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# -*- coding: utf-8 -*-
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"""Implementation of execution-related magic functions."""
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# Copyright (c) IPython Development Team.
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# Distributed under the terms of the Modified BSD License.
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from __future__ import print_function
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from __future__ import absolute_import
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import ast
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import bdb
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import gc
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import itertools
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import os
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import sys
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import time
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import timeit
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from pdb import Restart
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# cProfile was added in Python2.5
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try:
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import cProfile as profile
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import pstats
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except ImportError:
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# profile isn't bundled by default in Debian for license reasons
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try:
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import profile, pstats
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except ImportError:
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profile = pstats = None
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from IPython.core import debugger, oinspect
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from IPython.core import magic_arguments
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from IPython.core import page
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from IPython.core.error import UsageError
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from IPython.core.macro import Macro
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from IPython.core.magic import (Magics, magics_class, line_magic, cell_magic,
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line_cell_magic, on_off, needs_local_scope)
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from IPython.testing.skipdoctest import skip_doctest
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from IPython.utils import py3compat
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from IPython.utils.py3compat import builtin_mod, iteritems, PY3
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from IPython.utils.contexts import preserve_keys
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from IPython.utils.capture import capture_output
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from IPython.utils.ipstruct import Struct
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from IPython.utils.module_paths import find_mod
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from IPython.utils.path import get_py_filename, shellglob
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from IPython.utils.timing import clock, clock2
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from warnings import warn
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from logging import error
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if PY3:
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from io import StringIO
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else:
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from StringIO import StringIO
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#-----------------------------------------------------------------------------
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# Magic implementation classes
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#-----------------------------------------------------------------------------
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class TimeitResult(object):
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"""
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Object returned by the timeit magic with info about the run.
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Contains the following attributes :
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loops: (int) number of loops done per measurement
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repeat: (int) number of times the measurement has been repeated
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best: (float) best execution time / number
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all_runs: (list of float) execution time of each run (in s)
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compile_time: (float) time of statement compilation (s)
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"""
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def __init__(self, loops, repeat, best, worst, all_runs, compile_time, precision):
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self.loops = loops
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self.repeat = repeat
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self.best = best
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self.worst = worst
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self.all_runs = all_runs
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self.compile_time = compile_time
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self._precision = precision
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def _repr_pretty_(self, p , cycle):
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if self.loops == 1: # No s at "loops" if only one loop
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unic = u"%d loop, best of %d: %s per loop" % (self.loops, self.repeat,
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_format_time(self.best, self._precision))
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else:
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unic = u"%d loops, best of %d: %s per loop" % (self.loops, self.repeat,
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_format_time(self.best, self._precision))
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p.text(u'<TimeitResult : '+unic+u'>')
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class TimeitTemplateFiller(ast.NodeTransformer):
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"""Fill in the AST template for timing execution.
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This is quite closely tied to the template definition, which is in
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:meth:`ExecutionMagics.timeit`.
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"""
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def __init__(self, ast_setup, ast_stmt):
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self.ast_setup = ast_setup
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self.ast_stmt = ast_stmt
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def visit_FunctionDef(self, node):
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"Fill in the setup statement"
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self.generic_visit(node)
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if node.name == "inner":
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node.body[:1] = self.ast_setup.body
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return node
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def visit_For(self, node):
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"Fill in the statement to be timed"
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if getattr(getattr(node.body[0], 'value', None), 'id', None) == 'stmt':
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node.body = self.ast_stmt.body
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return node
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class Timer(timeit.Timer):
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"""Timer class that explicitly uses self.inner
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which is an undocumented implementation detail of CPython,
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not shared by PyPy.
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"""
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# Timer.timeit copied from CPython 3.4.2
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def timeit(self, number=timeit.default_number):
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"""Time 'number' executions of the main statement.
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To be precise, this executes the setup statement once, and
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then returns the time it takes to execute the main statement
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a number of times, as a float measured in seconds. The
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argument is the number of times through the loop, defaulting
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to one million. The main statement, the setup statement and
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the timer function to be used are passed to the constructor.
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"""
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it = itertools.repeat(None, number)
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gcold = gc.isenabled()
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gc.disable()
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try:
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timing = self.inner(it, self.timer)
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finally:
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if gcold:
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gc.enable()
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return timing
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@magics_class
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class ExecutionMagics(Magics):
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"""Magics related to code execution, debugging, profiling, etc.
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"""
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def __init__(self, shell):
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super(ExecutionMagics, self).__init__(shell)
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if profile is None:
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self.prun = self.profile_missing_notice
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# Default execution function used to actually run user code.
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self.default_runner = None
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def profile_missing_notice(self, *args, **kwargs):
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error("""\
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The profile module could not be found. It has been removed from the standard
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python packages because of its non-free license. To use profiling, install the
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python-profiler package from non-free.""")
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@skip_doctest
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@line_cell_magic
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def prun(self, parameter_s='', cell=None):
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"""Run a statement through the python code profiler.
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Usage, in line mode:
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%prun [options] statement
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Usage, in cell mode:
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%%prun [options] [statement]
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code...
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code...
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In cell mode, the additional code lines are appended to the (possibly
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empty) statement in the first line. Cell mode allows you to easily
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profile multiline blocks without having to put them in a separate
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function.
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The given statement (which doesn't require quote marks) is run via the
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python profiler in a manner similar to the profile.run() function.
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Namespaces are internally managed to work correctly; profile.run
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cannot be used in IPython because it makes certain assumptions about
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namespaces which do not hold under IPython.
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Options:
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-l <limit>
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you can place restrictions on what or how much of the
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profile gets printed. The limit value can be:
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* A string: only information for function names containing this string
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is printed.
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* An integer: only these many lines are printed.
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* A float (between 0 and 1): this fraction of the report is printed
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(for example, use a limit of 0.4 to see the topmost 40% only).
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You can combine several limits with repeated use of the option. For
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example, ``-l __init__ -l 5`` will print only the topmost 5 lines of
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information about class constructors.
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-r
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return the pstats.Stats object generated by the profiling. This
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object has all the information about the profile in it, and you can
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later use it for further analysis or in other functions.
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-s <key>
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sort profile by given key. You can provide more than one key
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by using the option several times: '-s key1 -s key2 -s key3...'. The
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default sorting key is 'time'.
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The following is copied verbatim from the profile documentation
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referenced below:
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When more than one key is provided, additional keys are used as
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secondary criteria when the there is equality in all keys selected
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before them.
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Abbreviations can be used for any key names, as long as the
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abbreviation is unambiguous. The following are the keys currently
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defined:
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============ =====================
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Valid Arg Meaning
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============ =====================
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"calls" call count
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"cumulative" cumulative time
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"file" file name
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"module" file name
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"pcalls" primitive call count
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"line" line number
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"name" function name
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"nfl" name/file/line
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"stdname" standard name
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"time" internal time
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============ =====================
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Note that all sorts on statistics are in descending order (placing
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most time consuming items first), where as name, file, and line number
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searches are in ascending order (i.e., alphabetical). The subtle
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distinction between "nfl" and "stdname" is that the standard name is a
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sort of the name as printed, which means that the embedded line
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numbers get compared in an odd way. For example, lines 3, 20, and 40
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would (if the file names were the same) appear in the string order
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"20" "3" and "40". In contrast, "nfl" does a numeric compare of the
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line numbers. In fact, sort_stats("nfl") is the same as
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sort_stats("name", "file", "line").
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-T <filename>
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save profile results as shown on screen to a text
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file. The profile is still shown on screen.
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-D <filename>
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save (via dump_stats) profile statistics to given
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filename. This data is in a format understood by the pstats module, and
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is generated by a call to the dump_stats() method of profile
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objects. The profile is still shown on screen.
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-q
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suppress output to the pager. Best used with -T and/or -D above.
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If you want to run complete programs under the profiler's control, use
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``%run -p [prof_opts] filename.py [args to program]`` where prof_opts
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contains profiler specific options as described here.
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You can read the complete documentation for the profile module with::
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In [1]: import profile; profile.help()
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"""
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opts, arg_str = self.parse_options(parameter_s, 'D:l:rs:T:q',
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list_all=True, posix=False)
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if cell is not None:
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arg_str += '\n' + cell
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arg_str = self.shell.input_splitter.transform_cell(arg_str)
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return self._run_with_profiler(arg_str, opts, self.shell.user_ns)
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def _run_with_profiler(self, code, opts, namespace):
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"""
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Run `code` with profiler. Used by ``%prun`` and ``%run -p``.
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Parameters
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----------
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code : str
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Code to be executed.
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opts : Struct
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Options parsed by `self.parse_options`.
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namespace : dict
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A dictionary for Python namespace (e.g., `self.shell.user_ns`).
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"""
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# Fill default values for unspecified options:
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opts.merge(Struct(D=[''], l=[], s=['time'], T=['']))
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prof = profile.Profile()
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try:
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prof = prof.runctx(code, namespace, namespace)
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sys_exit = ''
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except SystemExit:
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sys_exit = """*** SystemExit exception caught in code being profiled."""
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stats = pstats.Stats(prof).strip_dirs().sort_stats(*opts.s)
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lims = opts.l
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if lims:
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lims = [] # rebuild lims with ints/floats/strings
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for lim in opts.l:
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try:
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lims.append(int(lim))
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except ValueError:
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try:
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lims.append(float(lim))
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except ValueError:
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lims.append(lim)
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# Trap output.
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stdout_trap = StringIO()
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stats_stream = stats.stream
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try:
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stats.stream = stdout_trap
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stats.print_stats(*lims)
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finally:
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stats.stream = stats_stream
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output = stdout_trap.getvalue()
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output = output.rstrip()
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if 'q' not in opts:
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page.page(output)
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print(sys_exit, end=' ')
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dump_file = opts.D[0]
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text_file = opts.T[0]
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if dump_file:
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prof.dump_stats(dump_file)
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print('\n*** Profile stats marshalled to file',\
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repr(dump_file)+'.',sys_exit)
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if text_file:
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pfile = open(text_file,'w')
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pfile.write(output)
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pfile.close()
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print('\n*** Profile printout saved to text file',\
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repr(text_file)+'.',sys_exit)
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if 'r' in opts:
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return stats
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else:
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return None
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@line_magic
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def pdb(self, parameter_s=''):
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"""Control the automatic calling of the pdb interactive debugger.
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Call as '%pdb on', '%pdb 1', '%pdb off' or '%pdb 0'. If called without
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argument it works as a toggle.
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When an exception is triggered, IPython can optionally call the
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interactive pdb debugger after the traceback printout. %pdb toggles
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this feature on and off.
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The initial state of this feature is set in your configuration
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file (the option is ``InteractiveShell.pdb``).
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If you want to just activate the debugger AFTER an exception has fired,
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without having to type '%pdb on' and rerunning your code, you can use
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the %debug magic."""
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par = parameter_s.strip().lower()
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if par:
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try:
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new_pdb = {'off':0,'0':0,'on':1,'1':1}[par]
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except KeyError:
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print ('Incorrect argument. Use on/1, off/0, '
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'or nothing for a toggle.')
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return
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else:
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# toggle
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new_pdb = not self.shell.call_pdb
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# set on the shell
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self.shell.call_pdb = new_pdb
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print('Automatic pdb calling has been turned',on_off(new_pdb))
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@skip_doctest
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@magic_arguments.magic_arguments()
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@magic_arguments.argument('--breakpoint', '-b', metavar='FILE:LINE',
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help="""
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Set break point at LINE in FILE.
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"""
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)
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@magic_arguments.argument('statement', nargs='*',
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help="""
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Code to run in debugger.
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You can omit this in cell magic mode.
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"""
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)
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@line_cell_magic
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def debug(self, line='', cell=None):
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"""Activate the interactive debugger.
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This magic command support two ways of activating debugger.
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One is to activate debugger before executing code. This way, you
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can set a break point, to step through the code from the point.
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You can use this mode by giving statements to execute and optionally
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a breakpoint.
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The other one is to activate debugger in post-mortem mode. You can
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activate this mode simply running %debug without any argument.
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If an exception has just occurred, this lets you inspect its stack
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frames interactively. Note that this will always work only on the last
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traceback that occurred, so you must call this quickly after an
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exception that you wish to inspect has fired, because if another one
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occurs, it clobbers the previous one.
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If you want IPython to automatically do this on every exception, see
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the %pdb magic for more details.
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"""
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args = magic_arguments.parse_argstring(self.debug, line)
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if not (args.breakpoint or args.statement or cell):
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self._debug_post_mortem()
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else:
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code = "\n".join(args.statement)
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if cell:
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code += "\n" + cell
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self._debug_exec(code, args.breakpoint)
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def _debug_post_mortem(self):
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self.shell.debugger(force=True)
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def _debug_exec(self, code, breakpoint):
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if breakpoint:
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(filename, bp_line) = breakpoint.split(':', 1)
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bp_line = int(bp_line)
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else:
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(filename, bp_line) = (None, None)
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self._run_with_debugger(code, self.shell.user_ns, filename, bp_line)
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@line_magic
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|
|
def tb(self, s):
|
|
|
"""Print the last traceback with the currently active exception mode.
|
|
|
|
|
|
See %xmode for changing exception reporting modes."""
|
|
|
self.shell.showtraceback()
|
|
|
|
|
|
@skip_doctest
|
|
|
@line_magic
|
|
|
def run(self, parameter_s='', runner=None,
|
|
|
file_finder=get_py_filename):
|
|
|
"""Run the named file inside IPython as a program.
|
|
|
|
|
|
Usage::
|
|
|
|
|
|
%run [-n -i -e -G]
|
|
|
[( -t [-N<N>] | -d [-b<N>] | -p [profile options] )]
|
|
|
( -m mod | file ) [args]
|
|
|
|
|
|
Parameters after the filename are passed as command-line arguments to
|
|
|
the program (put in sys.argv). Then, control returns to IPython's
|
|
|
prompt.
|
|
|
|
|
|
This is similar to running at a system prompt ``python file args``,
|
|
|
but with the advantage of giving you IPython's tracebacks, and of
|
|
|
loading all variables into your interactive namespace for further use
|
|
|
(unless -p is used, see below).
|
|
|
|
|
|
The file is executed in a namespace initially consisting only of
|
|
|
``__name__=='__main__'`` and sys.argv constructed as indicated. It thus
|
|
|
sees its environment as if it were being run as a stand-alone program
|
|
|
(except for sharing global objects such as previously imported
|
|
|
modules). But after execution, the IPython interactive namespace gets
|
|
|
updated with all variables defined in the program (except for __name__
|
|
|
and sys.argv). This allows for very convenient loading of code for
|
|
|
interactive work, while giving each program a 'clean sheet' to run in.
|
|
|
|
|
|
Arguments are expanded using shell-like glob match. Patterns
|
|
|
'*', '?', '[seq]' and '[!seq]' can be used. Additionally,
|
|
|
tilde '~' will be expanded into user's home directory. Unlike
|
|
|
real shells, quotation does not suppress expansions. Use
|
|
|
*two* back slashes (e.g. ``\\\\*``) to suppress expansions.
|
|
|
To completely disable these expansions, you can use -G flag.
|
|
|
|
|
|
Options:
|
|
|
|
|
|
-n
|
|
|
__name__ is NOT set to '__main__', but to the running file's name
|
|
|
without extension (as python does under import). This allows running
|
|
|
scripts and reloading the definitions in them without calling code
|
|
|
protected by an ``if __name__ == "__main__"`` clause.
|
|
|
|
|
|
-i
|
|
|
run the file in IPython's namespace instead of an empty one. This
|
|
|
is useful if you are experimenting with code written in a text editor
|
|
|
which depends on variables defined interactively.
|
|
|
|
|
|
-e
|
|
|
ignore sys.exit() calls or SystemExit exceptions in the script
|
|
|
being run. This is particularly useful if IPython is being used to
|
|
|
run unittests, which always exit with a sys.exit() call. In such
|
|
|
cases you are interested in the output of the test results, not in
|
|
|
seeing a traceback of the unittest module.
|
|
|
|
|
|
-t
|
|
|
print timing information at the end of the run. IPython will give
|
|
|
you an estimated CPU time consumption for your script, which under
|
|
|
Unix uses the resource module to avoid the wraparound problems of
|
|
|
time.clock(). Under Unix, an estimate of time spent on system tasks
|
|
|
is also given (for Windows platforms this is reported as 0.0).
|
|
|
|
|
|
If -t is given, an additional ``-N<N>`` option can be given, where <N>
|
|
|
must be an integer indicating how many times you want the script to
|
|
|
run. The final timing report will include total and per run results.
|
|
|
|
|
|
For example (testing the script uniq_stable.py)::
|
|
|
|
|
|
In [1]: run -t uniq_stable
|
|
|
|
|
|
IPython CPU timings (estimated):
|
|
|
User : 0.19597 s.
|
|
|
System: 0.0 s.
|
|
|
|
|
|
In [2]: run -t -N5 uniq_stable
|
|
|
|
|
|
IPython CPU timings (estimated):
|
|
|
Total runs performed: 5
|
|
|
Times : Total Per run
|
|
|
User : 0.910862 s, 0.1821724 s.
|
|
|
System: 0.0 s, 0.0 s.
|
|
|
|
|
|
-d
|
|
|
run your program under the control of pdb, the Python debugger.
|
|
|
This allows you to execute your program step by step, watch variables,
|
|
|
etc. Internally, what IPython does is similar to calling::
|
|
|
|
|
|
pdb.run('execfile("YOURFILENAME")')
|
|
|
|
|
|
with a breakpoint set on line 1 of your file. You can change the line
|
|
|
number for this automatic breakpoint to be <N> by using the -bN option
|
|
|
(where N must be an integer). For example::
|
|
|
|
|
|
%run -d -b40 myscript
|
|
|
|
|
|
will set the first breakpoint at line 40 in myscript.py. Note that
|
|
|
the first breakpoint must be set on a line which actually does
|
|
|
something (not a comment or docstring) for it to stop execution.
|
|
|
|
|
|
Or you can specify a breakpoint in a different file::
|
|
|
|
|
|
%run -d -b myotherfile.py:20 myscript
|
|
|
|
|
|
When the pdb debugger starts, you will see a (Pdb) prompt. You must
|
|
|
first enter 'c' (without quotes) to start execution up to the first
|
|
|
breakpoint.
|
|
|
|
|
|
Entering 'help' gives information about the use of the debugger. You
|
|
|
can easily see pdb's full documentation with "import pdb;pdb.help()"
|
|
|
at a prompt.
|
|
|
|
|
|
-p
|
|
|
run program under the control of the Python profiler module (which
|
|
|
prints a detailed report of execution times, function calls, etc).
|
|
|
|
|
|
You can pass other options after -p which affect the behavior of the
|
|
|
profiler itself. See the docs for %prun for details.
|
|
|
|
|
|
In this mode, the program's variables do NOT propagate back to the
|
|
|
IPython interactive namespace (because they remain in the namespace
|
|
|
where the profiler executes them).
|
|
|
|
|
|
Internally this triggers a call to %prun, see its documentation for
|
|
|
details on the options available specifically for profiling.
|
|
|
|
|
|
There is one special usage for which the text above doesn't apply:
|
|
|
if the filename ends with .ipy[nb], the file is run as ipython script,
|
|
|
just as if the commands were written on IPython prompt.
|
|
|
|
|
|
-m
|
|
|
specify module name to load instead of script path. Similar to
|
|
|
the -m option for the python interpreter. Use this option last if you
|
|
|
want to combine with other %run options. Unlike the python interpreter
|
|
|
only source modules are allowed no .pyc or .pyo files.
|
|
|
For example::
|
|
|
|
|
|
%run -m example
|
|
|
|
|
|
will run the example module.
|
|
|
|
|
|
-G
|
|
|
disable shell-like glob expansion of arguments.
|
|
|
|
|
|
"""
|
|
|
|
|
|
# get arguments and set sys.argv for program to be run.
|
|
|
opts, arg_lst = self.parse_options(parameter_s,
|
|
|
'nidtN:b:pD:l:rs:T:em:G',
|
|
|
mode='list', list_all=1)
|
|
|
if "m" in opts:
|
|
|
modulename = opts["m"][0]
|
|
|
modpath = find_mod(modulename)
|
|
|
if modpath is None:
|
|
|
warn('%r is not a valid modulename on sys.path'%modulename)
|
|
|
return
|
|
|
arg_lst = [modpath] + arg_lst
|
|
|
try:
|
|
|
filename = file_finder(arg_lst[0])
|
|
|
except IndexError:
|
|
|
warn('you must provide at least a filename.')
|
|
|
print('\n%run:\n', oinspect.getdoc(self.run))
|
|
|
return
|
|
|
except IOError as e:
|
|
|
try:
|
|
|
msg = str(e)
|
|
|
except UnicodeError:
|
|
|
msg = e.message
|
|
|
error(msg)
|
|
|
return
|
|
|
|
|
|
if filename.lower().endswith(('.ipy', '.ipynb')):
|
|
|
with preserve_keys(self.shell.user_ns, '__file__'):
|
|
|
self.shell.user_ns['__file__'] = filename
|
|
|
self.shell.safe_execfile_ipy(filename)
|
|
|
return
|
|
|
|
|
|
# Control the response to exit() calls made by the script being run
|
|
|
exit_ignore = 'e' in opts
|
|
|
|
|
|
# Make sure that the running script gets a proper sys.argv as if it
|
|
|
# were run from a system shell.
|
|
|
save_argv = sys.argv # save it for later restoring
|
|
|
|
|
|
if 'G' in opts:
|
|
|
args = arg_lst[1:]
|
|
|
else:
|
|
|
# tilde and glob expansion
|
|
|
args = shellglob(map(os.path.expanduser, arg_lst[1:]))
|
|
|
|
|
|
sys.argv = [filename] + args # put in the proper filename
|
|
|
# protect sys.argv from potential unicode strings on Python 2:
|
|
|
if not py3compat.PY3:
|
|
|
sys.argv = [ py3compat.cast_bytes(a) for a in sys.argv ]
|
|
|
|
|
|
if 'i' in opts:
|
|
|
# Run in user's interactive namespace
|
|
|
prog_ns = self.shell.user_ns
|
|
|
__name__save = self.shell.user_ns['__name__']
|
|
|
prog_ns['__name__'] = '__main__'
|
|
|
main_mod = self.shell.user_module
|
|
|
|
|
|
# Since '%run foo' emulates 'python foo.py' at the cmd line, we must
|
|
|
# set the __file__ global in the script's namespace
|
|
|
# TK: Is this necessary in interactive mode?
|
|
|
prog_ns['__file__'] = filename
|
|
|
else:
|
|
|
# Run in a fresh, empty namespace
|
|
|
if 'n' in opts:
|
|
|
name = os.path.splitext(os.path.basename(filename))[0]
|
|
|
else:
|
|
|
name = '__main__'
|
|
|
|
|
|
# The shell MUST hold a reference to prog_ns so after %run
|
|
|
# exits, the python deletion mechanism doesn't zero it out
|
|
|
# (leaving dangling references). See interactiveshell for details
|
|
|
main_mod = self.shell.new_main_mod(filename, name)
|
|
|
prog_ns = main_mod.__dict__
|
|
|
|
|
|
# pickle fix. See interactiveshell for an explanation. But we need to
|
|
|
# make sure that, if we overwrite __main__, we replace it at the end
|
|
|
main_mod_name = prog_ns['__name__']
|
|
|
|
|
|
if main_mod_name == '__main__':
|
|
|
restore_main = sys.modules['__main__']
|
|
|
else:
|
|
|
restore_main = False
|
|
|
|
|
|
# This needs to be undone at the end to prevent holding references to
|
|
|
# every single object ever created.
|
|
|
sys.modules[main_mod_name] = main_mod
|
|
|
|
|
|
if 'p' in opts or 'd' in opts:
|
|
|
if 'm' in opts:
|
|
|
code = 'run_module(modulename, prog_ns)'
|
|
|
code_ns = {
|
|
|
'run_module': self.shell.safe_run_module,
|
|
|
'prog_ns': prog_ns,
|
|
|
'modulename': modulename,
|
|
|
}
|
|
|
else:
|
|
|
if 'd' in opts:
|
|
|
# allow exceptions to raise in debug mode
|
|
|
code = 'execfile(filename, prog_ns, raise_exceptions=True)'
|
|
|
else:
|
|
|
code = 'execfile(filename, prog_ns)'
|
|
|
code_ns = {
|
|
|
'execfile': self.shell.safe_execfile,
|
|
|
'prog_ns': prog_ns,
|
|
|
'filename': get_py_filename(filename),
|
|
|
}
|
|
|
|
|
|
try:
|
|
|
stats = None
|
|
|
if 'p' in opts:
|
|
|
stats = self._run_with_profiler(code, opts, code_ns)
|
|
|
else:
|
|
|
if 'd' in opts:
|
|
|
bp_file, bp_line = parse_breakpoint(
|
|
|
opts.get('b', ['1'])[0], filename)
|
|
|
self._run_with_debugger(
|
|
|
code, code_ns, filename, bp_line, bp_file)
|
|
|
else:
|
|
|
if 'm' in opts:
|
|
|
def run():
|
|
|
self.shell.safe_run_module(modulename, prog_ns)
|
|
|
else:
|
|
|
if runner is None:
|
|
|
runner = self.default_runner
|
|
|
if runner is None:
|
|
|
runner = self.shell.safe_execfile
|
|
|
|
|
|
def run():
|
|
|
runner(filename, prog_ns, prog_ns,
|
|
|
exit_ignore=exit_ignore)
|
|
|
|
|
|
if 't' in opts:
|
|
|
# timed execution
|
|
|
try:
|
|
|
nruns = int(opts['N'][0])
|
|
|
if nruns < 1:
|
|
|
error('Number of runs must be >=1')
|
|
|
return
|
|
|
except (KeyError):
|
|
|
nruns = 1
|
|
|
self._run_with_timing(run, nruns)
|
|
|
else:
|
|
|
# regular execution
|
|
|
run()
|
|
|
|
|
|
if 'i' in opts:
|
|
|
self.shell.user_ns['__name__'] = __name__save
|
|
|
else:
|
|
|
# update IPython interactive namespace
|
|
|
|
|
|
# Some forms of read errors on the file may mean the
|
|
|
# __name__ key was never set; using pop we don't have to
|
|
|
# worry about a possible KeyError.
|
|
|
prog_ns.pop('__name__', None)
|
|
|
|
|
|
with preserve_keys(self.shell.user_ns, '__file__'):
|
|
|
self.shell.user_ns.update(prog_ns)
|
|
|
finally:
|
|
|
# It's a bit of a mystery why, but __builtins__ can change from
|
|
|
# being a module to becoming a dict missing some key data after
|
|
|
# %run. As best I can see, this is NOT something IPython is doing
|
|
|
# at all, and similar problems have been reported before:
|
|
|
# http://coding.derkeiler.com/Archive/Python/comp.lang.python/2004-10/0188.html
|
|
|
# Since this seems to be done by the interpreter itself, the best
|
|
|
# we can do is to at least restore __builtins__ for the user on
|
|
|
# exit.
|
|
|
self.shell.user_ns['__builtins__'] = builtin_mod
|
|
|
|
|
|
# Ensure key global structures are restored
|
|
|
sys.argv = save_argv
|
|
|
if restore_main:
|
|
|
sys.modules['__main__'] = restore_main
|
|
|
else:
|
|
|
# Remove from sys.modules the reference to main_mod we'd
|
|
|
# added. Otherwise it will trap references to objects
|
|
|
# contained therein.
|
|
|
del sys.modules[main_mod_name]
|
|
|
|
|
|
return stats
|
|
|
|
|
|
def _run_with_debugger(self, code, code_ns, filename=None,
|
|
|
bp_line=None, bp_file=None):
|
|
|
"""
|
|
|
Run `code` in debugger with a break point.
|
|
|
|
|
|
Parameters
|
|
|
----------
|
|
|
code : str
|
|
|
Code to execute.
|
|
|
code_ns : dict
|
|
|
A namespace in which `code` is executed.
|
|
|
filename : str
|
|
|
`code` is ran as if it is in `filename`.
|
|
|
bp_line : int, optional
|
|
|
Line number of the break point.
|
|
|
bp_file : str, optional
|
|
|
Path to the file in which break point is specified.
|
|
|
`filename` is used if not given.
|
|
|
|
|
|
Raises
|
|
|
------
|
|
|
UsageError
|
|
|
If the break point given by `bp_line` is not valid.
|
|
|
|
|
|
"""
|
|
|
deb = debugger.Pdb(self.shell.colors)
|
|
|
# reset Breakpoint state, which is moronically kept
|
|
|
# in a class
|
|
|
bdb.Breakpoint.next = 1
|
|
|
bdb.Breakpoint.bplist = {}
|
|
|
bdb.Breakpoint.bpbynumber = [None]
|
|
|
if bp_line is not None:
|
|
|
# Set an initial breakpoint to stop execution
|
|
|
maxtries = 10
|
|
|
bp_file = bp_file or filename
|
|
|
checkline = deb.checkline(bp_file, bp_line)
|
|
|
if not checkline:
|
|
|
for bp in range(bp_line + 1, bp_line + maxtries + 1):
|
|
|
if deb.checkline(bp_file, bp):
|
|
|
break
|
|
|
else:
|
|
|
msg = ("\nI failed to find a valid line to set "
|
|
|
"a breakpoint\n"
|
|
|
"after trying up to line: %s.\n"
|
|
|
"Please set a valid breakpoint manually "
|
|
|
"with the -b option." % bp)
|
|
|
raise UsageError(msg)
|
|
|
# if we find a good linenumber, set the breakpoint
|
|
|
deb.do_break('%s:%s' % (bp_file, bp_line))
|
|
|
|
|
|
if filename:
|
|
|
# Mimic Pdb._runscript(...)
|
|
|
deb._wait_for_mainpyfile = True
|
|
|
deb.mainpyfile = deb.canonic(filename)
|
|
|
|
|
|
# Start file run
|
|
|
print("NOTE: Enter 'c' at the %s prompt to continue execution." % deb.prompt)
|
|
|
try:
|
|
|
if filename:
|
|
|
# save filename so it can be used by methods on the deb object
|
|
|
deb._exec_filename = filename
|
|
|
while True:
|
|
|
try:
|
|
|
deb.run(code, code_ns)
|
|
|
except Restart:
|
|
|
print("Restarting")
|
|
|
if filename:
|
|
|
deb._wait_for_mainpyfile = True
|
|
|
deb.mainpyfile = deb.canonic(filename)
|
|
|
continue
|
|
|
else:
|
|
|
break
|
|
|
|
|
|
|
|
|
except:
|
|
|
etype, value, tb = sys.exc_info()
|
|
|
# Skip three frames in the traceback: the %run one,
|
|
|
# one inside bdb.py, and the command-line typed by the
|
|
|
# user (run by exec in pdb itself).
|
|
|
self.shell.InteractiveTB(etype, value, tb, tb_offset=3)
|
|
|
|
|
|
@staticmethod
|
|
|
def _run_with_timing(run, nruns):
|
|
|
"""
|
|
|
Run function `run` and print timing information.
|
|
|
|
|
|
Parameters
|
|
|
----------
|
|
|
run : callable
|
|
|
Any callable object which takes no argument.
|
|
|
nruns : int
|
|
|
Number of times to execute `run`.
|
|
|
|
|
|
"""
|
|
|
twall0 = time.time()
|
|
|
if nruns == 1:
|
|
|
t0 = clock2()
|
|
|
run()
|
|
|
t1 = clock2()
|
|
|
t_usr = t1[0] - t0[0]
|
|
|
t_sys = t1[1] - t0[1]
|
|
|
print("\nIPython CPU timings (estimated):")
|
|
|
print(" User : %10.2f s." % t_usr)
|
|
|
print(" System : %10.2f s." % t_sys)
|
|
|
else:
|
|
|
runs = range(nruns)
|
|
|
t0 = clock2()
|
|
|
for nr in runs:
|
|
|
run()
|
|
|
t1 = clock2()
|
|
|
t_usr = t1[0] - t0[0]
|
|
|
t_sys = t1[1] - t0[1]
|
|
|
print("\nIPython CPU timings (estimated):")
|
|
|
print("Total runs performed:", nruns)
|
|
|
print(" Times : %10s %10s" % ('Total', 'Per run'))
|
|
|
print(" User : %10.2f s, %10.2f s." % (t_usr, t_usr / nruns))
|
|
|
print(" System : %10.2f s, %10.2f s." % (t_sys, t_sys / nruns))
|
|
|
twall1 = time.time()
|
|
|
print("Wall time: %10.2f s." % (twall1 - twall0))
|
|
|
|
|
|
@skip_doctest
|
|
|
@line_cell_magic
|
|
|
def timeit(self, line='', cell=None):
|
|
|
"""Time execution of a Python statement or expression
|
|
|
|
|
|
Usage, in line mode:
|
|
|
%timeit [-n<N> -r<R> [-t|-c] -q -p<P> -o] statement
|
|
|
or in cell mode:
|
|
|
%%timeit [-n<N> -r<R> [-t|-c] -q -p<P> -o] setup_code
|
|
|
code
|
|
|
code...
|
|
|
|
|
|
Time execution of a Python statement or expression using the timeit
|
|
|
module. This function can be used both as a line and cell magic:
|
|
|
|
|
|
- In line mode you can time a single-line statement (though multiple
|
|
|
ones can be chained with using semicolons).
|
|
|
|
|
|
- In cell mode, the statement in the first line is used as setup code
|
|
|
(executed but not timed) and the body of the cell is timed. The cell
|
|
|
body has access to any variables created in the setup code.
|
|
|
|
|
|
Options:
|
|
|
-n<N>: execute the given statement <N> times in a loop. If this value
|
|
|
is not given, a fitting value is chosen.
|
|
|
|
|
|
-r<R>: repeat the loop iteration <R> times and take the best result.
|
|
|
Default: 3
|
|
|
|
|
|
-t: use time.time to measure the time, which is the default on Unix.
|
|
|
This function measures wall time.
|
|
|
|
|
|
-c: use time.clock to measure the time, which is the default on
|
|
|
Windows and measures wall time. On Unix, resource.getrusage is used
|
|
|
instead and returns the CPU user time.
|
|
|
|
|
|
-p<P>: use a precision of <P> digits to display the timing result.
|
|
|
Default: 3
|
|
|
|
|
|
-q: Quiet, do not print result.
|
|
|
|
|
|
-o: return a TimeitResult that can be stored in a variable to inspect
|
|
|
the result in more details.
|
|
|
|
|
|
|
|
|
Examples
|
|
|
--------
|
|
|
::
|
|
|
|
|
|
In [1]: %timeit pass
|
|
|
10000000 loops, best of 3: 53.3 ns per loop
|
|
|
|
|
|
In [2]: u = None
|
|
|
|
|
|
In [3]: %timeit u is None
|
|
|
10000000 loops, best of 3: 184 ns per loop
|
|
|
|
|
|
In [4]: %timeit -r 4 u == None
|
|
|
1000000 loops, best of 4: 242 ns per loop
|
|
|
|
|
|
In [5]: import time
|
|
|
|
|
|
In [6]: %timeit -n1 time.sleep(2)
|
|
|
1 loop, best of 3: 2 s per loop
|
|
|
|
|
|
|
|
|
The times reported by %timeit will be slightly higher than those
|
|
|
reported by the timeit.py script when variables are accessed. This is
|
|
|
due to the fact that %timeit executes the statement in the namespace
|
|
|
of the shell, compared with timeit.py, which uses a single setup
|
|
|
statement to import function or create variables. Generally, the bias
|
|
|
does not matter as long as results from timeit.py are not mixed with
|
|
|
those from %timeit."""
|
|
|
|
|
|
opts, stmt = self.parse_options(line,'n:r:tcp:qo',
|
|
|
posix=False, strict=False)
|
|
|
if stmt == "" and cell is None:
|
|
|
return
|
|
|
|
|
|
timefunc = timeit.default_timer
|
|
|
number = int(getattr(opts, "n", 0))
|
|
|
repeat = int(getattr(opts, "r", timeit.default_repeat))
|
|
|
precision = int(getattr(opts, "p", 3))
|
|
|
quiet = 'q' in opts
|
|
|
return_result = 'o' in opts
|
|
|
if hasattr(opts, "t"):
|
|
|
timefunc = time.time
|
|
|
if hasattr(opts, "c"):
|
|
|
timefunc = clock
|
|
|
|
|
|
timer = Timer(timer=timefunc)
|
|
|
# this code has tight coupling to the inner workings of timeit.Timer,
|
|
|
# but is there a better way to achieve that the code stmt has access
|
|
|
# to the shell namespace?
|
|
|
transform = self.shell.input_splitter.transform_cell
|
|
|
|
|
|
if cell is None:
|
|
|
# called as line magic
|
|
|
ast_setup = self.shell.compile.ast_parse("pass")
|
|
|
ast_stmt = self.shell.compile.ast_parse(transform(stmt))
|
|
|
else:
|
|
|
ast_setup = self.shell.compile.ast_parse(transform(stmt))
|
|
|
ast_stmt = self.shell.compile.ast_parse(transform(cell))
|
|
|
|
|
|
ast_setup = self.shell.transform_ast(ast_setup)
|
|
|
ast_stmt = self.shell.transform_ast(ast_stmt)
|
|
|
|
|
|
# This codestring is taken from timeit.template - we fill it in as an
|
|
|
# AST, so that we can apply our AST transformations to the user code
|
|
|
# without affecting the timing code.
|
|
|
timeit_ast_template = ast.parse('def inner(_it, _timer):\n'
|
|
|
' setup\n'
|
|
|
' _t0 = _timer()\n'
|
|
|
' for _i in _it:\n'
|
|
|
' stmt\n'
|
|
|
' _t1 = _timer()\n'
|
|
|
' return _t1 - _t0\n')
|
|
|
|
|
|
timeit_ast = TimeitTemplateFiller(ast_setup, ast_stmt).visit(timeit_ast_template)
|
|
|
timeit_ast = ast.fix_missing_locations(timeit_ast)
|
|
|
|
|
|
# Track compilation time so it can be reported if too long
|
|
|
# Minimum time above which compilation time will be reported
|
|
|
tc_min = 0.1
|
|
|
|
|
|
t0 = clock()
|
|
|
code = self.shell.compile(timeit_ast, "<magic-timeit>", "exec")
|
|
|
tc = clock()-t0
|
|
|
|
|
|
ns = {}
|
|
|
exec(code, self.shell.user_ns, ns)
|
|
|
timer.inner = ns["inner"]
|
|
|
|
|
|
# This is used to check if there is a huge difference between the
|
|
|
# best and worst timings.
|
|
|
# Issue: https://github.com/ipython/ipython/issues/6471
|
|
|
worst_tuning = 0
|
|
|
if number == 0:
|
|
|
# determine number so that 0.2 <= total time < 2.0
|
|
|
number = 1
|
|
|
for _ in range(1, 10):
|
|
|
time_number = timer.timeit(number)
|
|
|
worst_tuning = max(worst_tuning, time_number / number)
|
|
|
if time_number >= 0.2:
|
|
|
break
|
|
|
number *= 10
|
|
|
all_runs = timer.repeat(repeat, number)
|
|
|
best = min(all_runs) / number
|
|
|
|
|
|
worst = max(all_runs) / number
|
|
|
if worst_tuning:
|
|
|
worst = max(worst, worst_tuning)
|
|
|
|
|
|
if not quiet :
|
|
|
# Check best timing is greater than zero to avoid a
|
|
|
# ZeroDivisionError.
|
|
|
# In cases where the slowest timing is lesser than a micosecond
|
|
|
# we assume that it does not really matter if the fastest
|
|
|
# timing is 4 times faster than the slowest timing or not.
|
|
|
if worst > 4 * best and best > 0 and worst > 1e-6:
|
|
|
print("The slowest run took %0.2f times longer than the "
|
|
|
"fastest. This could mean that an intermediate result "
|
|
|
"is being cached." % (worst / best))
|
|
|
if number == 1: # No s at "loops" if only one loop
|
|
|
print(u"%d loop, best of %d: %s per loop" % (number, repeat,
|
|
|
_format_time(best, precision)))
|
|
|
else:
|
|
|
print(u"%d loops, best of %d: %s per loop" % (number, repeat,
|
|
|
_format_time(best, precision)))
|
|
|
if tc > tc_min:
|
|
|
print("Compiler time: %.2f s" % tc)
|
|
|
if return_result:
|
|
|
return TimeitResult(number, repeat, best, worst, all_runs, tc, precision)
|
|
|
|
|
|
@skip_doctest
|
|
|
@needs_local_scope
|
|
|
@line_cell_magic
|
|
|
def time(self,line='', cell=None, local_ns=None):
|
|
|
"""Time execution of a Python statement or expression.
|
|
|
|
|
|
The CPU and wall clock times are printed, and the value of the
|
|
|
expression (if any) is returned. Note that under Win32, system time
|
|
|
is always reported as 0, since it can not be measured.
|
|
|
|
|
|
This function can be used both as a line and cell magic:
|
|
|
|
|
|
- In line mode you can time a single-line statement (though multiple
|
|
|
ones can be chained with using semicolons).
|
|
|
|
|
|
- In cell mode, you can time the cell body (a directly
|
|
|
following statement raises an error).
|
|
|
|
|
|
This function provides very basic timing functionality. Use the timeit
|
|
|
magic for more control over the measurement.
|
|
|
|
|
|
Examples
|
|
|
--------
|
|
|
::
|
|
|
|
|
|
In [1]: %time 2**128
|
|
|
CPU times: user 0.00 s, sys: 0.00 s, total: 0.00 s
|
|
|
Wall time: 0.00
|
|
|
Out[1]: 340282366920938463463374607431768211456L
|
|
|
|
|
|
In [2]: n = 1000000
|
|
|
|
|
|
In [3]: %time sum(range(n))
|
|
|
CPU times: user 1.20 s, sys: 0.05 s, total: 1.25 s
|
|
|
Wall time: 1.37
|
|
|
Out[3]: 499999500000L
|
|
|
|
|
|
In [4]: %time print 'hello world'
|
|
|
hello world
|
|
|
CPU times: user 0.00 s, sys: 0.00 s, total: 0.00 s
|
|
|
Wall time: 0.00
|
|
|
|
|
|
Note that the time needed by Python to compile the given expression
|
|
|
will be reported if it is more than 0.1s. In this example, the
|
|
|
actual exponentiation is done by Python at compilation time, so while
|
|
|
the expression can take a noticeable amount of time to compute, that
|
|
|
time is purely due to the compilation:
|
|
|
|
|
|
In [5]: %time 3**9999;
|
|
|
CPU times: user 0.00 s, sys: 0.00 s, total: 0.00 s
|
|
|
Wall time: 0.00 s
|
|
|
|
|
|
In [6]: %time 3**999999;
|
|
|
CPU times: user 0.00 s, sys: 0.00 s, total: 0.00 s
|
|
|
Wall time: 0.00 s
|
|
|
Compiler : 0.78 s
|
|
|
"""
|
|
|
|
|
|
# fail immediately if the given expression can't be compiled
|
|
|
|
|
|
if line and cell:
|
|
|
raise UsageError("Can't use statement directly after '%%time'!")
|
|
|
|
|
|
if cell:
|
|
|
expr = self.shell.input_transformer_manager.transform_cell(cell)
|
|
|
else:
|
|
|
expr = self.shell.input_transformer_manager.transform_cell(line)
|
|
|
|
|
|
# Minimum time above which parse time will be reported
|
|
|
tp_min = 0.1
|
|
|
|
|
|
t0 = clock()
|
|
|
expr_ast = self.shell.compile.ast_parse(expr)
|
|
|
tp = clock()-t0
|
|
|
|
|
|
# Apply AST transformations
|
|
|
expr_ast = self.shell.transform_ast(expr_ast)
|
|
|
|
|
|
# Minimum time above which compilation time will be reported
|
|
|
tc_min = 0.1
|
|
|
|
|
|
if len(expr_ast.body)==1 and isinstance(expr_ast.body[0], ast.Expr):
|
|
|
mode = 'eval'
|
|
|
source = '<timed eval>'
|
|
|
expr_ast = ast.Expression(expr_ast.body[0].value)
|
|
|
else:
|
|
|
mode = 'exec'
|
|
|
source = '<timed exec>'
|
|
|
t0 = clock()
|
|
|
code = self.shell.compile(expr_ast, source, mode)
|
|
|
tc = clock()-t0
|
|
|
|
|
|
# skew measurement as little as possible
|
|
|
glob = self.shell.user_ns
|
|
|
wtime = time.time
|
|
|
# time execution
|
|
|
wall_st = wtime()
|
|
|
if mode=='eval':
|
|
|
st = clock2()
|
|
|
out = eval(code, glob, local_ns)
|
|
|
end = clock2()
|
|
|
else:
|
|
|
st = clock2()
|
|
|
exec(code, glob, local_ns)
|
|
|
end = clock2()
|
|
|
out = None
|
|
|
wall_end = wtime()
|
|
|
# Compute actual times and report
|
|
|
wall_time = wall_end-wall_st
|
|
|
cpu_user = end[0]-st[0]
|
|
|
cpu_sys = end[1]-st[1]
|
|
|
cpu_tot = cpu_user+cpu_sys
|
|
|
# On windows cpu_sys is always zero, so no new information to the next print
|
|
|
if sys.platform != 'win32':
|
|
|
print("CPU times: user %s, sys: %s, total: %s" % \
|
|
|
(_format_time(cpu_user),_format_time(cpu_sys),_format_time(cpu_tot)))
|
|
|
print("Wall time: %s" % _format_time(wall_time))
|
|
|
if tc > tc_min:
|
|
|
print("Compiler : %s" % _format_time(tc))
|
|
|
if tp > tp_min:
|
|
|
print("Parser : %s" % _format_time(tp))
|
|
|
return out
|
|
|
|
|
|
@skip_doctest
|
|
|
@line_magic
|
|
|
def macro(self, parameter_s=''):
|
|
|
"""Define a macro for future re-execution. It accepts ranges of history,
|
|
|
filenames or string objects.
|
|
|
|
|
|
Usage:\\
|
|
|
%macro [options] name n1-n2 n3-n4 ... n5 .. n6 ...
|
|
|
|
|
|
Options:
|
|
|
|
|
|
-r: use 'raw' input. By default, the 'processed' history is used,
|
|
|
so that magics are loaded in their transformed version to valid
|
|
|
Python. If this option is given, the raw input as typed at the
|
|
|
command line is used instead.
|
|
|
|
|
|
-q: quiet macro definition. By default, a tag line is printed
|
|
|
to indicate the macro has been created, and then the contents of
|
|
|
the macro are printed. If this option is given, then no printout
|
|
|
is produced once the macro is created.
|
|
|
|
|
|
This will define a global variable called `name` which is a string
|
|
|
made of joining the slices and lines you specify (n1,n2,... numbers
|
|
|
above) from your input history into a single string. This variable
|
|
|
acts like an automatic function which re-executes those lines as if
|
|
|
you had typed them. You just type 'name' at the prompt and the code
|
|
|
executes.
|
|
|
|
|
|
The syntax for indicating input ranges is described in %history.
|
|
|
|
|
|
Note: as a 'hidden' feature, you can also use traditional python slice
|
|
|
notation, where N:M means numbers N through M-1.
|
|
|
|
|
|
For example, if your history contains (print using %hist -n )::
|
|
|
|
|
|
44: x=1
|
|
|
45: y=3
|
|
|
46: z=x+y
|
|
|
47: print x
|
|
|
48: a=5
|
|
|
49: print 'x',x,'y',y
|
|
|
|
|
|
you can create a macro with lines 44 through 47 (included) and line 49
|
|
|
called my_macro with::
|
|
|
|
|
|
In [55]: %macro my_macro 44-47 49
|
|
|
|
|
|
Now, typing `my_macro` (without quotes) will re-execute all this code
|
|
|
in one pass.
|
|
|
|
|
|
You don't need to give the line-numbers in order, and any given line
|
|
|
number can appear multiple times. You can assemble macros with any
|
|
|
lines from your input history in any order.
|
|
|
|
|
|
The macro is a simple object which holds its value in an attribute,
|
|
|
but IPython's display system checks for macros and executes them as
|
|
|
code instead of printing them when you type their name.
|
|
|
|
|
|
You can view a macro's contents by explicitly printing it with::
|
|
|
|
|
|
print macro_name
|
|
|
|
|
|
"""
|
|
|
opts,args = self.parse_options(parameter_s,'rq',mode='list')
|
|
|
if not args: # List existing macros
|
|
|
return sorted(k for k,v in iteritems(self.shell.user_ns) if\
|
|
|
isinstance(v, Macro))
|
|
|
if len(args) == 1:
|
|
|
raise UsageError(
|
|
|
"%macro insufficient args; usage '%macro name n1-n2 n3-4...")
|
|
|
name, codefrom = args[0], " ".join(args[1:])
|
|
|
|
|
|
#print 'rng',ranges # dbg
|
|
|
try:
|
|
|
lines = self.shell.find_user_code(codefrom, 'r' in opts)
|
|
|
except (ValueError, TypeError) as e:
|
|
|
print(e.args[0])
|
|
|
return
|
|
|
macro = Macro(lines)
|
|
|
self.shell.define_macro(name, macro)
|
|
|
if not ( 'q' in opts) :
|
|
|
print('Macro `%s` created. To execute, type its name (without quotes).' % name)
|
|
|
print('=== Macro contents: ===')
|
|
|
print(macro, end=' ')
|
|
|
|
|
|
@magic_arguments.magic_arguments()
|
|
|
@magic_arguments.argument('output', type=str, default='', nargs='?',
|
|
|
help="""The name of the variable in which to store output.
|
|
|
This is a utils.io.CapturedIO object with stdout/err attributes
|
|
|
for the text of the captured output.
|
|
|
|
|
|
CapturedOutput also has a show() method for displaying the output,
|
|
|
and __call__ as well, so you can use that to quickly display the
|
|
|
output.
|
|
|
|
|
|
If unspecified, captured output is discarded.
|
|
|
"""
|
|
|
)
|
|
|
@magic_arguments.argument('--no-stderr', action="store_true",
|
|
|
help="""Don't capture stderr."""
|
|
|
)
|
|
|
@magic_arguments.argument('--no-stdout', action="store_true",
|
|
|
help="""Don't capture stdout."""
|
|
|
)
|
|
|
@magic_arguments.argument('--no-display', action="store_true",
|
|
|
help="""Don't capture IPython's rich display."""
|
|
|
)
|
|
|
@cell_magic
|
|
|
def capture(self, line, cell):
|
|
|
"""run the cell, capturing stdout, stderr, and IPython's rich display() calls."""
|
|
|
args = magic_arguments.parse_argstring(self.capture, line)
|
|
|
out = not args.no_stdout
|
|
|
err = not args.no_stderr
|
|
|
disp = not args.no_display
|
|
|
with capture_output(out, err, disp) as io:
|
|
|
self.shell.run_cell(cell)
|
|
|
if args.output:
|
|
|
self.shell.user_ns[args.output] = io
|
|
|
|
|
|
def parse_breakpoint(text, current_file):
|
|
|
'''Returns (file, line) for file:line and (current_file, line) for line'''
|
|
|
colon = text.find(':')
|
|
|
if colon == -1:
|
|
|
return current_file, int(text)
|
|
|
else:
|
|
|
return text[:colon], int(text[colon+1:])
|
|
|
|
|
|
def _format_time(timespan, precision=3):
|
|
|
"""Formats the timespan in a human readable form"""
|
|
|
import math
|
|
|
|
|
|
if timespan >= 60.0:
|
|
|
# we have more than a minute, format that in a human readable form
|
|
|
# Idea from http://snipplr.com/view/5713/
|
|
|
parts = [("d", 60*60*24),("h", 60*60),("min", 60), ("s", 1)]
|
|
|
time = []
|
|
|
leftover = timespan
|
|
|
for suffix, length in parts:
|
|
|
value = int(leftover / length)
|
|
|
if value > 0:
|
|
|
leftover = leftover % length
|
|
|
time.append(u'%s%s' % (str(value), suffix))
|
|
|
if leftover < 1:
|
|
|
break
|
|
|
return " ".join(time)
|
|
|
|
|
|
|
|
|
# Unfortunately the unicode 'micro' symbol can cause problems in
|
|
|
# certain terminals.
|
|
|
# See bug: https://bugs.launchpad.net/ipython/+bug/348466
|
|
|
# Try to prevent crashes by being more secure than it needs to
|
|
|
# E.g. eclipse is able to print a ยต, but has no sys.stdout.encoding set.
|
|
|
units = [u"s", u"ms",u'us',"ns"] # the save value
|
|
|
if hasattr(sys.stdout, 'encoding') and sys.stdout.encoding:
|
|
|
try:
|
|
|
u'\xb5'.encode(sys.stdout.encoding)
|
|
|
units = [u"s", u"ms",u'\xb5s',"ns"]
|
|
|
except:
|
|
|
pass
|
|
|
scaling = [1, 1e3, 1e6, 1e9]
|
|
|
|
|
|
if timespan > 0.0:
|
|
|
order = min(-int(math.floor(math.log10(timespan)) // 3), 3)
|
|
|
else:
|
|
|
order = 3
|
|
|
return u"%.*g %s" % (precision, timespan * scaling[order], units[order])
|
|
|
|
