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"""Implementation of execution-related magic functions.
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"""
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#-----------------------------------------------------------------------------
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# Copyright (c) 2012 The IPython Development Team.
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#
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# Distributed under the terms of the Modified BSD License.
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#
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# The full license is in the file COPYING.txt, distributed with this software.
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#-----------------------------------------------------------------------------
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#-----------------------------------------------------------------------------
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# Imports
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#-----------------------------------------------------------------------------
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# Stdlib
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import __builtin__ as builtin_mod
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import bdb
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import os
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import sys
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import time
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from StringIO import StringIO
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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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# Our own packages
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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.io 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, unquote_filename
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from IPython.utils.timing import clock, clock2
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from IPython.utils.warn import warn, error
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#-----------------------------------------------------------------------------
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# Magic implementation classes
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#-----------------------------------------------------------------------------
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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, user_mode=True,
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opts=None,arg_lst=None,prog_ns=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>: 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: 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>: 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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Valid Arg Meaning
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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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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>: 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>: 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: 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_def = Struct(D=[''],l=[],s=['time'],T=[''])
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if user_mode: # regular user call
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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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namespace = self.shell.user_ns
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if cell is not None:
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arg_str += '\n' + cell
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else: # called to run a program by %run -p
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try:
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filename = get_py_filename(arg_lst[0])
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except IOError as e:
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try:
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msg = str(e)
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except UnicodeError:
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msg = e.message
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error(msg)
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return
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arg_str = 'execfile(filename,prog_ns)'
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namespace = {
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'execfile': self.shell.safe_execfile,
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'prog_ns': prog_ns,
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'filename': filename
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}
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opts.merge(opts_def)
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prof = profile.Profile()
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try:
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prof = prof.runctx(arg_str,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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if hasattr(stats,'stream'):
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# In newer versions of python, the stats object has a 'stream'
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# attribute to write into.
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stats.stream = stdout_trap
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stats.print_stats(*lims)
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else:
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# For older versions, we manually redirect stdout during printing
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sys_stdout = sys.stdout
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try:
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sys.stdout = stdout_trap
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stats.print_stats(*lims)
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finally:
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sys.stdout = sys_stdout
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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,
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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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dump_file = unquote_filename(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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`dump_file`+'.',sys_exit
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if text_file:
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text_file = unquote_filename(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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`text_file`+'.',sys_exit
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if opts.has_key('r'):
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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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@line_magic
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def debug(self, parameter_s=''):
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"""Activate the interactive debugger in post-mortem mode.
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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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self.shell.debugger(force=True)
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@line_magic
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def tb(self, s):
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"""Print the last traceback with the currently active exception mode.
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See %xmode for changing exception reporting modes."""
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self.shell.showtraceback()
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@skip_doctest
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@line_magic
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def run(self, parameter_s='', runner=None,
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file_finder=get_py_filename):
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"""Run the named file inside IPython as a program.
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Usage:\\
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%run [-n -i -t [-N<N>] -d [-b<N>] -p [profile options]] file [args]
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Parameters after the filename are passed as command-line arguments to
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the program (put in sys.argv). Then, control returns to IPython's
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prompt.
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This is similar to running at a system prompt:\\
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$ python file args\\
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but with the advantage of giving you IPython's tracebacks, and of
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loading all variables into your interactive namespace for further use
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(unless -p is used, see below).
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The file is executed in a namespace initially consisting only of
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__name__=='__main__' and sys.argv constructed as indicated. It thus
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sees its environment as if it were being run as a stand-alone program
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(except for sharing global objects such as previously imported
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modules). But after execution, the IPython interactive namespace gets
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updated with all variables defined in the program (except for __name__
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and sys.argv). This allows for very convenient loading of code for
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interactive work, while giving each program a 'clean sheet' to run in.
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Options:
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-n: __name__ is NOT set to '__main__', but to the running file's name
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without extension (as python does under import). This allows running
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scripts and reloading the definitions in them without calling code
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protected by an ' if __name__ == "__main__" ' clause.
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-i: run the file in IPython's namespace instead of an empty one. This
|
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is useful if you are experimenting with code written in a text editor
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which depends on variables defined interactively.
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-e: ignore sys.exit() calls or SystemExit exceptions in the script
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being run. This is particularly useful if IPython is being used to
|
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run unittests, which always exit with a sys.exit() call. In such
|
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cases you are interested in the output of the test results, not in
|
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seeing a traceback of the unittest module.
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-t: print timing information at the end of the run. IPython will give
|
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you an estimated CPU time consumption for your script, which under
|
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|
Unix uses the resource module to avoid the wraparound problems of
|
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|
time.clock(). Under Unix, an estimate of time spent on system tasks
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is also given (for Windows platforms this is reported as 0.0).
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If -t is given, an additional -N<N> option can be given, where <N>
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must be an integer indicating how many times you want the script to
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run. The final timing report will include total and per run results.
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|
For example (testing the script uniq_stable.py)::
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|
In [1]: run -t uniq_stable
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|
IPython CPU timings (estimated):\\
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|
User : 0.19597 s.\\
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|
System: 0.0 s.\\
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|
In [2]: run -t -N5 uniq_stable
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|
IPython CPU timings (estimated):\\
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Total runs performed: 5\\
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|
Times : Total Per run\\
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|
User : 0.910862 s, 0.1821724 s.\\
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System: 0.0 s, 0.0 s.
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|
-d: run your program under the control of pdb, the Python debugger.
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|
This allows you to execute your program step by step, watch variables,
|
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|
etc. Internally, what IPython does is similar to calling:
|
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|
|
|
pdb.run('execfile("YOURFILENAME")')
|
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|
|
|
|
with a breakpoint set on line 1 of your file. You can change the line
|
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|
number for this automatic breakpoint to be <N> by using the -bN option
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|
(where N must be an integer). For example::
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|
|
%run -d -b40 myscript
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|
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|
will set the first breakpoint at line 40 in myscript.py. Note that
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|
the first breakpoint must be set on a line which actually does
|
|
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something (not a comment or docstring) for it to stop execution.
|
|
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|
|
|
When the pdb debugger starts, you will see a (Pdb) prompt. You must
|
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first enter 'c' (without quotes) to start execution up to the first
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breakpoint.
|
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|
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|
Entering 'help' gives information about the use of the debugger. You
|
|
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can easily see pdb's full documentation with "import pdb;pdb.help()"
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|
at a prompt.
|
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|
|
|
-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, 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.
|
|
|
|
|
|
"""
|
|
|
|
|
|
# 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:',
|
|
|
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'):
|
|
|
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
|
|
|
|
|
|
# simulate shell expansion on arguments, at least tilde expansion
|
|
|
args = [ os.path.expanduser(a) for a in 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.new_main_mod(prog_ns)
|
|
|
else:
|
|
|
# Run in a fresh, empty namespace
|
|
|
if 'n' in opts:
|
|
|
name = os.path.splitext(os.path.basename(filename))[0]
|
|
|
else:
|
|
|
name = '__main__'
|
|
|
|
|
|
main_mod = self.shell.new_main_mod()
|
|
|
prog_ns = main_mod.__dict__
|
|
|
prog_ns['__name__'] = name
|
|
|
|
|
|
# Since '%run foo' emulates 'python foo.py' at the cmd line, we must
|
|
|
# set the __file__ global in the script's namespace
|
|
|
prog_ns['__file__'] = filename
|
|
|
|
|
|
# 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
|
|
|
|
|
|
try:
|
|
|
stats = None
|
|
|
with self.shell.readline_no_record:
|
|
|
if 'p' in opts:
|
|
|
stats = self.prun('', None, False, opts, arg_lst, prog_ns)
|
|
|
else:
|
|
|
if 'd' in opts:
|
|
|
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]
|
|
|
# Set an initial breakpoint to stop execution
|
|
|
maxtries = 10
|
|
|
bp = int(opts.get('b', [1])[0])
|
|
|
checkline = deb.checkline(filename, bp)
|
|
|
if not checkline:
|
|
|
for bp in range(bp + 1, bp + maxtries + 1):
|
|
|
if deb.checkline(filename, 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)
|
|
|
error(msg)
|
|
|
return
|
|
|
# if we find a good linenumber, set the breakpoint
|
|
|
deb.do_break('%s:%s' % (filename, bp))
|
|
|
|
|
|
# Mimic Pdb._runscript(...)
|
|
|
deb._wait_for_mainpyfile = True
|
|
|
deb.mainpyfile = deb.canonic(filename)
|
|
|
|
|
|
# Start file run
|
|
|
print "NOTE: Enter 'c' at the",
|
|
|
print "%s prompt to start your script." % deb.prompt
|
|
|
ns = {'execfile': py3compat.execfile, 'prog_ns': prog_ns}
|
|
|
try:
|
|
|
deb.run('execfile("%s", prog_ns)' % filename, ns)
|
|
|
|
|
|
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)
|
|
|
else:
|
|
|
if runner is None:
|
|
|
runner = self.default_runner
|
|
|
if runner is None:
|
|
|
runner = self.shell.safe_execfile
|
|
|
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
|
|
|
twall0 = time.time()
|
|
|
if nruns == 1:
|
|
|
t0 = clock2()
|
|
|
runner(filename, prog_ns, prog_ns,
|
|
|
exit_ignore=exit_ignore)
|
|
|
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:
|
|
|
runner(filename, prog_ns, prog_ns,
|
|
|
exit_ignore=exit_ignore)
|
|
|
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 : %10.2f %10.2f" % ('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)
|
|
|
|
|
|
else:
|
|
|
# regular execution
|
|
|
runner(filename, prog_ns, prog_ns, exit_ignore=exit_ignore)
|
|
|
|
|
|
if 'i' in opts:
|
|
|
self.shell.user_ns['__name__'] = __name__save
|
|
|
else:
|
|
|
# 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).
|
|
|
self.shell.cache_main_mod(prog_ns, filename)
|
|
|
# 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)
|
|
|
|
|
|
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
|
|
|
|
|
|
@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]] statement
|
|
|
or in cell mode:
|
|
|
%%timeit [-n<N> -r<R> [-t|-c]] 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
|
|
|
|
|
|
|
|
|
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 loops, 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."""
|
|
|
|
|
|
import timeit
|
|
|
import math
|
|
|
|
|
|
# XXX: Unfortunately the unicode 'micro' symbol can cause problems in
|
|
|
# certain terminals. Until we figure out a robust way of
|
|
|
# auto-detecting if the terminal can deal with it, use plain 'us' for
|
|
|
# microseconds. I am really NOT happy about disabling the proper
|
|
|
# 'micro' prefix, but crashing is worse... If anyone knows what the
|
|
|
# right solution for this is, I'm all ears...
|
|
|
#
|
|
|
# Note: using
|
|
|
#
|
|
|
# s = u'\xb5'
|
|
|
# s.encode(sys.getdefaultencoding())
|
|
|
#
|
|
|
# is not sufficient, as I've seen terminals where that fails but
|
|
|
# print s
|
|
|
#
|
|
|
# succeeds
|
|
|
#
|
|
|
# See bug: https://bugs.launchpad.net/ipython/+bug/348466
|
|
|
|
|
|
#units = [u"s", u"ms",u'\xb5',"ns"]
|
|
|
units = [u"s", u"ms",u'us',"ns"]
|
|
|
|
|
|
scaling = [1, 1e3, 1e6, 1e9]
|
|
|
|
|
|
opts, stmt = self.parse_options(line,'n:r:tcp:',
|
|
|
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))
|
|
|
if hasattr(opts, "t"):
|
|
|
timefunc = time.time
|
|
|
if hasattr(opts, "c"):
|
|
|
timefunc = clock
|
|
|
|
|
|
timer = timeit.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
|
|
|
setup = 'pass'
|
|
|
stmt = timeit.reindent(transform(stmt), 8)
|
|
|
else:
|
|
|
setup = timeit.reindent(transform(stmt), 4)
|
|
|
stmt = timeit.reindent(transform(cell), 8)
|
|
|
|
|
|
# From Python 3.3, this template uses new-style string formatting.
|
|
|
if sys.version_info >= (3, 3):
|
|
|
src = timeit.template.format(stmt=stmt, setup=setup)
|
|
|
else:
|
|
|
src = timeit.template % dict(stmt=stmt, setup=setup)
|
|
|
|
|
|
# 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 = compile(src, "<magic-timeit>", "exec")
|
|
|
tc = clock()-t0
|
|
|
|
|
|
ns = {}
|
|
|
exec code in self.shell.user_ns, ns
|
|
|
timer.inner = ns["inner"]
|
|
|
|
|
|
if number == 0:
|
|
|
# determine number so that 0.2 <= total time < 2.0
|
|
|
number = 1
|
|
|
for i in range(1, 10):
|
|
|
if timer.timeit(number) >= 0.2:
|
|
|
break
|
|
|
number *= 10
|
|
|
|
|
|
best = min(timer.repeat(repeat, number)) / number
|
|
|
|
|
|
if best > 0.0 and best < 1000.0:
|
|
|
order = min(-int(math.floor(math.log10(best)) // 3), 3)
|
|
|
elif best >= 1000.0:
|
|
|
order = 0
|
|
|
else:
|
|
|
order = 3
|
|
|
print u"%d loops, best of %d: %.*g %s per loop" % (number, repeat,
|
|
|
precision,
|
|
|
best * scaling[order],
|
|
|
units[order])
|
|
|
if tc > tc_min:
|
|
|
print "Compiler time: %.2f s" % tc
|
|
|
|
|
|
@skip_doctest
|
|
|
@needs_local_scope
|
|
|
@line_magic
|
|
|
def time(self,parameter_s, user_locals):
|
|
|
"""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 provides very basic timing functionality. In Python
|
|
|
2.3, the timeit module offers more control and sophistication, so this
|
|
|
could be rewritten to use it (patches welcome).
|
|
|
|
|
|
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
|
|
|
|
|
|
expr = self.shell.prefilter(parameter_s,False)
|
|
|
|
|
|
# Minimum time above which compilation time will be reported
|
|
|
tc_min = 0.1
|
|
|
|
|
|
try:
|
|
|
mode = 'eval'
|
|
|
t0 = clock()
|
|
|
code = compile(expr,'<timed eval>',mode)
|
|
|
tc = clock()-t0
|
|
|
except SyntaxError:
|
|
|
mode = 'exec'
|
|
|
t0 = clock()
|
|
|
code = compile(expr,'<timed exec>',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, user_locals)
|
|
|
end = clock2()
|
|
|
else:
|
|
|
st = clock2()
|
|
|
exec code in glob, user_locals
|
|
|
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
|
|
|
print "CPU times: user %.2f s, sys: %.2f s, total: %.2f s" % \
|
|
|
(cpu_user,cpu_sys,cpu_tot)
|
|
|
print "Wall time: %.2f s" % wall_time
|
|
|
if tc > tc_min:
|
|
|
print "Compiler : %.2f s" % tc
|
|
|
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 as the
|
|
|
command line is used instead.
|
|
|
|
|
|
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 (%hist prints it)::
|
|
|
|
|
|
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,'r',mode='list')
|
|
|
if not args: # List existing macros
|
|
|
return sorted(k for k,v in self.shell.user_ns.iteritems() 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)
|
|
|
print 'Macro `%s` created. To execute, type its name (without quotes).' % name
|
|
|
print '=== Macro contents: ==='
|
|
|
print macro,
|
|
|
|
|
|
@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."""
|
|
|
)
|
|
|
@cell_magic
|
|
|
def capture(self, line, cell):
|
|
|
"""run the cell, capturing stdout/err"""
|
|
|
args = magic_arguments.parse_argstring(self.capture, line)
|
|
|
out = not args.no_stdout
|
|
|
err = not args.no_stderr
|
|
|
with capture_output(out, err) as io:
|
|
|
self.shell.run_cell(cell)
|
|
|
if args.output:
|
|
|
self.shell.user_ns[args.output] = io
|
|
|
|
