# -*- coding: utf-8 -*- """Implementation of execution-related magic functions.""" # Copyright (c) IPython Development Team. # Distributed under the terms of the Modified BSD License. import ast import bdb import gc import itertools import os import sys import time import timeit import math from pdb import Restart # cProfile was added in Python2.5 try: import cProfile as profile import pstats except ImportError: # profile isn't bundled by default in Debian for license reasons try: import profile, pstats except ImportError: profile = pstats = None from IPython.core import oinspect from IPython.core import magic_arguments from IPython.core import page from IPython.core.error import UsageError from IPython.core.macro import Macro from IPython.core.magic import (Magics, magics_class, line_magic, cell_magic, line_cell_magic, on_off, needs_local_scope) from IPython.testing.skipdoctest import skip_doctest from IPython.utils import py3compat from IPython.utils.py3compat import builtin_mod, iteritems, PY3 from IPython.utils.contexts import preserve_keys from IPython.utils.capture import capture_output from IPython.utils.ipstruct import Struct from IPython.utils.module_paths import find_mod from IPython.utils.path import get_py_filename, shellglob from IPython.utils.timing import clock, clock2 from warnings import warn from logging import error if PY3: from io import StringIO else: from StringIO import StringIO #----------------------------------------------------------------------------- # Magic implementation classes #----------------------------------------------------------------------------- class TimeitResult(object): """ Object returned by the timeit magic with info about the run. Contains the following attributes : loops: (int) number of loops done per measurement repeat: (int) number of times the measurement has been repeated best: (float) best execution time / number all_runs: (list of float) execution time of each run (in s) compile_time: (float) time of statement compilation (s) """ def __init__(self, loops, repeat, best, worst, all_runs, compile_time, precision): self.loops = loops self.repeat = repeat self.best = best self.worst = worst self.all_runs = all_runs self.compile_time = compile_time self._precision = precision self.timings = [ dt / self.loops for dt in all_runs] @property def average(self): return math.fsum(self.timings) / len(self.timings) @property def stdev(self): mean = self.average return (math.fsum([(x - mean) ** 2 for x in self.timings]) / len(self.timings)) ** 0.5 def __str__(self): return (u"%s loop%s, average of %d: %s +- %s per loop (using standard deviation)" % (self.loops,"" if self.loops == 1 else "s", self.repeat, _format_time(self.average, self._precision), _format_time(self.stdev, self._precision))) def _repr_pretty_(self, p , cycle): unic = self.__str__() p.text(u'') class TimeitTemplateFiller(ast.NodeTransformer): """Fill in the AST template for timing execution. This is quite closely tied to the template definition, which is in :meth:`ExecutionMagics.timeit`. """ def __init__(self, ast_setup, ast_stmt): self.ast_setup = ast_setup self.ast_stmt = ast_stmt def visit_FunctionDef(self, node): "Fill in the setup statement" self.generic_visit(node) if node.name == "inner": node.body[:1] = self.ast_setup.body return node def visit_For(self, node): "Fill in the statement to be timed" if getattr(getattr(node.body[0], 'value', None), 'id', None) == 'stmt': node.body = self.ast_stmt.body return node class Timer(timeit.Timer): """Timer class that explicitly uses self.inner which is an undocumented implementation detail of CPython, not shared by PyPy. """ # Timer.timeit copied from CPython 3.4.2 def timeit(self, number=timeit.default_number): """Time 'number' executions of the main statement. To be precise, this executes the setup statement once, and then returns the time it takes to execute the main statement a number of times, as a float measured in seconds. The argument is the number of times through the loop, defaulting to one million. The main statement, the setup statement and the timer function to be used are passed to the constructor. """ it = itertools.repeat(None, number) gcold = gc.isenabled() gc.disable() try: timing = self.inner(it, self.timer) finally: if gcold: gc.enable() return timing @magics_class class ExecutionMagics(Magics): """Magics related to code execution, debugging, profiling, etc. """ def __init__(self, shell): super(ExecutionMagics, self).__init__(shell) if profile is None: self.prun = self.profile_missing_notice # Default execution function used to actually run user code. self.default_runner = None def profile_missing_notice(self, *args, **kwargs): error("""\ The profile module could not be found. It has been removed from the standard python packages because of its non-free license. To use profiling, install the python-profiler package from non-free.""") @skip_doctest @line_cell_magic def prun(self, parameter_s='', cell=None): """Run a statement through the python code profiler. Usage, in line mode: %prun [options] statement Usage, in cell mode: %%prun [options] [statement] code... code... In cell mode, the additional code lines are appended to the (possibly empty) statement in the first line. Cell mode allows you to easily profile multiline blocks without having to put them in a separate function. The given statement (which doesn't require quote marks) is run via the python profiler in a manner similar to the profile.run() function. Namespaces are internally managed to work correctly; profile.run cannot be used in IPython because it makes certain assumptions about namespaces which do not hold under IPython. Options: -l you can place restrictions on what or how much of the profile gets printed. The limit value can be: * A string: only information for function names containing this string is printed. * An integer: only these many lines are printed. * A float (between 0 and 1): this fraction of the report is printed (for example, use a limit of 0.4 to see the topmost 40% only). You can combine several limits with repeated use of the option. For example, ``-l __init__ -l 5`` will print only the topmost 5 lines of information about class constructors. -r return the pstats.Stats object generated by the profiling. This object has all the information about the profile in it, and you can later use it for further analysis or in other functions. -s sort profile by given key. You can provide more than one key by using the option several times: '-s key1 -s key2 -s key3...'. The default sorting key is 'time'. The following is copied verbatim from the profile documentation referenced below: When more than one key is provided, additional keys are used as secondary criteria when the there is equality in all keys selected before them. Abbreviations can be used for any key names, as long as the abbreviation is unambiguous. The following are the keys currently defined: ============ ===================== Valid Arg Meaning ============ ===================== "calls" call count "cumulative" cumulative time "file" file name "module" file name "pcalls" primitive call count "line" line number "name" function name "nfl" name/file/line "stdname" standard name "time" internal time ============ ===================== Note that all sorts on statistics are in descending order (placing most time consuming items first), where as name, file, and line number searches are in ascending order (i.e., alphabetical). The subtle distinction between "nfl" and "stdname" is that the standard name is a sort of the name as printed, which means that the embedded line numbers get compared in an odd way. For example, lines 3, 20, and 40 would (if the file names were the same) appear in the string order "20" "3" and "40". In contrast, "nfl" does a numeric compare of the line numbers. In fact, sort_stats("nfl") is the same as sort_stats("name", "file", "line"). -T save profile results as shown on screen to a text file. The profile is still shown on screen. -D save (via dump_stats) profile statistics to given filename. This data is in a format understood by the pstats module, and is generated by a call to the dump_stats() method of profile objects. The profile is still shown on screen. -q suppress output to the pager. Best used with -T and/or -D above. If you want to run complete programs under the profiler's control, use ``%run -p [prof_opts] filename.py [args to program]`` where prof_opts contains profiler specific options as described here. You can read the complete documentation for the profile module with:: In [1]: import profile; profile.help() """ opts, arg_str = self.parse_options(parameter_s, 'D:l:rs:T:q', list_all=True, posix=False) if cell is not None: arg_str += '\n' + cell arg_str = self.shell.input_splitter.transform_cell(arg_str) return self._run_with_profiler(arg_str, opts, self.shell.user_ns) def _run_with_profiler(self, code, opts, namespace): """ Run `code` with profiler. Used by ``%prun`` and ``%run -p``. Parameters ---------- code : str Code to be executed. opts : Struct Options parsed by `self.parse_options`. namespace : dict A dictionary for Python namespace (e.g., `self.shell.user_ns`). """ # Fill default values for unspecified options: opts.merge(Struct(D=[''], l=[], s=['time'], T=[''])) prof = profile.Profile() try: prof = prof.runctx(code, namespace, namespace) sys_exit = '' except SystemExit: sys_exit = """*** SystemExit exception caught in code being profiled.""" stats = pstats.Stats(prof).strip_dirs().sort_stats(*opts.s) lims = opts.l if lims: lims = [] # rebuild lims with ints/floats/strings for lim in opts.l: try: lims.append(int(lim)) except ValueError: try: lims.append(float(lim)) except ValueError: lims.append(lim) # Trap output. stdout_trap = StringIO() stats_stream = stats.stream try: stats.stream = stdout_trap stats.print_stats(*lims) finally: stats.stream = stats_stream output = stdout_trap.getvalue() output = output.rstrip() if 'q' not in opts: page.page(output) print(sys_exit, end=' ') dump_file = opts.D[0] text_file = opts.T[0] if dump_file: prof.dump_stats(dump_file) print('\n*** Profile stats marshalled to file',\ repr(dump_file)+'.',sys_exit) if text_file: pfile = open(text_file,'w') pfile.write(output) pfile.close() print('\n*** Profile printout saved to text file',\ repr(text_file)+'.',sys_exit) if 'r' in opts: return stats else: return None @line_magic def pdb(self, parameter_s=''): """Control the automatic calling of the pdb interactive debugger. Call as '%pdb on', '%pdb 1', '%pdb off' or '%pdb 0'. If called without argument it works as a toggle. When an exception is triggered, IPython can optionally call the interactive pdb debugger after the traceback printout. %pdb toggles this feature on and off. The initial state of this feature is set in your configuration file (the option is ``InteractiveShell.pdb``). If you want to just activate the debugger AFTER an exception has fired, without having to type '%pdb on' and rerunning your code, you can use the %debug magic.""" par = parameter_s.strip().lower() if par: try: new_pdb = {'off':0,'0':0,'on':1,'1':1}[par] except KeyError: print ('Incorrect argument. Use on/1, off/0, ' 'or nothing for a toggle.') return else: # toggle new_pdb = not self.shell.call_pdb # set on the shell self.shell.call_pdb = new_pdb print('Automatic pdb calling has been turned',on_off(new_pdb)) @skip_doctest @magic_arguments.magic_arguments() @magic_arguments.argument('--breakpoint', '-b', metavar='FILE:LINE', help=""" Set break point at LINE in FILE. """ ) @magic_arguments.argument('statement', nargs='*', help=""" Code to run in debugger. You can omit this in cell magic mode. """ ) @line_cell_magic def debug(self, line='', cell=None): """Activate the interactive debugger. This magic command support two ways of activating debugger. One is to activate debugger before executing code. This way, you can set a break point, to step through the code from the point. You can use this mode by giving statements to execute and optionally a breakpoint. The other one is to activate debugger in post-mortem mode. You can activate this mode simply running %debug without any argument. If an exception has just occurred, this lets you inspect its stack frames interactively. Note that this will always work only on the last traceback that occurred, so you must call this quickly after an exception that you wish to inspect has fired, because if another one occurs, it clobbers the previous one. If you want IPython to automatically do this on every exception, see the %pdb magic for more details. """ args = magic_arguments.parse_argstring(self.debug, line) if not (args.breakpoint or args.statement or cell): self._debug_post_mortem() else: code = "\n".join(args.statement) if cell: code += "\n" + cell self._debug_exec(code, args.breakpoint) def _debug_post_mortem(self): self.shell.debugger(force=True) def _debug_exec(self, code, breakpoint): if breakpoint: (filename, bp_line) = breakpoint.rsplit(':', 1) bp_line = int(bp_line) else: (filename, bp_line) = (None, None) self._run_with_debugger(code, self.shell.user_ns, filename, bp_line) @line_magic 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] | -d [-b] | -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`` option can be given, where 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 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 = self.shell.InteractiveTB.pdb if not deb: self.shell.InteractiveTB.pdb = self.shell.InteractiveTB.debugger_cls() deb = self.shell.InteractiveTB.pdb # 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 -r [-t|-c] -q -p

-o] statement or in cell mode: %%timeit [-n -r [-t|-c] -q -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: execute the given statement times in a loop. If this value is not given, a fitting value is chosen. -r: repeat the loop iteration 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

: use a precision of

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 100000000 loops, average of 7: 5.48 ns +- 0.354 ns per loop (using standard deviation) In [2]: u = None In [3]: %timeit u is None 10000000 loops, average of 7: 22.7 ns +- 2.33 ns per loop (using standard deviation) In [4]: %timeit -r 4 u == None 10000000 loops, average of 4: 27.5 ns +- 2.91 ns per loop (using standard deviation) In [5]: import time In [6]: %timeit -n1 time.sleep(2) 1 loop, average of 7: 2 s +- 4.71 µs per loop (using standard deviation) 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)) default_repeat = 7 if timeit.default_repeat < 7 else timeit.default_repeat repeat = int(getattr(opts, "r", 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, "", "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 if number == 0: # determine number so that 0.2 <= total time < 2.0 for index in range(0, 10): number = 10 ** index time_number = timer.timeit(number) if time_number >= 0.2: break all_runs = timer.repeat(repeat, number) best = min(all_runs) / number worst = max(all_runs) / number timeit_result = TimeitResult(number, repeat, best, worst, all_runs, tc, precision) 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)) print( timeit_result ) if tc > tc_min: print("Compiler time: %.2f s" % tc) if return_result: return timeit_result @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 = '' expr_ast = ast.Expression(expr_ast.body[0].value) else: mode = 'exec' source = '' 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""" 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 µ, 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])