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GNU Info File | 1996-11-14 | 50.5 KB | 1,320 lines

This is Info file pylibi, produced by Makeinfo-1.55 from the input file lib.texi. This file describes the built-in types, exceptions and functions and the standard modules that come with the Python system. It assumes basic knowledge about the Python language. For an informal introduction to the language, see the Python Tutorial. The Python Reference Manual gives a more formal definition of the language. (These manuals are not yet available in INFO or Texinfo format.) Copyright 1991-1995 by Stichting Mathematisch Centrum, Amsterdam, The Netherlands. All Rights Reserved Permission to use, copy, modify, and distribute this software and its documentation for any purpose and without fee is hereby granted, provided that the above copyright notice appear in all copies and that both that copyright notice and this permission notice appear in supporting documentation, and that the names of Stichting Mathematisch Centrum or CWI or Corporation for National Research Initiatives or CNRI not be used in advertising or publicity pertaining to distribution of the software without specific, written prior permission. While CWI is the initial source for this software, a modified version is made available by the Corporation for National Research Initiatives (CNRI) at the Internet address ftp://ftp.python.org. STICHTING MATHEMATISCH CENTRUM AND CNRI DISCLAIM ALL WARRANTIES WITH REGARD TO THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS, IN NO EVENT SHALL STICHTING MATHEMATISCH CENTRUM OR CNRI BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. File: pylibi, Node: Debugger Commands, Next: How It Works, Prev: The Python Debugger, Up: The Python Debugger Debugger Commands ================= The debugger recognizes the following commands. Most commands can be abbreviated to one or two letters; e.g. "`h(elp)'" means that either "`h'" or "`help'" can be used to enter the help command (but not "`he'" or "`hel'", nor "`H'" or "`Help' or "`HELP'"). Arguments to commands must be separated by whitespace (spaces or tabs). Optional arguments are enclosed in square brackets ("`[]'") in the command syntax; the square brackets must not be typed. Alternatives in the command syntax are separated by a vertical bar ("`|'"). Entering a blank line repeats the last command entered. Exception: if the last command was a "`list'" command, the next 11 lines are listed. Commands that the debugger doesn't recognize are assumed to be Python statements and are executed in the context of the program being debugged. Python statements can also be prefixed with an exclamation point ("`!'"). This is a powerful way to inspect the program being debugged; it is even possible to change a variable or call a function. When an exception occurs in such a statement, the exception name is printed but the debugger's state is not changed. h(elp) [COMMAND ] Without argument, print the list of available commands. With a COMMAND as argument, print help about that command. "`help pdb'" displays the full documentation file; if the environment variable `PAGER' is defined, the file is piped through that command instead. Since the COMMAND argument must be an identifier, "`help exec'" must be entered to get help on the "`!'" command. w(here) Print a stack trace, with the most recent frame at the bottom. An arrow indicates the current frame, which determines the context of most commands. d(own) Move the current frame one level down in the stack trace (to an older frame). u(p) Move the current frame one level up in the stack trace (to a newer frame). b(reak) [LINENO`|'FUNCTION ] With a LINENO argument, set a break there in the current file. With a FUNCTION argument, set a break at the entry of that function. Without argument, list all breaks. cl(ear) [LINENO ] With a LINENO argument, clear that break in the current file. Without argument, clear all breaks (but first ask confirmation). s(tep) Execute the current line, stop at the first possible occasion (either in a function that is called or on the next line in the current function). n(ext) Continue execution until the next line in the current function is reached or it returns. (The difference between `next' and `step' is that `step' stops inside a called function, while `next' executes called functions at (nearly) full speed, only stopping at the next line in the current function.) r(eturn) Continue execution until the current function returns. c(ont(inue)) Continue execution, only stop when a breakpoint is encountered. l(ist) [FIRST [, LAST ]] List source code for the current file. Without arguments, list 11 lines around the current line or continue the previous listing. With one argument, list 11 lines around at that line. With two arguments, list the given range; if the second argument is less than the first, it is interpreted as a count. a(rgs) Print the argument list of the current function. p EXPRESSION Evaluate the EXPRESSION in the current context and print its value. (Note: `print' can also be used, but is not a debugger command -- this executes the Python `print' statement.) [! STATEMENT] Execute the (one-line) STATEMENT in the context of the current stack frame. The exclamation point can be omitted unless the first word of the statement resembles a debugger command. To set a global variable, you can prefix the assignment command with a "`global'" command on the same line, e.g.: (Pdb) global list_options; list_options = ['-l'] (Pdb) q(uit) Quit from the debugger. The program being executed is aborted. File: pylibi, Node: How It Works, Prev: Debugger Commands, Up: The Python Debugger How It Works ============ Some changes were made to the interpreter: * sys.settrace(func) sets the global trace function * there can also a local trace function (see later) Trace functions have three arguments: (FRAME, EVENT, ARG) FRAME is the current stack frame EVENT is a string: `'call'', `'line'', `'return'' or `'exception'' ARG is dependent on the event type A trace function should return a new trace function or None. Class methods are accepted (and most useful!) as trace methods. The events have the following meaning: `'call'' A function is called (or some other code block entered). The global trace function is called; arg is the argument list to the function; the return value specifies the local trace function. `'line'' The interpreter is about to execute a new line of code (sometimes multiple line events on one line exist). The local trace function is called; arg in None; the return value specifies the new local trace function. `'return'' A function (or other code block) is about to return. The local trace function is called; arg is the value that will be returned. The trace function's return value is ignored. `'exception'' An exception has occurred. The local trace function is called; arg is a triple (exception, value, traceback); the return value specifies the new local trace function Note that as an exception is propagated down the chain of callers, an `'exception'' event is generated at each level. Stack frame objects have the following read-only attributes: f_code the code object being executed f_lineno the current line number (`-1' for `'call'' events) f_back the stack frame of the caller, or None f_locals dictionary containing local name bindings f_globals dictionary containing global name bindings Code objects have the following read-only attributes: co_code the code string co_names the list of names used by the code co_consts the list of (literal) constants used by the code co_filename the filename from which the code was compiled File: pylibi, Node: The Python Profiler, Next: Internet and WWW, Prev: The Python Debugger, Up: Top The Python Profiler ******************* Copyright (C) 1994, by InfoSeek Corporation, all rights reserved. Written by James Roskind(1) Permission to use, copy, modify, and distribute this Python software and its associated documentation for any purpose (subject to the restriction in the following sentence) without fee is hereby granted, provided that the above copyright notice appears in all copies, and that both that copyright notice and this permission notice appear in supporting documentation, and that the name of InfoSeek not be used in advertising or publicity pertaining to distribution of the software without specific, written prior permission. This permission is explicitly restricted to the copying and modification of the software to remain in Python, compiled Python, or other languages (such as C) wherein the modified or derived code is exclusively imported into a Python module. INFOSEEK CORPORATION DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL INFOSEEK CORPORATION BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. The profiler was written after only programming in Python for 3 weeks. As a result, it is probably clumsy code, but I don't know for sure yet 'cause I'm a beginner :-). I did work hard to make the code run fast, so that profiling would be a reasonable thing to do. I tried not to repeat code fragments, but I'm sure I did some stuff in really awkward ways at times. Please send suggestions for improvements to: `jar@netscape.com'. I won't promise *any* support. ...but I'd appreciate the feedback. * Menu: * Profiler Introduction:: * Profiler Changes:: * Instant Users Manual:: * Deterministic Profiling:: * Reference Manual:: * Limitations:: * Calibration:: * Profiler Extensions:: ---------- Footnotes ---------- (1) Updated and converted to LaTeX by Guido van Rossum. The references to the old profiler are left in the text, although it no longer exists. File: pylibi, Node: Profiler Introduction, Next: Profiler Changes, Prev: The Python Profiler, Up: The Python Profiler Introduction to the profiler ============================ A "profiler" is a program that describes the run time performance of a program, providing a variety of statistics. This documentation describes the profiler functionality provided in the modules `profile' and `pstats.' This profiler provides "deterministic profiling" of any Python programs. It also provides a series of report generation tools to allow users to rapidly examine the results of a profile operation. File: pylibi, Node: Profiler Changes, Next: Instant Users Manual, Prev: Profiler Introduction, Up: The Python Profiler How Is This Profiler Different From The Old Profiler? ===================================================== The big changes from old profiling module are that you get more information, and you pay less CPU time. It's not a trade-off, it's a trade-up. To be specific: Bugs removed: Local stack frame is no longer molested, execution time is now charged to correct functions. Accuracy increased: Profiler execution time is no longer charged to user's code, calibration for platform is supported, file reads are not done *by* profiler *during* profiling (and charged to user's code!). Speed increased: Overhead CPU cost was reduced by more than a factor of two (perhaps a factor of five), lightweight profiler module is all that must be loaded, and the report generating module (`pstats') is not needed during profiling. Recursive functions support: Cumulative times in recursive functions are correctly calculated; recursive entries are counted. Large growth in report generating UI: Distinct profiles runs can be added together forming a comprehensive report; functions that import statistics take arbitrary lists of files; sorting criteria is now based on keywords (instead of 4 integer options); reports shows what functions were profiled as well as what profile file was referenced; output format has been improved. File: pylibi, Node: Instant Users Manual, Next: Deterministic Profiling, Prev: Profiler Changes, Up: The Python Profiler Instant Users Manual ==================== This section is provided for users that "don't want to read the manual." It provides a very brief overview, and allows a user to rapidly perform profiling on an existing application. To profile an application with a main entry point of `foo()', you would add the following to your module: import profile profile.run("foo()") The above action would cause `foo()' to be run, and a series of informative lines (the profile) to be printed. The above approach is most useful when working with the interpreter. If you would like to save the results of a profile into a file for later examination, you can supply a file name as the second argument to the `run()' function: import profile profile.run("foo()", 'fooprof') When you wish to review the profile, you should use the methods in the `pstats' module. Typically you would load the statistics data as follows: import pstats p = pstats.Stats('fooprof') The class `Stats' (the above code just created an instance of this class) has a variety of methods for manipulating and printing the data that was just read into `p'. When you ran `profile.run()' above, what was printed was the result of three method calls: p.strip_dirs().sort_stats(-1).print_stats() The first method removed the extraneous path from all the module names. The second method sorted all the entries according to the standard module/line/name string that is printed (this is to comply with the semantics of the old profiler). The third method printed out all the statistics. You might try the following sort calls: p.sort_stats('name') p.print_stats() The first call will actually sort the list by function name, and the second call will print out the statistics. The following are some interesting calls to experiment with: p.sort_stats('cumulative').print_stats(10) This sorts the profile by cumulative time in a function, and then only prints the ten most significant lines. If you want to understand what algorithms are taking time, the above line is what you would use. If you were looking to see what functions were looping a lot, and taking a lot of time, you would do: p.sort_stats('time').print_stats(10) to sort according to time spent within each function, and then print the statistics for the top ten functions. You might also try: p.sort_stats('file').print_stats('__init__') This will sort all the statistics by file name, and then print out statistics for only the class init methods ('cause they are spelled with `__init__' in them). As one final example, you could try: p.sort_stats('time', 'cum').print_stats(.5, 'init') This line sorts statistics with a primary key of time, and a secondary key of cumulative time, and then prints out some of the statistics. To be specific, the list is first culled down to 50% (re: `.5') of its original size, then only lines containing `init' are maintained, and that sub-sub-list is printed. If you wondered what functions called the above functions, you could now (`p' is still sorted according to the last criteria) do: p.print_callers(.5, 'init') and you would get a list of callers for each of the listed functions. If you want more functionality, you're going to have to read the manual, or guess what the following functions do: p.print_callees() p.add('fooprof') File: pylibi, Node: Deterministic Profiling, Next: Reference Manual, Prev: Instant Users Manual, Up: The Python Profiler What Is Deterministic Profiling? ================================ "Deterministic profiling" is meant to reflect the fact that all "function call", "function return", and "exception" events are monitored, and precise timings are made for the intervals between these events (during which time the user's code is executing). In contrast, "statistical profiling" (which is not done by this module) randomly samples the effective instruction pointer, and deduces where time is being spent. The latter technique traditionally involves less overhead (as the code does not need to be instrumented), but provides only relative indications of where time is being spent. In Python, since there is an interpreter active during execution, the presence of instrumented code is not required to do deterministic profiling. Python automatically provides a "hook" (optional callback) for each event. In addition, the interpreted nature of Python tends to add so much overhead to execution, that deterministic profiling tends to only add small processing overhead in typical applications. The result is that deterministic profiling is not that expensive, yet provides extensive run time statistics about the execution of a Python program. Call count statistics can be used to identify bugs in code (surprising counts), and to identify possible inline-expansion points (high call counts). Internal time statistics can be used to identify "hot loops" that should be carefully optimized. Cumulative time statistics should be used to identify high level errors in the selection of algorithms. Note that the unusual handling of cumulative times in this profiler allows statistics for recursive implementations of algorithms to be directly compared to iterative implementations. File: pylibi, Node: Reference Manual, Next: Limitations, Prev: Deterministic Profiling, Up: The Python Profiler Reference Manual ================ The primary entry point for the profiler is the global function `profile.run()'. It is typically used to create any profile information. The reports are formatted and printed using methods of the class `pstats.Stats'. The following is a description of all of these standard entry points and functions. For a more in-depth view of some of the code, consider reading the later section on Profiler Extensions, which includes discussion of how to derive "better" profilers from the classes presented, or reading the source code for these modules. - profiler function: profile.run (STRING[, FILENAME[, ...]]) This function takes a single argument that has can be passed to the `exec' statement, and an optional file name. In all cases this routine attempts to `exec' its first argument, and gather profiling statistics from the execution. If no file name is present, then this function automatically prints a simple profiling report, sorted by the standard name string (file/line/function-name) that is presented in each line. The following is a typical output from such a call: main() 2706 function calls (2004 primitive calls) in 4.504 CPU seconds Ordered by: standard name ncalls tottime percall cumtime percall filename:lineno(function) 2 0.006 0.003 0.953 0.477 pobject.py:75(save_objects) 43/3 0.533 0.012 0.749 0.250 pobject.py:99(evaluate) ... The first line indicates that this profile was generated by the call: `profile.run('main()')', and hence the exec'ed string is `'main()''. The second line indicates that 2706 calls were monitored. Of those calls, 2004 were "primitive". We define "primitive" to mean that the call was not induced via recursion. The next line: `Ordered by: standard name', indicates that the text string in the far right column was used to sort the output. The column headings include: ncalls for the number of calls, tottime for the total time spent in the given function (and excluding time made in calls to sub-functions), percall is the quotient of `tottime' divided by `ncalls' cumtime is the total time spent in this and all subfunctions (i.e., from invocation till exit). This figure is accurate *even* for recursive functions. percall is the quotient of `cumtime' divided by primitive calls filename:lineno(function) provides the respective data of each function When there are two numbers in the first column (e.g.: `43/3'), then the latter is the number of primitive calls, and the former is the actual number of calls. Note that when the function does not recurse, these two values are the same, and only the single figure is printed. - profiler function: pstats.Stats (FILENAME[, ...]) This class constructor creates an instance of a "statistics object" from a FILENAME (or set of filenames). `Stats' objects are manipulated by methods, in order to print useful reports. The file selected by the above constructor must have been created by the corresponding version of `profile'. To be specific, there is *NO* file compatibility guaranteed with future versions of this profiler, and there is no compatibility with files produced by other profilers (e.g., the old system profiler). If several files are provided, all the statistics for identical functions will be coalesced, so that an overall view of several processes can be considered in a single report. If additional files need to be combined with data in an existing `Stats' object, the `add()' method can be used. * Menu: * The Stats Class:: File: pylibi, Node: The Stats Class, Prev: Reference Manual, Up: Reference Manual The `Stats' Class ----------------- - Method on Stats: strip_dirs () This method for the `Stats' class removes all leading path information from file names. It is very useful in reducing the size of the printout to fit within (close to) 80 columns. This method modifies the object, and the stripped information is lost. After performing a strip operation, the object is considered to have its entries in a "random" order, as it was just after object initialization and loading. If `strip_dirs()' causes two function names to be indistinguishable (i.e., they are on the same line of the same filename, and have the same function name), then the statistics for these two entries are accumulated into a single entry. - Method on Stats: add (FILENAME[, ...]) This method of the `Stats' class accumulates additional profiling information into the current profiling object. Its arguments should refer to filenames created by the corresponding version of `profile.run()'. Statistics for identically named (re: file, line, name) functions are automatically accumulated into single function statistics. - Method on Stats: sort_stats (KEY[, ...]) This method modifies the `Stats' object by sorting it according to the supplied criteria. The argument is typically a string identifying the basis of a sort (example: `"time"' or `"name"'). When more than one key is provided, then additional keys are used as secondary criteria when the there is equality in all keys selected before them. For example, sort_stats('name', 'file') will sort all the entries according to their function name, and resolve all ties (identical function names) by sorting by file name. 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")'. For compatibility with the old profiler, the numeric arguments `-1', `0', `1', and `2' are permitted. They are interpreted as `"stdname"', `"calls"', `"time"', and `"cumulative"' respectively. If this old style format (numeric) is used, only one sort key (the numeric key) will be used, and additional arguments will be silently ignored. - Method on Stats: reverse_order () This method for the `Stats' class reverses the ordering of the basic list within the object. This method is provided primarily for compatibility with the old profiler. Its utility is questionable now that ascending vs descending order is properly selected based on the sort key of choice. - Method on Stats: print_stats (RESTRICTION[, ...]) This method for the `Stats' class prints out a report as described in the `profile.run()' definition. The order of the printing is based on the last `sort_stats()' operation done on the object (subject to caveats in `add()' and `strip_dirs())'. The arguments provided (if any) can be used to limit the list down to the significant entries. Initially, the list is taken to be the complete set of profiled functions. Each restriction is either an integer (to select a count of lines), or a decimal fraction between 0.0 and 1.0 inclusive (to select a percentage of lines), or a regular expression (to pattern match the standard name that is printed). If several restrictions are provided, then they are applied sequentially. For example: print_stats(.1, "foo:") would first limit the printing to first 10% of list, and then only print functions that were part of filename `.*foo:'. In contrast, the command: print_stats("foo:", .1) would limit the list to all functions having file names `.*foo:', and then proceed to only print the first 10% of them. - Method on Stats: print_callers (RESTRICTIONS[, ...]) This method for the `Stats' class prints a list of all functions that called each function in the profiled database. The ordering is identical to that provided by `print_stats()', and the definition of the restricting argument is also identical. For convenience, a number is shown in parentheses after each caller to show how many times this specific call was made. A second non-parenthesized number is the cumulative time spent in the function at the right. - Method on Stats: print_callees (RESTRICTIONS[, ...]) This method for the `Stats' class prints a list of all function that were called by the indicated function. Aside from this reversal of direction of calls (re: called vs was called by), the arguments and ordering are identical to the `print_callers()' method. - Method on Stats: ignore () This method of the `Stats' class is used to dispose of the value returned by earlier methods. All standard methods in this class return the instance that is being processed, so that the commands can be strung together. For example: pstats.Stats('foofile').strip_dirs().sort_stats('cum') \ .print_stats().ignore() would perform all the indicated functions, but it would not return the final reference to the `Stats' instance.(1) ---------- Footnotes ---------- (1) This was once necessary, when Python would print any unused expression result that was not `None'. The method is still defined for backward compatibility. File: pylibi, Node: Limitations, Next: Calibration, Prev: Reference Manual, Up: The Python Profiler Limitations =========== There are two fundamental limitations on this profiler. The first is that it relies on the Python interpreter to dispatch "call", "return", and "exception" events. Compiled C code does not get interpreted, and hence is "invisible" to the profiler. All time spent in C code (including builtin functions) will be charged to the Python function that invoked the C code. If the C code calls out to some native Python code, then those calls will be profiled properly. The second limitation has to do with accuracy of timing information. There is a fundamental problem with deterministic profilers involving accuracy. The most obvious restriction is that the underlying "clock" is only ticking at a rate (typically) of about .001 seconds. Hence no measurements will be more accurate that that underlying clock. If enough measurements are taken, then the "error" will tend to average out. Unfortunately, removing this first error induces a second source of error... The second problem is that it "takes a while" from when an event is dispatched until the profiler's call to get the time actually *gets* the state of the clock. Similarly, there is a certain lag when exiting the profiler event handler from the time that the clock's value was obtained (and then squirreled away), until the user's code is once again executing. As a result, functions that are called many times, or call many functions, will typically accumulate this error. The error that accumulates in this fashion is typically less than the accuracy of the clock (i.e., less than one clock tick), but it *can* accumulate and become very significant. This profiler provides a means of calibrating itself for a given platform so that this error can be probabilistically (i.e., on the average) removed. After the profiler is calibrated, it will be more accurate (in a least square sense), but it will sometimes produce negative numbers (when call counts are exceptionally low, and the gods of probability work against you :-). ) Do *NOT* be alarmed by negative numbers in the profile. They should *only* appear if you have calibrated your profiler, and the results are actually better than without calibration. File: pylibi, Node: Calibration, Next: Profiler Extensions, Prev: Limitations, Up: The Python Profiler Calibration =========== The profiler class has a hard coded constant that is added to each event handling time to compensate for the overhead of calling the time function, and socking away the results. The following procedure can be used to obtain this constant for a given platform (see discussion in section Limitations above). import profile pr = profile.Profile() pr.calibrate(100) pr.calibrate(100) pr.calibrate(100) The argument to calibrate() is the number of times to try to do the sample calls to get the CPU times. If your computer is *very* fast, you might have to do: pr.calibrate(1000) or even: pr.calibrate(10000) The object of this exercise is to get a fairly consistent result. When you have a consistent answer, you are ready to use that number in the source code. For a Sun Sparcstation 1000 running Solaris 2.3, the magical number is about .00053. If you have a choice, you are better off with a smaller constant, and your results will "less often" show up as negative in profile statistics. The following shows how the trace_dispatch() method in the Profile class should be modified to install the calibration constant on a Sun Sparcstation 1000: def trace_dispatch(self, frame, event, arg): t = self.timer() t = t[0] + t[1] - self.t - .00053 # Calibration constant if self.dispatch[event](frame,t): t = self.timer() self.t = t[0] + t[1] else: r = self.timer() self.t = r[0] + r[1] - t # put back unrecorded delta return Note that if there is no calibration constant, then the line containing the callibration constant should simply say: t = t[0] + t[1] - self.t # no calibration constant You can also achieve the same results using a derived class (and the profiler will actually run equally fast!!), but the above method is the simplest to use. I could have made the profiler "self calibrating", but it would have made the initialization of the profiler class slower, and would have required some *very* fancy coding, or else the use of a variable where the constant `.00053' was placed in the code shown. This is a *VERY* critical performance section, and there is no reason to use a variable lookup at this point, when a constant can be used. File: pylibi, Node: Profiler Extensions, Prev: Calibration, Up: The Python Profiler Extensions -- Deriving Better Profilers ======================================= The `Profile' class of module `profile' was written so that derived classes could be developed to extend the profiler. Rather than describing all the details of such an effort, I'll just present the following two examples of derived classes that can be used to do profiling. If the reader is an avid Python programmer, then it should be possible to use these as a model and create similar (and perchance better) profile classes. If all you want to do is change how the timer is called, or which timer function is used, then the basic class has an option for that in the constructor for the class. Consider passing the name of a function to call into the constructor: pr = profile.Profile(your_time_func) The resulting profiler will call `your_time_func()' instead of `os.times()'. The function should return either a single number or a list of numbers (like what `os.times()' returns). If the function returns a single time number, or the list of returned numbers has length 2, then you will get an especially fast version of the dispatch routine. Be warned that you *should* calibrate the profiler class for the timer function that you choose. For most machines, a timer that returns a lone integer value will provide the best results in terms of low overhead during profiling. (os.times is *pretty* bad, 'cause it returns a tuple of floating point values, so all arithmetic is floating point in the profiler!). If you want to substitute a better timer in the cleanest fashion, you should derive a class, and simply put in the replacement dispatch method that better handles your timer call, along with the appropriate calibration constant :-). * Menu: * OldProfile Class:: * HotProfile Class:: File: pylibi, Node: OldProfile Class, Next: HotProfile Class, Prev: Profiler Extensions, Up: Profiler Extensions OldProfile Class ---------------- The following derived profiler simulates the old style profiler, providing errant results on recursive functions. The reason for the usefulness of this profiler is that it runs faster (i.e., less overhead) than the old profiler. It still creates all the caller stats, and is quite useful when there is *no* recursion in the user's code. It is also a lot more accurate than the old profiler, as it does not charge all its overhead time to the user's code. class OldProfile(Profile): def trace_dispatch_exception(self, frame, t): rt, rtt, rct, rfn, rframe, rcur = self.cur if rcur and not rframe is frame: return self.trace_dispatch_return(rframe, t) return 0 def trace_dispatch_call(self, frame, t): fn = `frame.f_code` self.cur = (t, 0, 0, fn, frame, self.cur) if self.timings.has_key(fn): tt, ct, callers = self.timings[fn] self.timings[fn] = tt, ct, callers else: self.timings[fn] = 0, 0, {} return 1 def trace_dispatch_return(self, frame, t): rt, rtt, rct, rfn, frame, rcur = self.cur rtt = rtt + t sft = rtt + rct pt, ptt, pct, pfn, pframe, pcur = rcur self.cur = pt, ptt+rt, pct+sft, pfn, pframe, pcur tt, ct, callers = self.timings[rfn] if callers.has_key(pfn): callers[pfn] = callers[pfn] + 1 else: callers[pfn] = 1 self.timings[rfn] = tt+rtt, ct + sft, callers return 1 def snapshot_stats(self): self.stats = {} for func in self.timings.keys(): tt, ct, callers = self.timings[func] nor_func = self.func_normalize(func) nor_callers = {} nc = 0 for func_caller in callers.keys(): nor_callers[self.func_normalize(func_caller)]=\ callers[func_caller] nc = nc + callers[func_caller] self.stats[nor_func] = nc, nc, tt, ct, nor_callers File: pylibi, Node: HotProfile Class, Prev: OldProfile Class, Up: Profiler Extensions HotProfile Class ---------------- This profiler is the fastest derived profile example. It does not calculate caller-callee relationships, and does not calculate cumulative time under a function. It only calculates time spent in a function, so it runs very quickly (re: very low overhead). In truth, the basic profiler is so fast, that is probably not worth the savings to give up the data, but this class still provides a nice example. class HotProfile(Profile): def trace_dispatch_exception(self, frame, t): rt, rtt, rfn, rframe, rcur = self.cur if rcur and not rframe is frame: return self.trace_dispatch_return(rframe, t) return 0 def trace_dispatch_call(self, frame, t): self.cur = (t, 0, frame, self.cur) return 1 def trace_dispatch_return(self, frame, t): rt, rtt, frame, rcur = self.cur rfn = `frame.f_code` pt, ptt, pframe, pcur = rcur self.cur = pt, ptt+rt, pframe, pcur if self.timings.has_key(rfn): nc, tt = self.timings[rfn] self.timings[rfn] = nc + 1, rt + rtt + tt else: self.timings[rfn] = 1, rt + rtt return 1 def snapshot_stats(self): self.stats = {} for func in self.timings.keys(): nc, tt = self.timings[func] nor_func = self.func_normalize(func) self.stats[nor_func] = nc, nc, tt, 0, {} File: pylibi, Node: Internet and WWW, Next: Restricted Execution, Prev: The Python Profiler, Up: Top Internet and WWW Services ************************* The modules described in this chapter provide various services to World-Wide Web (WWW) clients and/or services, and a few modules related to news and email. They are all implemented in Python. Some of these modules require the presence of the system-dependent module `sockets', which is currently only fully supported on Unix and Windows NT. Here is an overview: cgi -- Common Gateway Interface, used to interpret forms in server-side scripts. urllib -- Open an arbitrary object given by URL (requires sockets). httplib -- HTTP protocol client (requires sockets). ftplib -- FTP protocol client (requires sockets). gopherlib -- Gopher protocol client (requires sockets). nntplib -- NNTP protocol client (requires sockets). urlparse -- Parse a URL string into a tuple (addressing scheme identifier, network location, path, parameters, query string, fragment identifier). sgmllib -- Only as much of an SGML parser as needed to parse HTML. htmllib -- A (slow) parser for HTML documents. formatter -- Generic output formatter and device interface. rfc822 -- Parse RFC-822 style mail headers. mimetools -- Tools for parsing MIME style message bodies. * Menu: * cgi:: * urllib:: * httplib:: * ftplib:: * gopherlib:: * nntplib:: * urlparse:: * sgmllib:: * htmllib:: * formatter:: * rfc822:: * mimetools:: * binhex:: * uu:: * binascii:: * xdrlib:: File: pylibi, Node: cgi, Next: urllib, Prev: Internet and WWW, Up: Internet and WWW Standard Module `cgi' ===================== Support module for CGI (Common Gateway Interface) scripts. This module defines a number of utilities for use by CGI scripts written in Python. * Menu: * Introduction to the CGI module:: * Using the cgi module:: * Old classes:: * Functions:: * Caring about security:: * Installing your CGI script on a Unix system:: * Testing your CGI script:: * Debugging CGI scripts:: * Common problems and solutions:: File: pylibi, Node: Introduction to the CGI module, Next: Using the cgi module, Prev: cgi, Up: cgi Introduction ------------ A CGI script is invoked by an HTTP server, usually to process user input submitted through an HTML `<FORM>' or `<ISINPUT>' element. Most often, CGI scripts live in the server's special `cgi-bin' directory. The HTTP server places all sorts of information about the request (such as the client's hostname, the requested URL, the query string, and lots of other goodies) in the script's shell environment, executes the script, and sends the script's output back to the client. The script's input is connected to the client too, and sometimes the form data is read this way; at other times the form data is passed via the "query string" part of the URL. This module (`cgi.py') is intended to take care of the different cases and provide a simpler interface to the Python script. It also provides a number of utilities that help in debugging scripts, and the latest addition is support for file uploads from a form (if your browser supports it - Grail 0.3 and Netscape 2.0 do). The output of a CGI script should consist of two sections, separated by a blank line. The first section contains a number of headers, telling the client what kind of data is following. Python code to generate a minimal header section looks like this: print "Content-type: text/html" # HTML is following print # blank line, end of headers The second section is usually HTML, which allows the client software to display nicely formatted text with header, in-line images, etc. Here's Python code that prints a simple piece of HTML: print "<TITLE>CGI script output</TITLE>" print "<H1>This is my first CGI script</H1>" print "Hello, world!" (It may not be fully legal HTML according to the letter of the standard, but any browser will understand it.) File: pylibi, Node: Using the cgi module, Next: Old classes, Prev: Introduction to the CGI module, Up: cgi Using the cgi module -------------------- Begin by writing `import cgi'. Don't use `from cgi import *' - the module defines all sorts of names for its own use or for backward compatibility that you don't want in your namespace. It's best to use the `FieldStorage' class. The other classes define in this module are provided mostly for backward compatibility. Instantiate it exactly once, without arguments. This reads the form contents from standard input or the environment (depending on the value of various environment variables set according to the CGI standard). Since it may consume standard input, it should be instantiated only once. The `FieldStorage' instance can be accessed as if it were a Python dictionary. For instance, the following code (which assumes that the `Content-type' header and blank line have already been printed) checks that the fields `name' and `addr' are both set to a non-empty string: form = cgi.FieldStorage() form_ok = 0 if form.has_key("name") and form.has_key("addr"): if form["name"].value != "" and form["addr"].value != "": form_ok = 1 if not form_ok: print "<H1>Error</H1>" print "Please fill in the name and addr fields." return ...further form processing here... Here the fields, accessed through `form[key]', are themselves instances of `FieldStorage' (or `MiniFieldStorage', depending on the form encoding). If the submitted form data contains more than one field with the same name, the object retrieved by `form[key]' is not a `(Mini)FieldStorage' instance but a list of such instances. If you expect this possibility (i.e., when your HTML form comtains multiple fields with the same name), use the `type()' function to determine whether you have a single instance or a list of instances. For example, here's code that concatenates any number of username fields, separated by commas: username = form["username"] if type(username) is type([]): # Multiple username fields specified usernames = "" for item in username: if usernames: # Next item -- insert comma usernames = usernames + "," + item.value else: # First item -- don't insert comma usernames = item.value else: # Single username field specified usernames = username.value If a field represents an uploaded file, the value attribute reads the entire file in memory as a string. This may not be what you want. You can test for an uploaded file by testing either the filename attribute or the file attribute. You can then read the data at leasure from the file attribute: fileitem = form["userfile"] if fileitem.file: # It's an uploaded file; count lines linecount = 0 while 1: line = fileitem.file.readline() if not line: break linecount = linecount + 1 The file upload draft standard entertains the possibility of uploading multiple files from one field (using a recursive `multipart/*' encoding). When this occurs, the item will be a dictionary-like FieldStorage item. This can be determined by testing its type attribute, which should have the value `multipart/form-data' (or perhaps another string beginning with `multipart/' It this case, it can be iterated over recursively just like the top-level form object. When a form is submitted in the "old" format (as the query string or as a single data part of type `application/x-www-form-urlencoded'), the items will actually be instances of the class `MiniFieldStorage'. In this case, the list, file and filename attributes are always `None'. File: pylibi, Node: Old classes, Next: Functions, Prev: Using the cgi module, Up: cgi Old classes ----------- These classes, present in earlier versions of the `cgi' module, are still supported for backward compatibility. New applications should use the FieldStorage class. `SvFormContentDict': single value form content as dictionary; assumes each field name occurs in the form only once. `FormContentDict': multiple value form content as dictionary (the form items are lists of values). Useful if your form contains multiple fields with the same name. Other classes (`FormContent', `InterpFormContentDict') are present for backwards compatibility with really old applications only. If you still use these and would be inconvenienced when they disappeared from a next version of this module, drop me a note. File: pylibi, Node: Functions, Next: Caring about security, Prev: Old classes, Up: cgi Functions --------- These are useful if you want more control, or if you want to employ some of the algorithms implemented in this module in other circumstances. - function of module cgi: parse (FP) : Parse a query in the environment or from a file (default `sys.stdin'). - function of module cgi: parse_qs (QS) : parse a query string given as a string argument (data of type `application/x-www-form-urlencoded'). - function of module cgi: parse_multipart (FP, PDICT) : parse input of type `multipart/form-data' (for file uploads). Arguments are `fp' for the input file and `pdict' for the dictionary containing other parameters of `content-type' header Returns a dictionary just like `parse_qs()': keys are the field names, each value is a list of values for that field. This is easy to use but not much good if you are expecting megabytes to be uploaded - in that case, use the `FieldStorage' class instead which is much more flexible. Note that `content-type' is the raw, unparsed contents of the `content-type' header. Note that this does not parse nested multipart parts - use `FieldStorage' for that. - function of module cgi: parse_header (STRING) : parse a header like `Content-type' into a main content-type and a dictionary of parameters. - function of module cgi: test () : robust test CGI script, usable as main program. Writes minimal HTTP headers and formats all information provided to the script in HTML form. - function of module cgi: print_environ () : format the shell environment in HTML. - function of module cgi: print_form (FORM) : format a form in HTML. - function of module cgi: print_directory () : format the current directory in HTML. - function of module cgi: print_environ_usage () : print a list of useful (used by CGI) environment variables in HTML. - function of module cgi: escape () : convert the characters "`&'", "`<'" and "`>'" to HTML-safe sequences. Use this if you need to display text that might contain such characters in HTML. To translate URLs for inclusion in the HREF attribute of an `<A>' tag, use `urllib.quote()'. File: pylibi, Node: Caring about security, Next: Installing your CGI script on a Unix system, Prev: Functions, Up: cgi Caring about security --------------------- There's one important rule: if you invoke an external program (e.g. via the `os.system()' or `os.popen()' functions), make very sure you don't pass arbitrary strings received from the client to the shell. This is a well-known security hole whereby clever hackers anywhere on the web can exploit a gullible CGI script to invoke arbitrary shell commands. Even parts of the URL or field names cannot be trusted, since the request doesn't have to come from your form! To be on the safe side, if you must pass a string gotten from a form to a shell command, you should make sure the string contains only alphanumeric characters, dashes, underscores, and periods.