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[gil] ToolBox uploaded
by Christian Henning 14 Feb '13

14 Feb '13

Hi all, I finally have managed to upload the "toolbox" extension for boost::gil. The toolbox provides some helpful extension for gil that are too small to be an extension themselves. There are: * color spaces: cmyka, gray_alpha, hsl, hsv, lab, xyz * metafunctions: channel_type_to_index, is_bit_aligned, is_similar, is_homogeneous, get_num_bits, channel_type, get_pixel_type, pixel_bit_size * image types: indexed_image * color converters: gray -> rgba I would need some help hooking up the tests and doc generation. Let me know if you any questions or improvements. Regards, Christian

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Relational database library (was: [GSOC] Are we going to participate?)
by Mateusz Loskot 14 Feb '13

14 Feb '13

On 14 February 2013 09:40, Roland Bock <rbock(a)eudoxos.de> wrote: > On 2013-02-14 06:04, Michael Marcin wrote: >> - relational database library (what ever happened with boost and soci?) > FYI: > I am currently working on a C++11 SQL library. Once you declare your > table structures in C++, it will allow you to express queries very close > to SQL: For example: > > TabSample t; > auto results = sql::select(sql::distinct, t.b, t.c) > .from(t) > .where(t.a > 7 and t.b != "cheesecake") > .order_by(t.c).run(db); > > for (auto row: results) > { > int a = row.a; > std::string c = row.c; > } > > The library will take care of escaping (to prevent code injection), the > compiler will enforce type safety (e.g. disallow comparison of integral > and string values). There will also be a lot of sanity checks at compile > time, for instance you cannot call .from() twice. It will even be able > to tell you, if you are referencing tables, which are missing in > .from(), etc. > > No more string concatenation and hoping to get the syntax right. The > compiler will tell you, if you do it wrong. > No more indexed access to string results. Result rows will allow you to > access individual columns by name (and they will have a decent type). > [...] Roland, It all sounds exciting. I remember the discussion long time ago we had, with similar ideas brainstormed [1], where you also asked this question, later discussed with Maciej: "Maybe it would make sense to add such a layer as an option to SOCI?" FYI, in SOCI, we're planning [2] to move SOCI towards C++11 It likely may happen during SOCI 4.0.0 release. So, I'd be interested in taking another look at this and having such layer adopted in SOCI too. Where I could learn about your solution more? [1] http://lists.boost.org/Archives/boost/2010/09/170983.php [2] https://github.com/SOCI/soci/wiki/Roadmap Best regards, -- Mateusz Loskot, http://mateusz.loskot.net

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[Git] Documentation update
by Beman Dawes 14 Feb '13

14 Feb '13

The "Git and Modular Boost" documentation has been updated: * The GitHub mothership with be github.com/boostorg * Full history for the current trunk and branches/release will be available. * There are now active milestone and issue trackers for the conversion. See https://svn.boost.org/trac/boost/wiki/WikiStart#GitandModularBoost --Beman

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adding comment to boost_1_54_0.qbk?
by Oliver Kowalke 14 Feb '13

14 Feb '13

Is it allowed to add an entry for updated libraries in public_html/live/feed/history/boost_1_54_0.qbk? Oliver

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[smart ptr] Any interest in copy-on-write pointer for C++11?
by Ralph Tandetzky 14 Feb '13

14 Feb '13

Hi, is there any interest in a copy-on-write pointer implementation? I wrote a cow_ptr<T> template class for C++11 which has the following use cases: 1. They are well suited as pimpl pointers for classes with value semantics. With most other smart pointers it is always necessary to reimplement the copy constructor, and the copy assignment operator, sometimes also the destructor. This is not necessary here and you'll get the right semantics and even the additional performance gain due to lazy copying. A futher advantage is that constness of cow pointer member functions propagates to the Impl object naturally making it easier to write const-correct code. // central_class_with_value_semantics.h class CentralClassWithValueSemantics { public: // ... public interface goes here ... // private: struct Impl; // forward declaration cow_ptr<Impl> m; }; // central_class_with_value_semantics.cpp struct CentralClassWithValueSemantics::Impl { // ... definition of hidden members goes here ... // } This is called the pimpl idiom, or private implementation idiom, handle body idiom, or compiler firewall idiom. 2. For classes with members whose copy-operations are expensive and/or which take a lot of space in memory, these members can be wrapped in a cow pointer. An example are matrix or image classes whose data might be stored in a std::vector. The matrix header information may be changed without deep copy. This boosts performance and optimizes memory usage, but at the same time retains the value semantics one feels comfortable with. class Matrix { public: // ... public interface goes here ... // private: size_t nRows, nCols; // can be touched without deep copy. cow_ptr<std::vector<float>> data; // copy-on-write } In such classes the default version of copy and move construction will usually work just fine. 3. You can add cloning to a class hierarchy from the outside. With cow_ptr<Base> a( new Derived1 ); cow_ptr<Base> b( new Derived2 ); cow_ptr<Base> c; c = a; // performs a shallow copy. c->doSomething(); // makes a deep copy of a as a Derived1 class. // There is no slicing involved. you copy Base objects polymorphically. The class Base can even be abstract here. It is only required that Derived1 and Derived2 be CopyConstructible. 4. You can create arrays with elements that retain polymorphic behaviour and have genuine value sematics at the same time with std::vector<cow_ptr<Base>> polymorphic_array_with_value_semantics; -- There are probably other use cases. I published the code on github ( https://github.com/ralphtandetzky/cow_ptr) Any suggestions for improvements would be greatly appreciated. Regards, Ralph Tandetzky.

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[container] varray aka static_vector
by Andrew Hundt 13 Feb '13

13 Feb '13

As Adam mentioned in an earlier thread we have renamed static_vector to varray to better represent how it works like an array with size without requiring the values to be default constructible, and to better distinguish it from hybrid_vector. varray location: https://svn.boost.org/svn/boost/sandbox/varray/ Documentation: https://svn.boost.org/svn/boost/sandbox/varray/doc/html/index.html One open question is if the 3rd strategy parameter should be public or in the detail namespace. Based on some of the feedback we moved the strategy from container to container_detail, and made the public version varray<typename T, std::size_t N>. However, we wanted to get others' thoughts on this change. Additionally, should primitives be default initialized with the contents of memory like an array or value initialized to 0 like a standard container in the constructor, resize(), and emplace_back()? Generally, built-in types are initialized when the constructor is called explicitly. For example: int a; // a has the value of underlying memory int b = int(); // b has a value of 0 Right now the default behavior of our container is more like a vector than an array when creating values using the default ctor. Default constructable values are explicitly initialized in the varray ctor, resize(), emplace() and emplace_back() functions, even when they are trivially constructable. We find this behavior more intuitive and therefore we have made it the default. If the performance needs to be improved slightly the behavior may be changed or disabled in container_detail::varray_traits<V, C, S>. Here is an example demonstrating the current default behavior: vector<int> vec(3); varray<int, 3> varr(3); array<int, 3> arr; vec[0] == varr[0] and vec[0] != arr[0] Any feedback is very much appreciated. Cheers! Andrew Hundt Adam Wulkiewicz

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[bind] Boost.Bind compile error with msvc-11 and msvc-10
by Ian Emmons 13 Feb '13

13 Feb '13

I have attached a short, self-contained test case that uses Boost.Bind. this test case compiles and runs correctly with the following tool sets: darwin (gcc 4.2.1 on MacOS 10.8) clang (clang on MacOS 10.8) gcc (gcc 4.7 on Ubuntu 12.04) msvc-9 (Visual Studio 2008 on Windows 7) It fails to compile on these: msvc-11 (Visual Studio 2012 on Windows 7) msvc-10 (Visual Studio 2010 on Windows 7) I have also attached the error messages from msvc-11, which seem to indicate that Boost.Bind is the problem. Can anyone shed any light on this issue? Is this an error in my usage of Boost.Bind? A Boost.Bind bug? A Visual Studio bug? Something else? Thanks, Ian

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[Contract] Eagerly waiting...
by Robert Jones 13 Feb '13

13 Feb '13

Hi Lorenzo (or anyone who knows) AFAICS your contract library seems to have been approved for a while, but not made it into a release. Do you have an ETA for this? Thx, Rob.

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[container] boost::container::vector, hybrid_vector, and performance
by Andrew Hundt 13 Feb '13

13 Feb '13

After some of the varray discussion and interest in hybrid_vector I modified an existing synthetic benchmark from the C++Next site (cpp-next.com) to test varray, boost::container::vector, boost::container::vector<T, stack_allocator>, and std::vector. Essentially for each container it times constructing a container of containers of primitives, std::sort, std::rotate, and destruction. Note that the test calls reserve() when setting up each container so that each gets a fair shot and isn't unfairly bogged down by a simple allocation optimization. The short results summary: varray was generally at or near the top for performance due to its simplicity, and interestingly on almost all platforms std::vector significantly outperformed boost::container::vector, which edged out boost::container::vector<T, stack_allocator>. Of course these are synthetic tests of only a subset of these containers’ functionality using only primitives so it should not be construed to necessarily represent the performance of these containers for other uses. Nonetheless I thought it may be interesting for discussion. There is also an additional benchmark of Adam Wulkiewicz’s rtree using linux clang comparing varray, container::vector, and std::vector with similar results. One question this leads me to ask is if someone might be able to illustrate a significant advantage hybrid_vector could have over boost::container::vector<T, stack_allocator>? The test can be found at: https://svn.boost.org/svn/boost/sandbox/varray/example/bench_varray.cpp Results are below. Cheers! Andrew Hundt --------------------------------------------------------------------------------------------------------------------------- Apple LLVM version 4.2 (clang-425.0.24) (based on LLVM 3.2svn) Target: x86_64-apple-darwin12.2.0 Thread model: posix Processor 2.2 GHz Intel Core i7 Memory 16 GB 1333 MHz DDR3 Software OS X 10.8.2 (12C60) --------------------------------------------------------------------------------------------------------------------------- set stack size limit to: 17000000 bytes N = 1000 varray benchmark: construction took 10.576854s wall, 10.580000s user + 0.020000s system = 10.600000s CPU (100.2%) sort took 4.323139s wall, 4.290000s user + 0.010000s system = 4.300000s CPU (99.5%) rotate took 0.909033s wall, 0.910000s user + 0.000000s system = 0.910000s CPU (100.1%) destruction took 0.000725s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n/a%) done Total time = 15.824697s wall, 15.780000s user + 0.040000s system = 15.820000s CPU (100.0%) boost::container::vector benchmark construction took 11.656541s wall, 11.180000s user + 0.320000s system = 11.500000s CPU (98.7%) sort took 5.213782s wall, 4.940000s user + 0.000000s system = 4.940000s CPU (94.7%) rotate took 0.003201s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (312.4%) destruction took 2.482177s wall, 0.640000s user + 2.000000s system = 2.640000s CPU (106.4%) done Total time = 19.367074s wall, 16.770000s user + 2.330000s system = 19.100000s CPU (98.6%) boost::container::vector benchmark with stack_allocator construction took 11.698265s wall, 11.110000s user + 0.430000s system = 11.540000s CPU (98.6%) sort took 5.227070s wall, 5.180000s user + 0.000000s system = 5.180000s CPU (99.1%) rotate took 0.003778s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (264.7%) destruction took 2.493007s wall, 0.510000s user + 1.900000s system = 2.410000s CPU (96.7%) done Total time = 19.433571s wall, 16.810000s user + 2.340000s system = 19.150000s CPU (98.5%) std::vector benchmark construction took 11.605458s wall, 11.400000s user + 0.300000s system = 11.700000s CPU (100.8%) sort took 2.256151s wall, 2.250000s user + 0.000000s system = 2.250000s CPU (99.7%) rotate took 0.003200s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n/a%) destruction took 2.498035s wall, 0.370000s user + 2.040000s system = 2.410000s CPU (96.5%) done Total time = 16.374640s wall, 14.020000s user + 2.350000s system = 16.370000s CPU (100.0%) varray/boost::container::vector total time comparison: wall = 0.817093 user = 0.940966 system = 0.0171674 (user+system) = 0.828272 varray/(boost::container::vector + stack_allocator) total time comparison: wall = 0.814297 user = 0.938727 system = 0.017094 (user+system) = 0.82611 varray/std::vector total time comparison: wall = 0.966415 user = 1.12553 system = 0.0170213 (user+system) = 0.966402 --------------------------------------------------------------------------------------------------------------------------- Windows VS2012 Core2Duo P7350 2GHz, 3GB Ram --------------------------------------------------------------------------------------------------------------------------- N = 1000 varray benchmark: construction took 18.901735s wall, 18.798121s user + 0.000000s system = 18.798121s CPU (99.5%) sort took 3.093771s wall, 3.260421s user + 0.000000s system = 3.260421s CPU (105.4%) rotate took 0.336938s wall, 0.280802s user + 0.000000s system = 0.280802s CPU (83.3%) destruction took 0.000270s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n/a%) done Total time = 22.339054s wall, 22.339343s user + 0.000000s system = 22.339343s CPU (100.0%) boost::container::vector benchmark construction took 18.757410s wall, 18.595319s user + 0.202801s system = 18.798121s CPU (100.2%) sort took 0.120896s wall, 0.140401s user + 0.000000s system = 0.140401s CPU (116.1%) rotate took 0.004482s wall, 0.015600s user + 0.000000s system = 0.015600s CPU (348.0%) destruction took 0.282280s wall, 0.046800s user + 0.171601s system = 0.218401s CPU (77.4%) done Total time = 19.167624s wall, 18.798121s user + 0.374402s system = 19.172523s CPU (100.0%) boost::container::vector benchmark with stack_allocator construction took 18.811076s wall, 18.361318s user + 0.312002s system = 18.673320s CPU (99.3%) sort took 0.134590s wall, 0.218401s user + 0.000000s system = 0.218401s CPU (162.3%) rotate took 0.004111s wall, 0.015600s user + 0.000000s system = 0.015600s CPU (379.5%) destruction took 0.279619s wall, 0.031200s user + 0.280802s system = 0.312002s CPU (111.6%) done Total time = 19.231975s wall, 18.626519s user + 0.592804s system = 19.219323s CPU (99.9%) std::vector benchmark construction took 19.587929s wall, 19.359724s user + 0.234002s system = 19.593726s CPU (100.0%) sort took 0.112920s wall, 0.124801s user + 0.000000s system = 0.124801s CPU (110.5%) rotate took 0.002486s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n/a%) destruction took 0.262937s wall, 0.031200s user + 0.218401s system = 0.249602s CPU (94.9%) done Total time = 19.968799s wall, 19.515725s user + 0.452403s system = 19.968128s CPU (100.0%) varray/boost::container::vector total time comparison: wall = 1.16546 user = 1.18838 system = 0 (user+system) = 1.16517 varray/(boost::container::vector + stack_allocator) total time comparison: wall = 1.16156 user = 1.19933 system = 0 (user+system) = 1.16234 varray/std::vector total time comparison: wall = 1.1187 user = 1.14468 system = 0 (user+system) = 1.11875 --------------------------------------------------------------------------------------------------------------------------- Windows mingw_4_7 -O2 Core2Duo P7350 2GHz, 3GB Ram --------------------------------------------------------------------------------------------------------------------------- N = 1000 varray benchmark: construction took 13.752772s wall, 13.447286s user + 0.000000s system = 13.447286s CPU (97.8%) sort took 2.207612s wall, 2.449216s user + 0.000000s system = 2.449216s CPU (110.9%) rotate took 0.381684s wall, 0.452403s user + 0.000000s system = 0.452403s CPU (118.5%) destruction took 0.000265s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n/a%) done Total time = 16.348433s wall, 16.348905s user + 0.000000s system = 16.348905s CPU (100.0%) boost::container::vector benchmark construction took 16.736949s wall, 15.631300s user + 1.060807s system = 16.692107s CPU (99.7%) sort took 5.170353s wall, 5.226034s user + 0.000000s system = 5.226034s CPU (101.1%) rotate took 0.001871s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n/a%) destruction took 0.537667s wall, 0.124801s user + 0.374402s system = 0.499203s CPU (92.8%) done Total time = 22.453639s wall, 20.982135s user + 1.435209s system = 22.417344s CPU (99.8%) boost::container::vector benchmark with stack_allocator construction took 16.227005s wall, 15.412899s user + 0.624004s system = 16.036903s CPU (98.8%) sort took 4.376050s wall, 4.477229s user + 0.000000s system = 4.477229s CPU (102.3%) rotate took 0.001871s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n/a%) destruction took 0.523920s wall, 0.109201s user + 0.514803s system = 0.624004s CPU (119.1%) done Total time = 21.134513s wall, 19.999328s user + 1.138807s system = 21.138135s CPU (100.0%) std::vector benchmark construction took 15.182662s wall, 14.149291s user + 0.904806s system = 15.054096s CPU (99.2%) sort took 1.408413s wall, 1.528810s user + 0.000000s system = 1.528810s CPU (108.5%) rotate took 0.001668s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n/a%) destruction took 0.451160s wall, 0.046800s user + 0.421203s system = 0.468003s CPU (103.7%) done Total time = 17.049415s wall, 15.724901s user + 1.326008s system = 17.050909s CPU (100.0%) varray/boost::container::vector total time comparison: wall = 0.728097 user = 0.779182 system = 0 (user+system) = 0.729297 varray/(boost::container::vector + stack_allocator) total time comparison: wall = 0.773542 user = 0.817473 system = 0 (user+system) = 0.773432 varray/std::vector total time comparison: wall = 0.958885 user = 1.03968 system = 0 (user+system) = 0.958829 --------------------------------------------------------------------------------------------------------------------------- Linux clang_3_0_6 -O2 Core2Duo P7350 2GHz, 3GB Ram --------------------------------------------------------------------------------------------------------------------------- set stack size limit to: 9000000 bytes N = 1000 varray benchmark: construction took 27.455347s wall, 27.360000s user + 0.050000s system = 27.410000s CPU (99.8%) sort took 5.683607s wall, 5.750000s user + 0.010000s system = 5.760000s CPU (101.3%) rotate took 1.651716s wall, 1.560000s user + 0.000000s system = 1.560000s CPU (94.4%) destruction took 0.002050s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n/a%) done Total time = 34.810039s wall, 34.700000s user + 0.060000s system = 34.760000s CPU (99.9%) boost::container::vector benchmark construction took 39.073566s wall, 33.070000s user + 5.960000s system = 39.030000s CPU (99.9%) sort took 13.405286s wall, 13.440000s user + 0.020000s system = 13.460000s CPU (100.4%) rotate took 0.011300s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n/a%) destruction took 1.005573s wall, 0.810000s user + 0.150000s system = 0.960000s CPU (95.5%) done Total time = 53.509749s wall, 47.330000s user + 6.130000s system = 53.460000s CPU (99.9%) boost::container::vector benchmark with stack_allocator construction took 39.457245s wall, 33.660000s user + 5.790000s system = 39.450000s CPU (100.0%) sort took 13.454378s wall, 13.190000s user + 0.050000s system = 13.240000s CPU (98.4%) rotate took 0.013398s wall, 0.040000s user + 0.000000s system = 0.040000s CPU (298.5%) destruction took 1.007952s wall, 0.860000s user + 0.300000s system = 1.160000s CPU (115.1%) done Total time = 53.947026s wall, 47.750000s user + 6.140000s system = 53.890000s CPU (99.9%) std::vector benchmark construction took 31.662683s wall, 25.550000s user + 6.240000s system = 31.790000s CPU (100.4%) sort took 3.102917s wall, 2.870000s user + 0.080000s system = 2.950000s CPU (95.1%) rotate took 0.010094s wall, 0.020000s user + 0.000000s system = 0.020000s CPU (198.1%) destruction took 1.002616s wall, 0.770000s user + 0.210000s system = 0.980000s CPU (97.7%) done Total time = 35.793019s wall, 29.220000s user + 6.530000s system = 35.750000s CPU (99.9%) varray/boost::container::vector total time comparison: wall = 0.650536 user = 0.73315 system = 0.00978793 (user+system) = 0.650206 varray/(boost::container::vector + stack_allocator) total time comparison: wall = 0.645263 user = 0.726702 system = 0.00977199 (user+system) = 0.645018 varray/std::vector total time comparison: wall = 0.972537 user = 1.18754 system = 0.00918836 (user+system) = 0.972308 --------------------------------------------------------------------------------------------------------------------------- Linux gcc_4_7_2 -O2 Core2Duo P7350 2GHz, 3GB Ram --------------------------------------------------------------------------------------------------------------------------- set stack size limit to: 9000000 bytes N = 1000 varray benchmark: construction took 27.677216s wall, 27.560000s user + 0.050000s system = 27.610000s CPU (99.8%) sort took 5.934591s wall, 6.080000s user + 0.010000s system = 6.090000s CPU (102.6%) rotate took 1.720024s wall, 1.590000s user + 0.000000s system = 1.590000s CPU (92.4%) destruction took 0.002077s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n/a%) done Total time = 35.350653s wall, 35.240000s user + 0.060000s system = 35.300000s CPU (99.9%) boost::container::vector benchmark construction took 38.196097s wall, 32.530000s user + 5.830000s system = 38.360000s CPU (100.4%) sort took 12.107587s wall, 12.070000s user + 0.010000s system = 12.080000s CPU (99.8%) rotate took 0.011146s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n/a%) destruction took 1.054511s wall, 0.680000s user + 0.190000s system = 0.870000s CPU (82.5%) done Total time = 51.383338s wall, 45.290000s user + 6.030000s system = 51.320000s CPU (99.9%) boost::container::vector benchmark with stack_allocator construction took 37.421275s wall, 31.590000s user + 5.840000s system = 37.430000s CPU (100.0%) sort took 12.009866s wall, 11.930000s user + 0.020000s system = 11.950000s CPU (99.5%) rotate took 0.013459s wall, 0.010000s user + 0.000000s system = 0.010000s CPU (74.3%) destruction took 1.053435s wall, 0.770000s user + 0.300000s system = 1.070000s CPU (101.6%) done Total time = 50.511806s wall, 44.300000s user + 6.170000s system = 50.470000s CPU (99.9%) std::vector benchmark construction took 31.849724s wall, 25.920000s user + 5.760000s system = 31.680000s CPU (99.5%) sort took 3.244223s wall, 3.240000s user + 0.110000s system = 3.350000s CPU (103.3%) rotate took 0.010454s wall, 0.000000s user + 0.000000s system = 0.000000s CPU (n/a%) destruction took 1.054326s wall, 0.780000s user + 0.280000s system = 1.060000s CPU (100.5%) done Total time = 36.173664s wall, 29.950000s user + 6.150000s system = 36.100000s CPU (99.8%) varray/boost::container::vector total time comparison: wall = 0.687979 user = 0.778097 system = 0.00995025 (user+system) = 0.687841 varray/(boost::container::vector + stack_allocator) total time comparison: wall = 0.699849 user = 0.795485 system = 0.00972447 (user+system) = 0.699425 varray/std::vector total time comparison: wall = 0.977248 user = 1.17663 system = 0.0097561 (user+system) = 0.977839 --------------------------------------------------------------------------------------------------------------------------- Linux clang_3_0_6 -O2 Core2Duo P7350 2GHz, 3GB Ram Additional benchmark of Adam Wulkiewicz’s rtree --------------------------------------------------------------------------------------------------------------------------- ins[s] qB[s] qiwo[s] qn[s] qnP5[s] qnB[s] rem[s] mem[MB] - container::vector 10.8 14.4 6.9 3.1 4.8 6.0 5.7 184.1 - std::vector 10.9 13.5 6.9 3.4 5.2 5.9 5.4 274,8 - varray 10.2 12.6 6.3 2.8 4.5 5.8 4.9 207,1 So in this case varray is slightly faster from both and consumes less memory than std::vector.

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[iostreams] Check zlib/bzip2 availability on configuration checks
by Kohei Takahashi 13 Feb '13

13 Feb '13

Hi all, I attached a patch for preventing build error with the environment which provides no zlib and/or bzip2. This patch detects zlib.h and bzlib.h availability on configuration checks timing. It also should work with any environment variable (e.g. ZLIB_SOURCE,BZIP2_SOURCE, ...). Suggestions and comments welcome. Thanks, Kohei Takahashi

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