Mailman 3 May 2005 - SciPy-User

Problems with fftpack on OS X 10.4
by Tim Gray May 11, 2005

May 11, 2005

Hi, I am a very happy (new) user of scipy (CVS), matplotlib (.74), and python (2.4.1) for analysis and plotting of scientific data. I have been running a CVS version of scipy on my computer at work, which is running OS X 10.3.8. Everything has been wonderful there. However, on my personal computer, I recently upgraded to OS X 10.4. I reinstalled all my python packages and got everything working, except for scipy (again, the current CVS build). Scipy seemed to build ok, but when I call anything in fftpack, I get the error at the end of this post. I recompiled fftw (2.1.5) and ran make check (or test? they all passed) before rebuilding scipy, but the error remains. Any help would be appreciated. Thanks! ------- /Library/Frameworks/Python.framework/Versions/2.4/lib/python2.4/site.py in __call__(self, *args, **kwds) 326 def __call__(self, *args, **kwds): 327 import pydoc --> 328 return pydoc.help(*args, **kwds) 329 330 def sethelper(): /Library/Frameworks/Python.framework/Versions/2.4/lib/python2.4/site-packages/scipy_base/ppimport.py in _ppimport_pydoc_help_call(self, *args, **kwds) 384 _old_pydoc_help_call = _pydoc.help.__class__.__call__ 385 def _ppimport_pydoc_help_call(self,*args,**kwds): --> 386 return _old_pydoc_help_call(self, *map(_ppresolve_ignore_failure,args), 387 **kwds) 388 _ppimport_pydoc_help_call.__doc__ = _old_pydoc_help_call.__doc__ /Library/Frameworks/Python.framework/Versions/2.4/lib/python2.4/site-packages/scipy_base/ppimport.py in _ppresolve_ignore_failure(a) 370 371 def _ppresolve_ignore_failure(a): --> 372 return ppresolve(a,ignore_failure=1) 373 374 try: /Library/Frameworks/Python.framework/Versions/2.4/lib/python2.4/site-packages/scipy_base/ppimport.py in ppresolve(a, ignore_failure) 364 if hasattr(a,'_ppimport_importer') or \ 365 hasattr(a,'_ppimport_module'): --> 366 a = getattr(a,'_ppimport_module',a) 367 if hasattr(a,'_ppimport_attr'): 368 a = a._ppimport_attr /Library/Frameworks/Python.framework/Versions/2.4/lib/python2.4/site-packages/scipy_base/ppimport.py in __getattr__(self, name) 301 module = self.__dict__['_ppimport_module'] 302 except KeyError: --> 303 module = self._ppimport_importer() 304 return getattr(module, name) 305 /Library/Frameworks/Python.framework/Versions/2.4/lib/python2.4/site-packages/scipy_base/ppimport.py in _ppimport_importer(self) 256 module = sys.modules[name] 257 except KeyError: --> 258 raise ImportError,self.__dict__.get('_ppimport_exc_info')[1] 259 if module is not self: 260 exc_info = self.__dict__.get('_ppimport_exc_info') ImportError: Failure linking new module: /Library/Frameworks/Python.framework/Versions/2.4/lib/python2.4/site-packages/scipy/fftpack/_fftpack.so: Symbol not found: _fprintf$LDBLStub Referenced from: /Library/Frameworks/Python.framework/Versions/2.4/lib/python2.4/site-packages/scipy/fftpack/_fftpack.so Expected in: dynamic lookup

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conference
by Daniel Wheeler May 11, 2005

May 11, 2005

Are there any plans for a SciPy conference this year? ------------------------------------- Daniel Wheeler Telephone: (301) 975-8358

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Numarray image processing
by Jeremy Sanders May 11, 2005

May 11, 2005

Hi - I'm trying to think of an efficient way to do the following in numarray. I have an image, and I want to caclulate the mean of the pixels at a particular radius in the image (from a fixed point), and subtract this mean away from those pixels at that radius. Can anyone provide insight into how this can be done without looping over the pixels? I've managed to generate an image containing the radius of each pixel, but this isn't much use. Thanks Jeremy -- Jeremy Sanders <jeremy(a)jeremysanders.net> http://www.jeremysanders.net/ Cambridge, UK Public Key Server PGP Key ID: E1AAE053

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installation in FC3
by Ryan Krauss May 10, 2005

May 10, 2005

(my appologies to the moderator. I initially sent this message from the wrong account and it bounced back pending moderator approval.) Hey, I am having trouble getting scipy installed in Linux. I am a grad student in mechanical engineering and have been using Matlab in Windows to do much of my data analysis and some curve fitting/optimization. I would like to switch over to using scipy in linux instead, but I am having trouble getting things installed. I am fairly new to linux but have been using python for about 9 months. I am running Fedora Core 3 and kde on a Compaq Presario laptop (P4). I tried in stalling using rpms, but the Numeric rpm kept telling me that I needed python >=2.3, even though I am running 2.3.4. I finally got Numeric installed from source, but the scipy rpm gives a segmentation fault when I try import scipy from within python. So, I have beening doing everything to install scipy from source and thought things were going fairly well. I type python setup.py install and lots of stuff scrolls by and things are compiling and then I get the message: In file included from Lib/xplt/src/play/x11/colors.c:9: Lib/xplt/src/play/x11/playx.h:11:22: X11/Xlib.h: No such file or directory and then lots of related errors come up. I tried searching the scipy archives for X11 and Xlib.h, but couldn't find anything. I also tried "locate Xlib.h" but it is don't on my system. Any help would be appreciated, Ryan Krauss requested info from web site follows: [ryan@localhost ~]$ python -c 'from f2py2e.diagnose import run;run()' ------ os.name='posix' ------ sys.platform='linux2' ------ sys.version: 2.3.4 (#1, Oct 26 2004, 16:42:40) [GCC 3.4.2 20041017 (Red Hat 3.4.2-6.fc3)] ------ sys.prefix: /usr ------ sys.path=':/usr/lib/python23.zip:/usr/lib/python2.3:/usr/lib/python2.3/plat-linux2:/usr/lib/python2.3/lib-tk:/usr/lib/python2.3/lib-dynload:/usr/lib/python2.3/site-packages:/usr/lib/python2.3/site-packages/Numeric:/usr/lib/python2.3/site-packages/gtk-2.0:/usr/lib/python2.3/site-packages/wx-2.6-gtk2-unicode' ------ Failed to import numarray: No module named numarray Failed to import scipy_distutils: No module named scipy_distutils Found Numeric version '23.8' in /usr/lib/python2.3/site-packages/Numeric/Numeric.pyc Found f2py2e version '2.45.241_1926' in /usr/lib/python2.3/site-packages/f2py2e/f2py2e.pyc -------------------------------------- #1 -------------------------------------- [ryan@localhost ~]$ python -c 'import os,sys;print os.name,sys.platform' posix linux2 [ryan@localhost ~]$ uname -a Linux localhost.localdomain 2.6.11-1.14_FC3 #1 Thu Apr 7 19:23:49 EDT 2005 i686i686 i386 GNU/Linux ----------------------------------- #2 ----------------------------------- [ryan@localhost ~]$ gcc -v Reading specs from /usr/lib/gcc/i386-redhat-linux/3.4.2/specs Configured with: ../configure --prefix=/usr --mandir=/usr/share/man --infodir=/usr/share/info --enable-shared --enable-threads=posix --disable-checking --with-system-zlib --enable-__cxa_atexit --disable-libunwind-exceptions --enable-java-awt=gtk --host=i386-redhat-linux Thread model: posix gcc version 3.4.2 20041017 (Red Hat 3.4.2-6.fc3) [ryan@localhost ~]$ g77 --version GNU Fortran (GCC) 3.4.2 20041017 (Red Hat 3.4.2-6.fc3) Copyright (C) 2004 Free Software Foundation, Inc. --------------------------------- #3 --------------------------------- [ryan@localhost ~]$ python -c 'import sys;print sys.version' 2.3.4 (#1, Oct 26 2004, 16:42:40) [GCC 3.4.2 20041017 (Red Hat 3.4.2-6.fc3)] ---------------------------------- #4 ---------------------------------- [ryan@localhost ~]$ python -c 'import Numeric;print Numeric.__version__' 23.8 ----------------------------------- #5 ----------------------------------- [ryan@localhost ~]$ f2py -v 2.45.241_1926 ----------------------------------- #6 ----------------------------------- [ryan@localhost linalg]$ python setup_atlas_version.py build_ext --inplace --force Traceback (most recent call last): File "setup_atlas_version.py", line 5, in ? from scipy_distutils.misc_util import get_path, default_config_dict ImportError: No module named scipy_distutils.misc_util [ryan@localhost linalg]$ python -c 'import atlas_version' --------------------------------- #7 ---------------------------------\[ryan@localhost scipy_distutils]$ python system_info.py _pkg_config_info: NOT AVAILABLE agg2_info: ( src_dirs = .:/usr/local/src ) NOT AVAILABLE atlas_blas_info: ( library_dirs = /usr/local/lib/atlas/:/usr/local/lib:/usr/lib ) ( paths: /usr/local/lib/atlas ) ( paths: /usr/lib/sse2 ) ( paths: /usr/local/lib/atlas/libf77blas.a ) ( paths: /usr/local/lib/atlas/libcblas.a ) ( paths: /usr/local/lib/atlas/libatlas.a ) ( paths: /usr/local/lib/atlas/libf77blas.a ) ( paths: /usr/local/lib/atlas/libcblas.a ) ( paths: /usr/local/lib/atlas/libatlas.a ) system_info.atlas_blas_info ( include_dirs = /usr/local/lib/atlas/:/usr/local/include:/usr/include ) ( paths: /usr/local/lib/atlas/cblas.h,/usr/local/lib/atlas/cblas.h,/usr/local/lib/atlas/cblas.h ) FOUND: libraries = ['f77blas', 'cblas', 'atlas'] library_dirs = ['/usr/local/lib/atlas'] language = c include_dirs = ['/usr/local/lib/atlas'] atlas_blas_threads_info: ( library_dirs = /usr/local/lib/atlas/:/usr/local/lib:/usr/lib ) ( paths: /usr/local/lib/atlas ) ( paths: /usr/lib/sse2 ) ( paths: /usr/local/lib/atlas/libatlas.a ) ( paths: /usr/local/lib/atlas/libatlas.a ) system_info.atlas_blas_threads_info NOT AVAILABLE atlas_info: ( library_dirs = /usr/local/lib/atlas/:/usr/local/lib:/usr/lib ) ( paths: /usr/local/lib/atlas ) ( paths: /usr/lib/sse2 ) ( paths: /usr/local/lib/atlas/libf77blas.a ) ( paths: /usr/local/lib/atlas/libcblas.a ) ( paths: /usr/local/lib/atlas/libatlas.a ) ( paths: /usr/local/lib/atlas/liblapack.a ) system_info.atlas_info ( include_dirs = /usr/local/lib/atlas/:/usr/local/include:/usr/include ) ( paths: /usr/local/lib/atlas/cblas.h,/usr/local/lib/atlas/cblas.h,/usr/local/lib/atlas/cblas.h ) FOUND: libraries = ['lapack', 'f77blas', 'cblas', 'atlas'] library_dirs = ['/usr/local/lib/atlas/'] language = f77 include_dirs = ['/usr/local/lib/atlas'] atlas_threads_info: ( library_dirs = /usr/local/lib/atlas/:/usr/local/lib:/usr/lib ) ( paths: /usr/local/lib/atlas ) ( paths: /usr/lib/sse2 ) ( paths: /usr/local/lib/atlas/libatlas.a ) ( paths: /usr/local/lib/atlas/libatlas.a ) system_info.atlas_threads_info NOT AVAILABLE blas_info: ( library_dirs = /usr/local/lib:/usr/lib ) ( paths: /usr/lib/libblas.so ) FOUND: libraries = ['blas'] library_dirs = ['/usr/lib'] language = f77 blas_opt_info: Traceback (most recent call last): File "system_info.py", line 1430, in ? show_all() File "system_info.py", line 1427, in show_all r = c.get_info() File "/home/ryan/downloads/python/scipy/SciPy_complete-0.3.2/scipy_core/scipy_distutils/system_info.py", line 314, in get_info self.calc_info() File "/home/ryan/downloads/python/scipy/SciPy_complete-0.3.2/scipy_core/scipy_distutils/system_info.py", line 972, in calc_info atlas_version = get_atlas_version(**version_info) File "/home/ryan/downloads/python/scipy/SciPy_complete-0.3.2/scipy_core/scipy_distutils/system_info.py", line 809, in get_atlas_version from core import Extension, setup File "/home/ryan/downloads/python/scipy/SciPy_complete-0.3.2/scipy_core/scipy_distutils/core.py", line 5, in ? from scipy_distutils.dist import Distribution ImportError: No module named scipy_distutils.dist #7b: build_flib.py is not in this directory

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PyTables 1.0 released
by Francesc Altet May 10, 2005

May 10, 2005

========================= Announcing PyTables 1.0 ========================= The Carabos crew is very proud to announce the immediate availability of **PyTables release 1.0**. On this release you will find a series of exciting new features, being the most important the Undo/Redo capabilities, support for objects (and indexes!) with more than 2**31 rows, better I/O performance for Numeric objects, new time datatypes (useful for time-stamping fields), support for Octave HDF5 files and improved support for HDF5 native files. What it is ========== **PyTables** is a package for managing hierarchical datasets and designed to efficiently cope with extremely large amounts of data (with support for full 64-bit file addressing). It features an object-oriented interface that, combined with C extensions for the performance-critical parts of the code, makes it a very easy-to-use tool for high performance data storage and retrieval. It is built on top of the HDF5 library and the numarray package, and provides containers for both heterogeneous data (``Table``) and homogeneous data (``Array``, ``EArray``) as well as containers for keeping lists of objects of variable length (like Unicode strings or general Python objects) in a very efficient way (``VLArray``). It also sports a series of filters allowing you to compress your data on-the-fly by using different compressors and compression enablers. But perhaps the more interesting features are its powerful browsing and searching capabilities that allow you to perform data selections over heterogeneous datasets exceeding gigabytes of data in just tenths of second. Besides, the PyTables I/O is buffered, implemented in C and carefully tuned so that you can reach much better performance with PyTables than with your own home-grown wrappings to the HDF5 library. Changes more in depth ===================== Improvements: - New Undo/Redo feature (i.e. integrated support for undoing and/or redoing actions). This functionality lets you to put marks in specific places of your data operations, so that you can make your HDF5 file pop back (undo) to a specific mark (for example for inspecting how your data looked at that point). You can also go forward to a more recent marker (redo). You can even do jumps to the marker you want using just one instruction. - Reading Numeric objects from ``*Array`` and ``Table`` (Numeric columns) objects have a 50-100x speedup. With that, Louis Wicker reported that a speed of 350 MB/s can be achieved with Numeric objects (on a SGI Altix with a Raid 5 disk array) while with numarrays, this speed approaches 900 MB/s. This improvement has been possible mainly due to a nice recipe from Todd Miller. Thanks Todd! - With PyTables 1.0 you can finally create Tables, EArrays and VLArrays with more than 2**31 (~ 2 thousand millions) rows, as well as retrieve them. Before PyTables 1.0, retrieving data on these beasts was not well supported, in part due to limitations in some slicing functions in Python (that rely on 32-bit adressing). So, we have made the necessary modifications in these functions to support 64-bit indexes and integrated them into PyTables. As a result, our tests shows that this feature works just fine. - As a consequence of the above, you can now index columns of tables with more than 2**31 rows. For instance, indexes have been created for integer columns with 10**10 (yeah, 10 thousand million) rows in less than 1 hour using an Opteron @ 1.6 GHz system (~ 1 hour and half with a Xeon Intel32 @ 2.5 GHz platform). Enjoy! - Now PyTables supports the native HDF5 time types, both 32-bit signed integer and 64-bit fixed point timestamps. They are mapped to ``Int32`` and ``Float64`` values for easy manipulation. See the documentation for the ``Time32Col`` and ``Time64Col`` classes. - Massive internal reorganization of the methods that deal with the hierarchy. Hopefully, that will enable a better understanding of the code for anybody wanting to add/modify features. - The opening and copying of files with large number of objects has been made faster by correcting a typo in ``Table._open()``. Thanks to Ashley Walsh for sending a patch for this. - Now, one can modify rank-0 (scalar) ``EArray`` datasets. Thanks to Norbert Nemec for providing a patch for this. - You are allowed from this version on to add non-valid natural naming names as node or attribute names. A warning is issued to warn (but not forbid) you in such a case. Of course, you have to use the ``getattr()`` function so as to access such invalid natural names. - The indexes of ``Table`` and ``*Array`` datasets can be of long type besides of integer type. However, indexes in slices are still restricted to regular integer type. - The concept of ``READ_ONLY`` system attributes has disappeared. You can change them now at your own risk! However, you still cannot remove or rename system attributes. - Now, one can do reads in-between write loops using ``table.row`` instances. This is thanks to a decoupling in I/O buffering: now there is a buffer for reading and other for writing, so that no collisions take place anymore. Fixes #1186892. - Support for Octave HDF5 output format. Even complex arrays are supported. Thanks to Edward C. Jones for reporting this format. Backward-incompatible changes: - The format of indexes has been changed and indexes in files created with PyTables pre-1.0 versions are ignored now. However, ``ptrepack`` can still save your life because it is able to convert your old files into the new indexing format. Also, if you copy the affected tables to other locations (by using ``Leaf.copy()``), it will regenerate your indexes with the new format for you. - The API has changed a little bit (nothing serious) for some methods. See ``RELEASE-NOTES.txt`` for more details. Bug fixes: - Added partial support for native HDF5 chunked datasets. They can be read now, and even extended, but only along the first extensible dimension. This limitation may be removed when multiple extensible dimensions are supported in PyTables. - Formerly, when the name of a column in a table was subsumed in another column name, PyTables crashed while retrieving information of the former column. That has been fixed. - A bug prevented the use of indexed columns of tables that were in other hierarchical level than root. This is solved now. - When a ``Group`` was renamed you were not able to modify its attributes. This has been fixed. - When whether ``Table.modifyColumns()`` or ``Table.modifyRows()`` were called, a subsequent call to ``Table.flush()`` didn't really flush the modified data to disk. This works as intended now. - Fixed some issues when iterating over ``*Array`` objects using the ``List`` or ``Tuple`` flavor. Important note for Python 2.4 and Windows users =============================================== If you are willing to use PyTables with Python 2.4 in Windows platforms, you will need to get the HDF5 library compiled for MSVC 7.1, aka .NET 2003. It can be found at: ftp://ftp.ncsa.uiuc.edu/HDF/HDF5/current/bin/windows/5-164-win-net.ZIP Users of Python 2.3 on Windows will have to download the version of HDF5 compiled with MSVC 6.0 available in: ftp://ftp.ncsa.uiuc.edu/HDF/HDF5/current/bin/windows/5-164-win.ZIP Where can PyTables be applied? ============================== PyTables is not designed to work as a relational database competitor, but rather as a teammate. If you want to work with large datasets of multidimensional data (for example, for multidimensional analysis), or just provide a categorized structure for some portions of your cluttered RDBS, then give PyTables a try. It works well for storing data from data acquisition systems (DAS), simulation software, network data monitoring systems (for example, traffic measurements of IP packets on routers), very large XML files, or for creating a centralized repository for system logs, to name only a few possible uses. What is a table? ================ A table is defined as a collection of records whose values are stored in fixed-length fields. All records have the same structure and all values in each field have the same data type. The terms "fixed-length" and "strict data types" seem to be quite a strange requirement for a language like Python that supports dynamic data types, but they serve a useful function if the goal is to save very large quantities of data (such as is generated by many scientific applications, for example) in an efficient manner that reduces demand on CPU time and I/O resources. What is HDF5? ============= For those people who know nothing about HDF5, it is a general purpose library and file format for storing scientific data made at NCSA. HDF5 can store two primary objects: datasets and groups. A dataset is essentially a multidimensional array of data elements, and a group is a structure for organizing objects in an HDF5 file. Using these two basic constructs, one can create and store almost any kind of scientific data structure, such as images, arrays of vectors, and structured and unstructured grids. You can also mix and match them in HDF5 files according to your needs. Platforms ========= We are using Linux on top of Intel32 as the main development platform, but PyTables should be easy to compile/install on other UNIX machines. This package has also been successfully compiled and tested on a FreeBSD 5.4 with Opteron64 processors, a UltraSparc platform with Solaris 7 and Solaris 8, a SGI Origin3000 with Itanium processors running IRIX 6.5 (using the gcc compiler), Microsoft Windows and MacOSX (10.2 although 10.3 should work fine as well). In particular, it has been thoroughly tested on 64-bit platforms, like Linux-64 on top of an Intel Itanium, AMD Opteron (in 64-bit mode) or PowerPC G5 (in 64-bit mode) where all the tests pass successfully. Regarding Windows platforms, PyTables has been tested with Windows 2000 and Windows XP (using the Microsoft Visual C compiler), but it should also work with other flavors as well. Web site ======== Go to the PyTables web site for more details: http://pytables.sourceforge.net/ To know more about the company behind the PyTables development, see: http://www.carabos.com/ Share your experience ===================== Let us know of any bugs, suggestions, gripes, kudos, etc. you may have. ---- **Enjoy data!** -- The PyTables Team

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Changing the graph title with scipy - plt plot.
by Kazuhiko UEBAYASHI May 8, 2005

May 8, 2005

Hi, According to http://www.scipy.org/documentation/plottutorial.html #"Using plt and gplt" , we can change the graph title, doing >>> plt.title('First Order Bessel Function') . But It does n't work. Python interactive shell says >>> plt.title('First Order Bessel Function') Traceback (most recent call last): File "<stdin>", line 1, in ? File "/usr/lib/python2.3/site-packages/scipy/plt/interface.py", \ line 144, in title _active.title.text = name AttributeError: 'instancemethod' object has no attribute 'text' . But {x,y} title is changed by >> plt.xtitle('x axis'); plt.ytitle('y axis') . Does anyone know how to change the graph titile on python interactive shell using scipy, plt? --- Kazuhiko UEBAYASHI u_kazu(a)nifty.com

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ImportError for scipy.xplt
by Frank May 6, 2005

May 6, 2005

Hi all, I have a problem with importing the xplt part of scipy: Python 2.4.1 (#2, Apr 2 2005, 04:26:17) [GCC 3.3.5 (Debian 1:3.3.5-12)] on linux2 Type "help", "copyright", "credits" or "license" for more information. >>> from scipy.xplt import * Traceback (most recent call last): File "<stdin>", line 1, in ? File "/usr/lib/python2.4/site-packages/scipy_base/ppimport.py", line 303, in __getattr__ module = self._ppimport_importer() File "/usr/lib/python2.4/site-packages/scipy_base/ppimport.py", line 258, in _ppimport_importer raise ImportError,self.__dict__.get('_ppimport_exc_info')[1] ImportError: Array can not be safely cast to required type >>> Importing other parts of scipy works fine (from scipy.integrate import *). This happens on Debian Sarge (AMD64) with scipy-0.3.2 under python-2.4.1 and python-2.3.5 Has anybody experienced the same or a solution? Thanks, Frank

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Plot window suppression in scipy.gplt
by Oleksandr Moskalenko May 5, 2005

May 5, 2005

Hi, First, thanks for scipy - a great piece of software. I've run into a problem I can't resolve with docs and reading the source code. I've been going through the mailing lists through the search interface and used Google, but I couldn't find information on how I could suppress the plot window that pops up when running gplt.plot. I am producing a png hardcopy of the plot with gplt.output and that is all I need, really. This script will be used as a back-end for a web app and I don't want to see that window pop up on every form submission. Thanks, Alex

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pyvtk ?
by Gary Pajer May 5, 2005

May 5, 2005

Pearu, et al, I have been looking at MayaVi, and I want to explore further. pyvtk is suggested (and Pearu's name is attached to it) but ... http://cens.ioc.ee/projects/pyvtk/ has been unresponsive for a few days. Is this a server problem, or has it moved? Or has it been withdrawn? More generally, is MayaVi / pyvtk a recommended or practical way to interactively visualize 3d data sets? By 3d I mean functions of two variables... surface plots. Any other suggestions? Regards, Gary Pajer

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Compiling C++ extensions using Enthon-2.3.5
by Bryan Cole May 4, 2005

May 4, 2005

Hi, I'm trying to wrap a small C++ library on windows, using Enthon-2.3.5 (the test version) and the version of GCC distributed with this. When I run: python setup.py build_ext --compiler=mingw32 it doesn't call gcc (or g++) as expected but calls 'cc' (presumably a placeholder for the correct compiler executable) which is undefined. How do I tell distutils the correct path to the CPP compiler? Note, compiling C-extensions works great right out-the-box. "c:\python2.3\enthought\mingw\bin\gcc.exe" is called, as expected. It's just *.cpp source files that don't work. C++ extensions work fine with MSVC-6. I've not tried this with the "stable" Enthon version yet. cheers, Bryan

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