== INTRODUCTION: ==

This is a set of utils for:

• plotting from the gdb command line
• saving c data to .mat files from gdb command line
• exploring the stack frame

== OVERVIEW: ==

I have attempted to show support for:

• c array, c pointer
• STL vector
• Eigen vector, Eigen array
• Boost vector
• Boost complex vector

== REQUIREMENTS: ==

• gdb >= 7.0
• python
• numpy
• matplotlib ( for plotting )
• scipy ( if you want to save to .mat file )
• iPython ( if you want to send data for interactive analysis )

The examples are divided up so you only need the dependencies for the example you want to run:

• stl_examples : gcc/g++ and libstdc++
• boost_numerics_examples : boost numerics (boost-devel)
• eigen_examples : eigen3 library ( eigen3-devel )

HOWTO:

1. Configure gdb A. Add the lines in gdbinit to ~/.gdbinit, fix the paths NOTE: REPLACE 'THIS DIRECTORY' with the root directory of this repository B. If you want to save to mat file, uncomment the lines 'import savemat'

2. Build any examples you want:

   cd examples Raw Pointers : g++ -g raw_pointers.cpp -o raw_pointers_example STL Example : g++ -g stl_example.cpp -o stl_example Boost Numerics Example : g++ -g boost_numerics_example.cpp -o boost_example Eigen3 Example : g++ -g -I/usr/include/eigen3 eigen_example.cpp -o eigen_example

3. Debug the examples

   NOTE: data types that don't have built in sizes take '@n_elements' as arguments gdb raw_pointers_example b 52 r plot d@1024 e@100 # plot 1024 samples of d and 100 of e -- NOTE: there's no checking that you haven't walked off the end of your buffer!

   NOTE: each example will print out a useful line to break on, and things you may want to plot. Just run it to get that printout.

   gdb stl_example # launch example program b 91 # give it a break point and run it r plot v1 a1 # plot a bunch of data of different types savemat mymatfile v1 a1 # Save array eca2 to a matlab .mat file

4. Hack it The examples here are really simple. It's just not entirely intuitive. Things you could easily do: 1. Handle more types 2. Send matrices to images 3. Add more arguments to the commands - plot a range, plot a column, etc. 4. Make a single plot command that figures out the type and does the write things.

== OTHER USEFUL STUFF: ==

savemat - saves arbitrary buffers as a .mat file [ requires scipy ]

showframe - stack frame explorer

(gdb) show_frame {'T': ['TEST', 'local_computation', 0], 'a1': ['double [10]', 'local_computation', 0], 'eca2': ['Eigen::ArrayXcd', 'local_computation', 0], 'eda2': ['Eigen::ArrayXd', 'local_computation', 0], 'eigen_complex_array': ['Eigen::ArrayXcd', 'local_computation', 0], 'eigen_double_array': ['Eigen::ArrayXd', 'local_computation', 0], 'heap_array': ['double *', 'local_computation', 0], 'm': ['Eigen::MatrixXd', 'local_computation', 0], 'v1': ['std::vector<double, std::allocator >', 'local_computation', 10], 'v2': ['std::vector<std::complex, std::allocator<std::complex > >', 'local_computation', 10], 'v4': ['std::vector<double, std::allocator >', 'local_computation', 5], 'vc': ['boost::numeric::ublas::vector<std::complex, boost::numeric::ublas::unbounded_array<std::complex, std::allocator<std::complex > > >', 'local_computation', 10], 'vx': ['boost::numeric::ublas::vector<double, boost::numeric::ublas::unbounded_array<double, std::allocator > >', 'local_computation', 10]}

Enjoy