This page presents instructions on how to develop code using IMP. Developers who wish to contribute code back to IMP or distribute their code should also read the Contributing code to IMP page.

Getting around

The input files in the IMP directory are structured as follows:

tools contains various command line utilities for use by developers. They are documented below.
doc contains inputs for general IMP overview documentation (such as this page), as well as configuration scripts for doxygen.
applications contains various applications implementing using a variety of IMP modules.
each subdirectory of module/ defines a module; they all have the same structure. The directory for module name has the following structure
- README.md contains a module overview
- include contains the C++ header files
- src contains the C++ source files
- bin contains C++ source files each of which is built into an executable
- pyext contains files defining the Python interface to the module as well as Python source files (in pyext/src)
- test contains test files, that can be run with ctest.
- doc contains additional documentation that is provided via .dox files
- examples contains examples in Python, as well as any data needed for examples
- data contains any data files needed by the module

When IMP is built, a number of directories are created in the build directory. They are

include which includes all the headers. The headers for module name are placed in include/IMP/name
lib where the C++ and Python libraries are placed. Module name is built into a C++ library lib/libimp_name.so (or .dylib on a Mac) and a Python library with Python files located in lib/IMP/name and the binary part in lib/_IMP_name.so.
doc where the html documentation is placed in doc/html and the examples in doc/examples with a subdirectory for each module
data where each module gets a subdirectory for its data.

When IMP is installed, the structure from the build directory is moved over more or less intact except that the C++ and Python libraries are put in the (different) appropriate locations.

Writing new functions and classes

The easiest way to start writing new functions and classes is to create a new module using the make-module script. This creates a new module in the modules directory, complete with example code.

We highly recommend using a revision control system such as Subversion or GIT to keep track of changes to your module.

If, instead, you choose to add code to an existing module you need to consult with the person who people who control that module. Their names can be found on the module main page.

When designing the interface for your new code, you should

search IMP for similar functionality and, if there is any, adapt the existing interface for your purposes. For example, the existing IMP::atom::read_pdb() and IMP::atom::write_pdb() functions provide templates that should be used for the design of any functions that create particles from a file or write particles to a file. Since IMP::atom::BondDecorator, IMP::algebra::Segment3D and IMP::display::Geometry all use methods like IMP::algebra::Segment3D::get_point() to access the endpoints of a segment, any new object which defines similar point-based geometry should do likewise.

think about how other people are likely to use the code. For example, not all molecular hierarchies have atoms as their leaves, so make sure your code searches for arbitrary IMP::core::XYZDecorator particles rather than atoms if you only care about the geometry.

look for easy ways of splitting the functionality into pieces. It generally makes sense, for example, to split selection of the particles from the action taken on them, either by accepting a IMP::ParticleRefiner, or a IMP::SingletonContainer or just an arbitrary IMP::Particles object.

You may want to read the design example for some suggestions on how to go about implementing your functionality in IMP.

Managing your own module

When there is a significant group of new functionality, a new set of authors, or code that is dependent on a new external dependency, it is probably a good idea to put that code in its own module. To create a new module, run the make_module script from the main IMP source directory, passing the name of your new module. The module name should consist of lower case characters and numbers and the name should not start with a number. In addition the name "local" is special and is reserved to modules that are internal to code for handling a particular biological system or application. eg

./tools/make_module mymodule

The script adds a number of example classes to the new module, which can be read and deleted.

The next step is to update the information about the module stored in modules/mymodule/README.md. This includes the names of the authors and descriptions of what the module is supposed to do.

If the module makes use of external libraries, you can add code to check for the external library to modules/mymodule/SConscript. Add a check for a library called libextern.so (or libextern.dylib on a mac) which is accessed using the header extern.h, using a function call call_extern(1.0)

make sure the SConscript includes
import scons_tools.checks
then add the check with
scons_tools.checks.add_external_library(env, "Extern", "extern", "extern.h",

body="call_extern(1.0);")
if the library requires that other libraries be linked in also, you can add the arguments
extra_libs=["first_required_library", "second_required_library"]

That adds a check for the library. To use it in your module, you need to decide if it

is required or not. If it is required, add the argument

\code required_dependencies=["Extern"]

to the env.IMPModuleBuild call. This will make it so that your module is only built if the library is found. If the library is optional, instead add
optional_dependencies=["Extern"]

When writing C++ code that depends on extern, surround it with ``` #if defined(IMP_MODULENAME_USE_EXTERN) some code using extern #endif ``` And when writing Python code check if IMP.mymodule.use_extern is True.

Each module has an auto-generated header called modulename_config.h. This header contains basic definitions needed for the module and should be included (first) in each header file in the module. In addition, there is a header module_version.h which contains the version info as preprocessor symbols. This should not be included in module headers or cpp files as doing so will force frequent recompilations.

Debugging and testing your code

Ensuring that your code is correct can be very difficult, so IMP provides a number of tools to help you out.

The first set are assert-style macros:

IMP_USAGE_CHECK() which should be used to check that arguments to functions and methods satisfy the preconditions.

IMP_INTERNAL_CHECK() which should be used to verify internal state and return values to make sure they satisfy pre and post-conditions.

See Error reporting/checking page for more details. As a general guideline, any improper usage to produce at least a warning all return values should be checked by such code.

The second is logging macros such as:

IMP_LOG() which allows controlled display of messages about what the code is doing. See logging for more information.

Finally, each module has a set of unit tests. The tests are located in the modules/modulename/test directory. These tests should try, as much as possible to provide independent verification of the correctness of the code. Any file in that directory or a subdirectory whose name matches test_*.{py,cpp}, medium_test_*.{py,cpp} or expensive_test_*.{py,cpp} is considered a test. Normal tests should run in at most a few seconds on a typical machine, medium tests in 10 seconds or so and expensive tests in a couple of minutes.

Some tests will require input files or temporary files. Input files should be placed in a directory called input in the test directory. The test script should then call

self.get_input_file_name(file_name)

to get the true path to the file. Likewise, appropriate names for temporary files should be found by calling

self.get_tmp_file_name(file_name)

. Temporary files will be located in build/tmp. The test should remove temporary files after using them.

Code coverage

To assist in testing your code, we report the coverage of all IMP modules and applications as part of the nightly builds. Coverage is basically a report of which lines of code were executed by your tests; it is then straightforward to see which parts of the code have not been exercised by any test, so that you can write new tests to test those parts. (Of course, lines of code that are never executed have no guarantee of working correctly.)

Both the C++ and Python code coverage is reported. For C++ code, only the lines of code that were exercised are reported; for Python code, which conditional branches were taken are also shown (for example, whether both branches from an 'if' statement are followed).

Ideally, coverage reflects the lines of code in a module or application that were exercised only by running its own tests, rather than the tests of the entire IMP package, and generally speaking you should try to test a module using its own tests.

If you have code that for some reason you wish to exclude from coverage, you can add specially formatted comments to the code. For Python code, add a "pragma: no cover" comment to the line to exclude. For C++ code, an individual line can be excluded by adding LCOV_EXCL_LINE somewhere on that line, or a block can be excluded by surrounding it with lines containing LCOV_EXCL_START and LCOV_EXCL_STOP.

Coding conventions

Make sure you read the API conventions page first.

To ensure code consistency and readability, certain conventions must be adhered to when writing code for IMP. Some of these conventions are automatically checked for by source control before allowing a new commit, and can also be checked yourself in new code by running

scons standards

Indentation

All C++ headers and code should be indented in 'Linux' style, with 2-space indents. Do not use tabs. This is roughly the output of Artistic Style run like

astyle --convert-tabs --style=linux --indent=spaces=2 --unpad=paren --pad=oper

. Split lines if necessary to ensure that no line is longer than 80 characters.

\b Rationale: Different users have different-sized windows or
terminals, and different tab settings, but everybody can read 80
column output without tabs.

All Python code should conform to the
<a href="http://www.python.org/dev/peps/pep-0008/"> Python style guide</a>.
In essence this
translates to 4-space indents, no tabs, and similar class, method
and variable naming to the C++ code. You can ensure that your
Python code is correctly indented by using the

tools/reindent.py

script, available as part of the IMP distribution.

Names

See the names section of the \ref conventions "IMP conventions" page.
In addition, developers should be aware that
- all preprocessor symbols (things created by `#define`) must begin with \c IMP
  and no \p %IMP code should depend on preprocessor symbols which do not
  start with IMP.
- names of files that implement a single class should be named for that
  class; for %example the SpecialVector class could be implemented in
  \c SpecialVector.h and \c SpecialVector.cpp
- files that provide free functions or macros should be given names
  \c separated_by_underscores, for %example \c container_macros.h
- Functions which take a parameter which has units should have the
  unit as part of the function name, for %example
  IMP::atom::SimulationParameters::set_maximum_time_step_in_femtoseconds().
  Remember the Mars orbiter. The exception to this is distance
  and force numbers which should always be in angstroms and
  kcal/mol angstrom respectively unless otherwise stated.
.
\b Rationale: This makes it easier to tell between class names and
function names where this is ambiguous (particularly an issue with
the Python interface). The Python guys also mandate CamelCase for
their class names, so this avoids any need to rename classes
between C++ and Python to ensure clean Python code. Good naming is
especially important with preprocessor symbols since these have
file scope and so can change the meaning of other people's code.

Passing and storing data

When a class or function takes a set of particles which are expected to be those of a particular type of decorator, it should take a list of decorators instead. eg IMP::core::transform() takes a IMP::core::XYZ. This makes it clearer what attributes the particle is required to have as well as allows functions to be overloaded (so there can be an IMP::core::transform() which takes IMP::core::RigidBody particles instead).

IMP::Restraint and IMP::ScoreState classes should generally use a IMP::SingletonContainer (or other type of Container) to store the set of IMP::Particle objects that they act on.

Store collections of IMP::Object-derived objects of type Name using a Names. Declare functions that accept them to take a NamesTemp (Names is a NamesTemp). Names are reference counted (see IMP::RefCounted for details), NamesTemp are not. Store collections of particles using a Particles object, rather than decorators.

Display

All classes must have a show method which takes an optional std::ostream and prints information about the object (see IMP::Object::show() for an example). The helper macros, such as IMP_RESTRAINT() define such a method. In addition they must have operator<< defined. This can be easily done using the IMP_OUTPUT_OPERATOR() macro once the show method is defined. Note that operator<< writes human readable information. Add a write method if you want to provide output that can be read back in.

Errors

Classes and methods should use IMP exceptions to report errors. See IMP::Exception for a list of existing exceptions. See a list of functions to aid in error reporting and detection.

Namespaces

Use the provided IMPMODULE_BEGIN_NAMESPACE, IMPMODULE_END_NAMESPACE, IMPMODULE_BEGIN_INTERNAL_NAMESPACE and IMPMODULE_END_INTERNAL_NAMESPACE macros to put declarations in a namespace appropriate for module MODULE.

Each module has an internal namespace, module_name::internal and an internal include directory modulename/internal. Any function which is

not intended to be part of the API,
not documented,
liable to change without notice,
or not tested

should be declared in an internal header and placed in the internal namespace.

The functionality in such internal headers is

not exported to Python
and not part of of documented API

As a result, such functions do not need to obey all the coding conventions (but we recommend that they do).

Documenting your code

IMP is documented using Doxygen. See documenting source code with doxygen to get started. We use //! and /** ... * / blocks for documentation.

Python code should provide Python doc strings.

All headers not in internal directories are parsed through Doxygen. Any function that you do not want documented (for example, because it is not well tested), hide by surrounding with ``` #ifndef IMP_DOXYGEN void messy_poorly_thought_out_function(); #endif ```

We provide a number of extra Doxygen commands to aid in producing nice IMP documentation. The commands are used by writing \commandname{args} or \commandname if there are no arguments.

When you want to specify some command-line command do
```
    \command{the command text}
```
which produces
the command text

To produce a link to a page on the Sali lab web site do
```
    \salilab{imp, the IMP project}
```
which produces the IMP project

To produce a link to the outside world do
```
    \external{http://boost.org, Boost}
```
produces Boost

When writing the name IMP do
```
    \imp
```
so that no link is produced (IMP as opposed to IMP).

Sections of documentation that are only for people developing IMP code should be marked with
```
  \advanceddoc You can tweak this class in various ways in order to optimize its
  performance. 
```
Similarly advanced methods should be marked with
```
  \advancedmethod
```
To produce
Note
Familiarity with these methods is not required to use imp..
General warning messages can be produced using
```
  \warning Be afraid, be very afraid.
```
which produces
Warning: Be afraid, be very afraid.
To mark that some part of the API has not yet been well planned at may change using \unstable{Classname}. The documentation will include a disclaimer and the class or function will be added to a list of unstable classes. It is better to simply hide such things from Doxygen.

To mark a method as not having been well tested yet, use \untested{Classname}.

To mark a method as not having been implemented, use \untested{Classname}.

To note that a class supports comparisons (eg <, >, ==, != etc) use \comparable and then hide the comparison functions from Doxygen (there are a lot of them and they aren't very interesting).

Scoring

Restraints take the current conformation of the particles and return a score and, if requested, add to the derivatives of each of the particles used. Evaluation can be done each of two ways

whole model, where each of the particles is assumed to have changed using IMP::core::RestraintsScoringFunction and
incremental, where only a few of the particles are assumed to have changed using IMP::core::IncrementalScoringFunction

In whole model evaluation, each restraint is called one at a time and given a change to computes its score based on the current conformation of the particles and adds to each particles derivatives. That is, if \(R(P_i)\) is the score of the restraint on particle conformation \(i\) and \(R'(P_i)\) and there are no other restraints:

Stage	Score for R	Particle attribute	Particle derivative
before model evaluation	undefined	\(P_0\)	undefined
before restraint evaluation	0	\(P_0\)	0
after restraint evaluation	\(R(P_0)\)	\(P_0\)	\(R'(P_0)\)

Writing Examples

Writing examples is very important part of being an IMP developer and one of the best ways to help people use your code. To write a (Python) example, create a file myexample.py in the example directory of an appropriate module, along with a file myexample.readme. The readme should provide a brief overview of what the code in the module is trying to accomplish as well as key pieces of IMP functionality that it uses.

When writing examples, one should try (as appropriate) to do the following:

begin the example with import lines for the IMP modules used
have parameters describing the process taking place. These include names of PDB files, the resolution to perform computations at etc.
define a function create_representating which creates and returns the model with the needed particles along with a data structure so that key particles can be located. It should define nested functions as needed to encapsulate commonly used code
define a function create_restraints which creates the restraints to score conformations of the representation
define a function get_conformations to perform the sampling
define a function analyze_conformations to perform some sort of clustering and analysis of the resulting conformations
finally do the actual work of calling the create_representation and create_restraints functions and performing samping and analysis and displaying the solutions.

Obviously, not all examples need all of the above parts. See Nup84 for a canonical example.

The example should have enough comments that the reasoning behind each line of code is clear to someone who roughly understands how IMP in general works.

Useful Scripts

IMP provides a variety of scripts to aid the lives of developers.

Making a module

Creating such a module is the easiest way to get started
developing code for \p %IMP. First, choose a name for the module.
The name should only contain letters, numbers and underscores as it
needs to be a valid file name as well as an identifier in Python and C++.

To create the module do

./tools/make-module my_module

Then, if you run scons with localmodules=True, your new module will be built. The new module includes a number of examples and comments to help you add code to the module.

You can use your new module in a variety of ways:

add C++ code to your module by putting .h files in modules/my_module/include and .cpp files in modules/my_module/src. In order to use use your new functions and classes in Python, you must add a line include "IMP/my_module/myheader.h" near the end of the file modules/my_module/pyext/my_module.i.
write C++ programs using IMP by creating .cpp files in modules/my_module/bin. Each .cpp file placed there is built into a separate executable.
add Python code to your library by putting a .py file in modules/my_module/pyext/my_module/
add Python code to your library by by adding pythoncode blocks to modules/my_module/pyext/my_module.i.
add test code to your library by putting .py files in modules/my_module/test or a subdirectory.

If you feel your module is of interest to other IMP users and developers, see the contributing code to IMP section.

If you document your code, running

make IMP-doc

will build documentation of all of the modules including yours. To access the documentation, open doc/html/index.html.

Contributing code back to the repository

In order to be shared with others as part of the IMP distribution, code needs to be of higher quality and more thoroughly vetted than typical research code. As a result, it may make sense to keep the code as part of a private module until you better understand what capabilities can be cleanly offered to others.

The first set of questions to answer are

What exactly is the functionality I would like to contribute? Is it a single function, a single Restraint, a set of related classes and functions?

Is there similar functionality already in IMP? If so, it might make more sense to modify the existing code in cooperation with its author. At the very least, the new code needs to respect the conventions established by the prior code in order to maintain consistency.

Where should the new functionality go? It can either be added to an existing module or as part of a new module. If adding to an existing module, you must communicate with the authors of that module to get permission and coordinate changes.

Should the functionality be written in C++ or Python? In general, we suggest C++ if you are comfortable programming in that language as that makes the functionality available to more people.

You are encouraged to post to the imp-dev to find help answering these questions as it can be hard to grasp all the various pieces of functionality already in the repository.

All code contributed to IMP

must follow the IMP coding conventions
should follow general good C++ programming practices
must have unit tests
must pass all unit tests
must have documentation
must build on all supported compilers (roughly, recent versions of gcc and Visual C++) without warnings
should have examples
must not have warnings when Doxygen is run (scons doc)

The next suggestions provide more details about the above choices and how to implement them.

Submitting a module

Small pieces of functionality or extensions to existing functionality probably should be submitted to an existing module. Please contact the authors of the appropriate module and discuss the submission and how the code will be maintained.

A list of all current modules in the IMP repository can be found in the modules list or from the modules tab at the top of this page.

As always, if in doubt, post to imp-dev.

Patches to modules for which you have write access can be submitted directly by doing:

svn commit -m "message describing the patch" files or directories to submit

Submitting a module

If you have a large group of related functionality to submit, it may make sense to create a new module in svn. Please post to imp-dev to discuss your plans.

Once you have submitted code

Once you have submitted code, you should monitor the Nightly build status to make sure that your code builds on all platforms and passes the unit tests. Please fix all build problems as fast as possible.

The following sorts of changes must be announced on the imp-dev mailing list before being made

changes to existing kernel or core APIs
significant additions to kernel or core

We recommend that changes be posted to the list a day or two before they are made so as to give everyone adequate time to comment.

In addition to monitoring the imp-dev list, developers who have a module or are committing patches to svn may want to subscribe to the imp-commits email list which receives notices of all changes made to the IMP SVN repository.

Cross platform compatibility

IMP is designed to run on a wide variety of platforms. To detect problems on other platforms we provide nightly test runs on the supported platforms for code that is part of the IMP SVN repository.

In order to make it more likely that your code works on all the supported platforms:

use the headers and classes in IMP::compatibility when appropriate
avoid the use of and and or in C++ code, use && and || instead.
avoid friend declarations involving templates, use #if blocks on SWIG and IMP_DOXYGEN to hide code as needed instead.

C++ 11

IMP now turns on C++ 11 support when it can. However, since compilers are still quite variable in which C++ 11 features they support, it is not adviseable to use them directly in IMP code at this point. To aid in their use when practical we provide several helper macros:

IMP_OVERRIDE inserts the override keyword when available
IMP_FINAL inserts the final keyword when available

More will come.

Good programming practices

The contents of this page are aimed at C++ programmers, but most apply also to Python.

General resources

Two excellent sources for general C++ coding guidelines are

C++ Coding Standards by Sutter and Alexandrescu

Effective C++ by Meyers

IMP endeavors to follow all the of the guidelines published in those books. The Sali lab owns copies of both of these books that you are free to borrow.

IMP gotchas

Below are a suggestions prompted by bugs found in code submitted to IMP.

Never use 'using namespace' outside of a function; instead explicitly provide the namespace. (This avoids namespace pollution, and removes any ambiguity.)

Never use the preprocessor to define constants. Use const variables instead. Preprocessor symbols don't have scope or type and so can have unexpected effects.

Don't expect names to be unique, they are there for human viewing. If you need a unique identifier associated with an object or non-geometric value, just use the object or value itself.

Pass other objects by value or by const & (if the object is large) and store copies of them.

Never expose member variables in an object which has methods. All such member variables should be private.

Don't derive a class from another class simply to reuse some code that the base class provides - only do so if your derived class could make sense when cast to the base class. As above, reuse existing code by pulling it into a function.

Clearly mark any file that is created by a script so that other people know to edit the original file.

Always return a const value or const ref if you are not providing write access. Returning a const copy means the compiler will report an error if the caller tries to modify the return value without creating a copy of it.

Include files from the local module first, then files from the other IMP modules and kernel and finally outside includes. This makes any dependencies in your code obvious, and by including standard headers after IMP headers, any missing includes in the headers themselves show up early (rather than being masked by other headers you include).

#include <IMP/mymodule/mymodule_exports.h>
#include <IMP/mymodule/MyRestraint.h>
#include <IMP/Restraint.h>
#include <vector>

Use double variables for all computational intermediates.

Avoid using nested classes in the API as SWIG can't wrap them properly. If you must use use nested classes, you will have to do more work to provide a Python interface to your code.

Delay initialization of keys until they are actually needed (since all initialized keys take up memory within each particle, more or less). The best way to do this is to have them be static variables in a static function:
FloatKey get_my_float_key() {

static FloatKey k("hello");

return k;

}

One is the almost always the right number:
- Information should be stored in exactly one place. Duplicated information easily gets out of sync.
- A given piece of code should only appear once. Do not copy, paste and modify to create new functionality. Instead, figure out a way to reuse the existing code by pulling it into an internal function and adding extra parameters. If you don't, when you find bugs, you won't remember to fix them in all the copies of the code.
- There should be exactly one way to represent any particular state. If there is more than one way, anyone who writes library code which uses that type of state has to handle all ways. For example, there is only one scheme for representing proteins, namely the IMP::atom::Hierarchy.
- Each class/method should do exactly one thing. The presence of arguments which dramatically change the behavior of the class/method is a sign that it should be split. Splitting it can make the code simpler, expose the common code for others to use and make it harder to make mistakes by getting the mode flag wrong.
- Methods should take at most one argument of each type (and ideally only one argument). If there are several arguments of the same types (eg two different double parameters) it is easy for a user to mix up the order of arguments and the compiler will not complain. int and double count as equivalent types for this rule since the compiler will transparently convert an int into a double.

Exporting code to Python

IMP uses SWIG to wrap code C++ code and export it to Python. Since SWIG is relatively complicated, we provide a number of helper macros and an example file (see modules/example/pyext/swig.i-in). The key bits are

the information goes into a file called swig.i-in in the module pyext directory
the first part should be one IMP_SWIG_VALUE(), IMP_SWIG_OBJECT() or IMP_SWIG_DECORATOR() line per value type, object type or decorator object the module exports to Python. Each of these lines looks like
IMP_SWIG_VALUE(IMP::module_namespace, ClassName, ClassNames);
then there should be a number of include lines, one per header file in the module which exports a class or function to Python. The header files must be in order such that no class is used before a declaration for it is encountered (SWIG does not search #include files for the include headers)
finally, any templates that are to be exported to SWIG must have a template call. It should look something like
namespace IMP {

namespace module_namespace {

%template(PythonName) CPPName<Restraint, 3>;

}

}

Profiling your code

On linux you can use gperftools for code profiling. The key bits are:

install gperftools (available as a pre-built package on most platforms)
make sure debuggin symbols are being included in your build by to your config.py
cxxflags="-g"
create a IMP::benchmark::Profiler in the start of the scope you want to profile passing the name of a file
rebuild imp
run your program as usual, it will create a file in the current directory
to display your call graph run
pprof --web <program_executable> profile_output_file

or
pprof --gv <program_executable> profile_output_file

Understanding the internals of IMP

At some point you may want to understand how some aspect of IMP works under the hood. These pages explain various aspects of how IMP works.

Dependencies

Table of Contents

Getting around

Writing new functions and classes

Managing your own module

Debugging and testing your code

Code coverage

Coding conventions

Indentation

Names

Passing and storing data

Display

Errors

Namespaces

Documenting your code

Scoring

Writing Examples

Useful Scripts

Making a module

Contributing code back to the repository

Submitting a module

Submitting a module

Once you have submitted code

Cross platform compatibility

C++ 11

Good programming practices

General resources

IMP gotchas

Exporting code to Python

Profiling your code

Understanding the internals of IMP