Developing with IMP

This page presents instructions on how to develop code using IMP. Developers should also read Getting started as a developer.

Getting around IMP

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 easier-to-use command line functionality, using a variety of IMP modules.
each subdirectory of modules/ 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 or .md files
- examples contains examples in Python and C++, 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 code

The easiest way to start writing new functions and classes is to create a new module using make-module.py. This creates a new module in the modules directory. Alternatively, you can simply use the scratch module.

We highly recommend using a revision control system such as git or svn 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 or 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::Bond, 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::XYZ 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::kernel::Refiner, a IMP::kernel::SingletonContainer or just an arbitrary IMP::kernel::ParticleIndexes object.

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

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 check_standards.py.

Indentation

All C++ headers and code should be indented with 2-space indents. Do not use tabs. clang-format can help you do this formatting automatically.

All Python code should conform to the Python style guide. 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 cleanup_code.py script.

Names

See the introduction first. In addition, developers should be aware that

all preprocessor symbols must begin 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 SpecialVector.h and SpecialVector.cpp
files that provide free functions or macros should be given names separated_by_underscores, for examplecontainer_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.

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 values must have a show method which takes an optional std::ostream and prints information about the object (see IMP::base::Array::show() for an example). 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::base::Exception for a list of existing exceptions. See checks for more information.

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, eg IMP::base::internal and an internal include directory IMP/base/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 your code in doxygen to get started. We use //! and /** ... * / blocks for documentation. You are encouraged to use Doxygen's markdown support as much as possible.

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.

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}.

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 checks 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.

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.

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.

Examples must use methods like IMP::base::get_example_data() to access data in the example directory. This allows them to be run from anywhere.

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 do recursive inclusion)
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>;
  }
}
```

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 make-module.py 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.py mymodule

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. See the files modules/base/dependencies.py and modules/base/dependency/Log4CXX.description for examples.

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.

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 list 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, clang++ and Visual C++) without warnings
should have examples
must not have warnings when its doc is built

See getting started as a developer for more information on submitting code.

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.

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 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 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 the preprocessor, conditionally on the symbols 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

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 IMP::base::Object::get_name() 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 reference 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.

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