ipython_handler.py

 
import os
import tempfile
 
from hep_ipython_tools import calculation_queue, calculation, information, calculation_list
 
 
class IPythonHandler:
 
    """
    Handler class to start processes in an IPython notebook in a convenient way.
    From this whole framework you should not need to create any instances by yourself but rather use the
    given ipython handler for this.
 
    Create a handler object in the beginning of your NB and use the two methods `process()`
    and `process_parameter_space()` to turn parameters or a parameter creator function into a Calculation.
    Do not create calculations on you own::
 
        from tracking.validation.ipython_handler import handler
 
        calculation = handler.process(parameters)
 
    """
 
    def __init__(self):
        """
        Each created log file gets registered and deleted if there are more than 20 log files present
        or if the get_log function of the process is called (the log is saved elsewhere).
        As the log files are saved to /tmp you have probably not to care about deleting them.
        """
 
        
        self.log_fileslog_files = []
 
        
        self.informationinformation = information.EnvironmentInformation()
 
        
        self._calculation_type_calculation_type = calculation.Calculation
 
    def process(self, result_queue=None, **kwargs):
        """
        Turn a parameter set into a Calculation that you can start, stop or whatever you want.
 
        Arguments:
            result_queue: The CalculationQueue you want to use. Without giving
                this as a parameter the function creates one for you. Create
                one on your own with the function create_queue.
        """
 
        if result_queue is None:
            result_queue = calculation_queue.CalculationQueue()
 
        calculation = self._calculation_type_calculation_type()
        calculation.append(result_queue=result_queue, log_file_name=self.next_log_file_namenext_log_file_name(), parameters=None, **kwargs)
 
        return calculation
 
    def process_parameter_space(self, kwargs_creator_function, **parameter_lists):
        """
        Create a list of calculations by combining all parameters with all parameters you provide and
        feeding the tuple into the parameter_creator_function.
        If the kwargs_creator_function has a parameter named queue, the function feeds the corresponding
        created queue into the parameter_creator_function.
        The parameter_creator_function must return a dictionary for every combination of parameters it gets,
        which will be used to construct a process out of it.
        See ipython_handler_basf2/ipython_handler for an example.
 
        Please note that a list of calculations acts the same as a single calculation you would get from
        the process function. You can handle 10 calculations the same way you would handle a single one.
 
        The kwargs_creator_function can transform the incoming parameters into different ones. To make this
        more clear, the resulting dictionary created by the kwargs_creator_function is called kwargs.
        These are the ones, that will be used to create a calculation process, so they must be compatible to the
        calculation you chose (namely compatible with the append function of the _calculation_type).
 
        Arguments:
            kwargs_creator_function: A function with as many input parameters
                as parameters you provide.  If the function has an additional
                queue parameter it is fed with the corresponding queue for this
                calculation.
            parameter_lists: As many lists as you want. Every list is one
                parameter. If you do not want a specific parameter
                constellation to occur, you can return None in your
                parameter_creator_function for this combination.
 
        Usage::
 
            def kwargs_creator_function(par_1, par_2, par_3, queue):
                kwargs = {... f(par_1) ... g(par_2) ... h(par_3)}
                queue.put(..., ...)
                return kwargs
 
            calculations = handler.process_parameter_space(kwargs_creator_function,
                                                           par_1=[1, 2, 3], par_2=["x", "y", "z"], par_3=[3, 4, 5])
 
        The calculations will be created with the kwargs arguments.
        """
 
        all_kwargs, all_queues, all_parameters = calculation_list.create_all_calculations(kwargs_creator_function,
                                                                                          **parameter_lists)
 
        calculations = self._calculation_type_calculation_type()
 
        for kwargs, q, parameters in zip(all_kwargs, all_queues, all_parameters):
            calculations.append(result_queue=q, log_file_name=self.next_log_file_namenext_log_file_name(),
                                parameters=parameters, **kwargs)
 
        return calculations
 
    def next_log_file_name(self):
        """
        Return the name of the next log file.
        If there are more than 20 log files present,
        start deleting the oldest ones.
        """
        next_log_file = tempfile.mkstemp()
        self.log_fileslog_files.append(next_log_file)
        while len(self.log_fileslog_files) > 20:
            first_log_file = self.log_fileslog_files.pop(0)
            f = first_log_file[0]
            log_file_name = first_log_file[1]
 
            os.close(f)
            try:
                os.unlink(log_file_name)
            except OSError:
                pass
        return next_log_file[1]
 
    @staticmethod
    def create_queue():
        """
        Create a Calculation queue. You need to do this if you want to pass it to your modules
        and write to it while processing the events.
        """
        return calculation_queue.CalculationQueue()