Source code for hep_ipython_tools.ipython_handler

import os
import tempfile

from hep_ipython_tools import calculation_queue, calculation, information, calculation_list


[docs]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. """ #: A list of open log files. self.log_files = [] #: A shortcut for returning information on the environment. self.information = information.EnvironmentInformation() #: The calculation type to use self._calculation_type = calculation.Calculation
[docs] 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.append(result_queue=result_queue, log_file_name=self.next_log_file_name(), parameters=None, **kwargs) return calculation
[docs] 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() for kwargs, q, parameters in zip(all_kwargs, all_queues, all_parameters): calculations.append(result_queue=q, log_file_name=self.next_log_file_name(), parameters=parameters, **kwargs) return calculations
[docs] 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_files.append(next_log_file) while len(self.log_files) > 20: first_log_file = self.log_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]
[docs] @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()