mps_alisetup Namespace Reference

Functions
def	get_weight_configs

Variables
	aligmentConfig = args.alignmentConfig
tuple	args = parser.parse_args()
tuple	config = ConfigParser.ConfigParser()
tuple	configTemplate = config.get('general','configTemplate')
tuple	currentDir = os.getcwd()
string	eos = '/afs/cern.ch/project/eos/installation/cms/bin/eos.select'
dictionary	generalOptions = {}
tuple	globalTag = config.get('general','globaltag')
string	help = 'display detailed output of mps_setup'
string	helpEpilog
tuple	match = re.search(re.compile('mpproduction\/mp(.+?)$', re.M|re.I),currentDir)
tuple	milleScript = os.path.join(mpsScriptsDir, "mps_runMille_template.sh")
string	mpsdirname = ''
tuple	mpsScriptsDir = os.path.join("src", "Alignment", "MillePedeAlignmentAlgorithm", "scripts")
string	mssDir = '/store/caf/user/'
tuple	parser
tuple	pedeScript = os.path.join(mpsScriptsDir, "mps_runPede_rfcp_template.sh")
tuple	pedesettings
tuple	tmpFile = f.read()
tuple	weight_confs = get_weight_configs(config)

Function Documentation

def mps_alisetup.get_weight_configs

(

config

)

Extracts different weight configurations from `config`.

Arguments:
- `config`: ConfigParser object containing the alignment configuration

Definition at line 24 of file mps_alisetup.py.

References split.

def get_weight_configs(config):
    """Extracts different weight configurations from `config`.

    Arguments:
    - `config`: ConfigParser object containing the alignment configuration
    """

    weight_dict = collections.OrderedDict()
    common_weights = {}

    # loop over datasets to reassign weights
    for section in config.sections():
        if 'general' in section:
            continue
        elif section == "weights":
            for option in config.options(section):
                common_weights[option] = [x.strip() for x in
                                          config.get(section, option).split(",")]
        elif section.startswith("dataset:"):
            name = section[8:]  # get name of dataset by stripping of "dataset:"
            if config.has_option(section,'weight'):
                weight_dict[name] = [x.strip() for x in
                                     config.get(section, "weight").split(",")]
            else:
                weight_dict[name] = ['1.0']

    weights_list = [[(name, weight) for weight in  weight_dict[name]]
                    for name in weight_dict]

    common_weights_list = [[(name, weight) for weight in  common_weights[name]]
                           for name in common_weights]
    common_weights_dicts = []
    for item in itertools.product(*common_weights_list):
        d = {}
        for name,weight in item:
            d[name] = weight
        common_weights_dicts.append(d)

    configs = []
    for weight_conf in itertools.product(*weights_list):
        if len(common_weights) > 0:
            for common_weight in common_weights_dicts:
                configs.append([(dataset[0],
                                 reduce(lambda x,y: x.replace(y, common_weight[y]),
                                        common_weight, dataset[1]))
                                for dataset in weight_conf])
        else:
            configs.append(weight_conf)

    return configs



# ------------------------------------------------------------------------------
# set up argument parser and config parser