def prepareParameters(self): """ method prepares poincare plot parameters """ if self.stepper: self.stepper_size = extract_number(self.stepper, convert=int) self.stepper_unit = extract_alphabetic(self.stepper, convert=str.lower) self.movie_dir = nvl(self.movie_dir, self.output_dir, '') if isinstance(self.excluded_annotations, str): self.excluded_annotations = get_as_list(self.excluded_annotations) if self.filters_names is not None: map(self.addFilter, get_as_list(self.filters_names)) if self.output_dir: # remove leading and trailing whitespaces self.output_dir = self.output_dir.strip() if self.window_size: self.window_size_unit = extract_alphabetic(self.window_size, convert=str.lower) self.window_size_value = extract_number(self.window_size, convert=int) self.movie_multiprocessing_factor = nvl( self.movie_multiprocessing_factor, 3 if multiprocessing.cpu_count() > 1 else 0) if not self.data_file == None: self.group_data_filename = None if isinstance(self.output_precision, str): self.output_precision = get_as_tuple(self.output_precision, convert=int) elif self.output_precision == None: self.output_precision = DEFAULT_OUTPUT_PRECISION if self.add_headers == None: self.add_headers = True if len(self.statistics_classes) > 0: self.statistics_names = [ s.__name__.replace("Statistic", "") for s in self.statistics_classes ]
def __init__(self, output_file=None, output_dir=None, output_suffix=None, reference_filename=None, sort_headers=True, output_precision=None, print_output_file=False, ordinal_column_name=None, output_separator=None, add_headers=False, ordered_headers=None, message=None, output_prefix=None, ordered_headers_aliases=None): super(NumpyCSVFile, self).__init__(output_file, output_dir, output_suffix, reference_filename, sort_headers, ordinal_column_name=ordinal_column_name, output_separator=output_separator, add_headers=add_headers, ordered_headers=ordered_headers, output_prefix=output_prefix, ordered_headers_aliases=ordered_headers_aliases) self.array_data = None self.__output_precision__ = get_as_tuple(output_precision, convert=int) self.__print_output_file__ = print_output_file self.__message__ = message
def prepareParameters(self): """ method prepares poincare plot parameters """ if self.stepper: self.stepper_size = extract_number(self.stepper, convert=int) self.stepper_unit = extract_alphabetic(self.stepper, convert=str.lower) self.movie_dir = nvl(self.movie_dir, self.output_dir, "") if isinstance(self.excluded_annotations, str): self.excluded_annotations = get_as_list(self.excluded_annotations) if self.filters_names is not None: map(self.addFilter, get_as_list(self.filters_names)) if self.output_dir: # remove leading and trailing whitespaces self.output_dir = self.output_dir.strip() if self.window_size: self.window_size_unit = extract_alphabetic(self.window_size, convert=str.lower) self.window_size_value = extract_number(self.window_size, convert=int) self.movie_multiprocessing_factor = nvl( self.movie_multiprocessing_factor, 3 if multiprocessing.cpu_count() > 1 else 0 ) if not self.data_file == None: self.group_data_filename = None if isinstance(self.output_precision, str): self.output_precision = get_as_tuple(self.output_precision, convert=int) elif self.output_precision == None: self.output_precision = DEFAULT_OUTPUT_PRECISION if self.add_headers == None: self.add_headers = True if len(self.statistics_classes) > 0: self.statistics_names = [s.__name__.replace("Statistic", "") for s in self.statistics_classes]