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]