def expand_to_real_class_names(short_classes_names,
_base_class,
_class_suffix=''):
"""
method converts user's inputed classes names into
real statistics class names
e.g. _class_suffix = 'statistic'
"""
real_classes_names = []
real_names = [(
name,
name.lower(),
) for name in get_subclasses_names(_base_class)]
lower_names = [
(
name.lower(),
name.lower() + _class_suffix,
) # e.g. _class_suffix = 'statistic' @IgnorePep8
for name in get_as_list(short_classes_names)
]
for (class_lower_name, class_base_name) in lower_names:
for (real_name, real_lower_name) in real_names:
if real_lower_name in (class_lower_name, class_base_name):
real_classes_names.append(real_name)
break
else:
print('Uknown class: ' + class_lower_name)
return []
return real_classes_names
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 create_many_plots(_source_file, headers, separator, window_size=None):
"""
draws many plots base on data from _source_file and collection of headers
grouped by a semicolon, elements in a group are separated by a comma
"""
colors = ['red', 'green', 'blue', 'yellow', 'black', 'cyan', 'magenta']
first_line = get_first_lines(_source_file)
if len(first_line) == 0:
return
file_headers = [
header.strip().lower() for header in first_line[0].split(separator)
]
for sub_headers in get_as_list(headers, separator=';'):
labels = [label.strip().lower() for label in get_as_list(sub_headers)]
try:
cols = tuple([file_headers.index(label) for label in labels])
except ValueError:
continue
if separator == None:
values = np.loadtxt(_source_file,
skiprows=1,
unpack=True,
usecols=cols)
else:
values = np.loadtxt(_source_file,
skiprows=1,
unpack=True,
usecols=cols,
delimiter=separator)
plt.gca().set_color_cycle(colors[:len(labels)])
filename = get_filename(_source_file)
title = "%s %s" % (filename, nvl(window_size + " window size", ''))
plt.title(title)
for value in values:
plt.plot(value)
plt.legend(labels, loc='upper right')
plt.axes().set_xlabel('Time')
plot_filename = "%s_%s.png" % (filename, '_'.join(labels))
_file = as_path(fs.dirname(_source_file), plot_filename)
plt.savefig(_file)
plt.cla()
def create_many_plots(_source_file, headers, separator, window_size=None):
"""
draws many plots base on data from _source_file and collection of headers
grouped by a semicolon, elements in a group are separated by a comma
"""
colors = ['red', 'green', 'blue', 'yellow', 'black', 'cyan', 'magenta']
first_line = get_first_lines(_source_file)
if len(first_line) == 0:
return
file_headers = [header.strip().lower()
for header in first_line[0].split(separator)]
for sub_headers in get_as_list(headers, separator=';'):
labels = [label.strip().lower()
for label in get_as_list(sub_headers)]
try:
cols = tuple([file_headers.index(label) for label in labels])
except ValueError:
continue
if separator == None:
values = np.loadtxt(_source_file, skiprows=1, unpack=True,
usecols=cols)
else:
values = np.loadtxt(_source_file, skiprows=1, unpack=True,
usecols=cols, delimiter=separator)
plt.gca().set_color_cycle(colors[:len(labels)])
filename = get_filename(_source_file)
title = "%s %s" % (filename, nvl(window_size + " window size", ''))
plt.title(title)
for value in values:
plt.plot(value)
plt.legend(labels, loc='upper right')
plt.axes().set_xlabel('Time')
plot_filename = "%s_%s.png" % (filename, '_'.join(labels))
_file = as_path(fs.dirname(_source_file), plot_filename)
plt.savefig(_file)
plt.cla()
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 expand_to_real_filters_names(filters_names):
"""
method converts user's inputed filters names into
real filters class names
"""
if filters_names[0] == ALL_FILTERS or filters_names == ALL_FILTERS:
return get_filters_long_names()
real_filters_names = []
real_names = [(real_name, real_name.lower(), ) \
for real_name in get_filters_long_names()]
lower_names = [(name.lower(), name.lower() + 'filter', ) \
for name in get_as_list(filters_names)]
for (filter_lower_name, filter_base_name) in lower_names:
for (real_name, real_lower_name) in real_names:
if real_lower_name in (filter_lower_name, filter_base_name):
real_filters_names.append(real_name)
break
else:
print('Uknown filter: ' + filter_lower_name)
return []
return real_filters_names
def expand_to_real_class_names(short_classes_names, _base_class,
_class_suffix=''):
"""
method converts user's inputed classes names into
real statistics class names
e.g. _class_suffix = 'statistic'
"""
real_classes_names = []
real_names = [(name, name.lower(), )
for name in get_subclasses_names(_base_class)]
lower_names = [(name.lower(), name.lower() + _class_suffix, ) # e.g. _class_suffix = 'statistic' @IgnorePep8
for name in get_as_list(short_classes_names)]
for (class_lower_name, class_base_name) in lower_names:
for (real_name, real_lower_name) in real_names:
if real_lower_name in (class_lower_name, class_base_name):
real_classes_names.append(real_name)
break
else:
print('Uknown class: ' + class_lower_name)
return []
return real_classes_names
def expand_to_real_filters_names(filters_names):
"""
method converts user's inputed filters names into
real filters class names
"""
if filters_names[0] == ALL_FILTERS or filters_names == ALL_FILTERS:
return get_filters_long_names()
real_filters_names = []
real_names = [(real_name, real_name.lower(), ) \
for real_name in get_filters_long_names()]
lower_names = [(name.lower(), name.lower() + 'filter', ) \
for name in get_as_list(filters_names)]
for (filter_lower_name, filter_base_name) in lower_names:
for (real_name, real_lower_name) in real_names:
if real_lower_name in (filter_lower_name, filter_base_name):
real_filters_names.append(real_name)
break
else:
print('Uknown filter: ' + filter_lower_name)
return []
return real_filters_names
