def save_page(rst_text, prob_name, file_path):
"""
Helper function that saves the rst page into text and html after
converting it to html.
@param rst_text :: page to be converted/saved in rst
@param prob_name :: name of the problem
@param file_path :: path to where the file is going to be saved
@returns :: html/txt visual display page saved at file_path
"""
html = publish_string(rst_text, writer_name='html')
with open(file_path + '.' + FILENAME_EXT_TXT, 'w') as visual_rst:
print(rst_text, file=visual_rst)
logger.info(
'Saved {prob_name}.{extension} to {working_directory}'.format(
prob_name=prob_name,
extension=FILENAME_EXT_TXT,
working_directory=file_path[file_path.find('fitbenchmarking'
):]))
with open(file_path + '.' + FILENAME_EXT_HTML, 'w') as visual_html:
print(html, file=visual_html)
logger.info(
'Saved {prob_name}.{extension} to {working_directory}'.format(
prob_name=prob_name,
extension=FILENAME_EXT_HTML,
working_directory=file_path[file_path.find('fitbenchmarking'
):]))
def get_problem_files(data_dir):
"""
Gets all the problem definition files from the specified problem
set directory.
:param data_dir: directory containing the problems
:type data_dir: str
:return: array containing of paths to the problems
e.g. In NIST we would have
[low_difficulty/file1.txt, ..., ...]
:rtype: list of str
"""
test_data = glob.glob(data_dir + '/*.*')
if test_data == []:
raise NoDataError('"{}" not recognised as a dataset. '
'Check that it contains problem files '
'and try again.'.format(data_dir))
problems = [
os.path.join(data_dir, data) for data in test_data
if not data.endswith('META.txt')
]
problems.sort()
for problem in problems:
logger.info(problem)
return problems
def save_plot(save):
"""
Save the plot to a .png file or just display it in the
console if that option is available.
"""
if save == "":
plt.show()
else:
output_file = save.replace(",", "")
output_file = output_file.replace(" ", "_")
logger.info("Saved " + os.path.basename(output_file) + " to " +
output_file[output_file.find("fitbenchmarking"):])
plt.savefig(output_file.replace(" ", "_"))
def get_problem_files(data_dir):
"""
Gets all the problem definition files from the specified problem
set directory.
@param data_dir :: directory containing the problems
@returns :: array containing of paths to the problems
e.g. In NIST we would have
[low_difficulty/file1.txt, ..., ...]
"""
test_data = glob.glob(data_dir + '/*.*')
if test_data == []:
raise ValueError('"{}" not recognised as a dataset. '
'Check that it contains problem files '
'and try again.'.format(data_dir))
problems = [os.path.join(data_dir, data) for data in test_data]
problems.sort()
for problem in problems:
logger.info(problem)
return problems
def _parse_line_by_line(self):
"""
Parses the NIST file one line at the time.
:returns: the equation, data pattern, and starting values
sections of the file
:rtype: str, str, list of list of float
"""
lines = self.file.readlines()
idx, ignored_lines = 0, 0
while idx < len(lines):
line = lines[idx].strip()
idx += 1
if not line:
continue
if line.startswith('Model:'):
equation_text, idx = self._get_nist_model(lines, idx)
elif 'Starting values' in line or 'Starting Values' in line:
starting_values, idx = self._get_nist_starting_values(
lines, idx)
elif line.startswith("Data:"):
if " x" in line and " y " in line:
data_pattern_text, idx = self._get_data_txt(lines, idx)
elif line.startswith("Dataset Name:"):
name = line.split(':', 1)[1]
name = name.split('(', 1)[0]
name = name.strip()
else:
ignored_lines += 1
logger.info("%s lines were ignored in this problem file",
ignored_lines)
return equation_text, data_pattern_text, starting_values, name