def pair_avg(vals):
"""
Returns a list of the average between pairs of numbers in the input list. If there is an odd
final input value, it is dropped.
:param vals: A list of floats to pair and average.
:return: The average of adjacent pairs in the given input list.
"""
results = []
for pair in chunk(vals, 2, list):
if len(pair) == 2:
results.append(sum(pair) / 2)
else:
warning("'{}' is not a pair".format(pair))
return results
def pair_avg(vals):
"""
Returns a list of the average between pairs of numbers in the input list. If there is an odd
final input value, it is dropped.
:param vals: A list of floats to pair and average.
:return: The average of adjacent pairs in the given input list.
"""
results = []
for pair in chunk(vals, 2, list):
if len(pair) == 2:
results.append(sum(pair) / 2)
else:
warning("'{}' is not a pair".format(pair))
return results
def rmsd_split(meta_file,
steps,
tpl_dir=DEF_TPL_DIR,
overwrite=False,
base_dir=None):
"""
Reads the given meta file, fetches the RMSD files in the inventory, and creates a succession
of directories that split the original RMSD files into a larger number of chunks for each step
such that step 1 will create a split of 2 in 01_01 and 01_02, etc.
:param meta_file: The initial meta file.
:param steps: The number of averaging steps to perform.
:param tpl_dir: The directory that contains the submit templates.
:param overwrite: Whether to overwrite existing files.
:param base_dir: The base directory to write to (defaults to the meta file's dir)
"""
meta = read_meta(meta_file)
rmsd = read_meta_rmsd(meta)
sub_tpl_base = read_tpl(os.path.join(tpl_dir, DEF_BASE_SUBMIT_TPL))
sub_tpl_line = read_tpl(os.path.join(tpl_dir, DEF_PART_LINE_SUBMIT_TPL))
if not base_dir:
base_dir = meta[DIR_KEY]
for step in range(1, steps + 1):
for rmsd_fname, data in rmsd.items():
data_len = len(data)
chunk_num = step + 1
chunk_size = math.floor(data_len / chunk_num)
logger.debug(STEP_DBG_MSG, step, data_len, rmsd_fname, chunk_num,
chunk_size)
rmsd_chunks = [ch for ch in chunk(data, chunk_size, list)]
for step_part in range(1, chunk_num + 1):
rmsd_tgt_dir = os.path.join(
base_dir, SPLIT_DIR_FMT.format(step, step_part))
if not os.path.exists(rmsd_tgt_dir):
os.makedirs(rmsd_tgt_dir)
f_name = os.path.join(rmsd_tgt_dir, rmsd_fname)
if allow_write(f_name, overwrite=overwrite):
write_rmsd(rmsd_chunks[step_part - 1], f_name)
write_meta(base_dir, meta, step, overwrite)
write_submit(base_dir, sub_tpl_base, sub_tpl_line, step, overwrite)
def rmsd_split(meta_file, steps, tpl_dir=DEF_TPL_DIR, overwrite=False, base_dir=None):
"""
Reads the given meta file, fetches the RMSD files in the inventory, and creates a succession
of directories that split the original RMSD files into a larger number of chunks for each step
such that step 1 will create a split of 2 in 01_01 and 01_02, etc.
:param meta_file: The initial meta file.
:param steps: The number of averaging steps to perform.
:param tpl_dir: The directory that contains the submit templates.
:param overwrite: Whether to overwrite existing files.
:param base_dir: The base directory to write to (defaults to the meta file's dir)
"""
meta = read_meta(meta_file)
rmsd = read_meta_rmsd(meta)
sub_tpl_base = read_tpl(os.path.join(tpl_dir, DEF_BASE_SUBMIT_TPL))
sub_tpl_line = read_tpl(os.path.join(tpl_dir, DEF_PART_LINE_SUBMIT_TPL))
if not base_dir:
base_dir = meta[DIR_KEY]
for step in range(1, steps + 1):
for rmsd_fname, data in rmsd.items():
data_len = len(data)
chunk_num = step + 1
chunk_size = math.floor(data_len / chunk_num)
logger.debug(STEP_DBG_MSG, step, data_len, rmsd_fname, chunk_num, chunk_size)
rmsd_chunks = [ch for ch in chunk(data, chunk_size, list)]
for step_part in range(1, chunk_num + 1):
rmsd_tgt_dir = os.path.join(base_dir, SPLIT_DIR_FMT.
format(step, step_part))
if not os.path.exists(rmsd_tgt_dir):
os.makedirs(rmsd_tgt_dir)
f_name = os.path.join(rmsd_tgt_dir, rmsd_fname)
if allow_write(f_name, overwrite=overwrite):
write_rmsd(rmsd_chunks[step_part - 1], f_name)
write_meta(base_dir, meta, step, overwrite)
write_submit(base_dir, sub_tpl_base, sub_tpl_line, step, overwrite)