def launch_opt_jobs(self):
"""
Mimicing DihedralScanner.launch_opt_jobs,
"""
assert hasattr(self, 'next_jobs') and hasattr(
self, 'current_finished_job_results')
while len(self.opt_queue) > 0:
m, from_grid_id, to_grid_id = self.opt_queue.pop()
# check if this job already exists
m_geo_key = get_geo_key(m.xyzs[0])
if m_geo_key in self.task_cache[to_grid_id]:
final_geo, final_energy, job_folder = self.task_cache[
to_grid_id][m_geo_key]
result_m = Molecule()
result_m.elem = list(m.elem)
result_m.xyzs = [final_geo]
result_m.qm_energies = [final_energy]
result_m.build_topology()
grid_id = self.get_dihedral_id(result_m,
check_grid_id=to_grid_id)
if grid_id is None:
print(
f"Cached result from {job_folder} is ignored because optimized geometry is far from grid id {to_grid_id}"
)
else:
self.current_finished_job_results.push((result_m, grid_id),
priority=job_folder)
else:
# append the job to self.next_jobs, which is the output of torsiondrive-API
self.next_jobs[to_grid_id].append(m.xyzs[0].copy())
def rebuild_task_cache(self, grid_status):
"""
Take a dictionary of finished optimizations, rebuild task_cache dictionary
This function mimics the DihedralScanner.restore_task_cache()
Parameters:
------------
grid_status = dict(), key is the grid_id, value is a list of job_info. Each job_info is a tuple of (start_geo, end_geo, end_energy).
* Note: The order of the job_info is important when reproducing the same scan procedure.
Returns: None
------------
Upon finish, the new folder 'opt_tmp' will be created, with many empty folders corrsponding to the finished jobs.
self.task_cache will be populated with correct information for repreducing the entire scan process.
"""
for grid_id, job_info_list in grid_status.items():
tname = 'gid_' + '_'.join('%+04d' % gid for gid in grid_id)
tmp_folder_path = os.path.join(self.tmp_folder_name, tname)
for i_job, job_info in enumerate(job_info_list):
job_path = os.path.join(tmp_folder_path, str(i_job + 1))
(start_geo, end_geo, end_energy) = job_info
job_geo_key = get_geo_key(start_geo)
self.task_cache[grid_id][job_geo_key] = (end_geo, end_energy,
job_path)
def rebuild_task_cache(self, grid_status):
"""
Take a dictionary of finished optimizations, rebuild task_cache dictionary
This function mimics the DihedralScanner.restore_task_cache()
Parameters
-----------
grid_status = dict(), key is the grid_id, value is a list of job_info. Each job_info is a tuple of (start_geo, end_geo, end_energy).
* Note: The order of the job_info is important when reproducing the same scan procedure.
Notes
-----
Upon finish, self.task_cache will be populated with correct information for repreducing the entire scan process, i.e.
self.task_cache = {(30,-60): {geo_key: (final_geo, final_energy, final_gradient, job_folder)}}
"""
for grid_id, job_info_list in grid_status.items():
tname = 'gid_' + '_'.join('%+04d' % gid for gid in grid_id)
tmp_folder_path = os.path.join(self.tmp_folder_name, tname)
for i_job, job_info in enumerate(job_info_list):
job_path = os.path.join(tmp_folder_path, str(i_job + 1))
(start_geo, end_geo, end_energy) = job_info
job_geo_key = get_geo_key(start_geo)
self.task_cache[grid_id][job_geo_key] = (end_geo, end_energy,
None, job_path)