def pandda_input(request, dataset, method):
project = current_project(request)
processed_dir = project.pandda_processed_dataset_dir(method, dataset)
pdb_path = next(processed_dir.glob("*pandda-input.pdb"))
return download_http_response(pdb_path)
def refined_map(request, result_id, type):
project = current_project(request)
result = get_refine_result_by_id(project, result_id)
mtz_path = Path(project.get_refine_result_dir(result),
_density_filename("final", type))
return download_http_response(mtz_path, f"{result.name}{mtz_path.suffix}")
def pandda_average(request, dataset, method):
project = current_project(request)
zmap_path = Path(
project.pandda_processed_dataset_dir(method, dataset),
f"{dataset}-ground-state-average-map.native.ccp4",
)
return download_http_response(zmap_path)
def pandda_consensus_zmap(request, dataset, method):
project = current_project(request)
zmap_path = Path(
project.pandda_processed_dataset_dir(method, dataset),
f"{dataset}-z_map.native.ccp4",
)
return download_http_response(zmap_path)
def pandda_bdc(request, dataset, method):
project = current_project(request)
dataset_dir = project.pandda_processed_dataset_dir(method, dataset)
# pick one of the matching .ccp4 files,
# TODO: this gives us random ccp4 file of any
# TODO: potentional BDC files, is this a good way to roll?
ccp4_path = next(dataset_dir.glob("*BDC*.ccp4"))
return download_http_response(ccp4_path)
def pandda_fitted(request, dataset: str, method: str):
project = current_project(request)
modelled_structures_dir = Path(
project.pandda_processed_dataset_dir(method, dataset),
"modelled_structures",
)
#
# pick 'fitted-vNNNN.pdb' file, with highest NNNN number
#
pdb_path = max(modelled_structures_dir.glob("*fitted*.pdb"))
return download_http_response(pdb_path)
def ligand(request, result_id):
"""
view for fetching ligand PDBs generated by the ligand fitting tools
'fitting' is either 'ligfit' or 'rhofit', for the respective tool
"""
project = current_project(request)
result = get_ligfit_result_by_id(project, result_id)
tool = result.result.tool
refine_dir = project.get_refine_result_dir(
result.result.input.refine_result)
if tool == "ligandfit":
pdb_path = Path(refine_dir, "ligfit", "LigandFit_run_1_",
"ligand_fit_1_1.pdb")
elif tool == "rhofit":
pdb_path = Path(refine_dir, "rhofit", "best.pdb")
else:
assert False, f"unexpected ligand fitting tool {tool}"
return download_http_response(pdb_path)
def refined(request, result_id):
project = current_project(request)
result = get_refine_result_by_id(project, result_id)
pdb_path = Path(project.get_refine_result_dir(result), "final.pdb")
return download_http_response(pdb_path, f"{result.name}.pdb")
def get(request, id):
project = current_project(request)
pdb = get_pdb_by_id(project, id)
return download_http_response(str(project.get_pdb_file(pdb)))
