Merge pull request #9 from kiharalab/SWORDv2

fix bug in SWORDv2

Author: wang3702

.gitignore

@@ -1,2 +1,174 @@
 .idea/
 .DS_Store
+.pixi/
+Predict_Result/
+output_half/
+af3_models/
+
+# SWORD2
+*.dat
+*.nrg
+*.o
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/latest/usage/project/#working-with-version-control
+.pdm.toml
+.pdm-python
+.pdm-build/
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/

README.md

@@ -73,34 +73,52 @@ validated its generalizability with plausible performances.
 
 <details>
 
-
-
 ### System Requirements
-CPU: >=4 cores <br>
-Memory (RAM): >=12Gb. <br>
-GPU: any GPU supports CUDA with at least 12GB memory. <br>
-GPU is required for DiffModeler since most computations are done on GPU.
+- **CPU**: 4 cores or higher
+- **Memory**: 12GB RAM or higher
+- **GPU**: CUDA-compatible with minimum 12GB memory
+- **Note**: GPU is mandatory as DiffModeler performs most computations on GPU
 
 ## Installation  
 ### 1. [`Install git`](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git) 
 ### 2. Clone the repository in your computer 
-```
+```bash
 git clone git@github.com:kiharalab/DiffModeler.git && cd DiffModeler
 ```
 
 ### 3. Configure environment for DiffModeler.
-#### 3.1.1 Install anaconda
+### **Option A: Conda Environment**
+##### 3.A.1 Install anaconda
 Install anaconda from https://www.anaconda.com/download#downloads.
-#### 3.1.2 Install environment via yml file
-Then create the environment via
-```commandline
+##### 3.A.2 Install environment via yml file
+```bash
 conda env create -f environment.yml
 ```
-#### 3.1.3 Activate environment for running
+##### 3.A.3 Activate environment for running
 Each time when you want to run this software, simply activate the environment by
-```
+```bash
 conda activate DiffModeler
-conda deactivate(If you want to exit) 
+# To exit
+conda deactivate
+```
+#### **Option B: Pixi Environment**
+
+#### 3.B.1 Install pixi
+```bash
+curl -fsSL https://pixi.sh/install.sh | bash
+```
+
+##### 3.B.2 Install environment via toml file
+```bash
+pixi install
+```
+
+##### 3.B.3 Activate environment for running
+Each time when you want to run this software, simply activate the environment by
+```bash
+pixi shell
+# To exit
+exit
 ```
 
 ### 4. Download the pre-trained diffusion model

VESPER_CUDA/fitter.py

@@ -519,8 +519,9 @@ def _retrieve_data(self, result_list):
             result["Nm"] = Nm
             result["total"] = total
 
+
     @staticmethod
-    def _calc_ldp_recall_score_item(ldp_arr, ca_arr, rot_mtx, trans):
+    def _calc_ldp_recall_score_item(ldp_tree, ca_arr, rot_mtx, trans):
         """
         Calculate the recall score of LDP points given a rotation matrix and translation vector
         All arguments have to be torch tensors on GPU
@@ -529,22 +530,22 @@ def _calc_ldp_recall_score_item(ldp_arr, ca_arr, rot_mtx, trans):
         # rot_mtx: torch tensor of shape (3, 3)
         # trans: torch tensor of shape (3, )
         """
-        import torch
-
-        # rotated backbone CA
-        rot_backbone_ca = torch.matmul(ca_arr, rot_mtx) + trans
+        # import torch
+        #
+        # # rotated backbone CA
+        # rot_backbone_ca = torch.matmul(ca_arr, rot_mtx) + trans
+        rot_backbone_ca = np.dot(ca_arr, rot_mtx) + trans
+        distances, indices = ldp_tree.query(rot_backbone_ca, k=1)
+        coverage = np.sum(distances < 3.0) / len(rot_backbone_ca)
+        return coverage
 
-        # calculate all pairwise distances
-        dist_mtx = torch.cdist(rot_backbone_ca, ldp_arr, p=2)
-
-        # get distance from the closest LDP point for each CA atom
-        min_dist = torch.min(dist_mtx, dim=1).values
-
-        # count the coverage of CA atoms within 3.0 angstrom of LDP points in the total amount of CA atoms
-        return (min_dist < 3.0).sum().item() / len(rot_backbone_ca)
 
     def _calc_ldp_recall(self, results, sort=False, progress_bar=True):
         import torch
+        from scipy.spatial import KDTree
+        ldp_atoms = self.ldp_atoms.cpu().numpy()
+        ldp_tree = KDTree(ldp_atoms)
+        bb_ca = self.backbone_ca.cpu().numpy()
 
         if not progress_bar:
             iter_results = results  # calculate for each rotation
@@ -553,10 +554,8 @@ def _calc_ldp_recall(self, results, sort=False, progress_bar=True):
         for result in iter_results:
             r = R.from_euler("xyz", result["angle"], degrees=True)
             rot_mtx = (r.as_matrix()).T
-            rot_mtx = torch.from_numpy(rot_mtx).to(self.device)
-            # rot_mtx = euler_to_mtx(torch.tensor(result["angle"], device=self.device)).t()
             result["ldp_recall"] = self._calc_ldp_recall_score_item(
-                self.ldp_atoms, self.backbone_ca, rot_mtx, torch.from_numpy(result["real_trans"]).to(self.device)
+                ldp_tree, bb_ca, rot_mtx, result["real_trans"]
             )
 
         # sort by LDP recall