Jake update

README.md

@@ -10,7 +10,7 @@ MolDscript is a Python workflow that converts Density Functional Theory (DFT) an
 - Parse optimization, single-point, NBO, NMR, charge, FMO, and Fukui calculations without manual file editing.
 - Match conformer ensembles, apply SMARTS-based substructure filters, and compute DBSTEP buried volumes on demand.
 - Generate ensembles (Boltzmann weighted, min/mnax within population windows, lowest-energy snapshots) in a single run.
-- Emit descriptor CSVs alongside module logs (`MOLDSCRIPT_*.dat`) for traceability.
+- Emit descriptor CSVs alongside a single run audit log (`MOLDSCRIPT.dat`) for traceability.
 
 ## Installation
 ```shell
@@ -38,21 +38,23 @@ Prefer storing options in a key:value text file? Use `--varfile inputs.txt`; com
 - `--spc PATH` - single-point energies that replace optimization SCF energies.
 - `--nbo`, `--nmr`, `--charges`, `--fmo` PATH - add module-specific descriptors; pair with `--suffix_*` to specify filename tokens specific to calculation type (required for proper comformer matching). 
 - `--fukui_neutral`, `--fukui_reduced`, `--fukui_oxidized` PATH - supply all three charge states for vertical IE/EA and condensed Fukui functions. Again, pair with `--suffix_*` for proper conformer matching.
-- `--substructure SMARTS` - limit atom/bond descriptors to a SMARTS match; combine with `--volume` or `--vall` and optional `--radius` list for DBSTEP buried volumes.
-- `--boltz`, `--min_max`, `--lowe` - compute Boltzmann-weighted averages, min/max/range tables (using `--cut`), and lowest-energy snapshots. Adjust `--temp` (K) as needed.
-- `--output PREFIX` - prepend every generated filename; append a slash to target a directory. Use `--no_mol`, `--no_atom`, `--no_bond`, or `--no_bond_filter` to tailor CSV output.
+- `--substructure SMARTS` - limit atom/bond descriptors to a SMARTS match; combine with `--volume` or `--vall` and optional `--radius` list for DBSTEP buried volumes.
+- `--boltz`, `--min_max`, `--lowe` - compute Boltzmann-weighted averages, min/max/range tables (using `--cut`), and lowest-energy snapshots. Adjust `--temp` (K) as needed.
+- `--output PREFIX` - prepend every generated filename; append a slash to target a directory. Use `--no_mol`, `--no_atom`, `--no_bond`, or `--no_bond_filter` to tailor CSV output.
+- `--workers N` - parse independent quantum output files in parallel. Start with a modest value such as `--workers 4` for large batches, then increase if memory use is acceptable.
+- `--write_args arguments.txt` - save the effective options for the current run as a reusable `--varfile`.
 
 ## Output Artefacts
 - `molecule_level.csv`, `bond_level.csv`, `atom_level.csv` - aligned descriptors per calculation, bond pair, or atom.
 - `ensemble_*.csv`, `boltzmann_weights.csv` - created when `--boltz` is enabled.
 - `min_max_range_*.csv`, `lowest_energy_*.csv` - created when `--min_max` or `--lowe` are requested.
-- `MOLDSCRIPT_*.dat` - per-module logs capturing provenance and CPU-time summaries.
+- `MOLDSCRIPT.dat` - a single run log capturing provenance, parsed files, module sections, and CPU-time summaries.
 
 ## Documentation
 The Read the Docs site (coming soon) will provide the full user guide: [https://moldscript.readthedocs.io](https://moldscript.readthedocs.io)
 
 ## Dependencies
-Key Python dependencies include `pandas`, `cclib` (latest GitHub version for the most up-to-date package compatability), `dbstep`, `rdkit`, `networkx`, `numpy`, and `periodictable`.
+Key Python dependencies include `pandas`, `cclib` (latest GitHub version for the most up-to-date package compatability), `dbstep`, `rdkit`, `networkx`, `numpy`, `periodictable`, `rich`, and `tqdm`.
 
 ## Supported Quantum Packages
 - Gaussian

docs/source/README.rst

@@ -20,15 +20,15 @@ Key Capabilities
 - Merge descriptors across conformers and calculation types into aligned CSV datasets.
 - Restrict analysis to user-defined SMARTS substructures and optionally compute DBSTEP buried volumes.
 - Generate ensemble statistics such as Boltzmann-weighted averages, population windows, and lowest-energy snapshots.
-- Produce audit logs (``MOLDSCRIPT_*.dat``) alongside descriptor files for reproducibility.
+- Produce a single audit log (``MOLDSCRIPT.dat``) alongside descriptor files for reproducibility.
 
 Installation
 ------------
 1. Clone the repository: ``git clone https://github.com/patonlab/molDscript.git``.
 2. (Optional) create and activate a dedicated environment.
 3. Install the package from the repository root: ``pip install -e .`` (or ``pip install .`` for a standard install).
 
-Required Python dependencies are declared in ``setup.py`` and include ``pandas>=2.0.2``, ``cclib`` (latest from GitHub), ``dbstep``, ``rdkit``, ``networkx``, ``numpy``, and ``periodictable``. Install RDKit and Open Babel via conda-forge when pip wheels are not available:
+Required Python dependencies are declared in ``setup.py`` and include ``pandas>=2.0.2``, ``cclib`` (latest from GitHub), ``dbstep``, ``rdkit``, ``networkx``, ``numpy``, ``periodictable``, ``rich``, and ``tqdm``. Install RDKit and Open Babel via conda-forge when pip wheels are not available:
 
 .. code-block:: shell
 
@@ -180,7 +180,7 @@ Running molDscript creates the following artefacts in the working directory (or
 - ``atom_level.csv`` - atomic descriptors including charges, Fukui indices, NMR shielding, and buried volumes when requested.
 - ``boltzmann_weights.csv`` plus ``ensemble_*.csv`` tables when ``--boltz`` is enabled.
 - ``min_max_range_*.csv`` tables when ``--min_max`` is enabled and ``lowest_energy_*.csv`` tables when ``--lowe`` is requested.
-- Module logs named ``MOLDSCRIPT_<MODULE>.dat`` that document parsing steps and CPU-time summaries.
+- ``MOLDSCRIPT.dat`` - a single run log that documents command provenance, parsed files, module sections, and CPU-time summaries.
 
 Each run also reports the cumulative CPU time associated with the parsed quantum chemistry jobs.