diff --git a/.gitignore b/.gitignore
index f1c7688..87f3cc9 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,16 +1,31 @@
-build
-dist
-test*
-tmp*
-*.log
-*.zip
-*.txt
+# Python
+__pycache__/
 *.pyc
+*.egg-info/
+build/
+dist/
+
+# Environments
+.venv/
+
+# Tool caches
+.ruff_cache/
+.pytest_cache/
+.claude/
+
+# Data (large, downloaded separately)
+/data/
+
+# Test subset (symlinks to data/)
+tests/bmrb_subset/
+
+# Pipeline output
+tmp*/
 *.pkl
-*.txt
-__pycache__
-data
-*.csv
-notebooks
-*.egg-info
-2024-05-09
+*.log
+
+# Internal planning notes
+docs/_planning/
+
+# OS
+.DS_Store
diff --git a/CLAUDE.md b/CLAUDE.md
new file mode 100644
index 0000000..90d43f2
--- /dev/null
+++ b/CLAUDE.md
@@ -0,0 +1,75 @@
+# TriZOD — Project Instructions
+
+## Quick Start
+- Package manager: `uv`
+- Install: `uv sync --group dev`
+- Run CLI: `uv run trizod --help`
+- Run tests: `uv run pytest tests/ -v`
+- Lint: `uv run ruff check trizod/` and `uv run ruff format --check trizod/`
+
+## Before Every Commit
+1. `uv run ruff check trizod/ tests/`
+2. `uv run ruff format --check trizod/ tests/`
+3. `uv run pytest tests/ -v`
+All three must pass.
+
+## Architecture
+- `trizod/trizod.py` — pipeline orchestration, CLI, filtering (`filter_defaults` DataFrame, `prefilter_dataframe()`, `print_filter_losses()`, `compute_scores_row()`, `main()`)
+- `trizod/bmrb/bmrb.py` — BMRB NMR-STAR file parsing (Entity, Assembly, SampleConditions, ShiftTable, BmrbEntry)
+- `trizod/potenci/potenci.py` — POTENCI random coil shift predictions (public API: `get_pred_shifts()`)
+- `trizod/scoring/scoring.py` — Z-score computation, offset correction (AIC-based global + 9-residue rolling window)
+- `trizod/constants.py` — shared constants (BBATNS, AA mappings, weights)
+
+## Pipeline Flow
+1. Parse args (two-phase: preset first, then detailed args)
+2. Find & load BMRB files → `load_bmrb_entries()` (with pickle cache in `tmp/bmrb_entries/`)
+3. Prefilter entries → `prefilter_dataframe()` (applies all filter criteria from `filter_defaults`)
+4. Compute scores → `compute_scores_row()` per entry (POTENCI predictions cached in `tmp/potenci/`, wSCS cached in `tmp/wSCS/`)
+5. Post-filter (offset rejection) → entries exceeding `max-offset` are excluded or masked
+6. Output results (JSON + optional CSV) + `print_filter_losses()` report
+
+## Caching
+- `--cache-dir` defaults to `./tmp`
+- `tmp/bmrb_entries/` — pickled BmrbEntry objects
+- `tmp/potenci/` — POTENCI predictions as JSON (content-addressed by seq+T+pH+ion hash)
+- `tmp/wSCS/` — scored weighted chemical shifts as `.npz` (keyed by entry_id + shift table IDs)
+- POTENCI cache is filter-independent (depends only on sequence + conditions), so re-runs with different filter settings are mostly cache hits
+
+## Filtering
+- 4 stringency tiers: `unfiltered`, `tolerant`, `moderate`, `strict`
+- `filter_defaults` DataFrame in `trizod/trizod.py` defines defaults per tier
+- Individual filters overridable via CLI args
+- `print_filter_losses()` reports per-filter counts (filtered + uniquely filtered)
+- See `docs/filtering.md` for full reference
+
+## Offset Correction (not re-referencing)
+- `scoring.py` detects per-atom-type systematic biases between observed and POTENCI-predicted shifts
+- Two strategies: global offset (AIC test) and 9-residue rolling window; picks whichever yields lower Z-scores
+- This is NOT classical NMR re-referencing (DSS/TMS) — it adjusts Z-score calculation internally
+- The pipeline does NOT output re-referenced .str files or corrected chemical shifts
+
+## Conventions
+- Python >=3.9, ruff for linting/formatting
+- Scientific variable names allowed (T, pH, Ion, N, etc.) — see ruff ignore rules
+- Always show staged files and proposed commit message, then wait for user approval before committing
+
+## Testing
+- `tests/test_potenci.py` — POTENCI prediction accuracy and edge cases
+- `tests/test_smoke.py` — CLI entrypoints, single-entry pipeline integration
+- `tests/test_pipeline_regression.py` — 300-entry subset regression (requires data/)
+- Pipeline/regression tests require BMRB data in `data/bmrb_entries/`
+- 9 tests total, ~60-80s runtime
+
+## Documentation
+- `docs/pipeline.md` — detailed pipeline walkthrough (6 stages)
+- `docs/potenci.md` — POTENCI module: origin, API, performance, internals
+- `docs/filtering.md` — filter descriptions and default values per stringency level
+- `docs/_planning/` — internal planning notes (gitignored)
+- `docs/_planning/status-2026-03-25.md` — implementation status and roadmap
+
+## Data
+- BMRB entries: `data/bmrb_entries/` (17,388 files, not committed)
+- Baselines: `data/baseline/` (not committed)
+- Filter impact analysis: `data/filter_impact/` (not committed)
+- Test reference: `tests/reference/unfiltered.json` (committed)
+- Test subset IDs: `tests/quick_subset_ids.txt` (committed)
diff --git a/README.md b/README.md
index cdd3d99..bd9d2f1 100644
--- a/README.md
+++ b/README.md
@@ -4,68 +4,28 @@ Novel, continuous, per-residue disorder scores from protein NMR experiments stor
 
 ## Description
 
-Accurate quantification of intrinsic disorder, crucial for understanding functional protein dynamics, remains challenging. We introduce TriZOD, an innovative scoring system for protein disorder analysis, utilizing nuclear magnetic resonance (NMR) spectroscopy chemical shifts. Traditional methods provide binary, residue-specific annotations, missing the complex spectrum of protein disorder. TriZOD extends the CheZOD scoring framework with quantitative statistical descriptors, offering a nuanced analysis of intrinsically disordered regions. It calculates per-residue scores from chemical shift data of polypeptides in the Biological Magnetic Resonance Data Bank (BMRB). The CheZOD Z-score is a quantitative metric for how much a set of experimentally determined chemical shifts deviate from random coil chemical shifts. The TriZOD G-scores extend upon them to be independent of the number of available chemical shifts. They are normalized to range between 0 and 1, which is beneficial for interpretation and use in training disorder predictors. Additionally, TriZOD introduces a refined, automated selection of BMRB datasets, including filters for physicochemical properties, keywords, and chemical denaturants. We calculated G-scores for over 15,000 peptides in the BMRB, approximately 10-fold the size of previously published CheZOD datasets.
-Validation against DisProt annotations demonstrates substantial agreement yet highlights discrepancies, suggesting the need to reevaluate some disorder annotations. TriZOD advances protein disorder prediction by leveraging the full potential of the BMRB database, refining our understanding of disorder, and challenging existing annotations.
+TriZOD computes per-residue disorder scores from NMR chemical shift data in the Biological Magnetic Resonance Data Bank (BMRB). It extends the CheZOD scoring framework with quantitative statistical descriptors, offering nuanced analysis of intrinsically disordered regions. The CheZOD Z-score measures how much experimentally determined chemical shifts deviate from random coil predictions. The TriZOD G-score normalizes these to [0, 1], independent of the number of available shift types.
 
-## Installation
-
-## Usage
+## Architecture
 
-## Datasets
-
-The latest dataset is published under the DOI [10.6084/m9.figshare.25792035](https://www.doi.org/10.6084/m9.figshare.25792035).
+TriZOD consists of three core modules:
 
-This publication consists of four nested datasets of increasing filter stringency: Unfiltered, tolerant, moderate and strict. An overview of the applied filters is given below. The .json files contain all entries of the BMRB that are in accordance with the given filter levels. These are not redundancy reduced and also contain the test set entries and are therefore not intended for direct use as training sets in machine learning applications. Instead, for this purpose, please use only those entries with IDs found in the [filter_level]_rest_set.fasta files and extract the corresponding information such as TriZOD G-scores and/or physicochemical properties from the respective .json files. These fasta files contain the cluster representatives of the redundancy reduction procedure which was performed in an iterative fashion such that clusters with members found in all filter levels are shared among them and have the same cluster representatives. If necessary, all other cluster members can be retrieved from the given [filter_level]_rest_clu.tsv files. The file TriZOD_test_set.fasta contains the IDs and sequences of the TriZOD test set. It is intended that the corresponding data is taken from the strict dataset.
+- **`trizod/bmrb/`** — Parses BMRB NMR-STAR files into structured data (entities, assemblies, sample conditions, shift tables)
+- **`trizod/potenci/`** — Predicts random coil chemical shifts using the [POTENCI](https://github.com/protein-nmr/POTENCI) algorithm ([docs](docs/potenci.md))
+- **`trizod/scoring/`** — Computes per-residue CheZOD Z-scores and TriZOD G-scores from experimental vs. predicted shifts
 
-### Filter defaults
+The main pipeline (`trizod/trizod.py`) orchestrates these modules. See [docs/pipeline.md](docs/pipeline.md) for a detailed walkthrough.
 
-TriZOD filters the peptide shift data entries in the BMRB database given a set of filter criteria. Though these criteria can be set individually with corresponding command line arguments, it is most convinient to use one of four filter default options to adapt the overall stringency of the filters. The command line argument `--filter-defaults` sets default values for all data filtering criteria. The accepted options with increasing stringency are `unfiltered`, `tolerant`, `moderate` and `strict`. The affected filters are:
+## Installation
 
-| Filter | Description | 
-| :--- | --- |
-| temperature-range | Minimum and maximum temperature in Kelvin. |
-| ionic-strength-range | Minimum and maximum ionic strength in Mol. |
-| pH-range | Minimum and maximum pH. |
-| unit-assumptions | Assume units for Temp., Ionic str. and pH if they are not given and exclude entries instead. |
-| unit-corrections | Correct values for Temp., Ionic str. and pH if units are most likely wrong. |
-| default-conditions | Assume standard conditions if pH (7), ionic strength (0.1 M) or temperature (298 K) are missing and exclude entries instead. |
-| peptide-length-range | Minimum (and optionally maximum) peptide sequence length. |
-| min-backbone-shift-types | Minimum number of different backbone shift types (max 7). |
-| min-backbone-shift-positions | Minimum number of positions with at least one backbone shift. |
-| min-backbone-shift-fraction | Minimum fraction of positions with at least one backbone shift. |
-| max-noncanonical-fraction | Maximum fraction of non-canonical amino acids (X count as arbitrary canonical) in the amino acid sequence. |
-| max-x-fraction | Maximum fraction of X letters (arbitrary canonical amino acid) in the amino acid sequence. |
-| keywords-blacklist | Exclude entries with any of these keywords mentioned anywhere in the BMRB file, case ignored. |
-| chemical-denaturants | Exclude entries with any of these chemicals as substrings of sample components, case ignored. |
-| exp-method-whitelist | Include only entries with any of these keywords as substring of the experiment subtype, case ignored. |
-| exp-method-blacklist | Exclude entries with any of these keywords as substring of the experiment subtype, case ignored. |
-| max-offset | Maximum valid offset correction for any random coil chemical shift type. |
-| reject-shift-type-only | Upon exceeding the maximal offset set by <--max-offset>, exclude only the backbone shifts exceeding the offset instead of the whole entry. |
+## Usage
 
-The following table lists the respective filtering criteria for each of the four filter default options:
+## Datasets
 
-| Filter | unfiltered | tolerant | moderate | strict |
-| :--- | --- | --- | --- | --- |
-| temperature-range | [-inf,+inf] | [263,333] | [273,313] | [273,313] |
-| ionic-strength-range | [0,+inf] | [0,7] | [0,5] | [0,3] |
-| pH-range | [-inf,+inf] | [2,12] | [4,10] | [6,8] |
-| unit-assumptions | Yes | Yes | Yes | No |
-| unit-corrections | Yes | Yes | No | No |
-| default-conditions | Yes | Yes | Yes | No |
-| peptide-length-range | [5,+inf] | [5,+inf] | [10,+inf] | [15,+inf] |
-| min-backbone-shift-types | 1 | 2 | 3 | 5 |
-| min-backbone-shift-positions | 3 | 3 | 8 | 12 |
-| min-backbone-shift-fraction | 0.0 | 0.0 | 0.6 | 0.8 |
-| max-noncanonical-fraction | 1.0 | 0.1 | 0.025 | 0.0 |
-| max-x-fraction | 1.0 | 0.2 | 0.05 | 0.0 |
-| keywords-blacklist | [] | ['denatur'] | ['denatur', 'unfold', 'misfold'] | ['denatur', 'unfold', 'misfold', 'interacti', 'bound'] |
-| chemical-denaturants | [] | ['guanidin', 'GdmCl', 'Gdn-Hcl','urea'] | ['guanidin', 'GdmCl', 'Gdn-Hcl','urea'] | ['guanidin', 'GdmCl', 'Gdn-Hcl','urea','BME','2-ME','mercaptoethanol', 'TFA', 'trifluoroethanol', 'Potassium Pyrophosphate', 'acetic acid', 'CD3COOH', 'DTT', 'dithiothreitol', 'dss', 'deuterated sodium acetate'] |
-| exp-method-whitelist | ['', '.'] | ['','solution', 'structures'] | ['','solution', 'structures'] | ['solution', 'structures'] |
-| exp-method-blacklist | [] | ['solid', 'state'] | ['solid', 'state'] | ['solid', 'state'] |
-| max-offset | +inf | 3 | 3 | 2 |
-| reject-shift-type-only | Yes | Yes | No | No |
+The latest dataset is published under the DOI [10.6084/m9.figshare.25792035](https://www.doi.org/10.6084/m9.figshare.25792035).
 
-Please note that each of these filters can be set individually with respective command line options and that this will take precedence over the filter defaults set by the `--filter-defaults` option.
+Four nested datasets of increasing filter stringency are provided: unfiltered, tolerant, moderate, and strict. See [docs/filtering.md](docs/filtering.md) for the complete filter reference and default values.
 
 ## Project status
+
 Under active development
diff --git a/docs/filtering.md b/docs/filtering.md
new file mode 100644
index 0000000..2c5d7b2
--- /dev/null
+++ b/docs/filtering.md
@@ -0,0 +1,58 @@
+# TriZOD Filtering Reference
+
+TriZOD filters peptide shift data entries from the BMRB database using
+configurable criteria. The `--filter-defaults` CLI argument sets default values
+for all filters at one of four stringency levels: `unfiltered`, `tolerant`,
+`moderate`, and `strict`. Individual filters can be overridden with their
+respective CLI arguments.
+
+## Filter Descriptions
+
+| Filter                       | Description                                                                                                                                |
+| :--------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------ |
+| temperature-range            | Minimum and maximum temperature in Kelvin.                                                                                                 |
+| ionic-strength-range         | Minimum and maximum ionic strength in Mol.                                                                                                 |
+| pH-range                     | Minimum and maximum pH.                                                                                                                    |
+| unit-assumptions             | Assume units for Temp., Ionic str. and pH if they are not given and exclude entries instead.                                               |
+| unit-corrections             | Correct values for Temp., Ionic str. and pH if units are most likely wrong.                                                                |
+| default-conditions           | Assume standard conditions if pH (7), ionic strength (0.1 M) or temperature (298 K) are missing and exclude entries instead.               |
+| peptide-length-range         | Minimum (and optionally maximum) peptide sequence length.                                                                                  |
+| min-backbone-shift-types     | Minimum number of different backbone shift types (max 7).                                                                                  |
+| min-backbone-shift-positions | Minimum number of positions with at least one backbone shift.                                                                              |
+| min-backbone-shift-fraction  | Minimum fraction of positions with at least one backbone shift.                                                                            |
+| max-noncanonical-fraction    | Maximum fraction of non-canonical amino acids (X count as arbitrary canonical) in the amino acid sequence.                                 |
+| max-x-fraction               | Maximum fraction of X letters (arbitrary canonical amino acid) in the amino acid sequence.                                                 |
+| keywords-blacklist           | Exclude entries with any of these keywords mentioned anywhere in the BMRB file, case ignored.                                              |
+| chemical-denaturants         | Exclude entries with any of these chemicals as substrings of sample components, case ignored.                                              |
+| exp-method-whitelist         | Include only entries with any of these keywords as substring of the experiment subtype, case ignored.                                      |
+| exp-method-blacklist         | Exclude entries with any of these keywords as substring of the experiment subtype, case ignored.                                           |
+| exclude-paramagnetic         | Exclude entries flagged as paramagnetic in the BMRB assembly or entity metadata. Paramagnetic samples cause massive chemical shift perturbations that make Z-score computation meaningless. |
+| max-offset                   | Maximum valid offset correction for any random coil chemical shift type.                                                                   |
+| reject-shift-type-only       | Upon exceeding the maximal offset set by `--max-offset`, exclude only the backbone shifts exceeding the offset instead of the whole entry. |
+
+## Filter Defaults by Stringency Level
+
+| Filter                       | unfiltered  | tolerant                                | moderate                                | strict                                                                                                                                                                                                              |
+| :--------------------------- | ----------- | --------------------------------------- | --------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| temperature-range            | [-inf,+inf] | [263,333]                               | [273,313]                               | [273,313]                                                                                                                                                                                                           |
+| ionic-strength-range         | [0,+inf]    | [0,7]                                   | [0,5]                                   | [0,3]                                                                                                                                                                                                               |
+| pH-range                     | [-inf,+inf] | [2,12]                                  | [4,10]                                  | [6,8]                                                                                                                                                                                                               |
+| unit-assumptions             | Yes         | Yes                                     | Yes                                     | No                                                                                                                                                                                                                  |
+| unit-corrections             | Yes         | Yes                                     | No                                      | No                                                                                                                                                                                                                  |
+| default-conditions           | Yes         | Yes                                     | Yes                                     | No                                                                                                                                                                                                                  |
+| peptide-length-range         | [5,+inf]    | [5,+inf]                                | [10,+inf]                               | [15,+inf]                                                                                                                                                                                                           |
+| min-backbone-shift-types     | 1           | 2                                       | 3                                       | 4                                                                                                                                                                                                                   |
+| min-backbone-shift-positions | 3           | 3                                       | 8                                       | 12                                                                                                                                                                                                                  |
+| min-backbone-shift-fraction  | 0.0         | 0.0                                     | 0.6                                     | 0.8                                                                                                                                                                                                                 |
+| max-noncanonical-fraction    | 1.0         | 0.1                                     | 0.025                                   | 0.0                                                                                                                                                                                                                 |
+| max-x-fraction               | 1.0         | 0.2                                     | 0.05                                    | 0.0                                                                                                                                                                                                                 |
+| keywords-blacklist           | []          | ['denatur']                             | ['denatur', 'unfold', 'misfold']        | ['denatur', 'unfold', 'misfold', 'interacti', 'bound']                                                                                                                                                              |
+| chemical-denaturants         | []          | ['guanidin', 'GdmCl', 'Gdn-Hcl','urea'] | ['guanidin', 'GdmCl', 'Gdn-Hcl','urea'] | ['guanidin', 'GdmCl', 'Gdn-Hcl', 'urea', 'TFA', 'trifluoroethanol', 'Potassium Pyrophosphate'] |
+| exp-method-whitelist         | ['', '.']   | ['','solution', 'structures']           | ['','solution', 'structures']           | ['solution', 'structures']                                                                                                                                                                                          |
+| exp-method-blacklist         | []          | ['solid']                               | ['solid']                               | ['solid']                                                                                                                                                                                                           |
+| exclude-paramagnetic         | No          | Yes                                     | Yes                                     | Yes                                                                                                                                                                                                                 |
+| max-offset                   | +inf        | 3                                       | 3                                       | 2                                                                                                                                                                                                                   |
+| reject-shift-type-only       | Yes         | Yes                                     | No                                      | No                                                                                                                                                                                                                  |
+
+Each filter can be set individually with the respective CLI option, which takes
+precedence over `--filter-defaults`.
diff --git a/docs/pipeline.md b/docs/pipeline.md
new file mode 100644
index 0000000..c23d8c9
--- /dev/null
+++ b/docs/pipeline.md
@@ -0,0 +1,146 @@
+# TriZOD Pipeline
+
+Detailed walkthrough of the TriZOD data processing pipeline, from raw BMRB
+NMR-STAR files to per-residue disorder scores.
+
+## Overview
+
+```
+BMRB NMR-STAR files
+    │
+    ▼
+1. Parse entries (bmrb.py)
+    │
+    ▼
+2. Build peptide DataFrame
+    │
+    ▼
+3. Pre-filter
+    │
+    ▼
+4. Compute scores (POTENCI + scoring)
+    │
+    ▼
+5. Post-filter
+    │
+    ▼
+6. Output (CSV / JSON)
+```
+
+## Stage 1: Parse BMRB Entries
+
+**Module**: `trizod/bmrb/bmrb.py`
+
+Each BMRB NMR-STAR file is parsed into a `BmrbEntry` object containing:
+
+- **Entity** — molecular identity: name, sequence, polymer type, fragment info
+- **Assembly** — oligomeric state, molecular weight, thiol state
+- **SampleConditions** — temperature (K), pH, ionic strength (M), pressure
+- **ShiftTable(s)** — assigned chemical shifts per atom per residue
+
+The parser extracts structured data from the NMR-STAR tag-value format, handling
+missing fields, unit ambiguities (e.g. Celsius vs Kelvin), and multi-entity entries.
+
+## Stage 2: Build Peptide DataFrame
+
+**Module**: `trizod/trizod.py` → `fill_row_data()`
+
+Each parsed entry becomes a row in a pandas DataFrame with columns for:
+
+- Entry ID, sequence, sequence length
+- Sample conditions (temperature, pH, ionic strength)
+- Backbone chemical shifts (7 atom types: C, CA, CB, HA, H, N, HB)
+- Metadata (experiment type, keywords, sample components)
+
+Only backbone atom types defined in `BBATNS` are retained. Non-canonical amino
+acids are translated to their canonical equivalents via `CAN_TRANS`.
+
+## Stage 3: Pre-filter
+
+**Module**: `trizod/trizod.py` → `prefilter_dataframe()`
+
+Entries are filtered based on configurable criteria organized into four preset
+levels (unfiltered, tolerant, moderate, strict). See [filtering.md](filtering.md)
+for the complete filter reference.
+
+Filter categories:
+- **Physicochemical**: temperature, pH, ionic strength ranges
+- **Data quality**: minimum backbone shift types, positions, coverage fraction
+- **Sequence**: peptide length, non-canonical/X-residue fraction limits
+- **Content**: keyword blacklists, chemical denaturant detection, experiment method
+- **Unit handling**: assumptions, corrections, default condition imputation
+
+A filter loss report tracks how many entries each filter removes.
+
+## Stage 4: Compute Scores
+
+**Modules**: `trizod/potenci/potenci.py`, `trizod/scoring/scoring.py`
+
+This is the core computation, applied per entry via `compute_scores_row()`:
+
+### 4a. POTENCI Random Coil Prediction
+
+`potenci.get_pred_shifts()` predicts what chemical shifts would be expected for a
+disordered (random coil) version of the sequence, given the sample conditions
+(temperature, pH, ionic strength). This accounts for:
+
+- Sequence-dependent neighbor effects (5-residue sliding window)
+- Temperature corrections
+- pH-dependent titration of ionizable residues (D, E, H, C, K, R, Y)
+- Ionic strength via Debye-Hückel electrostatics
+
+POTENCI dominates pipeline runtime (~92%), primarily due to iterative pKa fitting
+with `scipy.optimize.curve_fit`.
+
+### 4b. Weighted Secondary Chemical Shifts
+
+For each residue and atom type, the secondary chemical shift (SCS) is:
+
+```
+SCS = observed_shift - predicted_shift
+```
+
+These are combined into a weighted sum using empirically derived weights from
+`trizod/constants.py`, producing a single per-residue weighted SCS value.
+
+### 4c. Offset Correction
+
+`scoring.get_offset_corrected_wscs()` corrects systematic offsets between
+observed and predicted shifts using two strategies:
+
+1. **Global offset** (`compute_offsets`): per-atom-type mean offset, accepted via
+   AIC test
+2. **Running offset** (`compute_running_offsets`): rolling window (size 9) at the
+   position of minimum standard deviation
+
+The method yielding the lower average Z-score is selected.
+
+### 4d. Z-score and G-score
+
+The CheZOD **Z-score** measures how many standard deviations a residue's weighted
+SCS deviates from the random coil expectation. Higher Z-scores indicate more
+ordered (structured) residues; lower scores indicate disorder.
+
+The TriZOD **G-score** normalizes Z-scores to [0, 1] range, independent of the
+number of available shift types, making scores comparable across entries with
+different data completeness.
+
+## Stage 5: Post-filter
+
+**Module**: `trizod/trizod.py`
+
+After scoring, entries may be rejected if:
+
+- The offset correction exceeds `--max-offset` (indicating unreliable data)
+- With `--reject-shift-type-only`: only the problematic atom type is dropped
+  rather than the entire entry
+
+## Stage 6: Output
+
+**Module**: `trizod/trizod.py` → `output_dataset()`
+
+Results are written as JSON (primary) and optionally CSV, containing per-entry:
+
+- Sequence, sample conditions, metadata
+- Per-residue Z-scores and G-scores
+- Filter status and offset corrections applied
diff --git a/docs/potenci.md b/docs/potenci.md
new file mode 100644
index 0000000..1a7f721
--- /dev/null
+++ b/docs/potenci.md
@@ -0,0 +1,266 @@
+# POTENCI — Random Coil Chemical Shift Prediction
+
+POTENCI (**P**rediction **O**f **T**otal **E**ffects on **N**MR **C**hemical shifts
+from **I**ntrinsically disordered proteins) predicts the **random coil chemical shifts**
+of backbone atoms in a protein sequence — i.e., the shifts you'd expect if the protein
+had no structure, just a flexible disordered chain.
+
+In the TriZOD pipeline, these predicted shifts are compared against experimentally
+measured shifts. Large deviations indicate structure; small deviations indicate disorder.
+
+## Origin
+
+Based on the POTENCI method published in:
+
+> Nielsen JT, Mulder FAA. *POTENCI: prediction of temperature, neighbor and pH-corrected
+> chemical shifts for intrinsically disordered proteins.* J Biomol NMR. 2018;70:141-165.
+> [doi:10.1007/s10858-018-0175-y](https://doi.org/10.1007/s10858-018-0175-y)
+
+Adapted from [protein-nmr/POTENCI](https://github.com/protein-nmr/POTENCI)
+(commit `17dd2e6`, file `pytenci1_3.py`), originally by Frans Mulder
+(fmulder@chem.au.dk). Adapted by Markus Haak (markus.haak@tum.de) and
+Tobias Senoner (tobias.senoner@tum.de).
+
+## Differences from upstream
+
+Our implementation is functionally equivalent (identical data tables, identical
+algorithm) but improved for integration into the TriZOD pipeline:
+
+| Aspect              | Upstream (`potenci.py3`)               | Ours                                                    |
+| ------------------- | -------------------------------------- | ------------------------------------------------------- |
+| Data tables         | Hardcoded multi-line strings           | CSV files in `data/`                                    |
+| pKa inner loop      | Nested Python loops                    | NumPy vectorized over pH values                         |
+| `eval()` usage      | Throughout (`eval(lin[4])`)            | Replaced with `float()`                                 |
+| Data caching        | None (re-parses strings each call)     | Module-level constants                                  |
+| Logging             | `VERB` flag + `print()`                | Python `logging` module                                 |
+| Numerical stability | `exp(x²) / (e*r)` can overflow         | Log-space: `exp(x² - log(e*r))`                         |
+| Naming              | camelCase / smooshed (`getpredshifts`) | snake_case (`get_pred_shifts`)                          |
+| Constants           | Single-letter names (`e`, `a`, `b`)    | Descriptive (`DIELECTRIC_WATER`, `MIN_CHARGE_DISTANCE`) |
+
+## Predicted atoms
+
+7 backbone atom types per residue:
+
+| Atom | Description | Notes |
+|------|-------------|-------|
+| `C`  | Carbonyl carbon (C') | |
+| `CA` | Alpha carbon | |
+| `CB` | Beta carbon | Absent for Glycine |
+| `N`  | Amide nitrogen | |
+| `H`  | Amide proton | Absent for Proline |
+| `HA` | Alpha proton | |
+| `HB` | Beta proton | |
+
+Terminal residues (first and last) are excluded from predictions because they
+lack the full 5-residue context window.
+
+## The prediction model (4 additive terms)
+
+For each interior residue, the predicted shift is:
+
+```
+shift = center_shift + neighbor_corrections + temperature_correction + pH_correction
+```
+
+### 1. Center shifts (`centshifts.csv`, 20 amino acids)
+
+Base random coil chemical shift for each amino acid x atom type. These are empirically
+determined reference values (e.g., Alanine CA = 52.53 ppm).
+
+### 2. Neighbor corrections (`neicorrs.csv` + `termcorrs.csv`)
+
+The identity of the 4 neighboring residues (i-2, i-1, i+1, i+2) perturbs the shift.
+Computed in `pred_pent_shift()` using a **5-residue sliding window (pentamer)**:
+
+```
+pentamer = [i-2, i-1, center, i+1, i+2]
+```
+
+For terminal residues, out-of-bounds positions are replaced by virtual `"n"` and `"c"`
+residues with their own correction coefficients (`termcorrs.csv`).
+
+On top of simple neighbor corrections, there are **combination corrections**
+(`combdevs.csv`, 247 entries) that capture non-additive effects. Amino acids are
+grouped into 6 classes (G, P, aromatic, aliphatic, positive, negative) and specific
+center+neighbor group pairs get extra correction terms.
+
+### 3. Temperature correction (`tempcoeffs.csv`)
+
+Linear scaling from a reference temperature of 298K:
+
+```
+correction = coeff / 1000 * (T - 298)
+```
+
+Coefficients are per amino acid x atom type. Not applied to HB.
+
+### 4. pH correction (the expensive part)
+
+Only applied when `use_ph_corr=True` (i.e., pH != 7.0). This accounts for the
+protonation state of titratable residues (D, E, H, C, K, R, Y, and N/C-termini)
+at non-neutral pH. This is where ~92% of compute time goes.
+
+#### Step 1: Predict pKa values (`calc_pkas_from_seq`)
+
+The algorithm iteratively predicts effective pKa values for all titratable sites:
+
+1. **Identify titratable residues** in the sequence (D, E, H, C, K, R, Y, n-term,
+   c-term) with their reference pKa values from `constants.py`.
+
+2. **Compute pairwise electrostatic interactions** using a Debye-Hückel model:
+   - Distance between sites estimated from sequence separation:
+     `d = 5.0 + sqrt(|i-j|) * 7.5` Angstrom
+   - Ionic strength dampens interactions (higher salt = weaker coupling)
+   - `_debye_huckel_W()` computes the screened Coulomb energy using the complementary
+     error function (`erfc`)
+
+3. **Iteratively fit pKa values** (5 cycles):
+   - For each titratable site, enumerate all 2^W protonation microstates in a sliding
+     window of W residues (W = min(5, n_sites))
+   - Compute the Boltzmann weight of each microstate from a free energy matrix G that
+     combines: intrinsic pKa, pH, pairwise interactions, and the current titration
+     state of sites outside the window
+   - Sum Boltzmann weights to get the titration fraction at each pH value (54 pH points
+     from 2.0 to 10.0)
+   - Fit the resulting titration curve to a Henderson-Hasselbalch equation using
+     `scipy.curve_fit` to extract effective pKa and Hill coefficient (nH)
+   - Use the new titration fractions as input for the next cycle
+
+**Why the sliding window?** With N titratable sites, the full partition function has 2^N
+microstates — exponential and infeasible for large proteins. The sliding window (size 5)
+approximates this by enumerating microstates locally, treating distant sites as fixed at
+their current titration state. The iterative cycles let information propagate across the
+sequence.
+
+#### Step 2: Compute shift corrections (`_get_ph_corrs`)
+
+For each titratable residue with predicted pKa:
+
+```
+frac     = titration_fraction(pH, predicted_pKa, nH)    # at actual pH
+frac_ref = titration_fraction(7.0, reference_pKa, nH)   # at reference pH
+delta    = (frac - frac_ref) * ph_shift_delta            # from phshifts.csv
+```
+
+This correction is applied to the titratable residue itself AND its immediate neighbors
+(preceding/succeeding), since protonation state changes propagate to nearby residues.
+
+## Output format
+
+`get_pred_shifts("AAGDTFKISELVK", 298.0, 7.0, 0.1)` returns:
+
+```python
+{
+    (2, 'A'): {'C': 177.5, 'CA': 52.7, 'CB': 19.2, 'H': 8.21, 'HA': 4.26, 'N': 123.5, 'HB': 1.32},
+    (3, 'G'): {'C': 173.7, 'CA': 45.3, 'H': 8.28, 'HA': 3.95, 'N': 109.7},  # no CB/HB
+    (4, 'D'): { ... },
+    ...
+    (12, 'V'): { ... },
+    # (1, 'A') and (13, 'K') excluded — terminal residues
+}
+```
+
+- **Keys**: `(residue_number, amino_acid)` — 1-based, terminals excluded
+- **Values**: dict of atom type to predicted shift in **ppm**
+- Glycine has no CB/HB; Proline has no H
+
+In the TriZOD pipeline, these predicted shifts are subtracted from experimental shifts
+to get **secondary chemical shifts (SCS)**, which are then converted to Z-scores
+indicating disorder.
+
+## Public API
+
+```python
+get_pred_shifts(seq, temperature, pH, ion, use_ph_corr=True)
+```
+
+**Parameters:**
+
+- `seq` — protein sequence (single-letter amino acids)
+- `temperature` — in Kelvin (e.g. 298.0)
+- `pH` — sample pH (e.g. 7.0)
+- `ion` — ionic strength in M (e.g. 0.1)
+- `use_ph_corr` — apply pH-dependent corrections (set `False` for pH=7.0)
+
+## Caching
+
+### Module-level caching (at import time)
+
+All CSV data tables are parsed once when the module is first imported and stored as
+module-level constants:
+
+| Constant | Source | Content |
+|----------|--------|---------|
+| `CENTER_SHIFTS` | `centshifts.csv` | Base shift per (amino acid, atom) |
+| `NEIGHBOR_CORRS` | `neicorrs.csv` + `termcorrs.csv` | Neighbor effect coefficients |
+| `TEMP_CORRS` | `tempcoeffs.csv` | Temperature coefficients |
+| `COMB_CORRS` | `combdevs.csv` | Combination correction terms |
+| `PH_SHIFTS` | `phshifts.csv` | pH shift deltas for titratable residues |
+| `_AA_GROUP` | computed | Amino acid to group mapping (6 groups) |
+| `_OUTER_MATRICES` / `_ALL_TUPLES` | computed | Pre-computed binary enumeration matrices for pKa fitting |
+
+### Pipeline-level caching (disk)
+
+Since POTENCI output depends only on `(seq, temperature, pH, ionic_strength)`, the
+pipeline caches results to disk via `--cache-dir`:
+
+- **Key**: SHA-256 hash of `"{seq}|{T}|{pH}|{ion}"` (truncated to 16 chars)
+- **Storage**: `{cache_dir}/potenci/{hash}.json`
+- **Precompute**: `scripts/precompute_potenci_cache.py` can batch-compute the cache
+  for the entire dataset upfront, with an index file (`_index.tsv`) to skip
+  already-processed BMRB entries on re-runs
+
+## Performance profile
+
+POTENCI dominates the TriZOD pipeline (~92% of total time), almost entirely
+in `calc_pkas_from_seq()` which iteratively fits pKa values for titratable
+residues using `scipy.optimize.curve_fit`.
+
+On the 300-entry test subset:
+
+- **With pH correction** (78% of entries): ~200ms/entry average
+- **Without pH correction** (pH=7.0): ~1ms/entry
+
+Sequence length and number of titratable residues (D, E, H, C, K, R, Y) are
+the main cost drivers.
+
+## Module structure
+
+```
+trizod/potenci/
+├── __init__.py          # Re-exports get_pred_shifts and constants
+├── constants.py         # Physical constants, reference pKa values
+├── potenci.py           # Core prediction code
+├── data/
+│   ├── centshifts.csv   # Center (random coil) chemical shifts per amino acid
+│   ├── neicorrs.csv     # Neighbor correction coefficients
+│   ├── termcorrs.csv    # N/C-terminal corrections
+│   ├── tempcoeffs.csv   # Temperature correction coefficients
+│   ├── combdevs.csv     # Combination deviation corrections
+│   └── phshifts.csv     # pH-dependent shift changes
+└── README.md
+```
+
+## Key internal functions
+
+| Function                 | Purpose                                                        |
+| ------------------------ | -------------------------------------------------------------- |
+| `calc_pkas_from_seq()`   | Iterative pKa prediction (the bottleneck)                      |
+| `_get_ph_corrs()`        | pH-dependent shift corrections using predicted pKas            |
+| `pred_pent_shift()`      | Predict shift for a 5-residue window                           |
+| `_build_pentamer()`      | Build 5-residue context window with terminal handling          |
+| `_get_temp_corr()`       | Temperature correction                                         |
+| `_titration_fraction()`  | Henderson-Hasselbalch titration fraction (used by `curve_fit`) |
+| `_debye_huckel_W()`      | Electrostatic interaction energy (Debye-Huckel)                |
+| `_w_to_logp()`           | Convert interaction energy to log-probability                  |
+| `_small_matrix_limits()` | Sliding window bounds for pKa fitting                          |
+| `_small_matrix_pos()`    | Position within sliding window                                 |
+
+## Testing
+
+Dedicated tests in `tests/test_potenci.py`:
+
+- **Reference value matching** — predictions compared against known values (commit `8905a85`)
+- **pH correction** — verifies pH-sensitive residues shift at non-neutral pH
+- **Terminal exclusion** — first/last residues are excluded from predictions
+- **Glycine constraints** — no CB/HB predictions for glycine
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..a52f462
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,69 @@
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
+
+[project]
+name = "trizod"
+version = "0.0.1"
+description = "Modern disorder scores for BMRB data"
+readme = "README.md"
+license = "AGPL-3.0-only"
+requires-python = ">=3.9"
+authors = [
+    { name = "Markus Haak", email = "markus.haak@tum.de" },
+    { name = "Tobias Senoner", email = "tobias.senoner@tum.de" },
+]
+classifiers = [
+    "Programming Language :: Python :: 3",
+    "License :: OSI Approved :: GNU Affero General Public License v3",
+    "Operating System :: OS Independent",
+    "Topic :: Scientific/Engineering :: Bio-Informatics",
+    "Intended Audience :: Science/Research",
+]
+dependencies = [
+    "matplotlib>=3.9.4",
+    "numpy>=2.0.2",
+    "pandarallel>=1.6.5",
+    "pandas>=2.3.3",
+    "pynmrstar>=3.5.0",
+    "scipy>=1.13.1",
+    "tqdm>=4.67.3",
+]
+
+[project.urls]
+Repository = "https://github.com/MarkusHaak/trizod"
+
+[project.scripts]
+trizod = "trizod.trizod:main"
+
+[tool.pytest.ini_options]
+testpaths = ["tests"]
+markers = [
+    "slow: marks tests that run the full dataset pipeline (deselect with '-m \"not slow\"')",
+]
+addopts = "-m 'not slow'"
+
+[tool.ruff]
+target-version = "py39"
+
+[tool.ruff.lint]
+select = ["E", "W", "F", "I", "N", "UP", "B", "C4", "SIM"]
+ignore = [
+    "N803",   # argument name should be lowercase (scientific convention: T, Ion, pH, etc.)
+    "N806",   # variable name should be lowercase (scientific convention: N, I, E, etc.)
+    "N802",   # function name should be lowercase (POTENCI naming: predPentShift, etc.)
+    "N816",   # mixed-case variable in global scope (AAstandard, CENTSHIFTS, etc.)
+    "N818",   # exception name should end in Error (legacy naming: Found, etc.)
+    "E741",   # ambiguous variable name (l, I, E — standard in scientific code)
+    "E501",   # line too long (formatter handles what it can; remaining are data/strings)
+    "SIM115", # open file with context handler (POTENCI CSV I/O)
+]
+
+[tool.ruff.lint.isort]
+known-first-party = ["trizod"]
+
+[dependency-groups]
+dev = [
+    "pytest>=8.4.2",
+    "ruff>=0.15.5",
+]
diff --git a/scripts/precompute_potenci_cache.py b/scripts/precompute_potenci_cache.py
new file mode 100644
index 0000000..b02384f
--- /dev/null
+++ b/scripts/precompute_potenci_cache.py
@@ -0,0 +1,192 @@
+#!/usr/bin/env python3
+"""Precompute POTENCI predictions and cache them for faster pipeline runs.
+
+Usage:
+    uv run python scripts/precompute_potenci_cache.py --input-dir data/bmrb_entries --cache-dir tmp
+    uv run python scripts/precompute_potenci_cache.py --input-dir tests/bmrb_subset --cache-dir tmp
+
+The cache is keyed by (seq, temperature, pH, ionic_strength) so it stays valid
+across filter/scoring changes. Only POTENCI input changes invalidate it.
+
+An index file (potenci/_index.tsv) tracks which entries have been processed,
+so re-runs skip parsing entirely for known entries.
+"""
+
+import argparse
+import logging
+import sys
+import time
+from pathlib import Path
+
+import numpy as np
+from tqdm import tqdm
+
+# Add project root to path
+sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
+
+import trizod.bmrb.bmrb as bmrb
+import trizod.potenci.potenci as potenci
+from trizod.trizod import (
+    _potenci_cache_key,
+    find_bmrb_files,
+    save_potenci_cache,
+)
+
+
+def load_index(potenci_dir):
+    """Load the entry→cache_key index. Returns set of processed entry IDs."""
+    index_fp = Path(potenci_dir) / "_index.tsv"
+    processed = set()
+    if index_fp.exists():
+        with index_fp.open() as f:
+            for line in f:
+                parts = line.strip().split("\t")
+                if parts:
+                    processed.add(parts[0])
+    return processed
+
+
+def append_index(potenci_dir, entry_id, keys):
+    """Append entry and its cache keys to the index file."""
+    index_fp = Path(potenci_dir) / "_index.tsv"
+    with index_fp.open("a") as f:
+        f.write(f"{entry_id}\t{','.join(keys)}\n")
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Precompute POTENCI prediction cache")
+    parser.add_argument(
+        "--input-dir", required=True, help="Directory with BMRB NMR-STAR files"
+    )
+    parser.add_argument(
+        "--cache-dir", default="./tmp", help="Cache directory (default: ./tmp)"
+    )
+    parser.add_argument(
+        "--file-pattern", default=r"bmr(\d+)_3\.str", help="BMRB file pattern"
+    )
+    parser.add_argument(
+        "-v",
+        "--verbose",
+        action="store_true",
+        help="Show BMRB parsing warnings and POTENCI debug messages",
+    )
+    parser.add_argument(
+        "--reindex",
+        action="store_true",
+        help="Ignore existing index and reprocess all entries",
+    )
+    args = parser.parse_args()
+
+    logging.basicConfig(level=logging.WARNING, format="%(levelname)s : %(message)s")
+    logger = logging.getLogger("precompute")
+    logger.setLevel(logging.INFO)
+    if not args.verbose:
+        logging.getLogger("trizod.bmrb").setLevel(logging.CRITICAL)
+        logging.getLogger("trizod.potenci").setLevel(logging.CRITICAL)
+        logging.getLogger("trizod").setLevel(logging.CRITICAL)
+
+    cache_dir = Path(args.cache_dir).resolve()
+    potenci_dir = cache_dir / "potenci"
+    potenci_dir.mkdir(parents=True, exist_ok=True)
+
+    logger.info(f"Finding BMRB files in {args.input_dir}")
+    bmrb_files = find_bmrb_files(args.input_dir, args.file_pattern)
+    logger.info(f"Found {len(bmrb_files)} BMRB files")
+
+    # Load index of already-processed entries
+    if args.reindex:
+        processed = set()
+        index_fp = potenci_dir / "_index.tsv"
+        if index_fp.exists():
+            index_fp.unlink()
+    else:
+        processed = load_index(potenci_dir)
+    if processed:
+        logger.info(f"Skipping {len(processed)} already-indexed entries")
+
+    computed, skipped, failed = 0, 0, 0
+    failed_entries = []
+    seen_keys = set()
+    start = time.time()
+
+    pbar = tqdm(bmrb_files.items(), desc="Precomputing POTENCI", unit="entry")
+    for entry_id, filepath in pbar:
+        if entry_id in processed:
+            skipped += 1
+            pbar.set_postfix(new=computed, skip=skipped, fail=failed)
+            continue
+
+        try:
+            entry = bmrb.BmrbEntry(entry_id, Path(filepath).parent)
+        except Exception as e:
+            failed += 1
+            failed_entries.append((entry_id, "parse", str(e)))
+            append_index(potenci_dir, entry_id, ["FAILED"])
+            continue
+
+        try:
+            peptide_shifts = entry.get_peptide_shifts()
+        except Exception as e:
+            failed += 1
+            failed_entries.append((entry_id, "shifts", str(e)))
+            append_index(potenci_dir, entry_id, ["FAILED"])
+            continue
+
+        entry_keys = []
+        for (_stID, _entity_assemID, entityID), (
+            _shifts,
+            condID,
+            _assemID,
+            _sampleIDs,
+        ) in peptide_shifts.items():
+            try:
+                entity = entry.entities[entityID]
+                seq = entity.seq
+                if not seq:
+                    continue
+                conds = entry.conditions.get(condID, None)
+                if conds is None:
+                    continue
+
+                temperature = conds.get_temperature()
+                pH = conds.get_pH()
+                ion = conds.get_ionic_strength()
+                if temperature is None or pH is None or ion is None:
+                    continue
+                if np.isnan(temperature) or np.isnan(pH) or np.isnan(ion):
+                    continue
+
+                key = _potenci_cache_key(seq, temperature, pH, ion)
+                if key in seen_keys:
+                    entry_keys.append(key)
+                    continue
+                seen_keys.add(key)
+
+                use_ph_corr = pH != 7.0
+                predshiftdct = potenci.get_pred_shifts(
+                    seq, temperature, pH, ion, use_ph_corr
+                )
+                save_potenci_cache(cache_dir, seq, temperature, pH, ion, predshiftdct)
+                entry_keys.append(key)
+                computed += 1
+            except Exception as e:
+                failed += 1
+                failed_entries.append((entry_id, "potenci", str(e)))
+
+        append_index(potenci_dir, entry_id, entry_keys)
+        pbar.set_postfix(new=computed, skip=skipped, fail=failed)
+
+    elapsed = time.time() - start
+    logger.info(
+        f"Done in {elapsed:.1f}s: {computed} computed, {skipped} skipped, {failed} failed"
+    )
+    if failed_entries:
+        log_fp = cache_dir / "potenci_failures.txt"
+        with log_fp.open("w") as f:
+            for entry_id, stage, error in failed_entries:
+                f.write(f"{entry_id}\t{stage}\t{error}\n")
+        logger.info(f"Failed entries written to {log_fp}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/setup.py b/setup.py
deleted file mode 100644
index 74c8bed..0000000
--- a/setup.py
+++ /dev/null
@@ -1,41 +0,0 @@
-import setuptools
-
-with open("README.md", "r") as f:
-    long_description = f.read()
-
-from trizod import META_DATA, __version__
-VERSION = __version__
-
-setuptools.setup(
-    name=META_DATA['package_name'].lower(),
-    version=VERSION,
-    author=META_DATA['author'],
-    author_email=META_DATA['author_email'],
-    description=META_DATA['description'],
-    long_description=long_description,
-    long_description_content_type="text/markdown",
-    url=META_DATA['github_url'],
-    packages=setuptools.find_packages(),
-    include_package_data=True,
-    package_data={'': ['potenci/data_tables/*.csv']},
-    install_requires=[
-        'pandas',
-        'numpy',
-        'matplotlib',
-        'tqdm',
-        'pandarallel',
-        'pynmrstar',
-        'scipy'
-    ],
-    classifiers=[
-        "Programming Language :: Python :: 3",
-        "License :: OSI Approved :: GNU AFFERO GENERAL PUBLIC LICENSE Version 3",
-        "Operating System :: OS Independent",
-        "Topic :: Scientific/Engineering :: Bio-Informatics",
-        "Intended Audience :: Science/Research"
-    ],
-    entry_points={
-    'console_scripts': [
-        'trizod=trizod.trizod:main',
-    ]},
-)
\ No newline at end of file
diff --git a/test/test_cache_equality.py b/test/test_cache_equality.py
deleted file mode 100644
index 9988efa..0000000
--- a/test/test_cache_equality.py
+++ /dev/null
@@ -1,49 +0,0 @@
-import os,sys
-import logging
-import numpy as np
-import pandas as pd
-import argparse
-from trizod.constants import BBATNS
-
-def parse_args():
-    parser = argparse.ArgumentParser(description='')
-
-    parser.add_argument('cache_dir',
-        help='Cache directory that shall be tested.')
-    parser.add_argument('cache_comp',
-        help='Cache directory to compare against.')
-
-    args = parser.parse_args()
-    return args
-
-def main():
-    cache_files = {f for f in os.listdir(os.path.join(args.cache_dir, 'wSCS')) if os.path.isfile(os.path.join(args.cache_dir, 'wSCS', f)) and f.endswith('.npz')}
-    comp_files = {f for f in os.listdir(os.path.join(args.cache_comp, 'wSCS')) if os.path.isfile(os.path.join(args.cache_comp, 'wSCS', f)) and f.endswith('.npz')}
-    common_files = list(cache_files & comp_files)
-    common_files.sort()
-    print(len(common_files))
-    res = []
-    for fn in common_files:
-        with open(os.path.join(args.cache_dir, 'wSCS', fn), 'rb') as f1, open(os.path.join(args.cache_comp, 'wSCS', fn), 'rb') as f2:
-            z = np.load(f1)
-            shw, ashwi, cmp_mask, olf, offf, shw0, ashwi0, ol0, off0 = z['shw'], z['ashwi'], z['cmp_mask'], z['olf'], z['offf'], z['shw0'], z['ashwi0'], z['ol0'], z['off0']
-            offf, off0 = {at:off for at,off in zip(BBATNS, offf)}, {at:off for at,off in zip(BBATNS, off0)}
-            
-            z =  np.load(f2)
-            shw_, ashwi_, cmp_mask_, olf_, offf_, shw0_, ashwi0_, ol0_, off0_ = z['shw'], z['ashwi'], z['cmp_mask'], z['olf'], z['offf'], z['shw0'], z['ashwi0'], z['ol0'], z['off0']
-            offf_, off0_ = {at:off for at,off in zip(BBATNS, offf_)}, {at:off for at,off in zip(BBATNS, off0_)}
-        if ashwi.shape != ashwi_.shape:
-            res.append("shape mismatch")
-            continue
-        if not np.allclose(ashwi, ashwi_):
-            res.append('not close')
-            continue
-        res.append('close')
-    df = pd.DataFrame(res, columns=['result'], index=common_files)
-    breakpoint()
-
-if __name__ == '__main__':
-    level = logging.INFO
-    logging.basicConfig(level=level, format=f'%(levelname)s : %(message)s')
-    args = parse_args()
-    main()
\ No newline at end of file
diff --git a/test/test_chezod_equality.py b/test/test_chezod_equality.py
deleted file mode 100644
index 77965fa..0000000
--- a/test/test_chezod_equality.py
+++ /dev/null
@@ -1,217 +0,0 @@
-import pytest
-import os,sys
-import logging
-import numpy as np
-import re
-
-import pprint
-sys.path.append("..")
-
-import trizod.potenci.potenci as potenci
-import trizod.scoring.scoring as scoring
-import trizod.bmrb.bmrb as bmrb
-
-class BiostarV2FileMissing(Exception):
-    pass
-class PotenciError(Exception):
-    pass
-class TriZODError(Exception):
-    pass
-class ChiZODError(Exception):
-    pass
-class BMRBEntryError(Exception):
-    pass
-class ZscoreDiversion(Exception):
-    pass
-
-bmrb_dir = os.path.join(os.path.dirname(__file__), 'data', 'bmrb_entries')
-ids_to_test = [int(dn[3:]) for dn in os.listdir(os.path.join(os.path.dirname(__file__), 'data', 'CheZOD_results_all')) if dn.startswith('bmr')]
-#ids_to_test = [id_ for id_ in ids_to_test if os.path.exists(os.path.join(bmrb_dir, f"bmr{id_}", f"bmr{id_}_3.str"))]
-ids_value_error = [
-'6753',
-'27272',
-'26904',
-'26953',
-]
-
-def parse_zscore_file(fn, seq=None, fill=999.):
-    """
-    Parses a zscore file and returns a (gap-filled if fill != False, fill value = fill) list of zscores
-    and the number of gaps compared to the sequence seq.
-    """
-    vals = []
-    gaps = 0
-    seq_ = []
-    with open(fn, 'r') as f:
-        i = 1
-        for line in f.readlines():
-            if line:
-                aa,pos,val = line.strip().split()
-                pos, val = int(pos), float(val)
-                if seq:
-                    if seq[pos-1].upper() != aa.upper():
-                        #print(f"Error: AA mismatch at position {i} of {id_}: ", seq[pos-1].upper(), "!=", aa.upper())
-                        raise ValueError
-                if fill is not False:
-                    while i < pos:
-                        vals.append(fill)
-                        i += 1
-                        gaps += 1
-                        seq_.append(" ")
-                vals.append(val)
-                seq_.append(aa)
-                i += 1
-    if seq and fill is not False:
-        while len(vals) < len(seq):
-            vals.append(fill)
-            gaps += 1
-            seq_.append(" ")
-    return vals, gaps, "".join(seq_).upper()
-
-def parse_shift_file(fn, seq=None):
-    '''
-    Parses a shift data file ceated by CheZOD and returns a set of all parsed shifts
-    '''
-    shift_data = {}
-    offsets = {}
-    with open(fn, 'r') as f:
-        for line in f.readlines():
-            if line.startswith('off'):
-                _,at,val = line.strip().split()
-                val = float(val)
-                offsets[at] = val
-                continue
-            pos,aa,at,val,_,_ = line.strip().split() #po is 1-based
-            pos,val = int(pos), float(val)
-            if seq:
-                if seq[pos-1].upper() != aa.upper():
-                    logging.warning(f'amino acid mismatch at position {pos-1}')
-                    raise ValueError(f'amino acid mismatch at position {pos-1}')
-            if (pos-1, at) in shift_data:
-                logging.warning(f'duplicate shift data entries')
-                raise ValueError(f'duplicate shift data entries')
-            shift_data[(pos-1, at)] = val
-    return shift_data, offsets
-    
-#@pytest.mark.parametrize("id_", ids_to_test)
-def test_equaltity_to_chezod(id_):
-    '''Checks if any protein-shift combination in the corresponding BMRB entry produces
-    equivalent Z-scores as those output by the original CheZOD script'''
-    str_fn = os.path.join(os.path.dirname(__file__), 'data', 'CheZOD_results_all', f'bmr{id_}', f'bmr{id_}.str')
-    if not os.path.exists(str_fn):
-        logging.warning(f"skipping entry {id_}, Biostar V2 file not found")
-        if __name__ == '__main__':
-            raise BiostarV2FileMissing
-        return
-    zscores_fn = os.path.join(os.path.dirname(__file__), 'data', 'CheZOD_results_all', f'bmr{id_}', f'zscores{id_}.txt')
-    if not os.path.exists(zscores_fn):
-        logging.warning(f"skipping entry {id_}, CheZOD zscore file not found")
-        if __name__ == '__main__':
-            raise ChiZODError
-        return 
-    shifts_fn = os.path.join(os.path.dirname(__file__), 'data', 'CheZOD_results_all', f'bmr{id_}', f'shifts{id_}.txt')
-    potenci_fn = [fn for fn in os.listdir(os.path.join(os.path.dirname(__file__), 'data', 'CheZOD_results_all', f'bmr{id_}')) if fn.startswith(f'outPOTENCI')][0]
-    m = re.search("_T([^_]*)_I([^_]*)_pH([^_]*)\.", potenci_fn)
-    chezod_conditions = (
-        float(m.groups()[0]),
-        float(m.groups()[1]),
-        float(m.groups()[2])
-    )
-    try:
-        entry = bmrb.BmrbEntry(id_, bmrb_dir)
-    except:
-        logging.warning(f"skipping entry {id_}, BMRB entry could not be parsed")
-        if __name__ == '__main__':
-            raise BMRBEntryError
-        return 
-    peptide_shifts = entry.get_peptide_shifts()
-    for (stID, entity_assemID, entityID) in peptide_shifts:
-        shifts, condID, assemID, sampleIDs = peptide_shifts[(stID, entity_assemID, entityID)]
-        # get polymer sequence and chemical backbone shifts
-        seq = entry.entities[entityID].seq
-        if seq is None:
-            logging.warning(f"skipping shifts for {(stID, condID, assemID, entity_assemID, entityID)}, no sequence information")
-            continue
-        elif len(seq) < 5:
-            logging.warning(f"skipping shifts for {(stID, condID, assemID, entity_assemID, entityID)}, sequence shorter than 5 residues")
-            continue
-        # try to parse CheZOD zscores with this seq - fails in case of amino acid mismatches!
-        try:
-            chezod_zscores,_,_ = parse_zscore_file(zscores_fn, seq=seq, fill=np.nan)
-            chezod_zscores = np.array(chezod_zscores)
-        except ValueError:
-            logging.warning(f"skipping shifts for {(stID, condID, assemID, entity_assemID, entityID)}, sequence mismatch")
-            continue
-        try:
-            chezod_shifts, chezod_offsets = parse_shift_file(shifts_fn, seq=seq)
-        except ValueError:
-            logging.warning(f"skipping shifts for {(stID, condID, assemID, entity_assemID, entityID)}, duplicates or sequence mismatch in chezod shifts file")
-            continue
-        # check if shift data is identical
-        bbshifts,_,_ = bmrb.get_valid_bbshifts(shifts, seq)
-        trizod_shifts = {(pos,at) : bbshifts[pos][at][0] for pos in bbshifts for at in bbshifts[pos] if pos not in [0,len(seq)-1]}
-        if set(chezod_shifts.keys()) != set(trizod_shifts.keys()):
-            logging.warning(f"skipping shifts for {(stID, condID, assemID, entity_assemID, entityID)}, backbone shift count not identical")
-            #breakpoint()
-            continue
-        all_close = True
-        for key in chezod_shifts.keys():
-            if not np.isclose(chezod_shifts[key], trizod_shifts[key], atol=0.1):
-                break
-        else:
-            all_close = True
-        if not all_close:
-            logging.warning(f"skipping shifts for {(stID, condID, assemID, entity_assemID, entityID)}, backbone shifts not identical")
-            continue
-            #breakpoint()
-        # use POTENCI to predict shifts
-        ion = entry.conditions[condID].get_ionic_strength()
-        pH = entry.conditions[condID].get_pH()
-        temperature = entry.conditions[condID].get_temperature()
-        if not np.allclose(np.array((temperature, ion, pH)), np.array(chezod_conditions), atol=0.01):
-            logging.warning(f"skipping shifts for {(stID, condID, assemID, entity_assemID, entityID)}, experiment conditions do not match")
-            continue
-        # reject if out of certain ranges
-        #if ion > 3. or pH > 13. or temperature < 273.15 or temperature > 373.15:
-        #    logging.error(f"skipping {(stID, condID, assemID, entity_assemID, entityID)} due to extreme experiment conditions")
-        #    continue
-        # predict random coil chemical shifts using POTENCI
-        usephcor = pH != 7.0
-        try:
-            predshiftdct = potenci.getpredshifts(seq,temperature,pH,ion,usephcor,pkacsvfile=None)
-        except:
-            logging.error(f"POTENCI failed for {(stID, condID, assemID, entity_assemID, entityID)} due to the following error:", exc_info=True)
-            if __name__ == '__main__':
-                raise PotenciError
-            continue
-        ret = scoring.get_offset_corrected_wSCS(seq, shifts, predshiftdct)
-        if ret is None:
-            logging.warning(f'skipping shifts for {(stID, condID, assemID, entity_assemID, entityID)} due to a previous error')
-            if __name__ == '__main__':
-                raise TriZODError
-            continue
-        shw, ashwi, cmp_mask, olf, offf = ret
-        ashwi3, k3 = scoring.convert_to_triplet_data(ashwi, cmp_mask)
-        zscores = scoring.compute_zscores(ashwi3, k3, cmp_mask)
-        # compare zscores
-        try:
-            np.testing.assert_allclose(chezod_zscores, zscores, atol=0.1, equal_nan=True)
-            break
-        except:
-            logging.exception(f'{id_}, {(id_, stID, condID, assemID, entity_assemID, entityID)}: Z-scores diverge substantially between CheZOD and TriZOD')
-            if __name__ == '__main__':
-                raise ZscoreDiversion(f'Z-scores diverge substantially between CheZOD and TriZOD')
-    else:
-        raise AssertionError(f'No matching entities found for BMRB entry {id_}')
-    return (id_, stID, condID, assemID, entity_assemID, entityID)
-
-if __name__ == '__main__':
-    logfile = os.path.join(os.path.dirname(__file__), "test_results.csv")
-    with open(logfile, 'w') as lf:
-        for id_ in ids_to_test:
-        #for id_ in ids_value_error:
-            try:
-                entity_info = test_equaltity_to_chezod(id_)
-                print("\t".join([str(id_), "pass", ",".join([str(i) for i in entity_info])]), file=lf)
-            except Exception as ex:
-                print("\t".join([str(id_),"fail",ex.__class__.__name__]), file=lf)
\ No newline at end of file
diff --git a/test/test_results_equality.py b/test/test_results_equality.py
deleted file mode 100644
index d4f8bb0..0000000
--- a/test/test_results_equality.py
+++ /dev/null
@@ -1,35 +0,0 @@
-import os,sys
-import logging
-import numpy as np
-import pandas as pd
-import argparse
-from trizod.constants import BBATNS
-
-def parse_args():
-    parser = argparse.ArgumentParser(description='')
-
-    parser.add_argument('results_file',
-        help='')
-    parser.add_argument('results_comp',
-        help='')
-
-    args = parser.parse_args()
-    return args
-
-def main():
-    df1 = pd.read_csv(args.results_file, index_col=[0,1,2,3,4,5])
-    df2 = pd.read_csv(args.results_comp, index_col=[0,1,2,3,4,5])
-    diff1 = df1.index.difference(df2.index)
-    logging.info(f"results file contains {diff1.size} entries that are not in the comparison file")
-    diff2 = df2.index.difference(df1.index)
-    logging.info(f"comparison file contains {diff2.size} entries that are not in the results file")
-    idx = df1.index.intersection(df2.index)
-    logging.info(f"{idx.size} entries are comparible")
-    close = np.isclose(df1.loc[idx].scores, df2.loc[idx].scores, equal_nan=True)
-    breakpoint()
-
-if __name__ == '__main__':
-    level = logging.INFO
-    logging.basicConfig(level=level, format=f'%(levelname)s : %(message)s')
-    args = parse_args()
-    main()
\ No newline at end of file
diff --git a/test/__init__.py b/tests/__init__.py
similarity index 100%
rename from test/__init__.py
rename to tests/__init__.py
diff --git a/tests/conftest.py b/tests/conftest.py
new file mode 100644
index 0000000..2d560f5
--- /dev/null
+++ b/tests/conftest.py
@@ -0,0 +1,40 @@
+from pathlib import Path
+
+import pytest
+
+PROJECT_ROOT = Path(__file__).resolve().parent.parent
+TESTS_DIR = Path(__file__).resolve().parent
+DATA_DIR = PROJECT_ROOT / "data"
+BMRB_DIR = DATA_DIR / "bmrb_entries"
+SUBSET_DIR = TESTS_DIR / "bmrb_subset"
+SUBSET_IDS_FILE = TESTS_DIR / "quick_subset_ids.txt"
+
+
+def has_bmrb_data():
+    """Check if BMRB data files are available."""
+    return BMRB_DIR.is_dir() and any(BMRB_DIR.iterdir())
+
+
+def ensure_subset_symlinks():
+    """Create symlinks for the test subset if they don't exist."""
+    if not has_bmrb_data() or not SUBSET_IDS_FILE.exists():
+        return
+    if SUBSET_DIR.is_dir() and any(SUBSET_DIR.iterdir()):
+        return  # already set up
+    SUBSET_DIR.mkdir(parents=True, exist_ok=True)
+    ids = SUBSET_IDS_FILE.read_text().strip().split("\n")
+    for bmrb_id in ids:
+        src = BMRB_DIR / f"bmr{bmrb_id}"
+        dst = SUBSET_DIR / f"bmr{bmrb_id}"
+        if src.is_dir() and not dst.exists():
+            dst.symlink_to(src.resolve())
+
+
+# Auto-create symlinks on import
+ensure_subset_symlinks()
+
+
+requires_bmrb_data = pytest.mark.skipif(
+    not has_bmrb_data(),
+    reason="BMRB data not available (data/bmrb_entries/)",
+)
diff --git a/tests/quick_subset_ids.txt b/tests/quick_subset_ids.txt
new file mode 100644
index 0000000..ed7169d
--- /dev/null
+++ b/tests/quick_subset_ids.txt
@@ -0,0 +1,300 @@
+10024
+10123
+11053
+11075
+11597
+1202
+1441
+1474
+1483
+15013
+15031
+15060
+15097
+15132
+15380
+15419
+15429
+15546
+15701
+15723
+15818
+15833
+15887
+15991
+16039
+16076
+16098
+16168
+16360
+16391
+16403
+1642
+16546
+16586
+16629
+16683
+16698
+16726
+16739
+16741
+16770
+16788
+16824
+16960
+17013
+17214
+17297
+17332
+17373
+17414
+17483
+17597
+1771
+17770
+17816
+17826
+17830
+17832
+17845
+17866
+17873
+17918
+17960
+17994
+18020
+1812
+18226
+1826
+18322
+18333
+1839
+18407
+18528
+18531
+18610
+18652
+18663
+18680
+18694
+18757
+18761
+18951
+18959
+19099
+19115
+19157
+19202
+19208
+1921
+19228
+19230
+19477
+19601
+19767
+1995
+19955
+19991
+19996
+20004
+20012
+2067
+21014
+2220
+237
+2395
+25024
+25104
+25177
+25261
+25311
+25418
+25437
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+25566
+25648
+25674
+25850
+25967
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+26535
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+26593
+26641
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+26684
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+26762
+26888
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+26921
+26926
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+26989
+27047
+27050
+27210
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+27323
+27346
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+27697
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+28101
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+30077
+301
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+30487
+30562
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+34074
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+34170
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+34263
+34308
+34380
+34475
+345
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+34608
+34720
+34870
+34931
+34946
+34988
+3525
+36079
+36309
+36327
+36341
+36361
+36362
+36404
+4158
+42
+4340
+4396
+4420
+443
+4493
+4572
+4589
+4797
+480
+4812
+4821
+4837
+4946
+5
+50089
+50138
+50366
+50385
+50392
+50399
+50457
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+50480
+50534
+50560
+5057
+50586
+50828
+50901
+51051
+5109
+51176
+5120
+51324
+51340
+51357
+51367
+51449
+51459
+51476
+51512
+51513
+51598
+51599
+51723
+51822
+51865
+51893
+51907
+51962
+51975
+52004
+5201
+52300
+52321
+52346
+52465
+52530
+52569
+52628
+52639
+52723
+5274
+52776
+52805
+52880
+52957
+53034
+53036
+53091
+53219
+53376
+53392
+5347
+5536
+5574
+5653
+5731
+5756
+595
+6097
+6457
+6494
+6576
+6617
+6666
+6669
+6675
+6746
+6747
+6837
+6881
+6885
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+7022
+7029
+7116
+7132
+849
diff --git a/tests/reference/unfiltered.json b/tests/reference/unfiltered.json
new file mode 100644
index 0000000..c7fe66c
--- /dev/null
+++ b/tests/reference/unfiltered.json
@@ -0,0 +1,344 @@
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diff --git a/tests/test_chezod_equality.py b/tests/test_chezod_equality.py
new file mode 100644
index 0000000..7e39394
--- /dev/null
+++ b/tests/test_chezod_equality.py
@@ -0,0 +1,10 @@
+"""
+TODO: Port legacy CheZOD equality test.
+
+Requires:
+- test/data/CheZOD_results_all/ — legacy CheZOD reference output files
+- Update unpacking of get_offset_corrected_wSCS() (now returns 9 values, old test expected 5)
+
+The test validates that TriZOD Z-scores match the original CheZOD implementation
+by comparing against pre-computed CheZOD reference data for known BMRB entries.
+"""
diff --git a/tests/test_full_dataset_regression.py b/tests/test_full_dataset_regression.py
new file mode 100644
index 0000000..af1e731
--- /dev/null
+++ b/tests/test_full_dataset_regression.py
@@ -0,0 +1,113 @@
+"""Full-dataset regression: compare pipeline output against baselines in data/baseline/.
+
+Run with:
+    uv run pytest tests/test_full_dataset_regression.py -v
+
+Requires:
+    - BMRB data in data/bmrb_entries/
+    - Baseline files in data/baseline/ (unfiltered.json, tolerant.json, etc.)
+    - Precomputed POTENCI cache in tmp/potenci/ (optional but recommended for speed)
+"""
+
+import json
+import subprocess
+import sys
+from pathlib import Path
+
+import pytest
+
+from tests.conftest import DATA_DIR, requires_bmrb_data
+
+BASELINE_DIR = DATA_DIR / "baseline"
+BMRB_DIR = DATA_DIR / "bmrb_entries"
+
+FILTER_LEVELS = ["unfiltered", "tolerant", "moderate", "strict"]
+
+
+def load_jsonl(path):
+    entries = {}
+    with open(path) as f:
+        for line in f:
+            entry = json.loads(line)
+            entries[entry["ID"]] = entry
+    return entries
+
+
+@pytest.mark.slow
+@requires_bmrb_data
+@pytest.mark.skipif(
+    not BASELINE_DIR.is_dir(),
+    reason="Baseline files not available (data/baseline/)",
+)
+class TestFullDatasetRegression:
+    @pytest.mark.parametrize("filter_level", FILTER_LEVELS)
+    def test_matches_baseline(self, tmp_path, filter_level):
+        baseline_file = BASELINE_DIR / f"{filter_level}.json"
+        if not baseline_file.exists():
+            pytest.skip(f"Baseline {baseline_file} not found")
+
+        output_prefix = str(tmp_path / filter_level)
+
+        # Use existing cache if available (speeds up from hours to minutes)
+        cache_dir = str(Path(__file__).resolve().parent.parent / "tmp")
+
+        cmd = [
+            sys.executable,
+            "-m",
+            "trizod.trizod",
+            "--input-dir",
+            str(BMRB_DIR),
+            "--filter-defaults",
+            filter_level,
+            "--output-prefix",
+            output_prefix,
+            "--output-format",
+            "json",
+            "--no-progress",
+            "--cache-dir",
+            cache_dir,
+        ]
+
+        result = subprocess.run(
+            cmd,
+            capture_output=True,
+            text=True,
+            timeout=7200,  # 2 hours max for full dataset
+        )
+        assert result.returncode == 0, f"Pipeline failed:\n{result.stderr}"
+
+        baseline = load_jsonl(baseline_file)
+        actual = load_jsonl(output_prefix + ".json")
+
+        assert len(actual) == len(baseline), (
+            f"Entry count mismatch for {filter_level}: "
+            f"{len(actual)} actual vs {len(baseline)} baseline"
+        )
+
+        zscore_mismatches = []
+        for id_ in baseline:
+            assert id_ in actual, f"Missing entry: {id_}"
+
+            ref = baseline[id_]
+            act = actual[id_]
+
+            assert act["seq"] == ref["seq"], f"{id_}: sequence mismatch"
+            assert act["temperature"] == pytest.approx(ref["temperature"]), (
+                f"{id_}: temperature mismatch"
+            )
+
+            ref_z = ref.get("zscores", [])
+            act_z = act.get("zscores", [])
+            assert len(act_z) == len(ref_z), f"{id_}: zscore length mismatch"
+
+            for i, (a, r) in enumerate(zip(act_z, ref_z)):
+                if a is None and r is None:
+                    continue
+                if a is None or r is None or abs(a - r) > 1e-6:
+                    zscore_mismatches.append(f"{id_} pos {i}: {a} != {r}")
+                    break
+
+        assert not zscore_mismatches, (
+            f"{len(zscore_mismatches)} entries with Z-score mismatches "
+            f"(showing first 10):\n" + "\n".join(zscore_mismatches[:10])
+        )
diff --git a/tests/test_pipeline_regression.py b/tests/test_pipeline_regression.py
new file mode 100644
index 0000000..b72ffab
--- /dev/null
+++ b/tests/test_pipeline_regression.py
@@ -0,0 +1,82 @@
+"""Regression test: run pipeline on 100-entry subset and compare against reference."""
+
+import json
+import subprocess
+import sys
+from pathlib import Path
+
+import pytest
+
+from tests.conftest import requires_bmrb_data
+
+TESTS_DIR = Path(__file__).resolve().parent
+REFERENCE_FILE = TESTS_DIR / "reference" / "unfiltered.json"
+SUBSET_DIR = TESTS_DIR / "bmrb_subset"
+
+
+def load_jsonl(path):
+    entries = {}
+    with open(path) as f:
+        for line in f:
+            entry = json.loads(line)
+            entries[entry["ID"]] = entry
+    return entries
+
+
+@requires_bmrb_data
+@pytest.mark.skipif(
+    not REFERENCE_FILE.exists(),
+    reason="Reference file not generated yet",
+)
+class TestPipelineRegression:
+    def test_unfiltered_matches_reference(self, tmp_path):
+        output_prefix = str(tmp_path / "unfiltered")
+        result = subprocess.run(
+            [
+                sys.executable,
+                "-m",
+                "trizod.trizod",
+                "--input-dir",
+                str(SUBSET_DIR),
+                "--filter-defaults",
+                "unfiltered",
+                "--output-prefix",
+                output_prefix,
+                "--output-format",
+                "json",
+                "--no-progress",
+                "--cache-dir",
+                str(tmp_path / "cache"),
+            ],
+            capture_output=True,
+            text=True,
+            timeout=120,
+        )
+        assert result.returncode == 0, f"Pipeline failed:\n{result.stderr}"
+
+        reference = load_jsonl(REFERENCE_FILE)
+        actual = load_jsonl(output_prefix + ".json")
+
+        assert len(actual) == len(reference), (
+            f"Entry count mismatch: {len(actual)} vs {len(reference)}"
+        )
+
+        for id_ in reference:
+            assert id_ in actual, f"Missing entry: {id_}"
+            ref = reference[id_]
+            act = actual[id_]
+
+            assert act["seq"] == ref["seq"], f"{id_}: sequence mismatch"
+            assert act["temperature"] == pytest.approx(ref["temperature"]), (
+                f"{id_}: temperature mismatch"
+            )
+
+            ref_z = ref.get("zscores", [])
+            act_z = act.get("zscores", [])
+            assert len(act_z) == len(ref_z), f"{id_}: zscore length mismatch"
+            for i, (a, r) in enumerate(zip(act_z, ref_z)):
+                if a is None and r is None:
+                    continue
+                assert a == pytest.approx(r, abs=1e-6), (
+                    f"{id_} pos {i}: zscore {a} != {r}"
+                )
diff --git a/tests/test_potenci.py b/tests/test_potenci.py
new file mode 100644
index 0000000..c463211
--- /dev/null
+++ b/tests/test_potenci.py
@@ -0,0 +1,95 @@
+"""Tests for the POTENCI random coil chemical shift prediction module."""
+
+import pytest
+
+import trizod.potenci.potenci as potenci
+
+# Reference values generated on main branch (commit 8905a85)
+# Sequence: AAGDTFKISELVK, T=298K, pH=7.0, ion=0.1M, no pH correction
+REFERENCE_SEQUENCE = "AAGDTFKISELVK"
+REFERENCE_TEMPERATURE = 298.0
+REFERENCE_PH = 7.0
+REFERENCE_ION = 0.1
+
+# Subset of reference values: (residue_num, aa, atom_type, value)
+REFERENCE_VALUES = [
+    (2, "A", "CA", 52.705700),
+    (2, "A", "N", 123.529426),
+    (3, "G", "CA", 45.270900),
+    (5, "T", "CB", 69.767200),
+    (7, "K", "H", 8.115990),
+    (10, "E", "C", 176.293150),
+    (12, "V", "HA", 4.098429),
+]
+
+
+class TestPredictions:
+    """Test POTENCI predictions against known reference values."""
+
+    def test_predictions_match_reference(self):
+        result = potenci.get_pred_shifts(
+            REFERENCE_SEQUENCE,
+            REFERENCE_TEMPERATURE,
+            REFERENCE_PH,
+            REFERENCE_ION,
+            use_ph_corr=False,
+        )
+
+        for res_num, aa, atom_type, expected in REFERENCE_VALUES:
+            actual = result[(res_num, aa)][atom_type]
+            assert actual == pytest.approx(expected, abs=1e-6), (
+                f"Mismatch for residue {res_num}{aa} {atom_type}: "
+                f"expected {expected}, got {actual}"
+            )
+
+    def test_predictions_with_ph_correction(self):
+        """POTENCI with pH correction should produce different results from pH=7."""
+        result_no_ph = potenci.get_pred_shifts(
+            REFERENCE_SEQUENCE,
+            REFERENCE_TEMPERATURE,
+            REFERENCE_PH,
+            REFERENCE_ION,
+            use_ph_corr=False,
+        )
+        result_with_ph = potenci.get_pred_shifts(
+            REFERENCE_SEQUENCE,
+            REFERENCE_TEMPERATURE,
+            5.5,  # different pH
+            REFERENCE_ION,
+            use_ph_corr=True,
+        )
+        # D (Asp) is pH-sensitive; its shifts should differ at pH 5.5 vs 7.0
+        d_ca_no_ph = result_no_ph[(4, "D")]["CA"]
+        d_ca_with_ph = result_with_ph[(4, "D")]["CA"]
+        assert d_ca_no_ph != pytest.approx(d_ca_with_ph, abs=0.01)
+
+
+class TestEdgeCases:
+    """Test POTENCI edge cases and structural constraints."""
+
+    def test_terminal_residues_excluded(self):
+        """POTENCI should not predict shifts for terminal residues."""
+        result = potenci.get_pred_shifts(
+            REFERENCE_SEQUENCE,
+            REFERENCE_TEMPERATURE,
+            REFERENCE_PH,
+            REFERENCE_ION,
+            use_ph_corr=False,
+        )
+        # First residue (1, 'A') and last residue (13, 'K') should be absent
+        assert (1, "A") not in result
+        residue_nums = [k[0] for k in result]
+        assert max(residue_nums) < len(REFERENCE_SEQUENCE)
+
+    def test_glycine_has_no_cb(self):
+        """Glycine should not have CB or HB predictions."""
+        result = potenci.get_pred_shifts(
+            REFERENCE_SEQUENCE,
+            REFERENCE_TEMPERATURE,
+            REFERENCE_PH,
+            REFERENCE_ION,
+            use_ph_corr=False,
+        )
+        gly_shifts = result[(3, "G")]
+        assert "CB" not in gly_shifts
+        assert "HB" not in gly_shifts
diff --git a/tests/test_smoke.py b/tests/test_smoke.py
new file mode 100644
index 0000000..4ccd263
--- /dev/null
+++ b/tests/test_smoke.py
@@ -0,0 +1,80 @@
+"""Smoke tests to verify basic functionality before any refactoring."""
+
+import subprocess
+import sys
+
+from tests.conftest import BMRB_DIR, requires_bmrb_data
+
+
+class TestCLI:
+    def test_help_runs(self):
+        result = subprocess.run(
+            [sys.executable, "-m", "trizod.trizod", "--help"],
+            capture_output=True,
+            text=True,
+        )
+        assert result.returncode == 0
+        assert "input-dir" in result.stdout
+
+    def test_trizod_entrypoint(self):
+        result = subprocess.run(
+            ["trizod", "--help"],
+            capture_output=True,
+            text=True,
+        )
+        assert result.returncode == 0
+
+
+@requires_bmrb_data
+class TestPipelineSingleEntry:
+    """Test pipeline on a single BMRB entry."""
+
+    def test_parse_bmrb_entry(self):
+        import trizod.bmrb.bmrb as bmrb
+
+        entry = bmrb.BmrbEntry("4493", BMRB_DIR / "bmr4493")
+        assert entry.id == "4493"
+        assert len(entry.entities) > 0
+        assert len(entry.shift_tables) > 0
+
+    def test_compute_scores_single_entry(self):
+        import trizod.bmrb.bmrb as bmrb
+        import trizod.potenci.potenci as potenci
+        import trizod.scoring.scoring as scoring
+
+        entry = bmrb.BmrbEntry("4493", BMRB_DIR / "bmr4493")
+        peptide_shifts = entry.get_peptide_shifts()
+        assert len(peptide_shifts) > 0
+
+        for (_stID, _entity_assemID, entityID), (
+            shifts,
+            condID,
+            _assemID,
+            _sampleIDs,
+        ) in peptide_shifts.items():
+            entity = entry.entities[entityID]
+            seq = entity.seq
+            conds = entry.conditions[condID]
+            temperature = conds.get_temperature()
+            pH = conds.get_pH()
+            ion = conds.get_ionic_strength()
+
+            use_ph_corr = pH != 7.0
+            predshiftdct = potenci.get_pred_shifts(
+                seq, temperature, pH, ion, use_ph_corr
+            )
+            ret = scoring.get_offset_corrected_shifts(seq, shifts, predshiftdct)
+            assert ret is not None, "Score computation returned None"
+            (
+                weighted_diffs_final,
+                abs_weighted_diffs_final,
+                cmp_mask,
+                outlier_mask_final,
+                offsets_final,
+                weighted_diffs_initial,
+                abs_weighted_diffs_initial,
+                outlier_mask_initial,
+                offsets_initial,
+            ) = ret
+            assert cmp_mask.any(), "No comparable backbone shifts found"
+            break  # only test first peptide
diff --git a/trizod/__init__.py b/trizod/__init__.py
index 28e6c08..4c32606 100644
--- a/trizod/__init__.py
+++ b/trizod/__init__.py
@@ -1,9 +1,9 @@
 __version__ = "0.0.1"
 
 META_DATA = {
-    "author": "Markus Haak",
-    "author_email": "m.haak@tum.de",
+    "authors": ["Markus Haak", "Tobias Senoner"],
+    "author_emails": ["markus.haak@tum.de", "tobias.senoner@tum.de"],
     "description": "Modern disorder scores for BMRB data",
     "package_name": "TriZOD",
-    "github_url": "https://git.rostlab.org/haak/TriZOD",
+    "github_url": "https://github.com/MarkusHaak/trizod",
 }
diff --git a/trizod/bmrb/__init__.py b/trizod/bmrb/__init__.py
index 1f77395..e935d7d 100644
--- a/trizod/bmrb/__init__.py
+++ b/trizod/bmrb/__init__.py
@@ -1 +1,3 @@
-from .bmrb import BmrbEntry, get_valid_bbshifts
\ No newline at end of file
+from .bmrb import BmrbEntry, get_valid_bbshifts
+
+__all__ = ["BmrbEntry", "get_valid_bbshifts"]
diff --git a/trizod/bmrb/bmrb.py b/trizod/bmrb/bmrb.py
index 7950b80..28576b7 100644
--- a/trizod/bmrb/bmrb.py
+++ b/trizod/bmrb/bmrb.py
@@ -1,278 +1,339 @@
-import os
 import logging
-import pynmrstar
+from pathlib import Path
+
 import numpy as np
 import pandas as pd
+import pynmrstar
+
 from trizod.constants import AA3TO1, BBATNS
 
-def get_tag_vals(sf, tag, warn=None, default=None, strip_str=False, indices=None, empty_val_str='.'):
+
+def get_tag_vals(
+    sf, tag, warn=None, default=None, strip_str=False, indices=None, empty_val_str="."
+):
     try:
         vals = sf.get_tag(tag)
         if strip_str:
             vals = [v.strip() for v in vals]
         if empty_val_str:
-            vals = [v if v != empty_val_str else '' for v in vals]
+            vals = [v if v != empty_val_str else "" for v in vals]
         if indices is not None:
-            if type(indices) == list:
+            if isinstance(indices, list):
                 vals = [vals[i] for i in indices]
             else:
                 vals = vals[indices]
         return vals
-    except:
+    except Exception:
         if warn:
-            logging.getLogger('trizod.bmrb').warning(warn)
-        logging.getLogger('trizod.bmrb').debug(f'failed to retrieve tag {tag}')
+            logging.getLogger("trizod.bmrb").warning(warn)
+        logging.getLogger("trizod.bmrb").debug(f"failed to retrieve tag {tag}")
     return default
 
-class Entity(object):
+
+class Entity:
     def __init__(self, sf):
-        super(Entity, self).__init__()
-        self.id = get_tag_vals(sf, '_Entity.ID', indices=0)
-        self.name = get_tag_vals(sf, '_Entity.Name', indices=0)
-        self.details = get_tag_vals(sf, '_Entity.Details', indices=0)
-        self.type = get_tag_vals(sf, '_Entity.Type', indices=0)
-        self.polymer_type = get_tag_vals(sf, '_Entity.Polymer_type', indices=0)
-        self.polymer_type_details = get_tag_vals(sf, '_Entity.Polymer_type_details', indices=0)
-        self.polymer_author_seq_details = get_tag_vals(sf, '_Entity.Polymer_author_seq_details', indices=0)
-        self.seq = get_tag_vals(sf, '_Entity.Polymer_seq_one_letter_code', indices=0)
-        if self.seq is not None and self.seq not in ['', '.']:
-            self.seq = self.seq.replace('\n', '').strip().upper()
+        super().__init__()
+        self.id = get_tag_vals(sf, "_Entity.ID", indices=0)
+        self.name = get_tag_vals(sf, "_Entity.Name", indices=0)
+        self.details = get_tag_vals(sf, "_Entity.Details", indices=0)
+        self.type = get_tag_vals(sf, "_Entity.Type", indices=0)
+        self.polymer_type = get_tag_vals(sf, "_Entity.Polymer_type", indices=0)
+        self.polymer_type_details = get_tag_vals(
+            sf, "_Entity.Polymer_type_details", indices=0
+        )
+        self.polymer_author_seq_details = get_tag_vals(
+            sf, "_Entity.Polymer_author_seq_details", indices=0
+        )
+        self.seq = get_tag_vals(sf, "_Entity.Polymer_seq_one_letter_code", indices=0)
+        if self.seq is not None and self.seq not in ["", "."]:
+            self.seq = self.seq.replace("\n", "").strip().upper()
         else:
             self.seq = None
-        self.fragment = get_tag_vals(sf, '_Entity.Fragment')
-        self.weight = get_tag_vals(sf, '_Entity.Formula_weight')
-        self.db_links = list(zip(
-            get_tag_vals(sf, '_Entity_db_link.Database_code', default=[]),
-            get_tag_vals(sf, '_Entity_db_link.Accession_code', default=[]),
-            get_tag_vals(sf, '_Entity_db_link.Entry_mol_code', default=[]),
-            get_tag_vals(sf, '_Entity_db_link.Seq_query_to_submitted_percent', default=[]),
-            get_tag_vals(sf, '_Entity_db_link.Seq_identity', default=[])
-            ))
-    
+        self.paramagnetic = get_tag_vals(sf, "_Entity.Paramagnetic", indices=0)
+        self.fragment = get_tag_vals(sf, "_Entity.Fragment")
+        self.weight = get_tag_vals(sf, "_Entity.Formula_weight")
+        self.db_links = list(
+            zip(
+                get_tag_vals(sf, "_Entity_db_link.Database_code", default=[]),
+                get_tag_vals(sf, "_Entity_db_link.Accession_code", default=[]),
+                get_tag_vals(sf, "_Entity_db_link.Entry_mol_code", default=[]),
+                get_tag_vals(
+                    sf, "_Entity_db_link.Seq_query_to_submitted_percent", default=[]
+                ),
+                get_tag_vals(sf, "_Entity_db_link.Seq_identity", default=[]),
+            )
+        )
+
     def __str__(self):
         if self.seq is not None:
-            seq_str = f', seq="{self.seq[:10] + "..." if type(self.seq) == str and len(self.seq) > 13 else self.seq}"'
+            seq_str = f', seq="{self.seq[:10] + "..." if isinstance(self.seq, str) and len(self.seq) > 13 else self.seq}"'
         else:
             seq_str = ""
         return f'(Entity {self.id}: name="{self.name}", type="{self.type}"{seq_str})'
-    
+
     def __repr__(self):
         return f"<Entity {self.id}>"
 
-class Assembly(object):
+
+class Assembly:
     def __init__(self, sf):
-        super(Assembly, self).__init__()
-        self.name = get_tag_vals(sf, '_Assembly.Name', indices=0)
-        self.id = get_tag_vals(sf, '_Assembly.ID', indices=0)
-        self.details = get_tag_vals(sf, '_Assembly.Details', indices=0)
-        self.n_components = get_tag_vals(sf, '_Assembly.Number_of_components', indices=0)
-        self.organic_ligands = get_tag_vals(sf, '_Assembly.Organic_ligands', indices=0)
-        self.metal_ions = get_tag_vals(sf, '_Assembly.Metal_ions', indices=0)
-        self.molecules_in_chemical_exchange = get_tag_vals(sf, '_Assembly.Molecules_in_chemical_exchange', indices=0)
-        self.entities = list(zip(
-            get_tag_vals(sf, '_Entity_assembly.ID', default=[]),
-            get_tag_vals(sf, '_Entity_assembly.Entity_ID', default=[]),
-            get_tag_vals(sf, '_Entity_assembly.Entity_label', default=[]),
-            get_tag_vals(sf, '_Entity_assembly.Physical_state', default=[])
-            ))
-    
+        super().__init__()
+        self.name = get_tag_vals(sf, "_Assembly.Name", indices=0)
+        self.id = get_tag_vals(sf, "_Assembly.ID", indices=0)
+        self.details = get_tag_vals(sf, "_Assembly.Details", indices=0)
+        self.n_components = get_tag_vals(
+            sf, "_Assembly.Number_of_components", indices=0
+        )
+        self.organic_ligands = get_tag_vals(sf, "_Assembly.Organic_ligands", indices=0)
+        self.metal_ions = get_tag_vals(sf, "_Assembly.Metal_ions", indices=0)
+        self.paramagnetic = get_tag_vals(sf, "_Assembly.Paramagnetic", indices=0)
+        self.molecules_in_chemical_exchange = get_tag_vals(
+            sf, "_Assembly.Molecules_in_chemical_exchange", indices=0
+        )
+        self.entities = list(
+            zip(
+                get_tag_vals(sf, "_Entity_assembly.ID", default=[]),
+                get_tag_vals(sf, "_Entity_assembly.Entity_ID", default=[]),
+                get_tag_vals(sf, "_Entity_assembly.Entity_label", default=[]),
+                get_tag_vals(sf, "_Entity_assembly.Physical_state", default=[]),
+            )
+        )
+
     def __str__(self):
-        return f'(Assembly {self.id}: entities {[(e[0], e[1]) for e in self.entities]}, {self.n_components} components)'
-    
+        return f"(Assembly {self.id}: entities {[(e[0], e[1]) for e in self.entities]}, {self.n_components} components)"
+
     def __repr__(self):
         return f"<Assembly {self.id}>"
 
-class SampleConditions(object):
+
+class SampleConditions:
     def __init__(self, sf):
-        super(SampleConditions, self).__init__()
-        self.id = get_tag_vals(sf, '_Sample_condition_list.ID', indices=0)
+        super().__init__()
+        self.id = get_tag_vals(sf, "_Sample_condition_list.ID", indices=0)
         self.ionic_strength = (None, None)
         self.pH = (None, None)
         self.pressure = (None, None)
         self.temperature = (None, None)
-        info = list(zip(
-            get_tag_vals(sf, '_Sample_condition_variable.Type', default=[]),
-            get_tag_vals(sf, '_Sample_condition_variable.Val', default=[]),
-            get_tag_vals(sf, '_Sample_condition_variable.Val_units', default=[])
-            ))
-        for t,val,unit in info:
-            if 'ionic strength' in t.lower():
+        info = list(
+            zip(
+                get_tag_vals(sf, "_Sample_condition_variable.Type", default=[]),
+                get_tag_vals(sf, "_Sample_condition_variable.Val", default=[]),
+                get_tag_vals(sf, "_Sample_condition_variable.Val_units", default=[]),
+            )
+        )
+        for t, val, unit in info:
+            if "ionic strength" in t.lower():
                 self.ionic_strength = (val, unit)
-            elif t.lower() == 'ph':
+            elif t.lower() == "ph":
                 self.pH = (val, unit)
-            elif t.lower() == 'pressure':
+            elif t.lower() == "pressure":
                 self.pressure = (val, unit)
-            elif 'temp' in t.lower():
+            elif "temp" in t.lower():
                 self.temperature = (val, unit)
             else:
-                logging.getLogger('trizod.bmrb').debug(f'Skipping sample condition {t} = {val} {unit}')
+                logging.getLogger("trizod.bmrb").debug(
+                    f"Skipping sample condition {t} = {val} {unit}"
+                )
 
     def convert_val(self, val):
         try:
             val = float(val)
-        except:
-            logging.getLogger('trizod.bmrb').debug(f'failed to convert value {val}')
+        except (ValueError, TypeError):
+            logging.getLogger("trizod.bmrb").debug(f"failed to convert value {val}")
             val = np.nan
         return val
 
     def get_pH(self, return_default=True):
         val = self.convert_val(self.pH[0])
         if np.isnan(val) and return_default:
-            logging.getLogger('trizod.bmrb').info(f'No information on pH for sample condition {self.id}, assuming 7.0')
+            logging.getLogger("trizod.bmrb").info(
+                f"No information on pH for sample condition {self.id}, assuming 7.0"
+            )
             return 7.0
         return val
-    
+
     def get_temperature(self, return_default=True, assume_si=True, fix_outliers=True):
         val = self.convert_val(self.temperature[0])
         if np.isnan(val):
             if return_default:
-                logging.getLogger('trizod.bmrb').info(f'No information on temperature for sample condition {self.id}, assuming 298 K')
-                return 298.
+                logging.getLogger("trizod.bmrb").info(
+                    f"No information on temperature for sample condition {self.id}, assuming 298 K"
+                )
+                return 298.0
             else:
                 return np.nan
-        if 'C' in self.temperature[1]:
-            const0, const1, factor = 0., 273.15, 1.
-        elif 'F' in self.temperature[1]:
-            const0, const1, factor = -32., 273.15, 1.8
-        elif self.temperature[1] == 'K':
-            const0, const1, factor = 0., 0., 1.
+        if "C" in self.temperature[1]:
+            const0, const1, factor = 0.0, 273.15, 1.0
+        elif "F" in self.temperature[1]:
+            const0, const1, factor = -32.0, 273.15, 1.8
+        elif self.temperature[1] == "K":
+            const0, const1, factor = 0.0, 0.0, 1.0
         elif assume_si:
-            const0, const1, factor = 0., 0., 1.
-            logging.getLogger('trizod.bmrb').info(f'Temperature unit unknown: {self.temperature[1]}, assuming K')
+            const0, const1, factor = 0.0, 0.0, 1.0
+            logging.getLogger("trizod.bmrb").info(
+                f"Temperature unit unknown: {self.temperature[1]}, assuming K"
+            )
         else:
             return None
-        if fix_outliers and const1 == 0.: # not explicitly °C or °F
-            if 15 <= val < 50:
-                logging.getLogger('trizod.bmrb').info(f'Very low temperature: {val}, assuming unit should be °C')
-                const0, const1, factor = 0., 273.15, 1.
+        if fix_outliers and const1 == 0.0:  # not explicitly °C or °F
+            if 1 <= val < 50:
+                logging.getLogger("trizod.bmrb").info(
+                    f"Very low temperature: {val}, assuming unit should be °C"
+                )
+                const0, const1, factor = 0.0, 273.15, 1.0
             elif 50 <= val <= 100:
-                const0, const1, factor = -32., 273.15, 1.8
-                logging.getLogger('trizod.bmrb').info(f'Low temperature: {val}, assuming unit should be °F')
+                const0, const1, factor = -32.0, 273.15, 1.8
+                logging.getLogger("trizod.bmrb").info(
+                    f"Low temperature: {val}, assuming unit should be °F"
+                )
         return (val + const0) * factor + const1
-    
+
     def get_pressure(self):
         # TODO: implement
         return
-    
-    def get_ionic_strength(self, return_default=True, assume_si=True, fix_outliers=True):
+
+    def get_ionic_strength(
+        self, return_default=True, assume_si=True, fix_outliers=True
+    ):
         val = self.convert_val(self.ionic_strength[0])
         if np.isnan(val):
             if return_default:
-                logging.getLogger('trizod.bmrb').info(f'No information on ionic strength for sample condition {self.id}, assuming 0.1 M')
+                logging.getLogger("trizod.bmrb").info(
+                    f"No information on ionic strength for sample condition {self.id}, assuming 0.1 M"
+                )
                 return 0.1
             else:
                 return None
-        if self.ionic_strength[1] == 'M':
-            const1, factor = 0., 1.
-        elif self.ionic_strength[1] == 'mM':
-            const1, factor = 0., 0.001
-        elif self.ionic_strength[1] == 'mu':
-            const1, factor = 0., 0.000001
+        if self.ionic_strength[1] == "M":
+            const1, factor = 0.0, 1.0
+        elif self.ionic_strength[1] == "mM":
+            const1, factor = 0.0, 0.001
+        elif self.ionic_strength[1] == "mu":
+            const1, factor = 0.0, 0.000001
         elif assume_si:
-            const1, factor = 0., 1.
-            logging.getLogger('trizod.bmrb').info(f'Ionic strength unit unknown: {self.ionic_strength[1]}, assuming K')
+            const1, factor = 0.0, 1.0
+            logging.getLogger("trizod.bmrb").info(
+                f"Ionic strength unit unknown: {self.ionic_strength[1]}, assuming K"
+            )
         else:
             return np.nan
-        if fix_outliers and factor == 1.:
-            if val > 5:
-                logging.getLogger('trizod.bmrb').info(f'High ionic strength: {val}, assuming unit should be mM')
-                factor = 0.001
+        if fix_outliers and factor == 1.0 and val > 5:
+            logging.getLogger("trizod.bmrb").info(
+                f"High ionic strength: {val}, assuming unit should be mM"
+            )
+            factor = 0.001
         return val * factor + const1
 
     def __str__(self):
-        return f'(Conditions {self.id}: pH {self.get_pH()}, {self.get_temperature()} K, {self.get_ionic_strength()} M)'
-    
+        return f"(Conditions {self.id}: pH {self.get_pH()}, {self.get_temperature()} K, {self.get_ionic_strength()} M)"
+
     def __repr__(self):
         return f"<Conditions {self.id}>"
 
-class Sample(object):
+
+class Sample:
     def __init__(self, sf):
-        super(Sample, self).__init__()
-        self.id = get_tag_vals(sf, '_Sample.ID', indices=0)
-        self.name = get_tag_vals(sf, '_Sample.Name', indices=0)
-        self.framecode = get_tag_vals(sf, '_Sample.Sf_framecode', indices=0)
-        self.details = get_tag_vals(sf, '_Sample.Details', indices=0)
-        self.type = get_tag_vals(sf, '_Sample.Type', indices=0)
-        self.sub_type = get_tag_vals(sf, '_Sample.Sub_type', indices=0)
-        self.components = list(zip(
-            get_tag_vals(sf, '_Sample_component.ID', default=[]),
-            get_tag_vals(sf, '_Sample_component.Assembly_ID', default=[]),
-            get_tag_vals(sf, '_Sample_component.Entity_ID', default=[]),
-            get_tag_vals(sf, '_Sample_component.Mol_common_name', default=[]),
-            get_tag_vals(sf, '_Sample_component.Concentration_val', default=[]),
-            get_tag_vals(sf, '_Sample_component.Concentration_val_units', default=[])
-            ))
-    
+        super().__init__()
+        self.id = get_tag_vals(sf, "_Sample.ID", indices=0)
+        self.name = get_tag_vals(sf, "_Sample.Name", indices=0)
+        self.framecode = get_tag_vals(sf, "_Sample.Sf_framecode", indices=0)
+        self.details = get_tag_vals(sf, "_Sample.Details", indices=0)
+        self.type = get_tag_vals(sf, "_Sample.Type", indices=0)
+        self.sub_type = get_tag_vals(sf, "_Sample.Sub_type", indices=0)
+        self.components = list(
+            zip(
+                get_tag_vals(sf, "_Sample_component.ID", default=[]),
+                get_tag_vals(sf, "_Sample_component.Assembly_ID", default=[]),
+                get_tag_vals(sf, "_Sample_component.Entity_ID", default=[]),
+                get_tag_vals(sf, "_Sample_component.Mol_common_name", default=[]),
+                get_tag_vals(sf, "_Sample_component.Concentration_val", default=[]),
+                get_tag_vals(
+                    sf, "_Sample_component.Concentration_val_units", default=[]
+                ),
+            )
+        )
+
     def __str__(self):
-        return f'(Sample {self.id}: type {self.type}, components: {[(c[0],c[1],c[2]) for c in self.components]})'
-    
+        return f"(Sample {self.id}: type {self.type}, components: {[(c[0], c[1], c[2]) for c in self.components]})"
+
     def __repr__(self):
         return f"<Sample {self.id}>"
 
-class ExperimentList(object):
+
+class ExperimentList:
     def __init__(self, sf):
-        super(ExperimentList, self).__init__()
-        self.id = get_tag_vals(sf, '_Experiment_list.ID', indices=0)
-        self.details = get_tag_vals(sf, '_Experiment_list.Details', indices=0)
-        self.experiments = list(zip(
-            get_tag_vals(sf, '_Experiment.ID', default=[]),
-            get_tag_vals(sf, '_Experiment.Name', default=[]),
-            get_tag_vals(sf, '_Experiment.Raw_data_flag', default=[]),
-            get_tag_vals(sf, '_Experiment.Sample_ID', default=[]),
-            get_tag_vals(sf, '_Experiment.Sample_label', default=[]),
-            get_tag_vals(sf, '_Experiment.Sample_state', default=[]),
-            get_tag_vals(sf, '_Experiment.Sample_condition_list_ID', default=[]),
-            get_tag_vals(sf, '_Experiment.Sample_condition_list_label', default=[]),
-            get_tag_vals(sf, '_Experiment.Mass_spectrometer_ID', default=[]),
-            get_tag_vals(sf, '_Experiment.Mass_spectrometer_label', default=[]),
-        ))
-    
+        super().__init__()
+        self.id = get_tag_vals(sf, "_Experiment_list.ID", indices=0)
+        self.details = get_tag_vals(sf, "_Experiment_list.Details", indices=0)
+        self.experiments = list(
+            zip(
+                get_tag_vals(sf, "_Experiment.ID", default=[]),
+                get_tag_vals(sf, "_Experiment.Name", default=[]),
+                get_tag_vals(sf, "_Experiment.Raw_data_flag", default=[]),
+                get_tag_vals(sf, "_Experiment.Sample_ID", default=[]),
+                get_tag_vals(sf, "_Experiment.Sample_label", default=[]),
+                get_tag_vals(sf, "_Experiment.Sample_state", default=[]),
+                get_tag_vals(sf, "_Experiment.Sample_condition_list_ID", default=[]),
+                get_tag_vals(sf, "_Experiment.Sample_condition_list_label", default=[]),
+                get_tag_vals(sf, "_Experiment.Mass_spectrometer_ID", default=[]),
+                get_tag_vals(sf, "_Experiment.Mass_spectrometer_label", default=[]),
+            )
+        )
+
     def __str__(self):
-        return f'(ExperimentList {self.id}: experiments: {[(c[0],c[1]) for c in self.experiments]})'
-    
+        return f"(ExperimentList {self.id}: experiments: {[(c[0], c[1]) for c in self.experiments]})"
+
     def __repr__(self):
         return f"<ExperimentList {self.id}>"
 
-class ShiftTable(object):
+
+class ShiftTable:
     def __init__(self, sf):
-        super(ShiftTable, self).__init__()
-        self.id = get_tag_vals(sf, '_Assigned_chem_shift_list.ID', indices=0)
-        self.conditions = get_tag_vals(sf, '_Assigned_chem_shift_list.Sample_condition_list_ID', indices=0)
-        self.experiments = list(zip(
-            get_tag_vals(sf, '_Chem_shift_experiment.Experiment_ID', default=[]),
-            get_tag_vals(sf, '_Chem_shift_experiment.Experiment_name', default=[]),
-            get_tag_vals(sf, '_Chem_shift_experiment.Sample_ID', default=[]),
-            get_tag_vals(sf, '_Chem_shift_experiment.Sample_state', default=[])
-            ))
-        self.shifts = list(zip(
-            get_tag_vals(sf, '_Atom_chem_shift.Entity_assembly_ID', default=[]),
-            get_tag_vals(sf, '_Atom_chem_shift.Entity_ID', default=[]),
-            get_tag_vals(sf, '_Atom_chem_shift.Seq_ID', default=[]),
-            get_tag_vals(sf, '_Atom_chem_shift.Comp_ID', default=[]),
-            get_tag_vals(sf, '_Atom_chem_shift.Atom_ID', default=[]),
-            get_tag_vals(sf, '_Atom_chem_shift.Atom_type', default=[]),
-            get_tag_vals(sf, '_Atom_chem_shift.Val', default=[]),
-            get_tag_vals(sf, '_Atom_chem_shift.Val_err', default=[]),
-            get_tag_vals(sf, '_Atom_chem_shift.Ambiguity_code', default=[])
-            ))
+        super().__init__()
+        self.id = get_tag_vals(sf, "_Assigned_chem_shift_list.ID", indices=0)
+        self.conditions = get_tag_vals(
+            sf, "_Assigned_chem_shift_list.Sample_condition_list_ID", indices=0
+        )
+        self.experiments = list(
+            zip(
+                get_tag_vals(sf, "_Chem_shift_experiment.Experiment_ID", default=[]),
+                get_tag_vals(sf, "_Chem_shift_experiment.Experiment_name", default=[]),
+                get_tag_vals(sf, "_Chem_shift_experiment.Sample_ID", default=[]),
+                get_tag_vals(sf, "_Chem_shift_experiment.Sample_state", default=[]),
+            )
+        )
+        self.shifts = list(
+            zip(
+                get_tag_vals(sf, "_Atom_chem_shift.Entity_assembly_ID", default=[]),
+                get_tag_vals(sf, "_Atom_chem_shift.Entity_ID", default=[]),
+                get_tag_vals(sf, "_Atom_chem_shift.Seq_ID", default=[]),
+                get_tag_vals(sf, "_Atom_chem_shift.Comp_ID", default=[]),
+                get_tag_vals(sf, "_Atom_chem_shift.Atom_ID", default=[]),
+                get_tag_vals(sf, "_Atom_chem_shift.Atom_type", default=[]),
+                get_tag_vals(sf, "_Atom_chem_shift.Val", default=[]),
+                get_tag_vals(sf, "_Atom_chem_shift.Val_err", default=[]),
+                get_tag_vals(sf, "_Atom_chem_shift.Ambiguity_code", default=[]),
+            )
+        )
         shifts = {}
-        for s in self.shifts:
-            if (s[0], s[1]) not in shifts:
-                shifts[(s[0], s[1])] = []
-            shifts[(s[0], s[1])].append(s)
+        for shift in self.shifts:
+            if (shift[0], shift[1]) not in shifts:
+                shifts[(shift[0], shift[1])] = []
+            shifts[(shift[0], shift[1])].append(shift)
         self.shifts = shifts
-    
+
     def __str__(self):
-        return f'(ShiftTable {self.id}: conditions {self.conditions}, shifts: {[(key, len(vals)) for key,vals in self.shifts.items()]})'
-    
+        return f"(ShiftTable {self.id}: conditions {self.conditions}, shifts: {[(key, len(vals)) for key, vals in self.shifts.items()]})"
+
     def __repr__(self):
         return f"<ShiftTable {self.id}>"
 
-class BmrbEntry(object):
+
+class BmrbEntry:
     def __init__(self, id_, bmrb_dir):
-        super(BmrbEntry, self).__init__()
+        super().__init__()
         self.source = None
-        
+
         self.id = id_
         self.type = None
         self.title = None
@@ -292,121 +353,198 @@ def __init__(self, id_, bmrb_dir):
         self.db_links = []
         self.n_components = None
         self.n_entities = None
-        self.entities = {} # Physical_state, Name, Type, [(Database_code, Accession_code), ...]
+        self.entities = {}  # Physical_state, Name, Type, [(Database_code, Accession_code), ...]
         self.conditions = {}
         self.shift_tables = {}
-        
-        self.entry_path = os.path.join(bmrb_dir, f"bmr{id_}")
-        fn3 = os.path.join(self.entry_path, f"bmr{id_}_3.str")
-        if not os.path.exists(fn3):
-            logging.getLogger('trizod.bmrb').debug(f'Bio-Star file for BMRB entry {id_} not found in directory {self.entry_path}')
+
+        self.entry_path = Path(bmrb_dir) / f"bmr{id_}"
+        fn3 = self.entry_path / f"bmr{id_}_3.str"
+        if not fn3.exists():
+            logging.getLogger("trizod.bmrb").debug(
+                f"Bio-Star file for BMRB entry {id_} not found in directory {self.entry_path}"
+            )
             # try to find str file in bmrb_dir
-            self.entry_path = bmrb_dir
-            fn3 = os.path.join(bmrb_dir, f"bmr{id_}_3.str")
-            if not os.path.exists(fn3):
-                logging.getLogger('trizod.bmrb').error(f'Bio-Star file for BMRB entry {id_} not found, file {fn3} does not exist')
-                raise ValueError
-
-        self.source = fn3
-        entry = pynmrstar.Entry.from_file(fn3)
+            self.entry_path = Path(bmrb_dir)
+            fn3 = self.entry_path / f"bmr{id_}_3.str"
+            if not fn3.exists():
+                logging.getLogger("trizod.bmrb").error(
+                    f"Bio-Star file for BMRB entry {id_} not found, file {fn3} does not exist"
+                )
+                raise ValueError(f"file not found: {fn3}")
+
+        self.source = str(fn3)
+        entry = pynmrstar.Entry.from_file(str(fn3))
         # entry info
-        entry_information = entry.get_saveframes_by_category('entry_information')
+        entry_information = entry.get_saveframes_by_category("entry_information")
         if entry_information:
-            self.type = get_tag_vals(entry_information[0], '_Entry.Type', indices=0)
-            self.title = get_tag_vals(entry_information[0], '_Entry.Title', indices=0)
-            self.details = get_tag_vals(entry_information[0], '_Entry.Title', indices=0)
-            self.submission_date = get_tag_vals(entry_information[0], '_Entry.Submission_date', indices=0)
-            self.nmr_star_version = get_tag_vals(entry_information[0], '_Entry.NMR_STAR_version', indices=0)
-            self.original_nmr_star_version = get_tag_vals(entry_information[0], '_Entry.Original_NMR_STAR_version', indices=0)
-            self.exp_method = get_tag_vals(entry_information[0], '_Entry.Experimental_method', indices=0)
-            self.exp_method_subtype = get_tag_vals(entry_information[0], '_Entry.Experimental_method_subtype', indices=0)
-            self.struct_keywords = get_tag_vals(entry_information[0], '_Struct_keywords.Keywords', default=[])
+            self.type = get_tag_vals(entry_information[0], "_Entry.Type", indices=0)
+            self.title = get_tag_vals(entry_information[0], "_Entry.Title", indices=0)
+            self.details = get_tag_vals(entry_information[0], "_Entry.Title", indices=0)
+            self.submission_date = get_tag_vals(
+                entry_information[0], "_Entry.Submission_date", indices=0
+            )
+            self.nmr_star_version = get_tag_vals(
+                entry_information[0], "_Entry.NMR_STAR_version", indices=0
+            )
+            self.original_nmr_star_version = get_tag_vals(
+                entry_information[0], "_Entry.Original_NMR_STAR_version", indices=0
+            )
+            self.exp_method = get_tag_vals(
+                entry_information[0], "_Entry.Experimental_method", indices=0
+            )
+            self.exp_method_subtype = get_tag_vals(
+                entry_information[0], "_Entry.Experimental_method_subtype", indices=0
+            )
+            self.struct_keywords = get_tag_vals(
+                entry_information[0], "_Struct_keywords.Keywords", default=[]
+            )
             # database links
-            self.db_links = list(zip(
-                get_tag_vals(entry_information[0], '_Related_entries.Database_name', default=[]),
-                get_tag_vals(entry_information[0], '_Related_entries.Database_accession_code', default=[]),
-                get_tag_vals(entry_information[0], '_Related_entries.Relationship', default=[])
-                ))
+            self.db_links = list(
+                zip(
+                    get_tag_vals(
+                        entry_information[0],
+                        "_Related_entries.Database_name",
+                        default=[],
+                    ),
+                    get_tag_vals(
+                        entry_information[0],
+                        "_Related_entries.Database_accession_code",
+                        default=[],
+                    ),
+                    get_tag_vals(
+                        entry_information[0],
+                        "_Related_entries.Relationship",
+                        default=[],
+                    ),
+                )
+            )
 
         # citation info
-        citations = entry.get_saveframes_by_category('citations')
+        citations = entry.get_saveframes_by_category("citations")
         if citations:
-            self.citation_title = get_tag_vals(citations[0], '_Citation.Title', indices=0)
-            self.citation_journal = get_tag_vals(citations[0], '_Citation.Journal_abbrev', indices=0)
-            self.citation_PubMed_ID = get_tag_vals(citations[0], '_Citation.PubMed_ID', indices=0)
-            self.citation_DOI = get_tag_vals(citations[0], '_Citation.DOI', indices=0)
-            self.citation_keywords = get_tag_vals(citations[0], '_Citation_keyword.Keyword', default=[])
+            self.citation_title = get_tag_vals(
+                citations[0], "_Citation.Title", indices=0
+            )
+            self.citation_journal = get_tag_vals(
+                citations[0], "_Citation.Journal_abbrev", indices=0
+            )
+            self.citation_PubMed_ID = get_tag_vals(
+                citations[0], "_Citation.PubMed_ID", indices=0
+            )
+            self.citation_DOI = get_tag_vals(citations[0], "_Citation.DOI", indices=0)
+            self.citation_keywords = get_tag_vals(
+                citations[0], "_Citation_keyword.Keyword", default=[]
+            )
 
         # Assembly info
-        entry_assemblies = entry.get_saveframes_by_category('assembly')
+        entry_assemblies = entry.get_saveframes_by_category("assembly")
         if len(entry_assemblies) == 0:
-            logging.getLogger('trizod.bmrb').error(f'BMRB entry {id_} contains no assembly information')
-            raise ValueError
+            logging.getLogger("trizod.bmrb").error(
+                f"BMRB entry {id_} contains no assembly information"
+            )
+            raise ValueError(f"no assembly information in {id_}")
         self.assemblies = [Assembly(sf) for sf in entry_assemblies]
-        if not len([a.id for a in self.assemblies]) == len({a.id for a in self.assemblies}):
-            logging.getLogger('trizod.bmrb').error("entry contains assemblies with non-unique ID")
-            raise ValueError
-        self.assemblies = {a.id:a for a in self.assemblies}
-        
-        entry_entities = entry.get_saveframes_by_category('entity')
+        if len([assembly.id for assembly in self.assemblies]) != len(
+            {assembly.id for assembly in self.assemblies}
+        ):
+            logging.getLogger("trizod.bmrb").error(
+                "entry contains assemblies with non-unique ID"
+            )
+            raise ValueError(f"non-unique assembly IDs in {id_}")
+        self.assemblies = {assembly.id: assembly for assembly in self.assemblies}
+
+        entry_entities = entry.get_saveframes_by_category("entity")
         if len(entry_entities) == 0:
-            logging.getLogger('trizod.bmrb').error(f'BMRB entry {id_} contains no entity information')
-            raise ValueError
+            logging.getLogger("trizod.bmrb").error(
+                f"BMRB entry {id_} contains no entity information"
+            )
+            raise ValueError(f"no entity information in {id_}")
         self.entities = [Entity(sf) for sf in entry_entities]
-        if not len([e.id for e in self.entities]) == len({e.id for e in self.entities}):
-            logging.getLogger('trizod.bmrb').error("entry contains entities with non-unique ID")
-            raise ValueError
-        self.entities = {e.id:e for e in self.entities}
+        if len([e.id for e in self.entities]) != len({e.id for e in self.entities}):
+            logging.getLogger("trizod.bmrb").error(
+                "entry contains entities with non-unique ID"
+            )
+            raise ValueError(f"non-unique entity IDs in {id_}")
+        self.entities = {e.id: e for e in self.entities}
 
-        entry_samples = entry.get_saveframes_by_category('sample')
+        entry_samples = entry.get_saveframes_by_category("sample")
         if len(entry_samples) == 0:
-            logging.getLogger('trizod.bmrb').warning(f'BMRB entry {id_} contains no sample information')
+            logging.getLogger("trizod.bmrb").warning(
+                f"BMRB entry {id_} contains no sample information"
+            )
         else:
             self.samples = [Sample(sf) for sf in entry_samples]
-            if not len([s.id for s in self.samples]) == len({s.id for s in self.samples}):
-                logging.getLogger('trizod.bmrb').error("entry contains samples with non-unique ID")
-                raise ValueError
-            self.samples = {s.id:s for s in self.samples}
+            if len([sample.id for sample in self.samples]) != len(
+                {sample.id for sample in self.samples}
+            ):
+                logging.getLogger("trizod.bmrb").error(
+                    "entry contains samples with non-unique ID"
+                )
+                raise ValueError(f"non-unique sample IDs in {id_}")
+            self.samples = {sample.id: sample for sample in self.samples}
 
-        entry_conditions = entry.get_saveframes_by_category('sample_conditions')
+        entry_conditions = entry.get_saveframes_by_category("sample_conditions")
         if len(entry_conditions) == 0:
-            logging.getLogger('trizod.bmrb').warning(f'BMRB entry {id_} contains no sample condition information')
+            logging.getLogger("trizod.bmrb").warning(
+                f"BMRB entry {id_} contains no sample condition information"
+            )
         else:
             self.conditions = [SampleConditions(sf) for sf in entry_conditions]
-            if not len([a.id for a in self.conditions]) == len({a.id for a in self.conditions}):
-                logging.getLogger('trizod.bmrb').error("entry contains conditions with non-unique ID")
-                raise ValueError
-            self.conditions = {a.id:a for a in self.conditions}
-        
-        entry_experiment_lists = entry.get_saveframes_by_category('experiment_list')
+            if len([condition.id for condition in self.conditions]) != len(
+                {condition.id for condition in self.conditions}
+            ):
+                logging.getLogger("trizod.bmrb").error(
+                    "entry contains conditions with non-unique ID"
+                )
+                raise ValueError(f"non-unique condition IDs in {id_}")
+            self.conditions = {condition.id: condition for condition in self.conditions}
+
+        entry_experiment_lists = entry.get_saveframes_by_category("experiment_list")
         if len(entry_experiment_lists) != 1:
-            logging.getLogger('trizod.bmrb').error(f'BMRB entry {id_} contains no or more than one experiment list, currently not supported')
-            raise ValueError # TODO: find a solution to include these as well
+            logging.getLogger("trizod.bmrb").error(
+                f"BMRB entry {id_} contains no or more than one experiment list, currently not supported"
+            )
+            # TODO: find a solution to include these as well
+            raise ValueError(
+                f"expected 1 experiment list in {id_}, found {len(entry_experiment_lists)}"
+            )
         self.experiment_list = ExperimentList(entry_experiment_lists[0])
-        self.experiment_dict = {e[0] : e for e in self.experiment_list.experiments}
+        self.experiment_dict = {e[0]: e for e in self.experiment_list.experiments}
 
-        entry_shift_tables = entry.get_saveframes_by_category('assigned_chemical_shifts')
+        entry_shift_tables = entry.get_saveframes_by_category(
+            "assigned_chemical_shifts"
+        )
         if len(entry_shift_tables) == 0:
-            logging.getLogger('trizod.bmrb').error(f'BMRB entry {id_} contains no chemical shift information')
-            raise ValueError
+            logging.getLogger("trizod.bmrb").error(
+                f"BMRB entry {id_} contains no chemical shift information"
+            )
+            raise ValueError(f"no chemical shift data in {id_}")
         self.shift_tables = [ShiftTable(sf) for sf in entry_shift_tables]
-        if not len([s.id for s in self.shift_tables]) == len({s.id for s in self.shift_tables}):
-            logging.getLogger('trizod.bmrb').error("entry contains shift tables with non-unique ID")
-            raise ValueError
-        self.shift_tables = {s.id:s for s in self.shift_tables}
-    
+        if len([shift_table.id for shift_table in self.shift_tables]) != len(
+            {shift_table.id for shift_table in self.shift_tables}
+        ):
+            logging.getLogger("trizod.bmrb").error(
+                "entry contains shift tables with non-unique ID"
+            )
+            raise ValueError(f"non-unique shift table IDs in {id_}")
+        self.shift_tables = {
+            shift_table.id: shift_table for shift_table in self.shift_tables
+        }
+
     def get_peptide_shifts(self):
         peptide_shifts = {}
-        for stID,st in self.shift_tables.items():
+        for stID, st in self.shift_tables.items():
             condID = st.conditions
             if condID is None or condID not in self.conditions:
-                logging.getLogger('trizod.bmrb').error(f'skipping shift table {stID} due to missing conditions entry: {condID}')
+                logging.getLogger("trizod.bmrb").error(
+                    f"skipping shift table {stID} due to missing conditions entry: {condID}"
+                )
                 continue
             exp_missing = False
             sampleIDs = []
             experimentIDs = []
-            #for eID in experimentIDs:
-            for eID,_,sID,_ in st.experiments:
+            # for eID in experimentIDs:
+            for eID, _, sID, _ in st.experiments:
                 if eID:
                     if eID not in self.experiment_dict:
                         exp_missing = True
@@ -415,20 +553,24 @@ def get_peptide_shifts(self):
                 if sID:
                     sampleIDs.append(sID)
             if exp_missing:
-                logging.getLogger('trizod.bmrb').error(f'skipping shift table {stID} due to missing experiment entry: {eID}')
+                logging.getLogger("trizod.bmrb").error(
+                    f"skipping shift table {stID} due to missing experiment entry: {eID}"
+                )
                 continue
             if len(sampleIDs) == 0:
-                logging.getLogger('trizod.bmrb').debug(f'missing sample ID references in shift table, trying to retrive from list of experiment IDs')
+                logging.getLogger("trizod.bmrb").debug(
+                    "missing sample ID references in shift table, trying to retrive from list of experiment IDs"
+                )
                 sampleIDs = [self.experiment_dict[eID][3] for eID in experimentIDs]
-            #if len(set(sampleIDs)) != 1:
+            # if len(set(sampleIDs)) != 1:
             #    #logging.getLogger('trizod.bmrb').error(f'skipping shift table {stID}, sampleIDs could not be safely determined')
             #    #print(self.id, sampleIDs)
             #    #continue
             #    # TODO: double-check that all experiments share the same experiment conditions
-            #sampleIDs = sampleIDs[0]
-            #try:
+            # sampleIDs = sampleIDs[0]
+            # try:
             sampleIDs = set(sampleIDs)
-            #except:
+            # except:
             #    breakpoint()
             sample_missing = False
             for sID in sampleIDs:
@@ -437,72 +579,116 @@ def get_peptide_shifts(self):
                     break
             if sample_missing:
                 # conservatively reject entries with false links to non-existing samples
-                logging.getLogger('trizod.bmrb').error(f'skipping shift table {stID}, sample ID {sID} unknown.')
+                logging.getLogger("trizod.bmrb").error(
+                    f"skipping shift table {stID}, sample ID {sID} unknown."
+                )
                 continue
             sampleIDs = [sID.strip() for sID in set(sampleIDs) if sID in self.samples]
             if not sampleIDs:
                 # do not require link to samples - only necessary if filtered for denaturants
-                logging.getLogger('trizod.bmrb').warning(f'sample ID(s) unknown for shift table {stID}.')
-            for (entity_assemID,entityID),shifts in st.shifts.items():
+                logging.getLogger("trizod.bmrb").warning(
+                    f"sample ID(s) unknown for shift table {stID}."
+                )
+            for (entity_assemID, entityID), shifts in st.shifts.items():
                 # check if there is ambiguity if the entityID tag in matching assemblies is not tested (might be empty)
-                assemID = [(self.assemblies[assem].id, e[1]) for assem in self.assemblies for e in self.assemblies[assem].entities if e[0] == entity_assemID and e[1] in ['', None, entityID]]
+                assemID = [
+                    (self.assemblies[assem].id, e[1])
+                    for assem in self.assemblies
+                    for e in self.assemblies[assem].entities
+                    if e[0] == entity_assemID and e[1] in ["", None, entityID]
+                ]
                 if len(assemID) > 1:
                     # if so, enforce same entityID
                     # should be extremely rare, only few entries have multiple assemblies...
-                    logging.getLogger('trizod.bmrb').info(f"ambiguity with respect to correct assemly, enforcing matching, non-empty entityID")
-                    assemID = [(self.assemblies[assem].id, e[1]) for assem in self.assemblies for e in self.assemblies[assem].entities if e[0] == entity_assemID and e[1] == entityID]
+                    logging.getLogger("trizod.bmrb").info(
+                        "ambiguity with respect to correct assemly, enforcing matching, non-empty entityID"
+                    )
+                    assemID = [
+                        (self.assemblies[assem].id, e[1])
+                        for assem in self.assemblies
+                        for e in self.assemblies[assem].entities
+                        if e[0] == entity_assemID and e[1] == entityID
+                    ]
                     if len(assemID) > 1:
-                        logging.getLogger('trizod.bmrb').error(f'skipping shifts for entity {entityID} due to ambiguity with respect to correct assemly: {assemID}')
+                        logging.getLogger("trizod.bmrb").error(
+                            f"skipping shifts for entity {entityID} due to ambiguity with respect to correct assemly: {assemID}"
+                        )
                         continue
                 if len(assemID) == 0:
-                    logging.getLogger('trizod.bmrb').error(f"skipping shift table {stID}, could not associate it with any assembly")
+                    logging.getLogger("trizod.bmrb").error(
+                        f"skipping shift table {stID}, could not associate it with any assembly"
+                    )
                     continue
                 assemID, entityID_ = assemID[0]
                 if entityID_ != entityID:
-                    logging.getLogger('trizod.bmrb').warning(f'Unambiguously using assembly {assemID} with empty entityID tag for assembly entity {entity_assemID}, assuming it is {entityID}')
-                
-                if entity_assemID is None or entity_assemID not in [e[0] for assem in self.assemblies for e in self.assemblies[assem].entities]: #self.assemblies:
-                    logging.getLogger('trizod.bmrb').error(f'skipping shifts for entity {entityID} due to missing assembly entity entry: {entity_assemID}')
+                    logging.getLogger("trizod.bmrb").warning(
+                        f"Unambiguously using assembly {assemID} with empty entityID tag for assembly entity {entity_assemID}, assuming it is {entityID}"
+                    )
+
+                if entity_assemID is None or entity_assemID not in [
+                    e[0]
+                    for assem in self.assemblies
+                    for e in self.assemblies[assem].entities
+                ]:  # self.assemblies:
+                    logging.getLogger("trizod.bmrb").error(
+                        f"skipping shifts for entity {entityID} due to missing assembly entity entry: {entity_assemID}"
+                    )
                     continue
                 if entityID is None or entityID not in self.entities:
-                    logging.getLogger('trizod.bmrb').error(f'skipping shifts for assembly {entity_assemID} due to missing entity entry: {entityID}')
+                    logging.getLogger("trizod.bmrb").error(
+                        f"skipping shifts for assembly {entity_assemID} due to missing entity entry: {entityID}"
+                    )
                     continue
                 entity = self.entities[entityID]
                 if not entity.type:
-                    logging.getLogger('trizod.bmrb').error(f'skipping shifts for assembly {entity_assemID} due to missing entity type: {entityID}')
+                    logging.getLogger("trizod.bmrb").error(
+                        f"skipping shifts for assembly {entity_assemID} due to missing entity type: {entityID}"
+                    )
                     continue
-                if entity.type == 'polymer':
+                if entity.type == "polymer":
                     if not entity.polymer_type:
-                        logging.getLogger('trizod.bmrb').error(f'skipping shifts for assembly {entity_assemID} due to missing polymer type for entity: {entityID}')
+                        logging.getLogger("trizod.bmrb").error(
+                            f"skipping shifts for assembly {entity_assemID} due to missing polymer type for entity: {entityID}"
+                        )
                         continue
-                    if entity.polymer_type == 'polypeptide(L)':
-                        peptide_shifts[(stID,entity_assemID,entityID)] = (shifts, condID, assemID, sampleIDs)
+                    if entity.polymer_type == "polypeptide(L)":
+                        peptide_shifts[(stID, entity_assemID, entityID)] = (
+                            shifts,
+                            condID,
+                            assemID,
+                            sampleIDs,
+                        )
         return peptide_shifts
 
-    
     def __str__(self):
-        def pplist(l):
-            if len(l) == 0 : return "[]"
-            elif len(l) == 1 : return f"[{str(l[0])}]"
-            else: return "[\n    " + "\n    ".join([str(e) for e in l]) + "\n]"
-        
-        s = f"bmr{self.id}:\n" + "\n  ".join([
-            f"id = {self.id}",
-            f"citation_title = {self.citation_title}",
-            f"citation_journal = {self.citation_journal}",
-            f"citation_PubMed_ID = {self.citation_PubMed_ID}",
-            f"citation_DOI = {self.citation_DOI}",
-            f"citation_keywords = {self.citation_keywords}",
-            f"assemblies = {pplist(list(self.assemblies.values()))}",
-            f"entities = {pplist(list(self.entities.values()))}",
-            f"samples = {pplist(list(self.samples.values()))}",
-            f"conditions = {pplist(list(self.conditions.values()))}",
-            f"shifts = {pplist(list(self.shift_tables.values()))}",
-        ])
+        def pplist(items):
+            if len(items) == 0:
+                return "[]"
+            elif len(items) == 1:
+                return f"[{str(items[0])}]"
+            else:
+                return "[\n    " + "\n    ".join([str(e) for e in items]) + "\n]"
+
+        s = f"bmr{self.id}:\n" + "\n  ".join(
+            [
+                f"id = {self.id}",
+                f"citation_title = {self.citation_title}",
+                f"citation_journal = {self.citation_journal}",
+                f"citation_PubMed_ID = {self.citation_PubMed_ID}",
+                f"citation_DOI = {self.citation_DOI}",
+                f"citation_keywords = {self.citation_keywords}",
+                f"assemblies = {pplist(list(self.assemblies.values()))}",
+                f"entities = {pplist(list(self.entities.values()))}",
+                f"samples = {pplist(list(self.samples.values()))}",
+                f"conditions = {pplist(list(self.conditions.values()))}",
+                f"shifts = {pplist(list(self.shift_tables.values()))}",
+            ]
+        )
         return s
-    
+
     def __repr__(self):
-        return f'<bmr{self.id}>'
+        return f"<bmr{self.id}>"
+
 
 def get_valid_bbshifts(shifts, seq, filter_amb=True, max_err=1.3, averaging=True):
     bb_atm_ids = BBATNS[:]
@@ -515,67 +701,104 @@ def get_valid_bbshifts(shifts, seq, filter_amb=True, max_err=1.3, averaging=True
     # 6: '_Atom_chem_shift.Val'
     # 7: '_Atom_chem_shift.Val_err'
     # 8: '_Atom_chem_shift.Ambiguity_code'
-    df = pd.DataFrame(shifts, columns=['entity_assemID','entityID','pos','aa3','atm_id','atm_type','val','err','ambc'])
+    df = pd.DataFrame(
+        shifts,
+        columns=[
+            "entity_assemID",
+            "entityID",
+            "pos",
+            "aa3",
+            "atm_id",
+            "atm_type",
+            "val",
+            "err",
+            "ambc",
+        ],
+    )
     # convert sequence index
     try:
-        df['pos'] = df['pos'].astype(int) - 1
-        assert np.all(df['pos'] >= 0)
+        df["pos"] = df["pos"].astype(int) - 1
+        assert np.all(df["pos"] >= 0)
     except (ValueError, AssertionError):
-        logging.getLogger('trizod.bmrb').error(f'conversion to integer failed for at least one position index')
+        logging.getLogger("trizod.bmrb").error(
+            "conversion to integer failed for at least one position index"
+        )
         return
-    if np.any(df['pos'] >= len(seq)):
-        logging.getLogger('trizod.bmrb').error(f'shift array sequence longer than polymer sequence')
+    if np.any(df["pos"] >= len(seq)):
+        logging.getLogger("trizod.bmrb").error(
+            "shift array sequence longer than polymer sequence"
+        )
         return
     # only process canonical bases, check if AAs match sequence
-    df = df.loc[df['aa3'].isin(AA3TO1.keys())]
-    df['aa1'] = df['aa3'].replace(AA3TO1)
-    if np.any(df['aa1'] != df['pos'].replace({i:aa for i,aa in enumerate(seq)})):
-        logging.getLogger('trizod.bmrb').error(f'canonical amino acid mismatch between sequence and shift array')
+    df = df.loc[df["aa3"].isin(AA3TO1.keys())]
+    df["aa1"] = df["aa3"].replace(AA3TO1)
+    if np.any(df["aa1"] != df["pos"].replace(dict(enumerate(seq)))):
+        logging.getLogger("trizod.bmrb").error(
+            "canonical amino acid mismatch between sequence and shift array"
+        )
         return
     # convert value and error
     try:
-        df['val'] = df['val'].astype(float) # will throw error on failed conversion
+        df["val"] = df["val"].astype(float)  # will throw error on failed conversion
     except ValueError:
-        logging.getLogger('trizod.bmrb').error(f'conversion to float failed for at least one shift value')
+        logging.getLogger("trizod.bmrb").error(
+            "conversion to float failed for at least one shift value"
+        )
         return
-    df['err'] = pd.to_numeric(df['err']) # will create NaN for non-numeric entries, which is intended
+    df["err"] = pd.to_numeric(
+        df["err"]
+    )  # will create NaN for non-numeric entries, which is intended
     # filter data with large measurement errors
     if max_err:
-        df = df.loc[(df['err'] <= max_err) | pd.isna(df['err'])]
+        df = df.loc[(df["err"] <= max_err) | pd.isna(df["err"])]
     # filter certain ambiguous codes
-    df = df.loc[df['ambc'].isin(['1', '2', '', '.'])]
+    df = df.loc[df["ambc"].isin(["1", "2", "", "."])]
     # filter non-backbone atoms
     # TODO: maybe move this up to fasten processing
-    df = df.loc[df['atm_id'].isin(bb_atm_ids + ['HA2', 'HA3' ,'HB1', 'HB2', 'HB3'])]
-    df['atm_id_single'] = df['atm_id']
+    df = df.loc[df["atm_id"].isin(bb_atm_ids + ["HA2", "HA3", "HB1", "HB2", "HB3"])]
+    df["atm_id_single"] = df["atm_id"]
     # look for non-standard shifts
-    df.loc[(df['aa3'] != 'ALA') & df['atm_id'].isin(['HB2', 'HB3']), 'atm_id_single'] = 'HB'
-    df.loc[(df['aa3'] == 'ALA') & df['atm_id'].isin(['HB1', 'HB2', 'HB3']), 'atm_id_single'] = 'HB'
-    df.loc[(df['aa3'] == 'GLY') & df['atm_id'].isin(['HA2', 'HA3']), 'atm_id_single'] = 'HA'
-    if np.any(~df['atm_id_single'].isin(bb_atm_ids)):
-        logging.getLogger('trizod.bmrb').error(f'found unexpected shift for a canonical amino acid in shift table')
+    df.loc[
+        (df["aa3"] != "ALA") & df["atm_id"].isin(["HB2", "HB3"]), "atm_id_single"
+    ] = "HB"
+    df.loc[
+        (df["aa3"] == "ALA") & df["atm_id"].isin(["HB1", "HB2", "HB3"]), "atm_id_single"
+    ] = "HB"
+    df.loc[
+        (df["aa3"] == "GLY") & df["atm_id"].isin(["HA2", "HA3"]), "atm_id_single"
+    ] = "HA"
+    if np.any(~df["atm_id_single"].isin(bb_atm_ids)):
+        logging.getLogger("trizod.bmrb").error(
+            "found unexpected shift for a canonical amino acid in shift table"
+        )
         return
     # duplicated shifts
-    dupl = df[['pos', 'atm_id_single', 'atm_id', 'val', 'err']].duplicated() # identical rows
+    dupl = df[
+        ["pos", "atm_id_single", "atm_id", "val", "err"]
+    ].duplicated()  # identical rows
     if np.any(dupl):
-        logging.getLogger('trizod.bmrb').warning(f'multiple identical shifts found for the same position and atom_id')
+        logging.getLogger("trizod.bmrb").warning(
+            "multiple identical shifts found for the same position and atom_id"
+        )
     df = df.loc[~dupl]
     # conflicting data
-    conflicting = df[['pos', 'atm_id_single', 'atm_id']].duplicated(keep=False)
+    conflicting = df[["pos", "atm_id_single", "atm_id"]].duplicated(keep=False)
     if np.any(conflicting):
-        logging.getLogger('trizod.bmrb').error(f'multiple shifts found for the same position and atom_id')
+        logging.getLogger("trizod.bmrb").error(
+            "multiple shifts found for the same position and atom_id"
+        )
         return
-    # averaging 
+    # averaging
     if averaging:
-        df = df.groupby(['pos', 'atm_id_single'])[['val']].agg('mean').reset_index()
+        df = df.groupby(["pos", "atm_id_single"])[["val"]].agg("mean").reset_index()
     else:
-        df['atm_id_single'] = df['atm_id']
-        bb_atm_ids = bb_atm_ids + ['HA2', 'HA3' ,'HB1', 'HB2', 'HB3']
+        df["atm_id_single"] = df["atm_id"]
+        bb_atm_ids = bb_atm_ids + ["HA2", "HA3", "HB1", "HB2", "HB3"]
     bbshifts_arr = np.zeros(shape=(len(seq), len(bb_atm_ids)))
     bbshifts_mask = np.full(shape=(len(seq), len(bb_atm_ids)), fill_value=False)
-    for i,atm_id in enumerate(bb_atm_ids):
-        sel = df['atm_id_single'] == atm_id
-        bbshifts_arr[df.loc[sel, 'pos'], i] = df.loc[sel, 'val']
-        bbshifts_mask[df.loc[sel, 'pos'], i] = True
-    #"""
-    return bbshifts_arr, bbshifts_mask
\ No newline at end of file
+    for i, atm_id in enumerate(bb_atm_ids):
+        sel = df["atm_id_single"] == atm_id
+        bbshifts_arr[df.loc[sel, "pos"], i] = df.loc[sel, "val"]
+        bbshifts_mask[df.loc[sel, "pos"], i] = True
+    # """
+    return bbshifts_arr, bbshifts_mask
diff --git a/trizod/constants.py b/trizod/constants.py
index 3a6d020..318c152 100644
--- a/trizod/constants.py
+++ b/trizod/constants.py
@@ -1,5 +1,34 @@
-BBATNS = ['C','CA','CB','HA','H','N','HB']
-REFINED_WEIGHTS = {'C':0.1846, 'CA':0.1982, 'CB':0.1544, 'HA':0.02631, 'H':0.06708, 'N':0.4722, 'HB':0.02154}
-AA3TO1 = {'CYS': 'C', 'GLN': 'Q', 'ILE': 'I', 'SER': 'S', 'VAL': 'V', 'MET': 'M', 'ASN': 'N', 'PRO': 'P', 'LYS': 'K', 'THR': 'T', 'PHE': 'F', 'ALA': 'A', 'HIS': 'H', 'GLY': 'G', 'ASP': 'D', 'LEU': 'L', 'ARG': 'R', 'TRP': 'W', 'GLU': 'E', 'TYR': 'Y'}
-AA1TO3 = {v:k for k,v in AA3TO1.items()}
-CAN_TRANS = str.maketrans("ARNDCQEGHILKMFPSTYWVX", "#####################")
\ No newline at end of file
+BBATNS = ["C", "CA", "CB", "HA", "H", "N", "HB"]
+REFINED_WEIGHTS = {
+    "C": 0.1846,
+    "CA": 0.1982,
+    "CB": 0.1544,
+    "HA": 0.02631,
+    "H": 0.06708,
+    "N": 0.4722,
+    "HB": 0.02154,
+}
+AA3TO1 = {
+    "CYS": "C",
+    "GLN": "Q",
+    "ILE": "I",
+    "SER": "S",
+    "VAL": "V",
+    "MET": "M",
+    "ASN": "N",
+    "PRO": "P",
+    "LYS": "K",
+    "THR": "T",
+    "PHE": "F",
+    "ALA": "A",
+    "HIS": "H",
+    "GLY": "G",
+    "ASP": "D",
+    "LEU": "L",
+    "ARG": "R",
+    "TRP": "W",
+    "GLU": "E",
+    "TYR": "Y",
+}
+AA1TO3 = {v: k for k, v in AA3TO1.items()}
+CAN_TRANS = str.maketrans("ARNDCQEGHILKMFPSTYWVX", "#####################")
diff --git a/trizod/potenci/README.md b/trizod/potenci/README.md
new file mode 100644
index 0000000..2b59116
--- /dev/null
+++ b/trizod/potenci/README.md
@@ -0,0 +1,3 @@
+# POTENCI
+
+See [docs/potenci.md](../../docs/potenci.md) for full documentation.
diff --git a/trizod/potenci/__init__.py b/trizod/potenci/__init__.py
index 829cb53..183838b 100644
--- a/trizod/potenci/__init__.py
+++ b/trizod/potenci/__init__.py
@@ -1,2 +1,22 @@
-from .potenci import getpredshifts
-from .constants import R, e, a, b, cutoff, ncycles
\ No newline at end of file
+from .constants import (
+    DIELECTRIC_WATER,
+    DISTANCE_SCALE,
+    GAS_CONSTANT,
+    MIN_CHARGE_DISTANCE,
+    PKA_FIT_CYCLES,
+    PKA_WINDOW_HALF,
+    REFERENCE_PKA,
+)
+from .potenci import BB_ATOMS, get_pred_shifts
+
+__all__ = [
+    "BB_ATOMS",
+    "DIELECTRIC_WATER",
+    "DISTANCE_SCALE",
+    "GAS_CONSTANT",
+    "MIN_CHARGE_DISTANCE",
+    "PKA_FIT_CYCLES",
+    "PKA_WINDOW_HALF",
+    "REFERENCE_PKA",
+    "get_pred_shifts",
+]
diff --git a/trizod/potenci/constants.py b/trizod/potenci/constants.py
index 25dcbef..7ac8c6b 100644
--- a/trizod/potenci/constants.py
+++ b/trizod/potenci/constants.py
@@ -1,10 +1,26 @@
-R = 8.314472
-e = 79.0
-a = 5.0
-b = 7.5
-cutoff = 2
-ncycles = 5
-
-#titratable groups
-##pK0 = {"n":7.5, "D":4.0, "E":4.4, "H":6.6, "C":8.6, "K":10.4, "R":12.0, "Y":9.6, "c":3.5} #was these values!
-pK0 = {"n":8.23, "D":3.86, "E":4.34, "H":6.45, "C":8.49, "K":10.34, "R":13.9, "Y":9.76, "c":3.55}
\ No newline at end of file
+"""Physical and algorithmic constants for the POTENCI pKa prediction."""
+
+# Physical constants
+GAS_CONSTANT = 8.314472  # J/(mol·K)
+DIELECTRIC_WATER = 79.0  # relative permittivity of water at ~25°C
+
+# Debye-Hückel charge interaction parameters (Ångströms)
+MIN_CHARGE_DISTANCE = 5.0  # minimum distance between titratable groups
+DISTANCE_SCALE = 7.5  # scaling factor for sequence-based distances
+
+# Iterative pKa fitting parameters
+PKA_WINDOW_HALF = 2  # half-width of sliding window (full window = 2*half + 1 = 5)
+PKA_FIT_CYCLES = 5  # number of iterative fitting cycles
+
+# Reference pKa values for titratable residues (including N/C-termini)
+REFERENCE_PKA = {
+    "n": 8.23,
+    "D": 3.86,
+    "E": 4.34,
+    "H": 6.45,
+    "C": 8.49,
+    "K": 10.34,
+    "R": 13.9,
+    "Y": 9.76,
+    "c": 3.55,
+}
diff --git a/trizod/potenci/data/centshifts.csv b/trizod/potenci/data/centshifts.csv
new file mode 100644
index 0000000..4ef31c5
--- /dev/null
+++ b/trizod/potenci/data/centshifts.csv
@@ -0,0 +1,21 @@
+aa,C,CA,CB,N,H,HA,HB
+A,177.44069,52.53002,19.21113,125.40155,8.20964,4.25629,1.31544
+C,174.33917,58.48976,28.06269,120.71212,8.29429,4.44261,2.85425
+D,176.02114,54.23920,41.18408,121.75726,8.28460,4.54836,2.60054
+E,176.19215,56.50755,30.30204,122.31578,8.35949,4.22124,1.92383
+F,175.42280,57.64849,39.55984,121.30500,8.10906,4.57507,3.00036
+G,173.83294,45.23929,None,110.09074,8.32746,3.91016,None
+H,175.00142,56.20256,30.60335,120.69141,8.27133,4.55872,3.03080
+I,175.88231,61.04925,38.68742,122.37586,8.06407,4.10574,1.78617
+K,176.22644,56.29413,33.02478,122.71282,8.24902,4.25873,1.71982
+L,177.06101,55.17464,42.29215,123.48611,8.14330,4.28545,1.54067
+M,175.90708,55.50643,32.83806,121.54592,8.24848,4.41483,1.97585
+N,174.94152,53.22822,38.87465,119.92746,8.37189,4.64308,2.72756
+P,176.67709,63.05232,32.03750,137.40612,None,4.36183,2.03318
+Q,175.63494,55.79861,29.44174,121.49225,8.30042,4.28006,1.97653
+R,175.92194,56.06785,30.81298,122.40365,8.26453,4.28372,1.73437
+S,174.31005,58.36048,63.82367,117.11419,8.25730,4.40101,3.80956
+T,174.27772,61.86928,69.80612,115.48126,8.11378,4.28923,4.15465
+V,175.80621,62.20156,32.77934,121.71912,8.06572,4.05841,1.99302
+W,175.92744,57.23836,29.56502,122.10991,7.97816,4.61061,3.18540
+Y,175.49651,57.82427,38.76184,121.43652,8.05749,4.51123,2.91782
diff --git a/trizod/potenci/data/combdevs.csv b/trizod/potenci/data/combdevs.csv
new file mode 100644
index 0000000..ec8318a
--- /dev/null
+++ b/trizod/potenci/data/combdevs.csv
@@ -0,0 +1,248 @@
+atn,neipos,centgroup,neigroup,segment,value,value2
+C,-1,G,r,xrGxx,0.2742,1.4856
+C,-1,G,-,x-Gxx,0.0522,0.2827
+C,-1,P,P,xPPxx,-0.0822,0.4450
+C,-1,P,r,xrPxx,0.2640,1.4303
+C,-1,r,P,xPrxx,-0.1027,0.5566
+C,-1,+,P,xP+xx,0.0714,0.3866
+C,-1,-,-,x--xx,-0.0501,0.2712
+C,-1,p,r,xrpxx,0.0582,0.3151
+C,1,G,r,xxGrx,0.0730,0.3955
+C,1,P,a,xxPax,-0.0981,0.5317
+C,1,P,+,xxP+x,-0.0577,0.3128
+C,1,P,p,xxPpx,-0.0619,0.3356
+C,1,r,r,xxrrx,-0.1858,1.0064
+C,1,r,a,xxrax,-0.1888,1.0226
+C,1,r,+,xxr+x,-0.1805,0.9779
+C,1,r,-,xxr-x,-0.1756,0.9512
+C,1,r,p,xxrpx,-0.1208,0.6544
+C,1,+,P,xx+Px,-0.0533,0.2886
+C,1,-,P,xx-Px,0.1867,1.0115
+C,1,p,P,xxpPx,0.2321,1.2574
+C,-2,G,r,rxGxx,-0.1457,0.7892
+C,-2,r,p,pxrxx,0.0555,0.3008
+C,2,P,P,xxPxP,0.1007,0.5455
+C,2,P,-,xxPx-,0.0634,0.3433
+C,2,r,P,xxrxP,-0.1447,0.7841
+C,2,a,r,xxaxr,-0.1488,0.8061
+C,2,a,-,xxax-,-0.0093,0.0506
+C,2,+,G,xx+xG,-0.0394,0.2132
+C,2,+,P,xx+xP,0.1016,0.5502
+C,2,+,a,xx+xa,0.0299,0.1622
+C,2,+,+,xx+x+,0.0427,0.2312
+C,2,-,a,xx-xa,0.0611,0.3308
+C,2,p,P,xxpxP,-0.0753,0.4078
+CA,-1,G,P,xPGxx,-0.0641,0.3233
+CA,-1,G,r,xrGxx,0.2107,1.0630
+CA,-1,P,P,xPPxx,-0.2042,1.0303
+CA,-1,P,p,xpPxx,0.0444,0.2240
+CA,-1,r,G,xGrxx,0.2030,1.0241
+CA,-1,r,+,x+rxx,-0.0811,0.4093
+CA,-1,-,P,xP-xx,0.0744,0.3755
+CA,-1,-,-,x--xx,-0.0263,0.1326
+CA,-1,p,p,xppxx,-0.0094,0.0475
+CA,1,G,P,xxGPx,1.3044,6.5813
+CA,1,G,-,xxG-x,-0.0632,0.3188
+CA,1,P,G,xxPGx,0.2642,1.3329
+CA,1,P,P,xxPPx,0.3025,1.5262
+CA,1,P,r,xxPrx,0.1455,0.7343
+CA,1,P,-,xxP-x,0.1188,0.5994
+CA,1,P,p,xxPpx,0.1201,0.6062
+CA,1,r,P,xxrPx,-0.1958,0.9878
+CA,1,r,-,xxr-x,-0.0931,0.4696
+CA,1,a,P,xxaPx,-0.1428,0.7204
+CA,1,a,-,xxa-x,-0.0262,0.1324
+CA,1,a,p,xxapx,0.0392,0.1977
+CA,1,+,P,xx+Px,-0.1059,0.5344
+CA,1,+,a,xx+ax,-0.0377,0.1901
+CA,1,+,+,xx++x,-0.0595,0.3001
+CA,1,-,P,xx-Px,-0.1156,0.5831
+CA,1,-,+,xx-+x,0.0316,0.1593
+CA,1,-,p,xx-px,0.0612,0.3090
+CA,1,p,r,xxprx,-0.0511,0.2576
+CA,-2,P,-,-xPxx,-0.1028,0.5185
+CA,-2,r,r,rxrxx,0.1933,0.9752
+CA,-2,-,G,Gx-xx,0.0559,0.2818
+CA,-2,-,p,px-xx,0.0391,0.1973
+CA,-2,p,a,axpxx,-0.0293,0.1479
+CA,-2,p,+,+xpxx,-0.0173,0.0873
+CA,2,G,-,xxGx-,0.0357,0.1802
+CA,2,+,G,xx+xG,-0.0315,0.1591
+CA,2,-,P,xx-xP,0.0426,0.2150
+CA,2,-,r,xx-xr,0.0784,0.3954
+CA,2,-,a,xx-xa,0.1084,0.5467
+CA,2,-,-,xx-x-,0.0836,0.4216
+CA,2,p,P,xxpxP,0.0685,0.3456
+CA,2,p,-,xxpx-,-0.0481,0.2428
+CB,-1,P,r,xrPxx,-0.2678,1.7345
+CB,-1,P,p,xpPxx,0.0355,0.2300
+CB,-1,r,P,xPrxx,-0.1137,0.7367
+CB,-1,a,p,xpaxx,0.0249,0.1613
+CB,-1,+,-,x-+xx,-0.0762,0.4935
+CB,-1,-,P,xP-xx,-0.0889,0.5757
+CB,-1,-,r,xr-xx,-0.0533,0.3451
+CB,-1,-,-,x--xx,0.0496,0.3215
+CB,-1,-,p,xp-xx,-0.0148,0.0960
+CB,-1,p,P,xPpxx,0.0119,0.0768
+CB,-1,p,r,xrpxx,-0.0673,0.4358
+CB,1,P,G,xxPGx,-0.0522,0.3379
+CB,1,P,P,xxPPx,-0.8458,5.4779
+CB,1,P,r,xxPrx,-0.1573,1.0187
+CB,1,r,r,xxrrx,0.1634,1.0581
+CB,1,a,G,xxaGx,-0.0393,0.2544
+CB,1,a,r,xxarx,0.0274,0.1777
+CB,1,a,-,xxa-x,0.0394,0.2553
+CB,1,a,p,xxapx,0.0149,0.0968
+CB,1,+,G,xx+Gx,-0.0784,0.5076
+CB,1,+,P,xx+Px,-0.1170,0.7580
+CB,1,-,P,xx-Px,-0.0913,0.5912
+CB,1,-,-,xx--x,0.0284,0.1838
+CB,1,p,P,xxpPx,0.0880,0.5697
+CB,1,p,p,xxppx,-0.0113,0.0733
+CB,-2,P,-,-xPxx,0.0389,0.2521
+CB,-2,P,p,pxPxx,0.0365,0.2362
+CB,-2,r,+,+xrxx,0.0809,0.5242
+CB,-2,a,-,-xaxx,-0.0452,0.2927
+CB,-2,+,-,-x+xx,-0.0651,0.4218
+CB,-2,-,G,Gx-xx,-0.0883,0.5717
+CB,-2,p,G,Gxpxx,0.0378,0.2445
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+CB,2,a,-,xxax-,-0.0298,0.1929
+CB,2,+,p,xx+xp,0.0189,0.1223
+CB,2,-,-,xx-x-,-0.0525,0.3400
+N,-1,G,P,xPGxx,0.2411,0.5105
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+N,-1,P,p,xpPxx,0.2609,0.5525
+N,-1,r,G,xGrxx,0.2417,0.5119
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+N,-1,r,p,xprxx,0.1440,0.3049
+N,-1,a,G,xGaxx,-0.5177,1.0963
+N,-1,a,r,xraxx,0.0890,0.1885
+N,-1,a,a,xaaxx,0.1393,0.2950
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+HB,-1,-,r,xr-xx,0.0233,1.0819
+HB,1,a,P,xxaPx,0.0287,1.3310
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+HB,-2,a,P,Pxaxx,-0.0026,0.1192
+HB,-2,a,r,rxaxx,-0.0098,0.4559
+HB,-2,-,-,-x-xx,0.0016,0.0751
+HB,2,P,r,xxPxr,-0.0595,2.7608
+HB,2,P,+,xxPx+,-0.0145,0.6744
+HB,2,P,-,xxPx-,0.0107,0.4976
+HB,2,a,+,xxax+,-0.0015,0.0691
+HB,2,p,r,xxpxr,0.0262,1.2178
diff --git a/trizod/potenci/data/neicorrs.csv b/trizod/potenci/data/neicorrs.csv
new file mode 100644
index 0000000..00cdc5e
--- /dev/null
+++ b/trizod/potenci/data/neicorrs.csv
@@ -0,0 +1,141 @@
+atn,aa,c0,c1,c2,c3
+C,A,0.06131,-0.04544,0.14646,0.01305
+C,C,0.04502,0.12592,-0.03407,-0.02654
+C,D,0.08180,-0.08589,0.22948,0.10934
+C,E,0.05388,0.22264,0.06962,0.01929
+C,F,-0.06286,-0.22396,-0.34442,0.00950
+C,G,0.12772,0.72041,0.16048,0.01324
+C,H,-0.00628,-0.03355,0.13309,-0.03906
+C,I,-0.11709,0.06591,-0.06361,-0.03628
+C,K,0.03368,0.15830,0.04518,-0.01576
+C,L,-0.03877,0.11608,0.02535,0.01976
+C,M,0.04611,0.25233,-0.00747,-0.01624
+C,N,0.07068,-0.06118,0.10077,0.05547
+C,P,-0.36018,-1.90872,0.16158,-0.05286
+C,Q,0.10861,0.19878,0.01596,-0.01757
+C,R,0.01933,0.13237,0.03606,-0.02468
+C,S,0.09888,0.28691,0.07601,0.01379
+C,T,0.05658,0.41659,-0.01103,-0.00114
+C,V,-0.11591,0.09565,-0.03355,-0.03368
+C,W,-0.01954,-0.19134,-0.37965,0.01582
+C,Y,-0.08380,-0.24519,-0.32700,-0.00577
+CA,A,0.03588,0.03480,-0.00468,-0.00920
+CA,C,0.02749,0.15742,0.14376,0.03681
+CA,D,-0.00751,0.12494,0.17354,0.14157
+CA,E,0.00985,0.13936,0.03289,-0.00702
+CA,F,0.01122,0.03732,-0.19586,-0.00377
+CA,G,-0.00885,0.23403,-0.03184,-0.01144
+CA,H,-0.02102,0.04621,0.03122,-0.02826
+CA,I,-0.00656,0.05965,-0.10588,-0.04372
+CA,K,0.01817,0.11216,-0.00341,-0.02950
+CA,L,0.04507,0.07829,-0.03526,0.00858
+CA,M,0.07553,0.18840,0.04987,-0.01749
+CA,N,-0.00649,0.11842,0.18729,0.06401
+CA,P,-0.27536,-2.02189,0.01327,-0.08732
+CA,Q,0.06365,0.15281,0.04575,-0.01356
+CA,R,0.04338,0.11783,0.00345,-0.02873
+CA,S,0.02867,0.07846,0.09443,0.02061
+CA,T,-0.01625,0.10626,0.03880,-0.00126
+CA,V,-0.04935,0.04248,-0.10195,-0.03778
+CA,W,0.00434,0.16188,-0.08742,0.03983
+CA,Y,0.02782,0.02846,-0.24750,0.00759
+CB,A,-0.00953,0.05704,-0.04838,0.00755
+CB,C,-0.00164,0.00760,-0.03293,-0.05613
+CB,D,0.02064,0.09849,-0.08746,-0.06691
+CB,E,0.01283,0.05404,-0.01342,0.02238
+CB,F,0.01028,0.03363,0.18112,0.01493
+CB,G,-0.02758,0.04383,0.06071,-0.02639
+CB,H,-0.01760,-0.02367,0.00343,0.00415
+CB,I,0.02783,0.01052,0.00641,0.05090
+CB,K,0.00350,0.02852,-0.00408,0.01218
+CB,L,0.01223,-0.02940,-0.07268,0.00884
+CB,M,-0.02925,-0.03912,-0.06587,0.03490
+CB,N,-0.02242,0.03403,-0.09759,-0.08018
+CB,P,0.08431,-0.35696,-0.04680,0.05192
+CB,Q,-0.01649,-0.01016,-0.03663,0.01723
+CB,R,-0.01887,0.00618,-0.00385,0.02884
+CB,S,-0.00921,0.07096,-0.06338,-0.03707
+CB,T,0.02601,0.04904,-0.01728,0.00781
+CB,V,0.03068,0.06325,0.01928,0.05011
+CB,W,-0.07651,-0.11334,0.13806,-0.03339
+CB,Y,0.00082,0.01466,0.18107,-0.01181
+N,A,0.09963,-0.00873,-2.31666,-0.14051
+N,C,0.11905,-0.01296,1.15573,0.01820
+N,D,0.11783,-0.11817,-1.16322,-0.37601
+N,E,0.10825,-0.00605,-0.41856,0.01187
+N,F,-0.12280,-0.27542,0.34635,0.09102
+N,G,0.10365,-0.05667,-1.50346,-0.00146
+N,H,-0.04145,-0.26494,0.26356,0.18198
+N,I,-0.09249,0.12136,2.75071,0.40643
+N,K,-0.02472,0.07224,-0.07057,0.12261
+N,L,0.01542,-0.12800,-0.85172,-0.15460
+N,M,-0.11266,-0.27311,-0.33192,0.09384
+N,N,-0.00295,-0.20562,-1.00652,-0.30971
+N,P,0.03252,1.35296,-1.17173,0.06026
+N,Q,0.00900,-0.09950,-0.07389,0.08415
+N,R,-0.07819,0.00802,-0.04821,0.08524
+N,S,0.12057,0.02242,0.48924,-0.25423
+N,T,0.04631,0.09935,1.02269,0.20228
+N,V,-0.03610,0.21959,2.42228,0.39686
+N,W,-0.15643,-0.19285,0.05515,-0.53172
+N,Y,-0.10497,-0.25228,0.46023,0.01399
+H,A,0.01337,-0.00605,-0.04371,-0.02485
+H,C,0.01324,0.05107,0.12857,0.00610
+H,D,0.02859,0.02436,-0.06510,0.02085
+H,E,0.02737,0.01790,0.03740,0.01969
+H,F,-0.02633,-0.08287,-0.11364,-0.03603
+H,G,0.02753,0.05640,-0.10477,0.06876
+H,H,-0.00124,-0.02861,0.04126,0.10004
+H,I,-0.02258,-0.00929,0.07962,0.01880
+H,K,-0.00512,-0.00744,0.04443,0.03434
+H,L,-0.01088,-0.01230,-0.03640,-0.03719
+H,M,-0.01961,-0.00749,-0.00097,0.02041
+H,N,0.01134,0.02121,-0.01837,-0.00629
+H,P,-0.01246,0.02956,0.13007,-0.00810
+H,Q,0.00783,0.00751,0.05643,0.02413
+H,R,-0.00734,0.00546,0.07003,0.04051
+H,S,0.02133,0.03964,0.04978,-0.03749
+H,T,0.00976,0.06072,0.03531,0.01657
+H,V,-0.01267,0.00994,0.09630,0.03420
+H,W,-0.02348,-0.09617,-0.24207,-0.18741
+H,Y,-0.01881,-0.07345,-0.14345,-0.06721
+HA,A,0.00350,-0.02371,-0.00654,0.00652
+HA,C,0.00660,0.01073,0.01921,0.00919
+HA,D,0.01717,-0.00854,-0.00802,-0.00597
+HA,E,0.01090,-0.01091,0.00472,0.00790
+HA,F,-0.02271,-0.06316,-0.03057,-0.02350
+HA,G,0.02155,-0.00151,0.02477,0.01526
+HA,H,-0.01132,-0.05617,-0.01514,0.01264
+HA,I,0.00459,0.00571,0.02984,0.00416
+HA,K,0.00492,-0.01788,0.00555,0.01259
+HA,L,-0.00599,-0.01558,0.00358,0.00167
+HA,M,0.00100,-0.02037,0.00678,0.00930
+HA,N,0.00651,-0.01499,-0.00361,0.00203
+HA,P,0.01542,0.28350,-0.01496,0.00796
+HA,Q,0.00711,-0.02142,0.00734,0.00971
+HA,R,-0.00472,-0.01414,0.00966,0.01180
+HA,S,0.01572,0.02791,0.03762,0.00133
+HA,T,0.01714,0.06590,0.03085,0.00143
+HA,V,0.00777,0.01505,0.02525,0.00659
+HA,W,-0.06818,-0.08412,-0.09386,-0.06072
+HA,Y,-0.02701,-0.05585,-0.03243,-0.02987
+HB,A,0.01473,0.01843,0.01428,0.00451
+HB,C,0.01180,0.03340,0.03081,0.00169
+HB,D,0.01786,0.01626,0.02221,0.01030
+HB,E,0.01796,0.01820,0.00835,-0.00045
+HB,F,-0.04867,-0.09154,-0.04858,-0.00164
+HB,G,0.01718,0.03852,0.01043,0.00051
+HB,H,-0.00817,-0.04557,-0.00820,0.00855
+HB,I,0.00446,0.00111,0.00049,-0.00283
+HB,K,0.01570,0.01156,0.00771,0.00646
+HB,L,0.00700,0.01236,0.00880,0.00150
+HB,M,0.01607,0.02294,0.01385,-0.00038
+HB,N,0.01893,0.01561,0.02760,0.01215
+HB,P,-0.01199,-0.02752,0.00891,-0.00033
+HB,Q,0.01636,0.01861,0.01177,-0.00099
+HB,R,0.01324,0.01526,0.01082,0.00378
+HB,S,0.01859,0.03487,0.02890,-0.00477
+HB,T,0.01624,0.04073,0.01936,-0.00348
+HB,V,0.00380,0.00271,-0.00144,-0.00315
+HB,W,-0.09045,-0.06895,-0.10934,-0.01948
+HB,Y,-0.05069,-0.06698,-0.05666,-0.01193
diff --git a/trizod/potenci/data/phshifts.csv b/trizod/potenci/data/phshifts.csv
new file mode 100644
index 0000000..0f14c02
--- /dev/null
+++ b/trizod/potenci/data/phshifts.csv
@@ -0,0 +1,85 @@
+resn,atn,shd,prev_nei,succ_nei
+D,H,-0.17,0.02,-0.03
+D,HA,-0.17,0.01,-0.01
+D,HB,-0.23,,
+D,CA,1.4,0.0,0.1
+D,CB,3.0,,
+D,CG,3.2,,
+D,C,1.1,-0.2,0.4
+D,N,1.5,0.3,0.1
+D,Np,0.1,,
+E,H,0.12,0.00,0.02
+E,HA,-0.10,0.01,0.00
+E,HB,-0.06,,
+E,HG,-0.22,,
+E,CA,1.0,0.0,0.0
+E,CB,1.5,,
+E,CG,3.5,,
+E,CD,4.1,,
+E,C,0.6,0.1,0.1
+E,N,1.0,0.2,0.1
+E,Np,0.1,,
+H,H,-0.2,-0.01,0.0
+H,HA,-0.2,-0.01,-0.06
+H,HB,-0.17,,
+H,HD2,-0.33,,
+H,HE1,-0.92,,
+H,CA,1.6,-0.1,0.1
+H,CB,2.4,,
+H,CG,4.2,,
+H,CD2,-0.3,,
+H,CE1,2.6,,
+H,C,1.5,0.0,0.6
+H,N,1.8,0.3,0.5
+H,Np,0.5,,
+H,ND1,56,,
+H,NE2,8,,
+C,H,0.0,,
+C,HA,-0.28,-0.01,-0.01
+C,HB,-0.09,,
+C,CA,2.1,0.0,0.1
+C,CB,1.7,,
+C,C,1.9,-0.4,0.5
+C,N,3.6,0.4,0.6
+C,Np,0.6,,
+Y,H,0.0,,
+Y,HA,-0.06,,
+Y,HB,-0.08,,
+Y,HD,-0.17,,
+Y,HE,-0.28,,
+Y,CA,0.3,,
+Y,CB,0.1,,
+Y,CG,-6.7,,
+Y,CD,-0.1,,
+Y,CE,3.3,,
+Y,CZ,10.4,,
+Y,C,0.4,,
+Y,N,0.6,,
+K,H,0.0,,
+K,HA,-0.04,,
+K,HB,-0.04,,
+K,HG,-0.08,,
+K,HD,-0.24,,
+K,HE,-0.40,,
+K,CA,0.4,,
+K,CB,0.3,,
+K,CG,0.4,,
+K,CD,5.0,,
+K,CE,1.0,,
+K,C,0.5,,
+K,N,0.7,,
+K,Np,0.1,,
+R,H,0.0,,
+R,HA,-0.07,,
+R,HB,0.05,,
+R,HG,0.05,,
+R,HD,-0.19,,
+R,CA,0.2,,
+R,CB,0.9,,
+R,CG,1.0,,
+R,CD,0.5,,
+R,CZ,4.0,,
+R,C,0.2,,
+R,N,0.4,,
+R,NE,5.9,,
+R,NG,22,,
diff --git a/trizod/potenci/data/tempcoeffs.csv b/trizod/potenci/data/tempcoeffs.csv
new file mode 100644
index 0000000..ab353eb
--- /dev/null
+++ b/trizod/potenci/data/tempcoeffs.csv
@@ -0,0 +1,21 @@
+aa,CA,CB,C,N,H,HA
+A,-2.2,4.7,-7.1,-5.3,-9.0,0.7
+C,-0.9,1.3,-2.6,-8.2,-7.0,0.0
+D,2.8,6.5,-4.8,-3.9,-6.2,-0.1
+E,0.9,4.6,-4.9,-3.7,-6.5,0.3
+F,-4.7,2.4,-6.9,-11.2,-7.5,0.4
+G,3.3,0.0,-3.2,-6.2,-9.1,0.0
+H,7.8,15.5,3.1,3.3,-7.8,0.4
+I,-2.0,4.6,-8.7,-12.7,-7.8,0.4
+K,-0.8,2.4,-7.1,-7.6,-7.5,0.4
+L,1.7,4.9,-8.2,-2.9,-7.5,0.1
+M,4.1,9.4,-8.2,-6.2,-7.1,-0.5
+N,2.8,5.1,-6.1,-3.3,-7.0,-2.9
+P,1.1,-0.2,-4.0,0.0,0.0,0.0
+Q,2.3,3.6,-5.7,-6.5,-7.2,0.3
+R,-1.4,3.5,-6.9,-5.3,-7.1,0.4
+S,-1.7,4.4,-4.7,-3.8,-7.6,0.1
+T,0.0,2.2,-5.2,-6.7,-7.3,0.0
+V,-2.8,2.5,-8.1,-14.2,-7.6,0.5
+W,-2.7,3.1,-7.9,-10.1,-7.8,0.4
+Y,-5.0,2.9,-7.7,-12.0,-7.7,0.5
diff --git a/trizod/potenci/data/termcorrs.csv b/trizod/potenci/data/termcorrs.csv
new file mode 100644
index 0000000..4cbbd49
--- /dev/null
+++ b/trizod/potenci/data/termcorrs.csv
@@ -0,0 +1,15 @@
+atn,term,value
+C,n,-0.15238
+C,c,-0.90166
+CB,n,0.12064
+CB,c,0.06854
+CA,n,-0.04616
+CA,c,-0.06680
+N,n,0.347176
+N,c,0.619141
+H,n,0.156786
+H,c,0.023189
+HB,n,0.0052692
+HB,c,0.0310875
+HA,n,0.048624
+HA,c,0.042019
diff --git a/trizod/potenci/legacy/potenci1_3.py b/trizod/potenci/legacy/potenci1_3.py
deleted file mode 100644
index 5ade904..0000000
--- a/trizod/potenci/legacy/potenci1_3.py
+++ /dev/null
@@ -1,970 +0,0 @@
-import sys
-import string
-from scipy.special import erfc
-from scipy.optimize import curve_fit
-import numpy as np
-##from matplotlib import pyplot as pl
-
-VERB=False #change this to True for verbose logfile
-
-#constants
-R = 8.314472
-e = 79.0
-a = 5.0
-b = 7.5
-cutoff = 2
-ncycles = 5
-
-def smallmatrixlimits(ires, cutoff, len):
-    ileft = max(1, ires - cutoff)
-    iright = min(ileft + 2 * cutoff, len)
-    if iright == len:
-        ileft = max(1, iright - 2 * cutoff)
-    return (ileft, iright)
-
-def smallmatrixpos(ires, cutoff, len):
-    resi = cutoff + 1
-    if ires < cutoff + 1:
-        resi = ires
-    if ires > len - cutoff:
-        resi = min(len, 2 * cutoff + 1) - (len - ires)
-    return resi
-
-def fun(pH, pK, nH):
-    return (10 ** ( nH*(pK - pH) ) ) / (1. + (10 **( nH*(pK - pH) ) ) )
-
-def W(r,Ion=0.1):
-    k = np.sqrt(Ion) / 3.08 #Ion=0.1 is default
-    x = k * r / np.sqrt(6)
-    return 332.286 * np.sqrt(6 / np.pi) * (1 - np.sqrt(np.pi) * x * np.exp(x ** 2) * erfc(x)) / (e * r)
-
-def w2logp(x,T=293.15):
-    return x * 4181.2 / (R * T * np.log(10)) 
-
-pK0 = {"n":8.23, "D":3.86, "E":4.34, "H":6.45, "C":8.49, "K":10.34, "R":13.9, "Y":9.76, "c":3.55}
-
-def calc_pkas_from_seq(seq=None, T=293.15, Ion=0.1):
-    #pH range
-    pHs = np.arange(1.99, 10.01, 0.15)
-    #titratable groups
-    ##pK0 = {"n":7.5, "D":4.0, "E":4.4, "H":6.6, "C":8.6, "K":10.4, "R":12.0, "Y":9.6, "c":3.5} #was these values!
-    pK0 = {"n":8.23, "D":3.86, "E":4.34, "H":6.45, "C":8.49, "K":10.34, "R":13.9, "Y":9.76, "c":3.55}
-  
-    pos = np.array([i for i in range(len(seq)) if seq[i] in pK0.keys()])
-    N = pos.shape[0]
-    I = np.diag(np.ones(N))
-    sites = ''.join([seq[i] for i in pos])
-    neg = np.array([i for i in range(len(sites)) if sites[i] in 'DEYc'])
-    l = np.array([abs(pos - pos[i]) for i in range(N)])
-    d = a + np.sqrt(l) * b
-  
-    tmp = W(d,Ion)
-    tmp[I == 1] = 0
-  
-    ww = w2logp(tmp,T) / 2
-  
-    chargesempty = np.zeros(pos.shape[0])
-    if len(neg): chargesempty[neg] = -1
-  
-    pK0s = [pK0[c] for c in sites]
-    nH0s = [0.9 for c in sites]
-  
-    titration = np.zeros((N,len(pHs)))
-    smallN = min(2 * cutoff+1, len(pos))
-    smallI = np.diag(np.ones(smallN))
-  
-    alltuples =  [[int(c) for c in np.binary_repr(i, smallN)]
-                  for i in range(2 ** (smallN))]
-    gmatrix = [np.zeros((smallN, smallN)) for p in range(len(pHs))]
-  
-    #perform iterative fitting.........................
-    for icycle in range(ncycles):
-        ##print (icycle)
-    
-        if icycle == 0:
-            fractionhold = np.array([[fun(pHs[p], pK0s[i], nH0s[i]) for i in range(N)] for p in range(len(pHs))])
-        else:
-            fractionhold = titration.transpose()
-    
-        for ires in range(1, N+1):
-            (ileft,iright) = smallmatrixlimits(ires, cutoff, N)
-            resi = smallmatrixpos(ires, cutoff, N)
-            for p in range(len(pHs)):
-                fraction = fractionhold[p].copy()
-                fraction[ileft - 1 : iright] = 0
-                charges = chargesempty + fraction  
-                ww0 = np.diag(np.dot(ww, charges) * 2) 
-                gmatrixfull =  ww + ww0 + pHs[p] * I - np.diag(pK0s) 
-                gmatrix[p] = gmatrixfull[ileft - 1 : iright, ileft - 1 : iright]
-      
-            E_all = np.array([sum([10 ** -(gmatrix[p] * np.outer(c,c)).sum() for c in alltuples]) for p in range(len(pHs))])
-            E_sel = np.array([sum([10 ** -(gmatrix[p] * np.outer(c,c)).sum() for c in alltuples if c[resi-1] == 1]) for p in range(len(pHs))])
-            titration[ires-1] = E_sel/E_all
-        sol=np.array([curve_fit(fun, pHs, titration[p], [pK0s[p], nH0s[p]])[0] for p in range(len(pK0s))])
-        (pKs, nHs) = sol.transpose()
-        ##print (sol)
-  
-    dct={}
-    for p,i in enumerate(pos):
-      ##print (p,i,seq[i],pKs[p],nHs[p])
-      dct[i-1]=(pKs[p],nHs[p],seq[i])
-  
-    return dct
-
-
-##--------------- POTENCI core code and data tables from here -----------------
-
-#AAstandard='ACDEFGHIKLMNPQRSTVY'
-AAstandard='ACDEFGHIKLMNPQRSTVWY'
-
-tablecent='''aa C CA CB N H HA HB
-A 177.44069  52.53002  19.21113 125.40155   8.20964   4.25629   1.31544
-C 174.33917  58.48976  28.06269 120.71212   8.29429   4.44261   2.85425
-D 176.02114  54.23920  41.18408 121.75726   8.28460   4.54836   2.60054
-E 176.19215  56.50755  30.30204 122.31578   8.35949   4.22124   1.92383
-F 175.42280  57.64849  39.55984 121.30500   8.10906   4.57507   3.00036
-G 173.83294  45.23929  None     110.09074   8.32746   3.91016   None
-H 175.00142  56.20256  30.60335 120.69141   8.27133   4.55872   3.03080
-I 175.88231  61.04925  38.68742 122.37586   8.06407   4.10574   1.78617
-K 176.22644  56.29413  33.02478 122.71282   8.24902   4.25873   1.71982
-L 177.06101  55.17464  42.29215 123.48611   8.14330   4.28545   1.54067
-M 175.90708  55.50643  32.83806 121.54592   8.24848   4.41483   1.97585
-N 174.94152  53.22822  38.87465 119.92746   8.37189   4.64308   2.72756
-P 176.67709  63.05232  32.03750 137.40612   None      4.36183   2.03318
-Q 175.63494  55.79861  29.44174 121.49225   8.30042   4.28006   1.97653
-R 175.92194  56.06785  30.81298 122.40365   8.26453   4.28372   1.73437
-S 174.31005  58.36048  63.82367 117.11419   8.25730   4.40101   3.80956
-T 174.27772  61.86928  69.80612 115.48126   8.11378   4.28923   4.15465
-V 175.80621  62.20156  32.77934 121.71912   8.06572   4.05841   1.99302
-W 175.92744  57.23836  29.56502 122.10991   7.97816   4.61061   3.18540
-Y 175.49651  57.82427  38.76184 121.43652   8.05749   4.51123   2.91782'''
-
-def initcorcents():
-    datc=string.split(tablecent,'\n')
-    ##aas=string.split(datc[0],'\t')[1:]
-    aas=string.split(datc[0])[1:]
-    dct={}
-    for i in range(20):
-        ##vals=string.split(datc[1+i],'\t')
-        vals=string.split(datc[1+i])
-        aai=vals[0]
-        dct[aai]={}
-        for j in range(7):
-            atnj=aas[j]
-            dct[aai][atnj]=eval(vals[1+j])
-    return dct
-        
-
-tablenei='''C A  0.06131 -0.04544  0.14646  0.01305
- C C  0.04502  0.12592 -0.03407 -0.02654
- C D  0.08180 -0.08589  0.22948  0.10934
- C E  0.05388  0.22264  0.06962  0.01929
- C F -0.06286 -0.22396 -0.34442  0.00950
- C G  0.12772  0.72041  0.16048  0.01324
- C H -0.00628 -0.03355  0.13309 -0.03906
- C I -0.11709  0.06591 -0.06361 -0.03628
- C K  0.03368  0.15830  0.04518 -0.01576
- C L -0.03877  0.11608  0.02535  0.01976
- C M  0.04611  0.25233 -0.00747 -0.01624
- C N  0.07068 -0.06118  0.10077  0.05547
- C P -0.36018 -1.90872  0.16158 -0.05286
- C Q  0.10861  0.19878  0.01596 -0.01757
- C R  0.01933  0.13237  0.03606 -0.02468
- C S  0.09888  0.28691  0.07601  0.01379
- C T  0.05658  0.41659 -0.01103 -0.00114
- C V -0.11591  0.09565 -0.03355 -0.03368
- C W -0.01954 -0.19134 -0.37965  0.01582
- C Y -0.08380 -0.24519 -0.32700 -0.00577
-CA A  0.03588  0.03480 -0.00468 -0.00920
-CA C  0.02749  0.15742  0.14376  0.03681
-CA D -0.00751  0.12494  0.17354  0.14157
-CA E  0.00985  0.13936  0.03289 -0.00702
-CA F  0.01122  0.03732 -0.19586 -0.00377
-CA G -0.00885  0.23403 -0.03184 -0.01144
-CA H -0.02102  0.04621  0.03122 -0.02826
-CA I -0.00656  0.05965 -0.10588 -0.04372
-CA K  0.01817  0.11216 -0.00341 -0.02950
-CA L  0.04507  0.07829 -0.03526  0.00858
-CA M  0.07553  0.18840  0.04987 -0.01749
-CA N -0.00649  0.11842  0.18729  0.06401
-CA P -0.27536 -2.02189  0.01327 -0.08732
-CA Q  0.06365  0.15281  0.04575 -0.01356
-CA R  0.04338  0.11783  0.00345 -0.02873
-CA S  0.02867  0.07846  0.09443  0.02061
-CA T -0.01625  0.10626  0.03880 -0.00126
-CA V -0.04935  0.04248 -0.10195 -0.03778
-CA W  0.00434  0.16188 -0.08742  0.03983
-CA Y  0.02782  0.02846 -0.24750  0.00759
-CB A -0.00953  0.05704 -0.04838  0.00755
-CB C -0.00164  0.00760 -0.03293 -0.05613
-CB D  0.02064  0.09849 -0.08746 -0.06691
-CB E  0.01283  0.05404 -0.01342  0.02238
-CB F  0.01028  0.03363  0.18112  0.01493
-CB G -0.02758  0.04383  0.06071 -0.02639
-CB H -0.01760 -0.02367  0.00343  0.00415
-CB I  0.02783  0.01052  0.00641  0.05090
-CB K  0.00350  0.02852 -0.00408  0.01218
-CB L  0.01223 -0.02940 -0.07268  0.00884
-CB M -0.02925 -0.03912 -0.06587  0.03490
-CB N -0.02242  0.03403 -0.09759 -0.08018
-CB P  0.08431 -0.35696 -0.04680  0.05192
-CB Q -0.01649 -0.01016 -0.03663  0.01723
-CB R -0.01887  0.00618 -0.00385  0.02884
-CB S -0.00921  0.07096 -0.06338 -0.03707
-CB T  0.02601  0.04904 -0.01728  0.00781
-CB V  0.03068  0.06325  0.01928  0.05011
-CB W -0.07651 -0.11334  0.13806 -0.03339
-CB Y  0.00082  0.01466  0.18107 -0.01181
- N A  0.09963 -0.00873 -2.31666 -0.14051
- N C  0.11905 -0.01296  1.15573  0.01820
- N D  0.11783 -0.11817 -1.16322 -0.37601
- N E  0.10825 -0.00605 -0.41856  0.01187
- N F -0.12280 -0.27542  0.34635  0.09102
- N G  0.10365 -0.05667 -1.50346 -0.00146
- N H -0.04145 -0.26494  0.26356  0.18198
- N I -0.09249  0.12136  2.75071  0.40643
- N K -0.02472  0.07224 -0.07057  0.12261
- N L  0.01542 -0.12800 -0.85172 -0.15460
- N M -0.11266 -0.27311 -0.33192  0.09384
- N N -0.00295 -0.20562 -1.00652 -0.30971
- N P  0.03252  1.35296 -1.17173  0.06026
- N Q  0.00900 -0.09950 -0.07389  0.08415
- N R -0.07819  0.00802 -0.04821  0.08524
- N S  0.12057  0.02242  0.48924 -0.25423
- N T  0.04631  0.09935  1.02269  0.20228
- N V -0.03610  0.21959  2.42228  0.39686
- N W -0.15643 -0.19285  0.05515 -0.53172
- N Y -0.10497 -0.25228  0.46023  0.01399
- H A  0.01337 -0.00605 -0.04371 -0.02485
- H C  0.01324  0.05107  0.12857  0.00610
- H D  0.02859  0.02436 -0.06510  0.02085
- H E  0.02737  0.01790  0.03740  0.01969
- H F -0.02633 -0.08287 -0.11364 -0.03603
- H G  0.02753  0.05640 -0.10477  0.06876
- H H -0.00124 -0.02861  0.04126  0.10004
- H I -0.02258 -0.00929  0.07962  0.01880
- H K -0.00512 -0.00744  0.04443  0.03434
- H L -0.01088 -0.01230 -0.03640 -0.03719
- H M -0.01961 -0.00749 -0.00097  0.02041
- H N  0.01134  0.02121 -0.01837 -0.00629
- H P -0.01246  0.02956  0.13007 -0.00810
- H Q  0.00783  0.00751  0.05643  0.02413
- H R -0.00734  0.00546  0.07003  0.04051
- H S  0.02133  0.03964  0.04978 -0.03749
- H T  0.00976  0.06072  0.03531  0.01657
- H V -0.01267  0.00994  0.09630  0.03420
- H W -0.02348 -0.09617 -0.24207 -0.18741
- H Y -0.01881 -0.07345 -0.14345 -0.06721
-HA A  0.00350 -0.02371 -0.00654  0.00652
-HA C  0.00660  0.01073  0.01921  0.00919
-HA D  0.01717 -0.00854 -0.00802 -0.00597
-HA E  0.01090 -0.01091  0.00472  0.00790
-HA F -0.02271 -0.06316 -0.03057 -0.02350
-HA G  0.02155 -0.00151  0.02477  0.01526
-HA H -0.01132 -0.05617 -0.01514  0.01264
-HA I  0.00459  0.00571  0.02984  0.00416
-HA K  0.00492 -0.01788  0.00555  0.01259
-HA L -0.00599 -0.01558  0.00358  0.00167
-HA M  0.00100 -0.02037  0.00678  0.00930
-HA N  0.00651 -0.01499 -0.00361  0.00203
-HA P  0.01542  0.28350 -0.01496  0.00796
-HA Q  0.00711 -0.02142  0.00734  0.00971
-HA R -0.00472 -0.01414  0.00966  0.01180
-HA S  0.01572  0.02791  0.03762  0.00133
-HA T  0.01714  0.06590  0.03085  0.00143
-HA V  0.00777  0.01505  0.02525  0.00659
-HA W -0.06818 -0.08412 -0.09386 -0.06072
-HA Y -0.02701 -0.05585 -0.03243 -0.02987
-HB A  0.01473  0.01843  0.01428  0.00451
-HB C  0.01180  0.03340  0.03081  0.00169
-HB D  0.01786  0.01626  0.02221  0.01030
-HB E  0.01796  0.01820  0.00835 -0.00045
-HB F -0.04867 -0.09154 -0.04858 -0.00164
-HB G  0.01718  0.03852  0.01043  0.00051
-HB H -0.00817 -0.04557 -0.00820  0.00855
-HB I  0.00446  0.00111  0.00049 -0.00283
-HB K  0.01570  0.01156  0.00771  0.00646
-HB L  0.00700  0.01236  0.00880  0.00150
-HB M  0.01607  0.02294  0.01385 -0.00038
-HB N  0.01893  0.01561  0.02760  0.01215
-HB P -0.01199 -0.02752  0.00891 -0.00033
-HB Q  0.01636  0.01861  0.01177 -0.00099
-HB R  0.01324  0.01526  0.01082  0.00378
-HB S  0.01859  0.03487  0.02890 -0.00477
-HB T  0.01624  0.04073  0.01936 -0.00348
-HB V  0.00380  0.00271 -0.00144 -0.00315
-HB W -0.09045 -0.06895 -0.10934 -0.01948
-HB Y -0.05069 -0.06698 -0.05666 -0.01193'''
-
-tabletermcorrs='''C n -0.15238
-C c -0.90166
-CB n 0.12064
-CB c 0.06854
-CA n -0.04616
-CA c -0.06680
-N n 0.347176
-N c 0.619141
-H n 0.156786
-H c 0.023189
-HB n 0.0052692
-HB c 0.0310875
-HA n 0.048624
-HA c 0.042019'''
-
-def initcorneis():
-    datc=string.split(tablenei,'\n')
-    dct={}
-    for i in range(20*7):
-        vals=string.split(datc[i])
-        atn=vals[0]
-        aai=vals[1]
-        if not aai in dct:dct[aai]={}
-        dct[aai][atn]=[eval(vals[2+j]) for j in range(4)]
-    datc=string.split(tabletermcorrs,'\n')
-    for i in range(len(datc)):
-        vals=string.split(datc[i])
-        atn=vals[0]
-        term=vals[1]
-        if not term in dct:dct[term]={}
-        if term=='n':  dct['n'][atn]=[None,None,None,eval(vals[-1])]
-        elif term=='c':dct['c'][atn]=[eval(vals[-1]),None,None,None]
-    return dct
-        
-
-tabletempk='''aa  CA   CB   C     N    H    HA
-A  -2.2  4.7 -7.1  -5.3 -9.0  0.7
-C  -0.9  1.3 -2.6  -8.2 -7.0  0.0
-D   2.8  6.5 -4.8  -3.9 -6.2 -0.1
-E   0.9  4.6 -4.9  -3.7 -6.5  0.3
-F  -4.7  2.4 -6.9 -11.2 -7.5  0.4
-G   3.3  0.0 -3.2  -6.2 -9.1  0.0
-H   7.8 15.5  3.1   3.3 -7.8  0.4
-I  -2.0  4.6 -8.7 -12.7 -7.8  0.4
-K  -0.8  2.4 -7.1  -7.6 -7.5  0.4
-L   1.7  4.9 -8.2  -2.9 -7.5  0.1
-M   4.1  9.4 -8.2  -6.2 -7.1 -0.5
-N   2.8  5.1 -6.1  -3.3 -7.0 -2.9
-P   1.1 -0.2 -4.0   0.0  0.0  0.0
-Q   2.3  3.6 -5.7  -6.5 -7.2  0.3
-R  -1.4  3.5 -6.9  -5.3 -7.1  0.4
-S  -1.7  4.4 -4.7  -3.8 -7.6  0.1
-T   0.0  2.2 -5.2  -6.7 -7.3  0.0
-V  -2.8  2.5 -8.1 -14.2 -7.6  0.5
-W  -2.7  3.1 -7.9 -10.1 -7.8  0.4
-Y  -5.0  2.9 -7.7 -12.0 -7.7  0.5'''
-
-def gettempkoeff():
-    datc=string.split(tabletempk,'\n')
-    buf=[string.split(lin) for lin in datc]
-    headers=buf[0][1:]
-    dct={}
-    for atn in headers:
-        dct[atn]={}
-    for lin in buf[1:]:
-        aa=lin[0]
-        for j,atn in enumerate(headers):
-            dct[atn][aa]=eval(lin[1+j])
-    return dct
-
-tablecombdevs='''C -1 G r xrGxx  0.2742  1.4856
- C -1 G - x-Gxx  0.0522  0.2827
- C -1 P P xPPxx -0.0822  0.4450
- C -1 P r xrPxx  0.2640  1.4303
- C -1 r P xPrxx -0.1027  0.5566
- C -1 + P xP+xx  0.0714  0.3866
- C -1 - - x--xx -0.0501  0.2712
- C -1 p r xrpxx  0.0582  0.3151
- C  1 G r xxGrx  0.0730  0.3955
- C  1 P a xxPax -0.0981  0.5317
- C  1 P + xxP+x -0.0577  0.3128
- C  1 P p xxPpx -0.0619  0.3356
- C  1 r r xxrrx -0.1858  1.0064
- C  1 r a xxrax -0.1888  1.0226
- C  1 r + xxr+x -0.1805  0.9779
- C  1 r - xxr-x -0.1756  0.9512
- C  1 r p xxrpx -0.1208  0.6544
- C  1 + P xx+Px -0.0533  0.2886
- C  1 - P xx-Px  0.1867  1.0115
- C  1 p P xxpPx  0.2321  1.2574
- C -2 G r rxGxx -0.1457  0.7892
- C -2 r p pxrxx  0.0555  0.3008
- C  2 P P xxPxP  0.1007  0.5455
- C  2 P - xxPx-  0.0634  0.3433
- C  2 r P xxrxP -0.1447  0.7841
- C  2 a r xxaxr -0.1488  0.8061
- C  2 a - xxax- -0.0093  0.0506
- C  2 + G xx+xG -0.0394  0.2132
- C  2 + P xx+xP  0.1016  0.5502
- C  2 + a xx+xa  0.0299  0.1622
- C  2 + + xx+x+  0.0427  0.2312
- C  2 - a xx-xa  0.0611  0.3308
- C  2 p P xxpxP -0.0753  0.4078
-CA -1 G P xPGxx -0.0641  0.3233
-CA -1 G r xrGxx  0.2107  1.0630
-CA -1 P P xPPxx -0.2042  1.0303
-CA -1 P p xpPxx  0.0444  0.2240
-CA -1 r G xGrxx  0.2030  1.0241
-CA -1 r + x+rxx -0.0811  0.4093
-CA -1 - P xP-xx  0.0744  0.3755
-CA -1 - - x--xx -0.0263  0.1326
-CA -1 p p xppxx -0.0094  0.0475
-CA  1 G P xxGPx  1.3044  6.5813
-CA  1 G - xxG-x -0.0632  0.3188
-CA  1 P G xxPGx  0.2642  1.3329
-CA  1 P P xxPPx  0.3025  1.5262
-CA  1 P r xxPrx  0.1455  0.7343
-CA  1 P - xxP-x  0.1188  0.5994
-CA  1 P p xxPpx  0.1201  0.6062
-CA  1 r P xxrPx -0.1958  0.9878
-CA  1 r - xxr-x -0.0931  0.4696
-CA  1 a P xxaPx -0.1428  0.7204
-CA  1 a - xxa-x -0.0262  0.1324
-CA  1 a p xxapx  0.0392  0.1977
-CA  1 + P xx+Px -0.1059  0.5344
-CA  1 + a xx+ax -0.0377  0.1901
-CA  1 + + xx++x -0.0595  0.3001
-CA  1 - P xx-Px -0.1156  0.5831
-CA  1 - + xx-+x  0.0316  0.1593
-CA  1 - p xx-px  0.0612  0.3090
-CA  1 p r xxprx -0.0511  0.2576
-CA -2 P - -xPxx -0.1028  0.5185
-CA -2 r r rxrxx  0.1933  0.9752
-CA -2 - G Gx-xx  0.0559  0.2818
-CA -2 - p px-xx  0.0391  0.1973
-CA -2 p a axpxx -0.0293  0.1479
-CA -2 p + +xpxx -0.0173  0.0873
-CA  2 G - xxGx-  0.0357  0.1802
-CA  2 + G xx+xG -0.0315  0.1591
-CA  2 - P xx-xP  0.0426  0.2150
-CA  2 - r xx-xr  0.0784  0.3954
-CA  2 - a xx-xa  0.1084  0.5467
-CA  2 - - xx-x-  0.0836  0.4216
-CA  2 p P xxpxP  0.0685  0.3456
-CA  2 p - xxpx- -0.0481  0.2428
-CB -1 P r xrPxx -0.2678  1.7345
-CB -1 P p xpPxx  0.0355  0.2300
-CB -1 r P xPrxx -0.1137  0.7367
-CB -1 a p xpaxx  0.0249  0.1613
-CB -1 + - x-+xx -0.0762  0.4935
-CB -1 - P xP-xx -0.0889  0.5757
-CB -1 - r xr-xx -0.0533  0.3451
-CB -1 - - x--xx  0.0496  0.3215
-CB -1 - p xp-xx -0.0148  0.0960
-CB -1 p P xPpxx  0.0119  0.0768
-CB -1 p r xrpxx -0.0673  0.4358
-CB  1 P G xxPGx -0.0522  0.3379
-CB  1 P P xxPPx -0.8458  5.4779
-CB  1 P r xxPrx -0.1573  1.0187
-CB  1 r r xxrrx  0.1634  1.0581
-CB  1 a G xxaGx -0.0393  0.2544
-CB  1 a r xxarx  0.0274  0.1777
-CB  1 a - xxa-x  0.0394  0.2553
-CB  1 a p xxapx  0.0149  0.0968
-CB  1 + G xx+Gx -0.0784  0.5076
-CB  1 + P xx+Px -0.1170  0.7580
-CB  1 - P xx-Px -0.0913  0.5912
-CB  1 - - xx--x  0.0284  0.1838
-CB  1 p P xxpPx  0.0880  0.5697
-CB  1 p p xxppx -0.0113  0.0733
-CB -2 P - -xPxx  0.0389  0.2521
-CB -2 P p pxPxx  0.0365  0.2362
-CB -2 r + +xrxx  0.0809  0.5242
-CB -2 a - -xaxx -0.0452  0.2927
-CB -2 + - -x+xx -0.0651  0.4218
-CB -2 - G Gx-xx -0.0883  0.5717
-CB -2 p G Gxpxx  0.0378  0.2445
-CB -2 p p pxpxx  0.0207  0.1341
-CB  2 r G xxrxG -0.0362  0.2344
-CB  2 r - xxrx- -0.0219  0.1419
-CB  2 a - xxax- -0.0298  0.1929
-CB  2 + p xx+xp  0.0189  0.1223
-CB  2 - - xx-x- -0.0525  0.3400
- N -1 G P xPGxx  0.2411  0.5105
- N -1 G + x+Gxx -0.1773  0.3754
- N -1 G - x-Gxx  0.1905  0.4035
- N -1 P P xPPxx -0.9177  1.9434
- N -1 P p xpPxx  0.2609  0.5525
- N -1 r G xGrxx  0.2417  0.5119
- N -1 r a xarxx -0.0139  0.0295
- N -1 r + x+rxx -0.4122  0.8729
- N -1 r p xprxx  0.1440  0.3049
- N -1 a G xGaxx -0.5177  1.0963
- N -1 a r xraxx  0.0890  0.1885
- N -1 a a xaaxx  0.1393  0.2950
- N -1 a p xpaxx -0.0825  0.1747
- N -1 + G xG+xx -0.4908  1.0394
- N -1 + a xa+xx  0.1709  0.3619
- N -1 + + x++xx  0.1868  0.3955
- N -1 + - x-+xx -0.0951  0.2014
- N -1 - P xP-xx -0.3027  0.6410
- N -1 - r xr-xx -0.1670  0.3537
- N -1 - + x+-xx -0.3501  0.7414
- N -1 - - x--xx  0.1266  0.2681
- N -1 p G xGpxx -0.1707  0.3614
- N -1 p - x-pxx  0.0011  0.0023
- N  1 G G xxGGx  0.2555  0.5412
- N  1 G P xxGPx -0.9725  2.0595
- N  1 G r xxGrx  0.0165  0.0349
- N  1 G p xxGpx  0.0703  0.1489
- N  1 r a xxrax -0.0237  0.0503
- N  1 a r xxarx -0.1816  0.3845
- N  1 a - xxa-x -0.1050  0.2224
- N  1 a p xxapx -0.1196  0.2533
- N  1 - r xx-rx -0.1762  0.3731
- N  1 - a xx-ax  0.0006  0.0013
- N  1 p P xxpPx  0.2797  0.5923
- N  1 p a xxpax  0.0938  0.1986
- N  1 p + xxp+x  0.1359  0.2878
- N -2 G r rxGxx -0.5140  1.0885
- N -2 G - -xGxx -0.0639  0.1354
- N -2 P P PxPxx -0.4215  0.8927
- N -2 r P Pxrxx -0.3696  0.7828
- N -2 r p pxrxx -0.1937  0.4101
- N -2 a - -xaxx -0.0351  0.0743
- N -2 a p pxaxx -0.1031  0.2183
- N -2 - G Gx-xx -0.2152  0.4558
- N -2 - P Px-xx -0.1375  0.2912
- N -2 - p px-xx -0.1081  0.2290
- N -2 p P Pxpxx -0.1489  0.3154
- N -2 p - -xpxx  0.0952  0.2015
- N  2 G - xxGx-  0.1160  0.2457
- N  2 r p xxrxp -0.1288  0.2728
- N  2 a P xxaxP  0.1632  0.3456
- N  2 + + xx+x+ -0.0106  0.0226
- N  2 + - xx+x-  0.0389  0.0824
- N  2 - a xx-xa -0.0815  0.1726
- N  2 p G xxpxG -0.0779  0.1649
- N  2 p p xxpxp -0.0683  0.1447
- H -1 G P xPGxx -0.0317  0.4730
- H -1 G r xrGxx  0.0549  0.8186
- H -1 G + x+Gxx -0.0192  0.2867
- H -1 G - x-Gxx  0.0138  0.2055
- H -1 r P xPrxx -0.0964  1.4367
- H -1 r - x-rxx -0.0245  0.3648
- H -1 a G xGaxx -0.0290  0.4320
- H -1 a a xaaxx  0.0063  0.0944
- H -1 + G xG+xx -0.0615  0.9168
- H -1 + r xr+xx -0.0480  0.7153
- H -1 + - x-+xx -0.0203  0.3030
- H -1 - + x+-xx -0.0232  0.3455
- H -1 p G xGpxx -0.0028  0.0411
- H -1 p P xPpxx -0.0121  0.1805
- H  1 G P xxGPx -0.1418  2.1144
- H  1 G r xxGrx  0.0236  0.3520
- H  1 G a xxGax  0.0173  0.2580
- H  1 a - xxa-x  0.0091  0.1349
- H  1 + P xx+Px -0.0422  0.6290
- H  1 + p xx+px  0.0191  0.2842
- H  1 - P xx-Px -0.0474  0.7065
- H  1 - a xx-ax  0.0102  0.1515
- H -2 G G GxGxx  0.0169  0.2517
- H -2 G r rxGxx -0.3503  5.2220
- H -2 a P Pxaxx  0.0216  0.3227
- H -2 a - -xaxx -0.0276  0.4118
- H -2 + - -x+xx -0.0260  0.3874
- H -2 - G Gx-xx  0.0273  0.4073
- H -2 - a ax-xx -0.0161  0.2400
- H -2 - - -x-xx -0.0285  0.4255
- H -2 p P Pxpxx -0.0101  0.1503
- H -2 p a axpxx -0.0157  0.2343
- H -2 p + +xpxx -0.0122  0.1815
- H -2 p p pxpxx  0.0107  0.1601
- H  2 G G xxGxG -0.0190  0.2826
- H  2 r G xxrxG  0.0472  0.7036
- H  2 r P xxrxP  0.0337  0.5027
- H  2 a + xxax+ -0.0159  0.2376
- H  2 + G xx+xG  0.0113  0.1685
- H  2 + r xx+xr -0.0307  0.4575
- H  2 - P xx-xP -0.0088  0.1318
-HA -1 P P xPPxx  0.0307  1.1685
-HA -1 P r xrPxx  0.0621  2.3592
-HA -1 r G xGrxx -0.0371  1.4092
-HA -1 r + x+rxx  0.0125  0.4733
-HA -1 r p xprxx -0.0199  0.7569
-HA -1 a G xGaxx  0.0073  0.2779
-HA -1 a a xaaxx  0.0044  0.1683
-HA -1 - G xG-xx  0.0116  0.4409
-HA -1 - r xr-xx  0.0228  0.8679
-HA -1 - p xp-xx  0.0074  0.2828
-HA  1 G G xxGGx  0.0175  0.6636
-HA  1 G - xxG-x  0.0107  0.4081
-HA  1 P a xxPax  0.0089  0.3369
-HA  1 - r xx-rx  0.0113  0.4291
-HA -2 G G GxGxx -0.0154  0.5847
-HA -2 P - -xPxx  0.0136  0.5179
-HA -2 r G Gxrxx -0.0159  0.6045
-HA -2 + + +x+xx -0.0137  0.5190
-HA -2 p - -xpxx -0.0068  0.2592
-HA -2 p p pxpxx  0.0046  0.1763
-HB -1 P r xrPxx  0.0460  2.1365
-HB -1 a - x-axx  0.0076  0.3551
-HB -1 + - x-+xx  0.0110  0.5122
-HB -1 - r xr-xx  0.0233  1.0819
-HB  1 a P xxaPx  0.0287  1.3310
-HB  1 + P xx+Px  0.0324  1.5056
-HB  1 + r xx+rx -0.0231  1.0709
-HB  1 p r xxprx  0.0077  0.3586
-HB  1 p + xxp+x -0.0074  0.3426
-HB -2 a P Pxaxx -0.0026  0.1192
-HB -2 a r rxaxx -0.0098  0.4559
-HB -2 - - -x-xx  0.0016  0.0751
-HB  2 P r xxPxr -0.0595  2.7608
-HB  2 P + xxPx+ -0.0145  0.6744
-HB  2 P - xxPx-  0.0107  0.4976
-HB  2 a + xxax+ -0.0015  0.0691
-HB  2 p r xxpxr  0.0262  1.2178'''
-
-tablephshifts='''
-D (pKa 3.86)
-D H  8.55 8.38 -0.17 0.02 -0.03
-D HA 4.78 4.61 -0.17 0.01 -0.01
-D HB 2.93 2.70 -0.23
-D CA 52.9 54.3 1.4 0.0 0.1
-D CB 38.0 41.1 3.0
-D CG 177.1 180.3 3.2
-D C  175.8 176.9 1.1 -0.2 0.4
-D N  118.7 120.2 1.5  0.3 0.1
-D Np na na 0.1
-E (pKa 4.34)
-E H  8.45 8.57  0.12 0.00 0.02
-E HA 4.39 4.29 -0.10 0.01 0.00
-E HB 2.08 2.02 -0.06
-E HG 2.49 2.27 -0.22
-E CA 56.0 56.9 1.0 0.0 0.0
-E CB 28.5 30.0 1.5
-E CG 32.7 36.1 3.5
-E CD 179.7 183.8 4.1
-E C  176.5 177.0 0.6  0.1 0.1 
-E N  119.9 120.9 1.0  0.2 0.1
-E Np na na 0.1
-H (pKa 6.45)
-H H  8.55 8.35 -0.2  -0.01  0.0
-H HA 4.75 4.59 -0.2  -0.01 -0.06
-H HB 3.25 3.08 -0.17
-H HD2 7.30 6.97 -0.33
-H HE1 8.60 7.68 -0.92
-H CA 55.1 56.7 1.6 -0.1 0.1
-H CB 28.9 31.3 2.4
-H CG 131.0 135.3 4.2
-H CD2 120.3 120.0 -0.3
-H CE1 136.6 139.2 2.6
-H C 174.8 176.2 1.5  0.0 0.6
-H N 117.9 119.7 1.8  0.3 0.5
-H Np na na 0.5
-H ND1 175.8 231.3 56
-H NE2 173.1 181.1 8
-C (pKa 8.49)
-C H 8.49 8.49 0.0
-C HA 4.56 4.28 -0.28 -0.01 -0.01
-C HB 2.97 2.88 -0.09
-C CA 58.5 60.6 2.1 0.0 0.1
-C CB 28.0 29.7 1.7
-C C 175.0 176.9 1.9 -0.4 0.5
-C N 118.7 122.2 3.6  0.4 0.6
-C Np na na 0.6
-Y (pKa 9.76)
-Y H  8.16 8.16 0.0
-Y HA 4.55 4.49 -0.06
-Y HB 3.02 2.94 -0.08
-Y HD 7.14 6.97 -0.17
-Y HE 6.85 6.57 -0.28
-Y CA 58.0 58.2 0.3
-Y CB 38.6 38.7 0.1
-Y CG 130.5 123.8 -6.7
-Y CD 133.3 133.2 -0.1
-Y CE 118.4 121.7 3.3
-Y CZ 157.0 167.4 10.4
-Y C 176.3 176.7 0.4
-Y N 120.1 120.7 0.6
-K (pKa 10.34)
-K H  8.4  8.4  0.0
-K HA 4.34 4.30 -0.04
-K HB 1.82 1.78 -0.04
-K HG 1.44 1.36 -0.08
-K HD 1.68 1.44 -0.24
-K HE 3.00 2.60 -0.40
-K CA 56.4 56.9 0.4
-K CB 32.8 33.2 0.3
-K CG 24.7 25.0 0.4
-K CD 28.9 33.9 5.0
-K CE 42.1 43.1 1.0
-K C 177.0 177.5 0.5
-K N 121.0 121.7 0.7
-K Np na na 0.1
-R (pKa 13.9)
-R H  7.81 7.81 0.0
-R HA 3.26 3.19 -0.07
-R HB 1.60 1.55 0.05
-R HG 1.60 1.55 0.05
-R HD 3.19 3.00 -0.19
-R CA 58.4 58.6 0.2
-R CB 34.4 35.2 0.9
-R CG 27.2 28.1 1.0
-R CD 43.8 44.3 0.5
-R CZ 159.6 163.5 4.0
-R C 185.8 186.1 0.2
-R N 122.4 122.8 0.4
-R NE 85.6 91.5 5.9
-R NG 71.2 93.2 22'''
-
-def initcorrcomb():
-    datc=string.split(tablecombdevs,'\n')
-    buf=[string.split(lin) for lin in datc]
-    dct={}
-    for lin in buf:
-        atn=lin[0]
-        if not atn in dct:dct[atn]={}
-        neipos=string.atoi(lin[1])
-        centgroup=lin[2]
-        neigroup= lin[3]
-        key=(neipos,centgroup,neigroup)#(k,l,m)
-        segment=lin[4]
-        dct[atn][segment]=key,eval(lin[-2])
-    return dct
-        
-TEMPCORRS=gettempkoeff()
-##dct[atn][aa]=eval(lin[1+j])
-CENTSHIFTS=initcorcents()
-##dct[aai][atnj]=eval(vals[1+j])
-NEICORRS =initcorneis()
-##dct[aai][atn]=[eval(vals[2+j]) for j in range(4)]
-COMBCORRS=initcorrcomb()
-##dct[atn][segment]=key,eval(lin[-1])
-
-def predPentShift(pent,atn):
-    aac=pent[2]
-    sh=CENTSHIFTS[aac][atn]
-    allneipos=[2,1,-1,-2]
-    for i in range(4):
-        aai=pent[2+allneipos[i]]
-        if aai in NEICORRS:
-            corr=NEICORRS[aai][atn][i]
-            sh+=corr
-    groups=['G','P','FYW','LIVMCA','KR','DE']##,'NQSTHncX']
-    labels='GPra+-p' #(Gly,Pro,Arom,Aliph,pos,neg,polar)
-    grstr=''
-    for i in range(5):
-        aai=pent[i]
-        found=False
-        for j,gr in enumerate(groups):
-            if aai in gr:
-                grstr+=labels[j]
-                found=True
-                break
-        if not found:grstr+='p'#polar
-    centgr=grstr[2]
-    for segm in COMBCORRS[atn]:
-        key,combval=COMBCORRS[atn][segm]
-        neipos,centgroup,neigroup=key#(k,l,m)
-        if centgroup==centgr and grstr[2+neipos]==neigroup:
-            if (centgr,neigroup)<>('p','p') or pent[2] in 'ST':
-                #pp comb only used when center is Ser or Thr!
-                sh+=combval
-    return sh
-        
-def gettempcorr(aai,atn,tempdct,temp):
-    return tempdct[atn][aai]/1000*(temp-298)
-
-def get_phshifts():
-    datc=string.split(tablephshifts,'\n')
-    buf=[string.split(lin) for lin in datc]
-    dct={}
-    na=None
-    for lin in buf:
-        if len(lin)>3:
-            resn=lin[0]
-            atn=lin[1]
-            sh0=eval(lin[2])
-            sh1=eval(lin[3])
-            shd=eval(lin[4])
-            if not resn in dct:dct[resn]={}
-            dct[resn][atn]=shd
-            if len(lin)>6:#neighbor data
-                for n in range(2):
-                    shdn=eval(lin[5+n])
-                    nresn=resn+'ps'[n]
-                    if not nresn in dct:dct[nresn]={}
-                    dct[nresn][atn]=shdn
-    return dct
-
-def initfilcsv(filename):
-    file=open(filename,'r')
-    buffer=file.readlines()
-    file.close()
-    for i in range(len(buffer)):
-        buffer[i]=string.split(buffer[i][:-1],',')
-    return buffer
-
-def write_csv_pkaoutput(pkadct,seq,temperature,ion):
-        seq=seq[:min(150,len(seq))]
-        name='outpepKalc_%s_T%6.2f_I%4.2f.csv'%(seq,temperature,ion)
-        out=open(name,'w')
-        out.write('Site,pKa value,pKa shift,Hill coefficient\n')
-        for i in pkadct:
-            pKa,nH,resi=pkadct[i]
-            reskey=resi+str(i+1)
-            diff=pKa-pK0[resi]
-            out.write('%s,%5.3f,%5.3f,%5.3f\n'%(reskey,pKa,diff,nH))
-        out.close()
-
-def read_csv_pkaoutput(seq,temperature,ion,name=None):
-    seq=seq[:min(150,len(seq))]
-    if VERB:print 'reading csv',name
-    if name==None:name='outpepKalc_%s_T%6.2f_I%4.2f.csv'%(seq,temperature,ion)
-    try:out=open(name,'r')
-    except IOError:return None
-    buf=initfilcsv(name)
-    for lnum,data in enumerate(buf):
-        if len(data)>0 and data[0]=='Site':break
-    pkadct={}
-    for data in buf[lnum+1:]:
-        reskey,pKa,diff,nH=data
-        i=string.atoi(reskey[1:])-1
-        resi=reskey[0]
-        pKaval=eval(pKa)
-        nHval=eval(nH)
-        pkadct[i]=pKaval,nHval,resi
-    return pkadct
-
-def getphcorrs(seq,temperature,pH,ion,pkacsvfilename=None):
-    bbatns=['C','CA','CB','HA','H','N','HB']
-    dct=get_phshifts()
-    Ion=max(0.0001,ion)
-    pkadct=read_csv_pkaoutput(seq,temperature,ion,pkacsvfilename)
-    if pkadct==None:
-        pkadct=calc_pkas_from_seq('n'+seq+'c',temperature,Ion)
-        write_csv_pkaoutput(pkadct,seq,temperature,ion)
-    outdct={}
-    for i in pkadct:
-        if VERB:print 'pkares: %6.3f %6.3f %1s'%pkadct[i],i
-        pKa,nH,resi=pkadct[i]
-        frac =fun(pH,pKa,nH)
-        frac7=fun(7.0,pK0[resi],nH)
-        if resi in 'nc':jump=0.0#so far
-        else:
-            for atn in bbatns:
-                if not atn in outdct:outdct[atn]={}
-                if VERB:print 'data:',atn,pKa,nH,resi,i,atn,pH
-                dctresi=dct[resi]
-                try:
-                    delta=dctresi[atn]
-                    jump =frac *delta
-                    jump7=frac7*delta
-                    key=(resi,atn)
-                except KeyError:
-                    ##if not (resi in 'RKCY' and atn=='H') and not (resi == 'R' and atn=='N'):
-                    print 'warning no key:',resi,i,atn
-                    delta=999;jump=999;jump7=999
-                if delta<99:
-                    jumpdelta=jump-jump7
-                    if not i in outdct[atn]:outdct[atn][i]=[resi,jumpdelta]
-                    else:
-                        outdct[atn][i][0]=resi
-                        outdct[atn][i][1]+=jumpdelta
-                    if VERB:print '%3s %5.2f %6.4f %s %3d %5s %8.5f %8.5f %4.2f'%(atn,pKa,nH,resi,i,atn,jump,jump7,pH)
-                    if resi+'p' in dct and atn in dct[resi+'p']:
-                        for n in range(2):
-                            ni=i+2*n-1
-                            ##if ni is somewhere in seq...
-                            nresi=resi+'ps'[n]
-                            ndelta=dct[nresi][atn]
-                            jump =frac *ndelta
-                            jump7=frac7*ndelta
-                            jumpdelta=jump-jump7
-                            if not ni in outdct[atn]:outdct[atn][ni]=[None,jumpdelta]
-                            else:outdct[atn][ni][1]+=jumpdelta
-    return outdct
-
-def getpredshifts(seq,temperature,pH,ion,usephcor=True,pkacsvfile=None,identifier=''):
-    tempdct=gettempkoeff()
-    bbatns =['C','CA','CB','HA','H','N','HB']
-    if usephcor:
-        phcorrs=getphcorrs(seq,temperature,pH,ion,pkacsvfile)
-    else:phcorrs={}
-    shiftdct={}
-    for i in range(1,len(seq)-1):
-        if seq[i] in AAstandard:#else: do nothing
-            res=str(i+1)
-            trip=seq[i-1]+seq[i]+seq[i+1]
-            phcorr=None
-            shiftdct[(i+1,seq[i])]={}
-            for at in bbatns:
-                if not (trip[1],at) in [('G','CB'),('G','HB'),('P','H')]:
-                    if i==1:
-                        pent='n'+     trip+seq[i+2]
-                    elif i==len(seq)-2:
-                        pent=seq[i-2]+trip+'c'
-                    else:
-                        pent=seq[i-2]+trip+seq[i+2]
-                    shp=predPentShift(pent,at)
-                    if shp<>None:
-                        if at<>'HB':shp+=gettempcorr(trip[1],at,tempdct,temperature)
-                        if at in phcorrs and i in phcorrs[at]:
-                            phdata=phcorrs[at][i]
-                            resi=phdata[0]
-                            ##assert resi==seq[i]
-                            if seq[i] in 'CDEHRKY' and resi<>seq[i]:
-                                print 'WARNING: residue mismatch',resi,seq[i],i,phdata,at
-                            phcorr=phdata[1]
-                            if abs(phcorr)<9.9:
-                                shp-=phcorr
-                        shiftdct[(i+1,seq[i])][at]=shp
-                        if VERB:print 'predictedshift: %5s %3d %1s %2s %8.4f'%(identifier,i,seq[i],at,shp),phcorr
-    return shiftdct
-
-def writeOutput(name,dct):
-        out=open(name,'w')
-        bbatns =['N','C','CA','CB','H','HA','HB']
-        out.write('#NUM AA   N ')
-        out.write(' %7s %7s %7s %7s %7s %7s\n'%tuple(bbatns[1:]))
-        reskeys=dct.keys();reskeys.sort()
-        for resnum,resn in reskeys:
-          shdct=dct[(resnum,resn)]
-          if len(shdct)>0:
-            out.write('%-4d %1s '%(resnum,resn))
-            for at in bbatns:
-              shp=0.0
-              if at in shdct:shp=shdct[at]
-              out.write(' %7.3f'%shp)
-          out.write('\n')
-        out.close()
-
-def main():
-    ##README##......
-    #requires: python2.x with numpy and scipy
-    #usage: potenci1_2.py seqstring pH temperature ionicstrength [pkacsvfile] > logfile
-    #optional filename in csv format contained predicted pKa values and Hill parameters,
-    #the format of the pkacsvfile must be the same as the output for pepKalc,
-    #only lines after "Site" is read. If this is not found no pH corrections are applied.
-    #Output: Table textfile in SHIFTY format (space separated)
-    #average of methylene protons are provided for Gly HA2/HA3 and HB2/HB3.
-    #NOTE:pH corrections is applied if pH is not 7.0
-    #NOTE:pKa predictions are stored locally and reloaded if the seq, temp and ion is the same.
-    #NOTE:at least 5 residues are required. Chemical shift predictions are not given for terminal residues.
-    #NOTE:change the value of VERB in the top of this script to have verbose logfile
-    args=sys.argv[1:]#first is scriptname
-    if len(args)<4:
-        print 'FAILED: please provide 4 arguments (exiting)' 
-        print 'usage: potenci1_2.py seqstring pH temperature ionicstrength [pkacsvfile] > logfile'
-        raise SystemExit
-    seq=args[0] #one unbroken line with single-letter amino acid labels
-    pH=string.atof(args[1])#e.g. 7.0
-    temperature=string.atof(args[2])#e.g. 298.0 / K
-    ion=string.atof(args[3])#e.g. 0.1 / M
-    pkacsvfile=None
-    if len(args)>4:pkacsvfile=args[4]
-    ##name='outPOTENCI_%s_T%6.2f_I%4.2f_pH%4.2f.txt'%(seq,temperature,ion,pH)
-    name='outPOTENCI_%s_T%6.2f_I%4.2f_pH%4.2f.txt'%(seq[:min(150,len(seq))],temperature,ion,pH)
-    usephcor = pH<6.99 or pH>7.01
-    if len(seq)<5:
-        print 'FAILED: at least 5 residues are required (exiting)' 
-        raise SystemExit
-    #------------- now ready to generate predicted shifts ---------------------
-    print 'predicting random coil chemical shift with POTENCI using:',seq,pH,temperature,ion,pkacsvfile
-    shiftdct=getpredshifts(seq,temperature,pH,ion,usephcor,pkacsvfile)
-    #------------- write output nicely is SHIFTY format -----------------------$
-    writeOutput(name,shiftdct)
-    print 'chemical shift succesfully predicted, see output:',name
-
-
-main()
diff --git a/trizod/potenci/potenci.py b/trizod/potenci/potenci.py
index ea7abce..858bba3 100644
--- a/trizod/potenci/potenci.py
+++ b/trizod/potenci/potenci.py
@@ -1,1084 +1,445 @@
-#!/bin/bash python3
+"""POTENCI — random coil chemical shift prediction for proteins.
 
-# version of the POTENCI script, adapted by haak@rostlab.org
-# original by fmulder@chem.au.dk
-# original taken from https://github.com/protein-nmr/POTENCI on 03.05.2023, commit 17dd2e6f3733c702323894697238c87e6723f934
-# original version (filename): pytenci1_3.py
+Adapted from https://github.com/protein-nmr/POTENCI (commit 17dd2e6).
+Original author: fmulder@chem.au.dk
+Adapted by: markus.haak@tum.de & tobias.senoner@tum.de
 
-import sys
-import string
-from scipy.special import erfc
-from scipy.optimize import curve_fit
-from scipy import sparse
-import numpy as np
-import pandas as pd
-import os
-##from matplotlib import pyplot as pl
-from trizod.potenci.constants import R, e, a, b, cutoff, ncycles, pK0
+Public API:
+    get_pred_shifts(seq, temperature, pH, ion, ...) -> dict
+"""
+
+import csv
 import logging
-import pkgutil
-from io import StringIO
-
-outer_matrices = []
-alltuples_ = []
-for smallN in range(0,6):
-    alltuples = np.array([[int(c) for c in np.binary_repr(i, smallN)] for i in range(2 ** (smallN))])
-    outerm = np.array([np.outer(c,c) for c in alltuples])
-    outer_matrices.append(outerm)
-    alltuples_.append(alltuples)
-
-def smallmatrixlimits(ires, cutoff, len):
-    ileft = max(1, ires - cutoff)
-    iright = min(ileft + 2 * cutoff, len)
-    if iright == len:
-        ileft = max(1, iright - 2 * cutoff)
-    return (ileft, iright)
-
-def smallmatrixpos(ires, cutoff, len):
-    resi = cutoff + 1
-    if ires < cutoff + 1:
-        resi = ires
-    if ires > len - cutoff:
-        resi = min(len, 2 * cutoff + 1) - (len - ires)
-    return resi
-
-def fun(pH, pK, nH):
-    #return (10 ** ( nH*(pK - pH) ) ) / (1. + (10 **( nH*(pK - pH) ) ) )
-    return 1. - 1. / ((10 ** ( nH*(pK - pH) ) ) + 1.) # identical
-
-def log_fun(pH, pK, nH):
-    return -np.log10(1 + 10**(nH*(pH - pK)))
-
-def W(r,Ion=0.1):
-    k = np.sqrt(Ion) / 3.08 #Ion=0.1 is default
-    x = k.astype(np.float64) * r.astype(np.float64) / np.sqrt(6)
-    i1 = 332.286 * np.sqrt(6 / np.pi)
-    i2_3 = erfc(x)
-    i2_2 = np.sqrt(np.pi) * x
-
-    i3 = (e * r)
-    i4 = np.exp(((x ** 2) - np.log(i3))) # always equal to np.exp(x ** 2) / (e * r), but intermediates are smaller
-    i4 = np.nan_to_num(i4) # to convert inf values to the largest possible value
-    return i1 * ((1 / i3) - np.nan_to_num(i4 * i2_2 * i2_3))
-
-def w2logp(x,T=293.15):
-    return x * 4181.2 / (R * T * np.log(10)) 
+import warnings
+from pathlib import Path
 
-def calc_pkas_from_seq(seq=None, T=293.15, Ion=0.1):
-    #pH range
-    pHs = np.arange(1.99, 10.01, 0.15)
+import numpy as np
+from scipy.optimize import OptimizeWarning, curve_fit
+from scipy.special import erfc
 
-    pos = np.array([i for i in range(len(seq)) if seq[i] in pK0.keys()])
-    N = pos.shape[0]
-    I = np.diag(np.ones(N))
-    sites = ''.join([seq[i] for i in pos])
-    neg = np.array([i for i in range(len(sites)) if sites[i] in 'DEYc'])
-    l = np.array([abs(pos - pos[i]) for i in range(N)])
-    d = a + np.sqrt(l) * b
+from trizod.potenci.constants import (
+    DIELECTRIC_WATER,
+    DISTANCE_SCALE,
+    GAS_CONSTANT,
+    MIN_CHARGE_DISTANCE,
+    PKA_FIT_CYCLES,
+    PKA_WINDOW_HALF,
+    REFERENCE_PKA,
+)
+
+logger = logging.getLogger("trizod.potenci")
+
+# ── Constants ────────────────────────────────────────────────────────────
+
+BB_ATOMS = ["C", "CA", "CB", "HA", "H", "N", "HB"]
+
+AA_STANDARD = "ACDEFGHIKLMNPQRSTVWY"
+
+# Atom/residue combinations that have no prediction
+_SKIP_ATOM_PAIRS = {("G", "CB"), ("G", "HB"), ("P", "H")}
+
+# ── Data loading ─────────────────────────────────────────────────────────
+
+_DATA_DIR = Path(__file__).resolve().parent / "data"
+
+
+def _load_csv(filename):
+    """Load a CSV file from the data directory."""
+    filepath = _DATA_DIR / filename
+    with filepath.open() as f:
+        return list(csv.DictReader(f))
+
+
+def _load_center_shifts():
+    result = {}
+    for row in _load_csv("centshifts.csv"):
+        aa = row["aa"]
+        result[aa] = {}
+        for atom in BB_ATOMS:
+            val = row[atom]
+            result[aa][atom] = None if val == "None" else float(val)
+    return result
+
+
+def _load_neighbor_corrs():
+    result = {}
+    for row in _load_csv("neicorrs.csv"):
+        atom = row["atn"]
+        aa = row["aa"]
+        if aa not in result:
+            result[aa] = {}
+        result[aa][atom] = [float(row[f"c{j}"]) for j in range(4)]
+    for row in _load_csv("termcorrs.csv"):
+        atom = row["atn"]
+        term = row["term"]
+        val = float(row["value"])
+        if term not in result:
+            result[term] = {}
+        if term == "n":
+            result["n"][atom] = [None, None, None, val]
+        elif term == "c":
+            result["c"][atom] = [val, None, None, None]
+    return result
+
+
+def _load_temp_coeffs():
+    rows = _load_csv("tempcoeffs.csv")
+    headers = [k for k in rows[0] if k != "aa"]
+    result = {}
+    for atom in headers:
+        result[atom] = {}
+    for row in rows:
+        aa = row["aa"]
+        for atom in headers:
+            result[atom][aa] = float(row[atom])
+    return result
+
+
+def _load_comb_devs():
+    result = {}
+    for row in _load_csv("combdevs.csv"):
+        atom = row["atn"]
+        if atom not in result:
+            result[atom] = {}
+        segment = row["segment"]
+        key = (int(row["neipos"]), row["centgroup"], row["neigroup"])
+        result[atom][segment] = key, float(row["value"])
+    return result
+
+
+def _load_ph_shifts():
+    rows = _load_csv("phshifts.csv")
+    result = {}
+    for row in rows:
+        res_name = row["resn"]
+        atom = row["atn"]
+        shift_delta = float(row["shd"])
+        if res_name not in result:
+            result[res_name] = {}
+        result[res_name][atom] = shift_delta
+        prev_nei = row["prev_nei"]
+        succ_nei = row["succ_nei"]
+        if prev_nei and succ_nei:
+            for nei_side, nei_val in enumerate([prev_nei, succ_nei]):
+                neighbor_key = res_name + "ps"[nei_side]
+                if neighbor_key not in result:
+                    result[neighbor_key] = {}
+                result[neighbor_key][atom] = float(nei_val)
+    return result
+
+
+# Module-level caches (loaded once at import time)
+TEMP_CORRS = _load_temp_coeffs()
+CENTER_SHIFTS = _load_center_shifts()
+NEIGHBOR_CORRS = _load_neighbor_corrs()
+COMB_CORRS = _load_comb_devs()
+PH_SHIFTS = _load_ph_shifts()
+
+# Amino acid → group lookup for combination corrections in pred_pent_shift()
+_AA_GROUP = {}
+for _group_aas, _group_label in zip(["G", "P", "FYW", "LIVMCA", "KR", "DE"], "GPra+-"):
+    for _aa in _group_aas:
+        _AA_GROUP[_aa] = _group_label
+
+# Neighbor position offsets relative to center (index 2) of pentamer
+_NEI_OFFSETS = [2, 1, -1, -2]
+
+# Pre-compute binary tuples and outer-product matrices for pKa calculation.
+# Window size ranges from 0..5; used as sliding window in calc_pkas_from_seq.
+_OUTER_MATRICES = []
+_ALL_TUPLES = []
+for _window_size in range(0, 6):
+    _tuples = np.array(
+        [
+            [int(c) for c in np.binary_repr(i, _window_size)]
+            for i in range(2**_window_size)
+        ]
+    )
+    _OUTER_MATRICES.append(np.array([np.outer(c, c) for c in _tuples]))
+    _ALL_TUPLES.append(_tuples)
+
+
+# ── Shift prediction helpers ─────────────────────────────────────────────
+
+
+def _get_temp_corr(aa, atom, temperature):
+    """Get temperature correction for a residue/atom at given temperature."""
+    return TEMP_CORRS[atom][aa] / 1000 * (temperature - 298)
+
+
+def pred_pent_shift(pentamer, atom):
+    """Predict chemical shift for a 5-residue window and atom type."""
+    center_aa = pentamer[2]
+    shift = CENTER_SHIFTS[center_aa][atom]
+    for offset_idx in range(4):
+        neighbor_aa = pentamer[2 + _NEI_OFFSETS[offset_idx]]
+        if neighbor_aa in NEIGHBOR_CORRS:
+            shift += NEIGHBOR_CORRS[neighbor_aa][atom][offset_idx]
+    group_str = "".join(_AA_GROUP.get(pentamer[pos], "p") for pos in range(5))
+    center_group = group_str[2]
+    for segment in COMB_CORRS[atom]:
+        key, comb_value = COMB_CORRS[atom][segment]
+        nei_pos, expected_center, nei_group = key
+        if (
+            expected_center == center_group
+            and group_str[2 + nei_pos] == nei_group
+            and ((center_group, nei_group) != ("p", "p") or pentamer[2] in "ST")
+        ):
+            # pp combination only used when center is Ser or Thr
+            shift += comb_value
+    return shift
+
+
+def _build_pentamer(seq, pos):
+    """Build the 5-residue context window for position pos (1-based interior)."""
+    triplet = seq[pos - 1] + seq[pos] + seq[pos + 1]
+    if pos == 1 and pos == len(seq) - 2:
+        return triplet, "n" + triplet + "c"
+    elif pos == 1:
+        return triplet, "n" + triplet + seq[pos + 2]
+    elif pos == len(seq) - 2:
+        return triplet, seq[pos - 2] + triplet + "c"
+    else:
+        return triplet, seq[pos - 2] + triplet + seq[pos + 2]
 
-    tmp = W(d,Ion)
-    tmp[I == 1] = 0
 
-    ww = w2logp(tmp,T) / 2
+# ── pKa prediction (Debye-Hückel + iterative fitting) ───────────────────
 
-    chargesempty = np.zeros(pos.shape[0])
-    if len(neg): chargesempty[neg] = -1
 
-    pK0s = np.array([pK0[c] for c in sites])
-    nH0s = np.array([0.9 for c in sites])
+def _titration_fraction(pH, pK, nH):
+    """Henderson-Hasselbalch titration fraction (used by curve_fit)."""
+    with np.errstate(over="ignore"):
+        return 1.0 - 1.0 / ((10 ** (nH * (pK - pH))) + 1.0)
 
-    titration = np.zeros((N,len(pHs)))
 
-    smallN = min(2 * cutoff + 1, len(pos))  
-    alltuples = alltuples_[smallN]
-    outerm = outer_matrices[smallN]
-    gmatrix = [np.zeros((smallN, smallN)) for _ in range(len(pHs))]
+def _debye_huckel_W(distances, Ion=0.1):
+    """Electrostatic interaction energy (Debye-Hückel model)."""
+    kappa = np.sqrt(Ion) / 3.08
+    scaled_dist = kappa.astype(np.float64) * distances.astype(np.float64) / np.sqrt(6)
+    prefactor = 332.286 * np.sqrt(6 / np.pi)
+    erfc_val = erfc(scaled_dist)
+    sqrt_pi_x = np.sqrt(np.pi) * scaled_dist
 
-    #perform iterative fitting.........................
-    for icycle in range(ncycles):
-        ##print (icycle)
+    dielectric_r = DIELECTRIC_WATER * distances
+    # Log-space to avoid overflow: exp(x²)/(ε*r) = exp(x² - log(ε*r))
+    exp_term = np.exp((scaled_dist**2) - np.log(dielectric_r))
+    exp_term = np.nan_to_num(exp_term)
+    return prefactor * (
+        (1 / dielectric_r) - np.nan_to_num(exp_term * sqrt_pi_x * erfc_val)
+    )
 
-        if icycle == 0:
-            fractionhold = np.array([[fun(pHs[p], pK0s[i], nH0s[i]) for i in range(N)] for p in range(len(pHs))])
-        else:
-            fractionhold = titration.transpose()
-
-        for ires in range(1, N+1):
-            (ileft,iright) = smallmatrixlimits(ires, cutoff, N)
-            resi = smallmatrixpos(ires, cutoff, N)
-            fraction = fractionhold.copy()
-            fraction[:,ileft - 1:iright] = 0
-            charges = fraction + chargesempty
-            ww0 = 2 * (ww * np.expand_dims(charges, axis=1)).sum(axis=-1)
-            ww0 = (np.expand_dims(ww0, 1) * I) # array of diagonal matrices
-            gmatrixfull = (ww + ww0 + np.expand_dims(pHs,(1,2)) * I - np.diag(pK0s))
-            gmatrix = gmatrixfull[:, ileft - 1 : iright, ileft - 1 : iright]
-            
-            E = (10 ** -(np.expand_dims(gmatrix, axis=1) * outerm).sum(axis=(2,3)))#.sum(axis=-1)
-            E_all = E.sum(axis=-1)
-            E_sel = E[:,(alltuples[:,resi-1] == 1)].sum(axis=-1)
-            titration[ires-1] = E_sel/E_all
-        sol = np.array([curve_fit(fun, pHs, titration[p], [pK0s[p], nH0s[p]], maxfev=5000)[0] for p in range(len(pK0s))])
-        (pKs, nHs) = sol.transpose()
-
-    dct={}
-    for p,i in enumerate(pos):
-        dct[i-1]=(pKs[p],nHs[p],seq[i])
-  
-    return dct
-
-
-##--------------- POTENCI core code and data tables from here -----------------
-
-#AAstandard='ACDEFGHIKLMNPQRSTVY'
-AAstandard='ACDEFGHIKLMNPQRSTVWY'
-
-tablecent='''aa C CA CB N H HA HB
-A 177.44069  52.53002  19.21113 125.40155   8.20964   4.25629   1.31544
-C 174.33917  58.48976  28.06269 120.71212   8.29429   4.44261   2.85425
-D 176.02114  54.23920  41.18408 121.75726   8.28460   4.54836   2.60054
-E 176.19215  56.50755  30.30204 122.31578   8.35949   4.22124   1.92383
-F 175.42280  57.64849  39.55984 121.30500   8.10906   4.57507   3.00036
-G 173.83294  45.23929  None     110.09074   8.32746   3.91016   None
-H 175.00142  56.20256  30.60335 120.69141   8.27133   4.55872   3.03080
-I 175.88231  61.04925  38.68742 122.37586   8.06407   4.10574   1.78617
-K 176.22644  56.29413  33.02478 122.71282   8.24902   4.25873   1.71982
-L 177.06101  55.17464  42.29215 123.48611   8.14330   4.28545   1.54067
-M 175.90708  55.50643  32.83806 121.54592   8.24848   4.41483   1.97585
-N 174.94152  53.22822  38.87465 119.92746   8.37189   4.64308   2.72756
-P 176.67709  63.05232  32.03750 137.40612   None      4.36183   2.03318
-Q 175.63494  55.79861  29.44174 121.49225   8.30042   4.28006   1.97653
-R 175.92194  56.06785  30.81298 122.40365   8.26453   4.28372   1.73437
-S 174.31005  58.36048  63.82367 117.11419   8.25730   4.40101   3.80956
-T 174.27772  61.86928  69.80612 115.48126   8.11378   4.28923   4.15465
-V 175.80621  62.20156  32.77934 121.71912   8.06572   4.05841   1.99302
-W 175.92744  57.23836  29.56502 122.10991   7.97816   4.61061   3.18540
-Y 175.49651  57.82427  38.76184 121.43652   8.05749   4.51123   2.91782'''
-
-def initcorcents():
-    datc=tablecent.split('\n')
-    aas=datc[0].split()[1:]
-    dct={}
-    for i in range(20):
-        vals=datc[1+i].split()
-        aai=vals[0]
-        dct[aai]={}
-        for j in range(7):
-            atnj=aas[j]
-            dct[aai][atnj]=eval(vals[1+j])
-    return dct
-        
-
-tablenei='''C A  0.06131 -0.04544  0.14646  0.01305
- C C  0.04502  0.12592 -0.03407 -0.02654
- C D  0.08180 -0.08589  0.22948  0.10934
- C E  0.05388  0.22264  0.06962  0.01929
- C F -0.06286 -0.22396 -0.34442  0.00950
- C G  0.12772  0.72041  0.16048  0.01324
- C H -0.00628 -0.03355  0.13309 -0.03906
- C I -0.11709  0.06591 -0.06361 -0.03628
- C K  0.03368  0.15830  0.04518 -0.01576
- C L -0.03877  0.11608  0.02535  0.01976
- C M  0.04611  0.25233 -0.00747 -0.01624
- C N  0.07068 -0.06118  0.10077  0.05547
- C P -0.36018 -1.90872  0.16158 -0.05286
- C Q  0.10861  0.19878  0.01596 -0.01757
- C R  0.01933  0.13237  0.03606 -0.02468
- C S  0.09888  0.28691  0.07601  0.01379
- C T  0.05658  0.41659 -0.01103 -0.00114
- C V -0.11591  0.09565 -0.03355 -0.03368
- C W -0.01954 -0.19134 -0.37965  0.01582
- C Y -0.08380 -0.24519 -0.32700 -0.00577
-CA A  0.03588  0.03480 -0.00468 -0.00920
-CA C  0.02749  0.15742  0.14376  0.03681
-CA D -0.00751  0.12494  0.17354  0.14157
-CA E  0.00985  0.13936  0.03289 -0.00702
-CA F  0.01122  0.03732 -0.19586 -0.00377
-CA G -0.00885  0.23403 -0.03184 -0.01144
-CA H -0.02102  0.04621  0.03122 -0.02826
-CA I -0.00656  0.05965 -0.10588 -0.04372
-CA K  0.01817  0.11216 -0.00341 -0.02950
-CA L  0.04507  0.07829 -0.03526  0.00858
-CA M  0.07553  0.18840  0.04987 -0.01749
-CA N -0.00649  0.11842  0.18729  0.06401
-CA P -0.27536 -2.02189  0.01327 -0.08732
-CA Q  0.06365  0.15281  0.04575 -0.01356
-CA R  0.04338  0.11783  0.00345 -0.02873
-CA S  0.02867  0.07846  0.09443  0.02061
-CA T -0.01625  0.10626  0.03880 -0.00126
-CA V -0.04935  0.04248 -0.10195 -0.03778
-CA W  0.00434  0.16188 -0.08742  0.03983
-CA Y  0.02782  0.02846 -0.24750  0.00759
-CB A -0.00953  0.05704 -0.04838  0.00755
-CB C -0.00164  0.00760 -0.03293 -0.05613
-CB D  0.02064  0.09849 -0.08746 -0.06691
-CB E  0.01283  0.05404 -0.01342  0.02238
-CB F  0.01028  0.03363  0.18112  0.01493
-CB G -0.02758  0.04383  0.06071 -0.02639
-CB H -0.01760 -0.02367  0.00343  0.00415
-CB I  0.02783  0.01052  0.00641  0.05090
-CB K  0.00350  0.02852 -0.00408  0.01218
-CB L  0.01223 -0.02940 -0.07268  0.00884
-CB M -0.02925 -0.03912 -0.06587  0.03490
-CB N -0.02242  0.03403 -0.09759 -0.08018
-CB P  0.08431 -0.35696 -0.04680  0.05192
-CB Q -0.01649 -0.01016 -0.03663  0.01723
-CB R -0.01887  0.00618 -0.00385  0.02884
-CB S -0.00921  0.07096 -0.06338 -0.03707
-CB T  0.02601  0.04904 -0.01728  0.00781
-CB V  0.03068  0.06325  0.01928  0.05011
-CB W -0.07651 -0.11334  0.13806 -0.03339
-CB Y  0.00082  0.01466  0.18107 -0.01181
- N A  0.09963 -0.00873 -2.31666 -0.14051
- N C  0.11905 -0.01296  1.15573  0.01820
- N D  0.11783 -0.11817 -1.16322 -0.37601
- N E  0.10825 -0.00605 -0.41856  0.01187
- N F -0.12280 -0.27542  0.34635  0.09102
- N G  0.10365 -0.05667 -1.50346 -0.00146
- N H -0.04145 -0.26494  0.26356  0.18198
- N I -0.09249  0.12136  2.75071  0.40643
- N K -0.02472  0.07224 -0.07057  0.12261
- N L  0.01542 -0.12800 -0.85172 -0.15460
- N M -0.11266 -0.27311 -0.33192  0.09384
- N N -0.00295 -0.20562 -1.00652 -0.30971
- N P  0.03252  1.35296 -1.17173  0.06026
- N Q  0.00900 -0.09950 -0.07389  0.08415
- N R -0.07819  0.00802 -0.04821  0.08524
- N S  0.12057  0.02242  0.48924 -0.25423
- N T  0.04631  0.09935  1.02269  0.20228
- N V -0.03610  0.21959  2.42228  0.39686
- N W -0.15643 -0.19285  0.05515 -0.53172
- N Y -0.10497 -0.25228  0.46023  0.01399
- H A  0.01337 -0.00605 -0.04371 -0.02485
- H C  0.01324  0.05107  0.12857  0.00610
- H D  0.02859  0.02436 -0.06510  0.02085
- H E  0.02737  0.01790  0.03740  0.01969
- H F -0.02633 -0.08287 -0.11364 -0.03603
- H G  0.02753  0.05640 -0.10477  0.06876
- H H -0.00124 -0.02861  0.04126  0.10004
- H I -0.02258 -0.00929  0.07962  0.01880
- H K -0.00512 -0.00744  0.04443  0.03434
- H L -0.01088 -0.01230 -0.03640 -0.03719
- H M -0.01961 -0.00749 -0.00097  0.02041
- H N  0.01134  0.02121 -0.01837 -0.00629
- H P -0.01246  0.02956  0.13007 -0.00810
- H Q  0.00783  0.00751  0.05643  0.02413
- H R -0.00734  0.00546  0.07003  0.04051
- H S  0.02133  0.03964  0.04978 -0.03749
- H T  0.00976  0.06072  0.03531  0.01657
- H V -0.01267  0.00994  0.09630  0.03420
- H W -0.02348 -0.09617 -0.24207 -0.18741
- H Y -0.01881 -0.07345 -0.14345 -0.06721
-HA A  0.00350 -0.02371 -0.00654  0.00652
-HA C  0.00660  0.01073  0.01921  0.00919
-HA D  0.01717 -0.00854 -0.00802 -0.00597
-HA E  0.01090 -0.01091  0.00472  0.00790
-HA F -0.02271 -0.06316 -0.03057 -0.02350
-HA G  0.02155 -0.00151  0.02477  0.01526
-HA H -0.01132 -0.05617 -0.01514  0.01264
-HA I  0.00459  0.00571  0.02984  0.00416
-HA K  0.00492 -0.01788  0.00555  0.01259
-HA L -0.00599 -0.01558  0.00358  0.00167
-HA M  0.00100 -0.02037  0.00678  0.00930
-HA N  0.00651 -0.01499 -0.00361  0.00203
-HA P  0.01542  0.28350 -0.01496  0.00796
-HA Q  0.00711 -0.02142  0.00734  0.00971
-HA R -0.00472 -0.01414  0.00966  0.01180
-HA S  0.01572  0.02791  0.03762  0.00133
-HA T  0.01714  0.06590  0.03085  0.00143
-HA V  0.00777  0.01505  0.02525  0.00659
-HA W -0.06818 -0.08412 -0.09386 -0.06072
-HA Y -0.02701 -0.05585 -0.03243 -0.02987
-HB A  0.01473  0.01843  0.01428  0.00451
-HB C  0.01180  0.03340  0.03081  0.00169
-HB D  0.01786  0.01626  0.02221  0.01030
-HB E  0.01796  0.01820  0.00835 -0.00045
-HB F -0.04867 -0.09154 -0.04858 -0.00164
-HB G  0.01718  0.03852  0.01043  0.00051
-HB H -0.00817 -0.04557 -0.00820  0.00855
-HB I  0.00446  0.00111  0.00049 -0.00283
-HB K  0.01570  0.01156  0.00771  0.00646
-HB L  0.00700  0.01236  0.00880  0.00150
-HB M  0.01607  0.02294  0.01385 -0.00038
-HB N  0.01893  0.01561  0.02760  0.01215
-HB P -0.01199 -0.02752  0.00891 -0.00033
-HB Q  0.01636  0.01861  0.01177 -0.00099
-HB R  0.01324  0.01526  0.01082  0.00378
-HB S  0.01859  0.03487  0.02890 -0.00477
-HB T  0.01624  0.04073  0.01936 -0.00348
-HB V  0.00380  0.00271 -0.00144 -0.00315
-HB W -0.09045 -0.06895 -0.10934 -0.01948
-HB Y -0.05069 -0.06698 -0.05666 -0.01193'''
-
-tabletermcorrs='''C n -0.15238
-C c -0.90166
-CB n 0.12064
-CB c 0.06854
-CA n -0.04616
-CA c -0.06680
-N n 0.347176
-N c 0.619141
-H n 0.156786
-H c 0.023189
-HB n 0.0052692
-HB c 0.0310875
-HA n 0.048624
-HA c 0.042019'''
-
-def initcorneis():
-    datc=tablenei.split('\n')
-    dct={}
-    for i in range(20*7):
-        vals=datc[i].split()
-        atn=vals[0]
-        aai=vals[1]
-        if not aai in dct:dct[aai]={}
-        dct[aai][atn]=[eval(vals[2+j]) for j in range(4)]
-    datc=tabletermcorrs.split('\n')
-    for i in range(len(datc)):
-        vals=datc[i].split()
-        atn=vals[0]
-        term=vals[1]
-        if not term in dct:dct[term]={}
-        if term=='n':  dct['n'][atn]=[None,None,None,eval(vals[-1])]
-        elif term=='c':dct['c'][atn]=[eval(vals[-1]),None,None,None]
-    return dct
-        
-
-tabletempk='''aa  CA   CB   C     N    H    HA
-A  -2.2  4.7 -7.1  -5.3 -9.0  0.7
-C  -0.9  1.3 -2.6  -8.2 -7.0  0.0
-D   2.8  6.5 -4.8  -3.9 -6.2 -0.1
-E   0.9  4.6 -4.9  -3.7 -6.5  0.3
-F  -4.7  2.4 -6.9 -11.2 -7.5  0.4
-G   3.3  0.0 -3.2  -6.2 -9.1  0.0
-H   7.8 15.5  3.1   3.3 -7.8  0.4
-I  -2.0  4.6 -8.7 -12.7 -7.8  0.4
-K  -0.8  2.4 -7.1  -7.6 -7.5  0.4
-L   1.7  4.9 -8.2  -2.9 -7.5  0.1
-M   4.1  9.4 -8.2  -6.2 -7.1 -0.5
-N   2.8  5.1 -6.1  -3.3 -7.0 -2.9
-P   1.1 -0.2 -4.0   0.0  0.0  0.0
-Q   2.3  3.6 -5.7  -6.5 -7.2  0.3
-R  -1.4  3.5 -6.9  -5.3 -7.1  0.4
-S  -1.7  4.4 -4.7  -3.8 -7.6  0.1
-T   0.0  2.2 -5.2  -6.7 -7.3  0.0
-V  -2.8  2.5 -8.1 -14.2 -7.6  0.5
-W  -2.7  3.1 -7.9 -10.1 -7.8  0.4
-Y  -5.0  2.9 -7.7 -12.0 -7.7  0.5'''
-
-def gettempkoeff():
-    datc=tabletempk.split('\n')
-    buf=[lin.split() for lin in datc]
-    headers=buf[0][1:]
-    dct={}
-    for atn in headers:
-        dct[atn]={}
-    for lin in buf[1:]:
-        aa=lin[0]
-        for j,atn in enumerate(headers):
-            dct[atn][aa]=eval(lin[1+j])
-    return dct
-
-tablecombdevs='''C -1 G r xrGxx  0.2742  1.4856
- C -1 G - x-Gxx  0.0522  0.2827
- C -1 P P xPPxx -0.0822  0.4450
- C -1 P r xrPxx  0.2640  1.4303
- C -1 r P xPrxx -0.1027  0.5566
- C -1 + P xP+xx  0.0714  0.3866
- C -1 - - x--xx -0.0501  0.2712
- C -1 p r xrpxx  0.0582  0.3151
- C  1 G r xxGrx  0.0730  0.3955
- C  1 P a xxPax -0.0981  0.5317
- C  1 P + xxP+x -0.0577  0.3128
- C  1 P p xxPpx -0.0619  0.3356
- C  1 r r xxrrx -0.1858  1.0064
- C  1 r a xxrax -0.1888  1.0226
- C  1 r + xxr+x -0.1805  0.9779
- C  1 r - xxr-x -0.1756  0.9512
- C  1 r p xxrpx -0.1208  0.6544
- C  1 + P xx+Px -0.0533  0.2886
- C  1 - P xx-Px  0.1867  1.0115
- C  1 p P xxpPx  0.2321  1.2574
- C -2 G r rxGxx -0.1457  0.7892
- C -2 r p pxrxx  0.0555  0.3008
- C  2 P P xxPxP  0.1007  0.5455
- C  2 P - xxPx-  0.0634  0.3433
- C  2 r P xxrxP -0.1447  0.7841
- C  2 a r xxaxr -0.1488  0.8061
- C  2 a - xxax- -0.0093  0.0506
- C  2 + G xx+xG -0.0394  0.2132
- C  2 + P xx+xP  0.1016  0.5502
- C  2 + a xx+xa  0.0299  0.1622
- C  2 + + xx+x+  0.0427  0.2312
- C  2 - a xx-xa  0.0611  0.3308
- C  2 p P xxpxP -0.0753  0.4078
-CA -1 G P xPGxx -0.0641  0.3233
-CA -1 G r xrGxx  0.2107  1.0630
-CA -1 P P xPPxx -0.2042  1.0303
-CA -1 P p xpPxx  0.0444  0.2240
-CA -1 r G xGrxx  0.2030  1.0241
-CA -1 r + x+rxx -0.0811  0.4093
-CA -1 - P xP-xx  0.0744  0.3755
-CA -1 - - x--xx -0.0263  0.1326
-CA -1 p p xppxx -0.0094  0.0475
-CA  1 G P xxGPx  1.3044  6.5813
-CA  1 G - xxG-x -0.0632  0.3188
-CA  1 P G xxPGx  0.2642  1.3329
-CA  1 P P xxPPx  0.3025  1.5262
-CA  1 P r xxPrx  0.1455  0.7343
-CA  1 P - xxP-x  0.1188  0.5994
-CA  1 P p xxPpx  0.1201  0.6062
-CA  1 r P xxrPx -0.1958  0.9878
-CA  1 r - xxr-x -0.0931  0.4696
-CA  1 a P xxaPx -0.1428  0.7204
-CA  1 a - xxa-x -0.0262  0.1324
-CA  1 a p xxapx  0.0392  0.1977
-CA  1 + P xx+Px -0.1059  0.5344
-CA  1 + a xx+ax -0.0377  0.1901
-CA  1 + + xx++x -0.0595  0.3001
-CA  1 - P xx-Px -0.1156  0.5831
-CA  1 - + xx-+x  0.0316  0.1593
-CA  1 - p xx-px  0.0612  0.3090
-CA  1 p r xxprx -0.0511  0.2576
-CA -2 P - -xPxx -0.1028  0.5185
-CA -2 r r rxrxx  0.1933  0.9752
-CA -2 - G Gx-xx  0.0559  0.2818
-CA -2 - p px-xx  0.0391  0.1973
-CA -2 p a axpxx -0.0293  0.1479
-CA -2 p + +xpxx -0.0173  0.0873
-CA  2 G - xxGx-  0.0357  0.1802
-CA  2 + G xx+xG -0.0315  0.1591
-CA  2 - P xx-xP  0.0426  0.2150
-CA  2 - r xx-xr  0.0784  0.3954
-CA  2 - a xx-xa  0.1084  0.5467
-CA  2 - - xx-x-  0.0836  0.4216
-CA  2 p P xxpxP  0.0685  0.3456
-CA  2 p - xxpx- -0.0481  0.2428
-CB -1 P r xrPxx -0.2678  1.7345
-CB -1 P p xpPxx  0.0355  0.2300
-CB -1 r P xPrxx -0.1137  0.7367
-CB -1 a p xpaxx  0.0249  0.1613
-CB -1 + - x-+xx -0.0762  0.4935
-CB -1 - P xP-xx -0.0889  0.5757
-CB -1 - r xr-xx -0.0533  0.3451
-CB -1 - - x--xx  0.0496  0.3215
-CB -1 - p xp-xx -0.0148  0.0960
-CB -1 p P xPpxx  0.0119  0.0768
-CB -1 p r xrpxx -0.0673  0.4358
-CB  1 P G xxPGx -0.0522  0.3379
-CB  1 P P xxPPx -0.8458  5.4779
-CB  1 P r xxPrx -0.1573  1.0187
-CB  1 r r xxrrx  0.1634  1.0581
-CB  1 a G xxaGx -0.0393  0.2544
-CB  1 a r xxarx  0.0274  0.1777
-CB  1 a - xxa-x  0.0394  0.2553
-CB  1 a p xxapx  0.0149  0.0968
-CB  1 + G xx+Gx -0.0784  0.5076
-CB  1 + P xx+Px -0.1170  0.7580
-CB  1 - P xx-Px -0.0913  0.5912
-CB  1 - - xx--x  0.0284  0.1838
-CB  1 p P xxpPx  0.0880  0.5697
-CB  1 p p xxppx -0.0113  0.0733
-CB -2 P - -xPxx  0.0389  0.2521
-CB -2 P p pxPxx  0.0365  0.2362
-CB -2 r + +xrxx  0.0809  0.5242
-CB -2 a - -xaxx -0.0452  0.2927
-CB -2 + - -x+xx -0.0651  0.4218
-CB -2 - G Gx-xx -0.0883  0.5717
-CB -2 p G Gxpxx  0.0378  0.2445
-CB -2 p p pxpxx  0.0207  0.1341
-CB  2 r G xxrxG -0.0362  0.2344
-CB  2 r - xxrx- -0.0219  0.1419
-CB  2 a - xxax- -0.0298  0.1929
-CB  2 + p xx+xp  0.0189  0.1223
-CB  2 - - xx-x- -0.0525  0.3400
- N -1 G P xPGxx  0.2411  0.5105
- N -1 G + x+Gxx -0.1773  0.3754
- N -1 G - x-Gxx  0.1905  0.4035
- N -1 P P xPPxx -0.9177  1.9434
- N -1 P p xpPxx  0.2609  0.5525
- N -1 r G xGrxx  0.2417  0.5119
- N -1 r a xarxx -0.0139  0.0295
- N -1 r + x+rxx -0.4122  0.8729
- N -1 r p xprxx  0.1440  0.3049
- N -1 a G xGaxx -0.5177  1.0963
- N -1 a r xraxx  0.0890  0.1885
- N -1 a a xaaxx  0.1393  0.2950
- N -1 a p xpaxx -0.0825  0.1747
- N -1 + G xG+xx -0.4908  1.0394
- N -1 + a xa+xx  0.1709  0.3619
- N -1 + + x++xx  0.1868  0.3955
- N -1 + - x-+xx -0.0951  0.2014
- N -1 - P xP-xx -0.3027  0.6410
- N -1 - r xr-xx -0.1670  0.3537
- N -1 - + x+-xx -0.3501  0.7414
- N -1 - - x--xx  0.1266  0.2681
- N -1 p G xGpxx -0.1707  0.3614
- N -1 p - x-pxx  0.0011  0.0023
- N  1 G G xxGGx  0.2555  0.5412
- N  1 G P xxGPx -0.9725  2.0595
- N  1 G r xxGrx  0.0165  0.0349
- N  1 G p xxGpx  0.0703  0.1489
- N  1 r a xxrax -0.0237  0.0503
- N  1 a r xxarx -0.1816  0.3845
- N  1 a - xxa-x -0.1050  0.2224
- N  1 a p xxapx -0.1196  0.2533
- N  1 - r xx-rx -0.1762  0.3731
- N  1 - a xx-ax  0.0006  0.0013
- N  1 p P xxpPx  0.2797  0.5923
- N  1 p a xxpax  0.0938  0.1986
- N  1 p + xxp+x  0.1359  0.2878
- N -2 G r rxGxx -0.5140  1.0885
- N -2 G - -xGxx -0.0639  0.1354
- N -2 P P PxPxx -0.4215  0.8927
- N -2 r P Pxrxx -0.3696  0.7828
- N -2 r p pxrxx -0.1937  0.4101
- N -2 a - -xaxx -0.0351  0.0743
- N -2 a p pxaxx -0.1031  0.2183
- N -2 - G Gx-xx -0.2152  0.4558
- N -2 - P Px-xx -0.1375  0.2912
- N -2 - p px-xx -0.1081  0.2290
- N -2 p P Pxpxx -0.1489  0.3154
- N -2 p - -xpxx  0.0952  0.2015
- N  2 G - xxGx-  0.1160  0.2457
- N  2 r p xxrxp -0.1288  0.2728
- N  2 a P xxaxP  0.1632  0.3456
- N  2 + + xx+x+ -0.0106  0.0226
- N  2 + - xx+x-  0.0389  0.0824
- N  2 - a xx-xa -0.0815  0.1726
- N  2 p G xxpxG -0.0779  0.1649
- N  2 p p xxpxp -0.0683  0.1447
- H -1 G P xPGxx -0.0317  0.4730
- H -1 G r xrGxx  0.0549  0.8186
- H -1 G + x+Gxx -0.0192  0.2867
- H -1 G - x-Gxx  0.0138  0.2055
- H -1 r P xPrxx -0.0964  1.4367
- H -1 r - x-rxx -0.0245  0.3648
- H -1 a G xGaxx -0.0290  0.4320
- H -1 a a xaaxx  0.0063  0.0944
- H -1 + G xG+xx -0.0615  0.9168
- H -1 + r xr+xx -0.0480  0.7153
- H -1 + - x-+xx -0.0203  0.3030
- H -1 - + x+-xx -0.0232  0.3455
- H -1 p G xGpxx -0.0028  0.0411
- H -1 p P xPpxx -0.0121  0.1805
- H  1 G P xxGPx -0.1418  2.1144
- H  1 G r xxGrx  0.0236  0.3520
- H  1 G a xxGax  0.0173  0.2580
- H  1 a - xxa-x  0.0091  0.1349
- H  1 + P xx+Px -0.0422  0.6290
- H  1 + p xx+px  0.0191  0.2842
- H  1 - P xx-Px -0.0474  0.7065
- H  1 - a xx-ax  0.0102  0.1515
- H -2 G G GxGxx  0.0169  0.2517
- H -2 G r rxGxx -0.3503  5.2220
- H -2 a P Pxaxx  0.0216  0.3227
- H -2 a - -xaxx -0.0276  0.4118
- H -2 + - -x+xx -0.0260  0.3874
- H -2 - G Gx-xx  0.0273  0.4073
- H -2 - a ax-xx -0.0161  0.2400
- H -2 - - -x-xx -0.0285  0.4255
- H -2 p P Pxpxx -0.0101  0.1503
- H -2 p a axpxx -0.0157  0.2343
- H -2 p + +xpxx -0.0122  0.1815
- H -2 p p pxpxx  0.0107  0.1601
- H  2 G G xxGxG -0.0190  0.2826
- H  2 r G xxrxG  0.0472  0.7036
- H  2 r P xxrxP  0.0337  0.5027
- H  2 a + xxax+ -0.0159  0.2376
- H  2 + G xx+xG  0.0113  0.1685
- H  2 + r xx+xr -0.0307  0.4575
- H  2 - P xx-xP -0.0088  0.1318
-HA -1 P P xPPxx  0.0307  1.1685
-HA -1 P r xrPxx  0.0621  2.3592
-HA -1 r G xGrxx -0.0371  1.4092
-HA -1 r + x+rxx  0.0125  0.4733
-HA -1 r p xprxx -0.0199  0.7569
-HA -1 a G xGaxx  0.0073  0.2779
-HA -1 a a xaaxx  0.0044  0.1683
-HA -1 - G xG-xx  0.0116  0.4409
-HA -1 - r xr-xx  0.0228  0.8679
-HA -1 - p xp-xx  0.0074  0.2828
-HA  1 G G xxGGx  0.0175  0.6636
-HA  1 G - xxG-x  0.0107  0.4081
-HA  1 P a xxPax  0.0089  0.3369
-HA  1 - r xx-rx  0.0113  0.4291
-HA -2 G G GxGxx -0.0154  0.5847
-HA -2 P - -xPxx  0.0136  0.5179
-HA -2 r G Gxrxx -0.0159  0.6045
-HA -2 + + +x+xx -0.0137  0.5190
-HA -2 p - -xpxx -0.0068  0.2592
-HA -2 p p pxpxx  0.0046  0.1763
-HB -1 P r xrPxx  0.0460  2.1365
-HB -1 a - x-axx  0.0076  0.3551
-HB -1 + - x-+xx  0.0110  0.5122
-HB -1 - r xr-xx  0.0233  1.0819
-HB  1 a P xxaPx  0.0287  1.3310
-HB  1 + P xx+Px  0.0324  1.5056
-HB  1 + r xx+rx -0.0231  1.0709
-HB  1 p r xxprx  0.0077  0.3586
-HB  1 p + xxp+x -0.0074  0.3426
-HB -2 a P Pxaxx -0.0026  0.1192
-HB -2 a r rxaxx -0.0098  0.4559
-HB -2 - - -x-xx  0.0016  0.0751
-HB  2 P r xxPxr -0.0595  2.7608
-HB  2 P + xxPx+ -0.0145  0.6744
-HB  2 P - xxPx-  0.0107  0.4976
-HB  2 a + xxax+ -0.0015  0.0691
-HB  2 p r xxpxr  0.0262  1.2178'''
-
-tablephshifts='''
-D (pKa 3.86)
-D H  8.55 8.38 -0.17 0.02 -0.03
-D HA 4.78 4.61 -0.17 0.01 -0.01
-D HB 2.93 2.70 -0.23
-D CA 52.9 54.3 1.4 0.0 0.1
-D CB 38.0 41.1 3.0
-D CG 177.1 180.3 3.2
-D C  175.8 176.9 1.1 -0.2 0.4
-D N  118.7 120.2 1.5  0.3 0.1
-D Np na na 0.1
-E (pKa 4.34)
-E H  8.45 8.57  0.12 0.00 0.02
-E HA 4.39 4.29 -0.10 0.01 0.00
-E HB 2.08 2.02 -0.06
-E HG 2.49 2.27 -0.22
-E CA 56.0 56.9 1.0 0.0 0.0
-E CB 28.5 30.0 1.5
-E CG 32.7 36.1 3.5
-E CD 179.7 183.8 4.1
-E C  176.5 177.0 0.6  0.1 0.1 
-E N  119.9 120.9 1.0  0.2 0.1
-E Np na na 0.1
-H (pKa 6.45)
-H H  8.55 8.35 -0.2  -0.01  0.0
-H HA 4.75 4.59 -0.2  -0.01 -0.06
-H HB 3.25 3.08 -0.17
-H HD2 7.30 6.97 -0.33
-H HE1 8.60 7.68 -0.92
-H CA 55.1 56.7 1.6 -0.1 0.1
-H CB 28.9 31.3 2.4
-H CG 131.0 135.3 4.2
-H CD2 120.3 120.0 -0.3
-H CE1 136.6 139.2 2.6
-H C 174.8 176.2 1.5  0.0 0.6
-H N 117.9 119.7 1.8  0.3 0.5
-H Np na na 0.5
-H ND1 175.8 231.3 56
-H NE2 173.1 181.1 8
-C (pKa 8.49)
-C H 8.49 8.49 0.0
-C HA 4.56 4.28 -0.28 -0.01 -0.01
-C HB 2.97 2.88 -0.09
-C CA 58.5 60.6 2.1 0.0 0.1
-C CB 28.0 29.7 1.7
-C C 175.0 176.9 1.9 -0.4 0.5
-C N 118.7 122.2 3.6  0.4 0.6
-C Np na na 0.6
-Y (pKa 9.76)
-Y H  8.16 8.16 0.0
-Y HA 4.55 4.49 -0.06
-Y HB 3.02 2.94 -0.08
-Y HD 7.14 6.97 -0.17
-Y HE 6.85 6.57 -0.28
-Y CA 58.0 58.2 0.3
-Y CB 38.6 38.7 0.1
-Y CG 130.5 123.8 -6.7
-Y CD 133.3 133.2 -0.1
-Y CE 118.4 121.7 3.3
-Y CZ 157.0 167.4 10.4
-Y C 176.3 176.7 0.4
-Y N 120.1 120.7 0.6
-K (pKa 10.34)
-K H  8.4  8.4  0.0
-K HA 4.34 4.30 -0.04
-K HB 1.82 1.78 -0.04
-K HG 1.44 1.36 -0.08
-K HD 1.68 1.44 -0.24
-K HE 3.00 2.60 -0.40
-K CA 56.4 56.9 0.4
-K CB 32.8 33.2 0.3
-K CG 24.7 25.0 0.4
-K CD 28.9 33.9 5.0
-K CE 42.1 43.1 1.0
-K C 177.0 177.5 0.5
-K N 121.0 121.7 0.7
-K Np na na 0.1
-R (pKa 13.9)
-R H  7.81 7.81 0.0
-R HA 3.26 3.19 -0.07
-R HB 1.60 1.55 0.05
-R HG 1.60 1.55 0.05
-R HD 3.19 3.00 -0.19
-R CA 58.4 58.6 0.2
-R CB 34.4 35.2 0.9
-R CG 27.2 28.1 1.0
-R CD 43.8 44.3 0.5
-R CZ 159.6 163.5 4.0
-R C 185.8 186.1 0.2
-R N 122.4 122.8 0.4
-R NE 85.6 91.5 5.9
-R NG 71.2 93.2 22'''
-
-def initcorrcomb():
-    datc=tablecombdevs.split('\n')
-    buf=[lin.split() for lin in datc]
-    dct={}
-    for lin in buf:
-        atn=lin[0]
-        if not atn in dct:dct[atn]={}
-        neipos=int(lin[1])
-        centgroup=lin[2]
-        neigroup= lin[3]
-        key=(neipos,centgroup,neigroup)#(k,l,m)
-        segment=lin[4]
-        dct[atn][segment]=key,eval(lin[-2])
-    return dct
-        
-TEMPCORRS=gettempkoeff()
-CENTSHIFTS=initcorcents()
-NEICORRS =initcorneis()
-COMBCORRS=initcorrcomb()
-
-# data = pkgutil.get_data(__name__, "data_tables/phshifts2.csv")
-# PHSHIFTS = pd.read_csv(StringIO(data.decode()), header=0, comment='#', index_col=['resn', 'atn'])
-
-def predPentShift(pent,atn):
-    aac=pent[2]
-    sh=CENTSHIFTS[aac][atn]
-    allneipos=[2,1,-1,-2]
-    for i in range(4):
-        aai=pent[2+allneipos[i]]
-        if aai in NEICORRS:
-            corr=NEICORRS[aai][atn][i]
-            sh+=corr
-    groups=['G','P','FYW','LIVMCA','KR','DE']##,'NQSTHncX']
-    labels='GPra+-p' #(Gly,Pro,Arom,Aliph,pos,neg,polar)
-    grstr=''
-    for i in range(5):
-        aai=pent[i]
-        found=False
-        for j,gr in enumerate(groups):
-            if aai in gr:
-                grstr+=labels[j]
-                found=True
-                break
-        if not found:grstr+='p'#polar
-    centgr=grstr[2]
-    for segm in COMBCORRS[atn]:
-        key,combval=COMBCORRS[atn][segm]
-        neipos,centgroup,neigroup=key#(k,l,m)
-        if centgroup==centgr and grstr[2+neipos]==neigroup:
-            if (centgr,neigroup)!=('p','p') or pent[2] in 'ST':
-                #pp comb only used when center is Ser or Thr!
-                sh+=combval
-    return sh
-        
-def gettempcorr(aai,atn,tempdct,temp):
-    return tempdct[atn][aai]/1000*(temp-298)
-
-def get_phshifts():
-    datc=tablephshifts.split('\n')
-    buf=[lin.split() for lin in datc]
-    dct={}
-    na=None
-    for lin in buf:
-        if len(lin)>3:
-            resn=lin[0]
-            atn=lin[1]
-            sh0=eval(lin[2])
-            sh1=eval(lin[3])
-            shd=eval(lin[4])
-            if not resn in dct:dct[resn]={}
-            dct[resn][atn]=shd
-            if len(lin)>6:#neighbor data
-                for n in range(2):
-                    shdn=eval(lin[5+n])
-                    nresn=resn+'ps'[n]
-                    if not nresn in dct:dct[nresn]={}
-                    dct[nresn][atn]=shdn
-    return dct
-
-def initfilcsv(filename):
-    file=open(filename,'r')
-    buffer=file.readlines()
-    file.close()
-    for i in range(len(buffer)):
-        buffer[i]=buffer[i][:-1].split(',')
-    return buffer
-
-def write_csv_pkaoutput(pkadct,seq,temperature,ion):
-        seq=seq[:min(150,len(seq))]
-        name='outpepKalc_%s_T%6.2f_I%4.2f.csv'%(seq,temperature,ion)
-        out=open(name,'w')
-        out.write('Site,pKa value,pKa shift,Hill coefficient\n')
-        for i in pkadct:
-            pKa,nH,resi=pkadct[i]
-            reskey=resi+str(i+1)
-            diff=pKa-pK0[resi]
-            out.write('%s,%5.3f,%5.3f,%5.3f\n'%(reskey,pKa,diff,nH))
-        out.close()
-
-def read_csv_pkaoutput(seq,temperature,ion,name=None):
-    seq=seq[:min(150,len(seq))]
-    logging.getLogger('trizod.potenci').debug(f'reading csv {name}')
-    if name==None:name='outpepKalc_%s_T%6.2f_I%4.2f.csv'%(seq,temperature,ion)
-    try:out=open(name,'r')
-    except IOError:return None
-    buf=initfilcsv(name)
-    for lnum,data in enumerate(buf):
-        if len(data)>0 and data[0]=='Site':break
-    pkadct={}
-    for data in buf[lnum+1:]:
-        reskey,pKa,diff,nH=data
-        i=int(reskey[1:])-1
-        resi=reskey[0]
-        pKaval=eval(pKa)
-        nHval=eval(nH)
-        pkadct[i]=pKaval,nHval,resi
-    return pkadct
-
-def getphcorrs(seq,temperature,pH,ion,pkacsvfilename=None):
-    bbatns=['C','CA','CB','HA','H','N','HB']
-    dct=get_phshifts()
-    Ion=max(0.0001,ion)
-    if pkacsvfilename == False:
-        pkadct=None
-    else:
-        pkadct=read_csv_pkaoutput(seq,temperature,ion,pkacsvfilename)
-    if pkadct==None:
-        pkadct=calc_pkas_from_seq('n'+seq+'c',temperature,Ion)
-        if pkacsvfilename != False:
-            write_csv_pkaoutput(pkadct,seq,temperature,ion)
-    outdct={}
-    for i in pkadct:
-        logging.getLogger('trizod.potenci').debug('pkares: %6.3f %6.3f %1s'%pkadct[i] + str(i))
-        pKa,nH,resi=pkadct[i]
-        frac =fun(pH,pKa,nH)
-        frac7=fun(7.0,pK0[resi],nH)
-        if resi in 'nc':jump=0.0#so far
-        else:
-            for atn in bbatns:
-                if not atn in outdct:outdct[atn]={}
-                logging.getLogger('trizod.potenci').debug(f'data: {atn}, {pKa}, {nH}, {resi}, {i}, {atn}, {pH}')
-                dctresi=dct[resi]
-                try:
-                    delta=dctresi[atn]
-                    # delta = PHSHIFTS.loc[(resi,atn), 'shd']
-                    jump =frac *delta
-                    jump7=frac7*delta
-                    key=(resi,atn)
-                except KeyError:
-                    ##if not (resi in 'RKCY' and atn=='H') and not (resi == 'R' and atn=='N'):
-                    logging.getLogger('trizod.potenci').waring(f'no key: {resi}, {i}, {atn}')
-                    delta=999;jump=999;jump7=999
-                if delta<99:
-                    jumpdelta=jump-jump7
-                    if not i in outdct[atn]:outdct[atn][i]=[resi,jumpdelta]
-                    else:
-                        outdct[atn][i][0]=resi
-                        outdct[atn][i][1]+=jumpdelta
-                    logging.getLogger('trizod.potenci').debug('%3s %5.2f %6.4f %s %3d %5s %8.5f %8.5f %4.2f'%(atn,pKa,nH,resi,i,atn,jump,jump7,pH))
-                    if resi+'p' in dct and atn in dct[resi+'p']:
-                    # if (resi+'p', atn) in PHSHIFTS.index:
-                        for n in range(2):
-                            ni=i+2*n-1
-                            ##if ni is somewhere in seq...
-                            nresi=resi+'ps'[n]
-                            ndelta=dct[nresi][atn]
-                            # ndelta = PHSHIFTS.loc[(nresi,atn), 'shd']
-                            jump =frac *ndelta
-                            jump7=frac7*ndelta
-                            jumpdelta=jump-jump7
-                            if not ni in outdct[atn]:outdct[atn][ni]=[None,jumpdelta]
-                            else:outdct[atn][ni][1]+=jumpdelta
-    return outdct
-
-def getphcorrs_arr(seq,temperature,pH,ion):
-    bbatns=['C','CA','CB','HA','H','N','HB']
-    dct=get_phshifts()
-    
-    Ion=max(0.0001,ion)
-
-    pkadct=calc_pkas_from_seq('n'+seq+'c',temperature,Ion)
-    #outdct={}
-    residues = [[None]*7 for i in range(len(seq))]
-    outarr = np.zeros(shape=(len(seq), len(bbatns)), dtype=np.float)
-    for i in pkadct:
-        logging.getLogger('trizod.potenci').debug('pkares: %6.3f %6.3f %1s'%pkadct[i] + str(i))
-        pKa,nH,resi=pkadct[i]
-        frac  = fun(pH,pKa,nH)
-        frac7 = fun(7.0,pK0[resi],nH)
-        if resi in 'nc':
-            jump = 0.0#so far
+
+def _w_to_logp(energy, T=293.15):
+    """Convert interaction energy to log-probability shift."""
+    return energy * 4181.2 / (GAS_CONSTANT * T * np.log(10))
+
+
+def _small_matrix_limits(res_idx, half_window, n_sites):
+    """Get left/right bounds for a sliding window around a residue."""
+    left = max(1, res_idx - half_window)
+    right = min(left + 2 * half_window, n_sites)
+    if right == n_sites:
+        left = max(1, right - 2 * half_window)
+    return (left, right)
+
+
+def _small_matrix_pos(res_idx, half_window, n_sites):
+    """Get position of a residue within its sliding window."""
+    pos = half_window + 1
+    if res_idx < half_window + 1:
+        pos = res_idx
+    if res_idx > n_sites - half_window:
+        pos = min(n_sites, 2 * half_window + 1) - (n_sites - res_idx)
+    return pos
+
+
+def calc_pkas_from_seq(seq=None, T=293.15, Ion=0.1):
+    """Iteratively predict pKa values for titratable residues in a sequence."""
+    ph_values = np.arange(1.99, 10.01, 0.15)
+
+    titratable_pos = np.array([i for i in range(len(seq)) if seq[i] in REFERENCE_PKA])
+    n_sites = titratable_pos.shape[0]
+    identity = np.diag(np.ones(n_sites))
+    sites = "".join([seq[i] for i in titratable_pos])
+    neg_indices = np.array([i for i in range(len(sites)) if sites[i] in "DEYc"])
+    seq_separations = np.array(
+        [abs(titratable_pos - titratable_pos[i]) for i in range(n_sites)]
+    )
+    distances = MIN_CHARGE_DISTANCE + np.sqrt(seq_separations) * DISTANCE_SCALE
+
+    w_energies = _debye_huckel_W(distances, Ion)
+    w_energies[identity == 1] = 0
+
+    log_interactions = _w_to_logp(w_energies, T) / 2
+
+    base_charges = np.zeros(n_sites)
+    if len(neg_indices):
+        base_charges[neg_indices] = -1
+
+    pka_initial = np.array([REFERENCE_PKA[residue] for residue in sites])
+    hill_initial = np.array([0.9 for _ in sites])
+
+    titration = np.zeros((n_sites, len(ph_values)))
+
+    window_size = min(2 * PKA_WINDOW_HALF + 1, len(titratable_pos))
+    tuples = _ALL_TUPLES[window_size]
+    outer_mats = _OUTER_MATRICES[window_size]
+
+    for _cycle in range(PKA_FIT_CYCLES):
+        if _cycle == 0:
+            prev_fractions = np.array(
+                [
+                    [
+                        _titration_fraction(
+                            ph_values[ph_idx],
+                            pka_initial[site_idx],
+                            hill_initial[site_idx],
+                        )
+                        for site_idx in range(n_sites)
+                    ]
+                    for ph_idx in range(len(ph_values))
+                ]
+            )
         else:
-            for col,atn in enumerate(bbatns):
-                #if not atn in outdct:outdct[atn]={}
-                logging.getLogger('trizod.potenci').debug(f'data: {atn}, {pKa}, {nH}, {resi}, {i}, {atn}, {pH}')
-                dctresi = dct[resi]
-                try:
-                    delta = dctresi[atn]
-                    jump  = frac *delta
-                    jump7 = frac7*delta
-                    #key=(resi,atn)
-                except KeyError:
-                    ##if not (resi in 'RKCY' and atn=='H') and not (resi == 'R' and atn=='N'):
-                    logging.getLogger('trizod.potenci').waring(f'no key: {resi}, {i}, {atn}')
-                    delta=999;jump=999;jump7=999
-                if delta<99:
-                    jumpdelta = jump - jump7
-                    #if not i in outdct[atn]:outdct[atn][i]=[resi,jumpdelta]
-                    #else:
-                    #    outdct[atn][i][0]=resi
-                    #    outdct[atn][i][1]+=jumpdelta
-                    residues[i][col] = resi
-                    outarr[i][col] += jumpdelta
-                    logging.getLogger('trizod.potenci').debug('%3s %5.2f %6.4f %s %3d %5s %8.5f %8.5f %4.2f'%(atn,pKa,nH,resi,i,atn,jump,jump7,pH))
-                    if resi+'p' in dct and atn in dct[resi+'p']:
-                        for n in range(2):
-                            ni=i+2*n-1
-                            ##if ni is somewhere in seq...
-                            nresi=resi+'ps'[n]
-                            ndelta=dct[nresi][atn]
-                            jump = frac  * ndelta
-                            jump7= frac7 * ndelta
-                            jumpdelta = jump - jump7
-                            #if not ni in outdct[atn]:outdct[atn][ni]=[None,jumpdelta]
-                            #else:outdct[atn][ni][1]+=jumpdelta
-                            residues[i][col] = None
-                            outarr[ni][col] += jumpdelta
-    return outarr
-
-def getpredshifts(seq,temperature,pH,ion,usephcor=True,pkacsvfile=None,identifier=''):
-    tempdct=gettempkoeff()
-    bbatns = ['C','CA','CB','HA','H','N','HB']
-    if usephcor:
-        phcorrs=getphcorrs(seq,temperature,pH,ion,pkacsvfile)
-    else:
-        phcorrs={}
-    shiftdct={}
-    for i in range(1,len(seq)-1):
-        if seq[i] in AAstandard:#else: do nothing
-            res=str(i+1)
-            trip=seq[i-1]+seq[i]+seq[i+1]
-            phcorr=None
-            shiftdct[(i+1,seq[i])]={}
-            for at in bbatns:
-                if not (trip[1],at) in [('G','CB'),('G','HB'),('P','H')]:
-                    if i == 1:
-                        pent = 'n'      + trip + seq[i+2]
-                    elif i==len(seq)-2:
-                        pent = seq[i-2] + trip + 'c'
-                    else:
-                        pent = seq[i-2] + trip + seq[i+2]
-                    shp=predPentShift(pent,at)
-                    if shp!=None:
-                        if at!='HB':shp+=gettempcorr(trip[1],at,tempdct,temperature)
-                        if at in phcorrs and i in phcorrs[at]:
-                            phdata=phcorrs[at][i]
-                            resi=phdata[0]
-                            ##assert resi==seq[i]
-                            if seq[i] in 'CDEHRKY' and resi != seq[i]:
-                                logging.getLogger('trizod.potenci').warning(f'residue mismatch: {resi},{seq[i]},{i},{phdata},{at}')
-                            phcorr=phdata[1]
-                            if abs(phcorr)<9.9:
-                                shp-=phcorr
-                        shiftdct[(i+1,seq[i])][at]=shp
-                        logging.getLogger('trizod.potenci').debug('predictedshift: %5s %3d %1s %2s %8.4f'%(identifier,i,seq[i],at,shp) + ' ' + str(phcorr))
-    return shiftdct
-
-def getpredshifts_arr(seq,temperature,pH,ion,usephcor=True,pkacsvfile=None,identifier=''):
-    tempdct=gettempkoeff()
-    bbatns = ['C','CA','CB','HA','H','N','HB']
-    if usephcor:
-        phcorrs=getphcorrs_arr(seq,temperature,pH,ion,pkacsvfile)
-    else:
-        phcorrs={}
-    shiftdct={}
-    for i in range(1,len(seq)-1):
-        if seq[i] in AAstandard:#else: do nothing
-            res=str(i+1)
-            trip=seq[i-1]+seq[i]+seq[i+1]
-            phcorr=None
-            shiftdct[(i+1,seq[i])]={}
-            for at in bbatns:
-                if not (trip[1],at) in [('G','CB'),('G','HB'),('P','H')]:
-                    if i == 1:
-                        pent = 'n'      + trip + seq[i+2]
-                    elif i==len(seq)-2:
-                        pent = seq[i-2] + trip + 'c'
+            prev_fractions = titration.transpose()
+
+        for res_idx in range(1, n_sites + 1):
+            (left, right) = _small_matrix_limits(res_idx, PKA_WINDOW_HALF, n_sites)
+            window_pos = _small_matrix_pos(res_idx, PKA_WINDOW_HALF, n_sites)
+            fraction = prev_fractions.copy()
+            fraction[:, left - 1 : right] = 0
+            charges = fraction + base_charges
+            interaction_diag = 2 * (
+                log_interactions * np.expand_dims(charges, axis=1)
+            ).sum(axis=-1)
+            interaction_diag = np.expand_dims(interaction_diag, 1) * identity
+            g_matrix_full = (
+                log_interactions
+                + interaction_diag
+                + np.expand_dims(ph_values, (1, 2)) * identity
+                - np.diag(pka_initial)
+            )
+            g_matrix = g_matrix_full[:, left - 1 : right, left - 1 : right]
+
+            boltzmann = 10 ** -(np.expand_dims(g_matrix, axis=1) * outer_mats).sum(
+                axis=(2, 3)
+            )
+            boltzmann_total = boltzmann.sum(axis=-1)
+            boltzmann_selected = boltzmann[:, (tuples[:, window_pos - 1] == 1)].sum(
+                axis=-1
+            )
+            titration[res_idx - 1] = boltzmann_selected / boltzmann_total
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore", OptimizeWarning)
+            fit_results = np.array(
+                [
+                    curve_fit(
+                        _titration_fraction,
+                        ph_values,
+                        titration[site_idx],
+                        [pka_initial[site_idx], hill_initial[site_idx]],
+                        maxfev=5000,
+                    )[0]
+                    for site_idx in range(len(pka_initial))
+                ]
+            )
+        (pKs, nHs) = fit_results.transpose()
+
+    result = {}
+    for site_idx, seq_pos in enumerate(titratable_pos):
+        result[seq_pos - 1] = (pKs[site_idx], nHs[site_idx], seq[seq_pos])
+
+    return result
+
+
+# ── pH corrections ───────────────────────────────────────────────────────
+
+
+def _get_ph_corrs(seq, temperature, pH, ion):
+    """Compute pH-dependent chemical shift corrections.
+
+    Returns dict[atom_type -> dict[residue_index -> (residue_name, delta_shift)]].
+    """
+    Ion = max(0.0001, ion)
+    pka_dict = calc_pkas_from_seq("n" + seq + "c", temperature, Ion)
+
+    corrections = {}
+    for res_idx in pka_dict:
+        pKa, nH, res = pka_dict[res_idx]
+        if res in "nc":
+            continue  # terminal residues: no correction (yet)
+
+        frac = _titration_fraction(pH, pKa, nH)
+        frac_ref = _titration_fraction(7.0, REFERENCE_PKA[res], nH)
+
+        for atom in BB_ATOMS:
+            if atom not in corrections:
+                corrections[atom] = {}
+            if atom not in PH_SHIFTS[res]:
+                continue
+            delta = PH_SHIFTS[res][atom]
+            delta_jump = frac * delta - frac_ref * delta
+            if res_idx not in corrections[atom]:
+                corrections[atom][res_idx] = [res, delta_jump]
+            else:
+                corrections[atom][res_idx][0] = res
+                corrections[atom][res_idx][1] += delta_jump
+
+            # Neighbor effects (preceding and succeeding residues)
+            if res + "p" in PH_SHIFTS and atom in PH_SHIFTS[res + "p"]:
+                for nei_side in range(2):
+                    neighbor_idx = res_idx + 2 * nei_side - 1
+                    neighbor_key = res + "ps"[nei_side]
+                    neighbor_delta = PH_SHIFTS[neighbor_key][atom]
+                    neighbor_jump = frac * neighbor_delta - frac_ref * neighbor_delta
+                    if neighbor_idx not in corrections[atom]:
+                        corrections[atom][neighbor_idx] = [None, neighbor_jump]
                     else:
-                        pent = seq[i-2] + trip + seq[i+2]
-                    shp=predPentShift(pent,at)
-                    if shp!=None:
-                        if at!='HB':shp+=gettempcorr(trip[1],at,tempdct,temperature)
-                        if at in phcorrs and i in phcorrs[at]:
-                            phdata=phcorrs[at][i]
-                            resi=phdata[0]
-                            ##assert resi==seq[i]
-                            if seq[i] in 'CDEHRKY' and resi!=seq[i]:
-                                logging.getLogger('trizod.potenci').warning(f'residue mismatch: {resi},{seq[i]},{i},{phdata},{at}')
-                            phcorr=phdata[1]
-                            if abs(phcorr)<9.9:
-                                shp-=phcorr
-                        shiftdct[(i+1,seq[i])][at]=shp
-                        logging.getLogger('trizod.potenci').debug('predictedshift: %5s %3d %1s %2s %8.4f'%(identifier,i,seq[i],at,shp) + ' ' + str(phcorr))
-    return shiftdct
-
-def writeOutput(name,dct):
-    out=open(name,'w')
-    bbatns =['N','C','CA','CB','H','HA','HB']
-    out.write('#NUM AA   N ')
-    out.write(' %7s %7s %7s %7s %7s %7s\n'%tuple(bbatns[1:]))
-    reskeys=list(dct.keys())
-    reskeys.sort()
-    for resnum,resn in reskeys:
-        shdct=dct[(resnum,resn)]
-        if len(shdct)>0:
-            out.write('%-4d %1s '%(resnum,resn))
-            for at in bbatns:
-                shp=0.0
-                if at in shdct:shp=shdct[at]
-                out.write(' %7.3f'%shp)
-        out.write('\n')
-    out.close()
-
-def main():
-    ##README##......
-    #requires: python2.x with numpy and scipy
-    #usage: potenci1_2.py seqstring pH temperature ionicstrength [pkacsvfile] > logfile
-    #optional filename in csv format contained predicted pKa values and Hill parameters,
-    #the format of the pkacsvfile must be the same as the output for pepKalc,
-    #only lines after "Site" is read. If this is not found no pH corrections are applied.
-    #Output: Table textfile in SHIFTY format (space separated)
-    #average of methylene protons are provided for Gly HA2/HA3 and HB2/HB3.
-    #NOTE:pH corrections is applied if pH is not 7.0
-    #NOTE:pKa predictions are stored locally and reloaded if the seq, temp and ion is the same.
-    #NOTE:at least 5 residues are required. Chemical shift predictions are not given for terminal residues.
-    args=sys.argv[1:]#first is scriptname
-    if len(args)<4:
-        logging.getLogger('trizod.potenci').error('FAILED: please provide 4 arguments (exiting)') 
-        logging.getLogger('trizod.potenci').info('usage: potenci1_2.py seqstring pH temperature ionicstrength [pkacsvfile] > logfile')
-        raise SystemExit
-    seq=args[0] #one unbroken line with single-letter amino acid labels
-    pH=float(args[1])#e.g. 7.0
-    temperature=float(args[2])#e.g. 298.0 / K
-    ion=float(args[3])#e.g. 0.1 / M
-    pkacsvfile=None
-    if len(args)>4:pkacsvfile=args[4]
-    ##name='outPOTENCI_%s_T%6.2f_I%4.2f_pH%4.2f.txt'%(seq,temperature,ion,pH)
-    name='outPOTENCI_%s_T%6.2f_I%4.2f_pH%4.2f.txt'%(seq[:min(150,len(seq))],temperature,ion,pH)
-    usephcor = pH<6.99 or pH>7.01
-    if len(seq)<5:
-        logging.getLogger('trizod.potenci').error('FAILED: at least 5 residues are required (exiting)') 
-        raise SystemExit
-    #------------- now ready to generate predicted shifts ---------------------
-    logging.getLogger('trizod.potenci').info('predicting random coil chemical shift with POTENCI using:',seq,pH,temperature,ion,pkacsvfile)
-    shiftdct=getpredshifts(seq,temperature,pH,ion,usephcor,pkacsvfile)
-    #------------- write output nicely is SHIFTY format -----------------------$
-    writeOutput(name,shiftdct)
-    logging.getLogger('trizod.potenci').info('chemical shift succesfully predicted, see output:',name)
-
-if __name__ == '__main__':
-    main()
+                        corrections[atom][neighbor_idx][1] += neighbor_jump
+    return corrections
+
+
+# ── Public API ───────────────────────────────────────────────────────────
+
+
+def get_pred_shifts(seq, temperature, pH, ion, use_ph_corr=True, **kwargs):
+    """Predict random coil chemical shifts for a protein sequence.
+
+    Parameters:
+        seq: protein sequence (single-letter amino acids)
+        temperature: in Kelvin (e.g. 298.0)
+        pH: sample pH (e.g. 7.0)
+        ion: ionic strength in M (e.g. 0.1)
+        use_ph_corr: apply pH-dependent corrections (set False for pH=7.0)
+
+    Returns:
+        dict[(residue_num, aa_letter)] -> dict[atom_type -> shift_value]
+    """
+    ph_corrs = _get_ph_corrs(seq, temperature, pH, ion) if use_ph_corr else {}
+
+    shift_dict = {}
+    for pos in range(1, len(seq) - 1):
+        if seq[pos] not in AA_STANDARD:
+            continue
+        triplet, pentamer = _build_pentamer(seq, pos)
+        shift_dict[(pos + 1, seq[pos])] = {}
+        for atom in BB_ATOMS:
+            if (triplet[1], atom) in _SKIP_ATOM_PAIRS:
+                continue
+            shift = pred_pent_shift(pentamer, atom)
+            if shift is None:
+                continue
+            if atom != "HB":
+                shift += _get_temp_corr(triplet[1], atom, temperature)
+            if atom in ph_corrs and pos in ph_corrs[atom]:
+                ph_data = ph_corrs[atom][pos]
+                corr_res = ph_data[0]
+                if seq[pos] in "CDEHRKY" and corr_res != seq[pos]:
+                    logger.warning(
+                        f"residue mismatch: {corr_res},{seq[pos]},{pos},{ph_data},{atom}"
+                    )
+                ph_corr = ph_data[1]
+                if abs(ph_corr) < 9.9:
+                    shift -= ph_corr
+            shift_dict[(pos + 1, seq[pos])][atom] = shift
+    return shift_dict
diff --git a/trizod/scoring/__init__.py b/trizod/scoring/__init__.py
index 1e8c5a9..bedd6e5 100644
--- a/trizod/scoring/__init__.py
+++ b/trizod/scoring/__init__.py
@@ -1 +1,3 @@
-from .scoring import convert_to_triplet_data, compute_zscores, compute_pscores
\ No newline at end of file
+from .scoring import compute_pscores, compute_zscores, convert_to_triplet_data
+
+__all__ = ["compute_pscores", "compute_zscores", "convert_to_triplet_data"]
diff --git a/trizod/scoring/scoring.py b/trizod/scoring/scoring.py
index 1cddd96..c957d2f 100644
--- a/trizod/scoring/scoring.py
+++ b/trizod/scoring/scoring.py
@@ -1,189 +1,308 @@
+import logging
+import warnings
+
 import numpy as np
 import pandas as pd
-import logging
-import trizod.bmrb.bmrb as bmrb
 import scipy
-import warnings
-from trizod.constants import BBATNS, REFINED_WEIGHTS #, Z_CORRECTION
-
-def convChi2CDF(rss,k):
-    with np.errstate(divide='ignore', invalid='ignore'):
-        # I expect to see RuntimeWarnings in this block
-        # k can be 0 at some 
-        res = ((((rss/k)**(1.0/6))-0.50*((rss/k)**(1.0/3))+1.0/3*((rss/k)**(1.0/2)))\
-            - (5.0/6-1.0/9/k-7.0/648/(k**2)+25.0/2187/(k**3)))\
-            / np.sqrt(1.0/18/k+1.0/162/(k**2)-37.0/11664/(k**3))
-    return res
-
-def comp2pred_arr(predshiftdct, bbshifts_arr, bbshifts_mask):
-    #cmparr = np.zeros(shape=(len(seq), len(BBATNS)))
-    # convert predshift dict to np array (TODO: do this in potenci...)
+
+import trizod.bmrb.bmrb as bmrb
+from trizod.constants import BBATNS, REFINED_WEIGHTS  # , Z_CORRECTION
+
+
+def chi2_cdf_approx(rss, k):
+    """Wilson-Hilferty approximation to the chi-squared CDF."""
+    with np.errstate(divide="ignore", invalid="ignore"):
+        # RuntimeWarnings expected: k can be 0
+        result = (
+            (
+                ((rss / k) ** (1.0 / 6))
+                - 0.50 * ((rss / k) ** (1.0 / 3))
+                + 1.0 / 3 * ((rss / k) ** (1.0 / 2))
+            )
+            - (5.0 / 6 - 1.0 / 9 / k - 7.0 / 648 / (k**2) + 25.0 / 2187 / (k**3))
+        ) / np.sqrt(1.0 / 18 / k + 1.0 / 162 / (k**2) - 37.0 / 11664 / (k**3))
+    return result
+
+
+def compare_to_predicted(predshiftdct, bbshifts_arr, bbshifts_mask):
+    """Compute observed - predicted shift differences."""
     predshift_arr = np.zeros(shape=bbshifts_arr.shape)
     predshift_mask = np.full(shape=bbshifts_mask.shape, fill_value=False)
-    for res,aa in predshiftdct:
+    for res, aa in predshiftdct:
         i = res - 1
-        for j,at in enumerate(BBATNS):
-            if at in predshiftdct[(res,aa)]:
-                if predshiftdct[(res,aa)][at] is not None:
-                    predshift_arr[i, j] = predshiftdct[(res,aa)][at]
-                    predshift_mask[i, j] = True
-    cmparr =  np.subtract(bbshifts_arr, predshift_arr, where=bbshifts_mask & predshift_mask, out=bbshifts_arr)
-    return cmparr, BBATNS, bbshifts_mask & predshift_mask
-
-def compute_running_offsets(cmparr, mask, minAIC=999.):
-    w_ = np.array([REFINED_WEIGHTS[at] for at in BBATNS]) # ensure same order
-    shw_ = cmparr / w_
-    df = pd.DataFrame(shw_).mask(~mask)
-    # compute rolling stadard deviation over detected shifts (missing values are ignored and streched by rolling window)
-    at_stdc = []
-    at_roff = []
-    at_std0 = []
-    for i in range(7): # TODO: not necessary to compute anything but at_stdc for all values. Only the selected position would suffice
+        for j, atom_type in enumerate(BBATNS):
+            if (
+                atom_type in predshiftdct[(res, aa)]
+                and predshiftdct[(res, aa)][atom_type] is not None
+            ):
+                predshift_arr[i, j] = predshiftdct[(res, aa)][atom_type]
+                predshift_mask[i, j] = True
+    diff_arr = np.subtract(
+        bbshifts_arr,
+        predshift_arr,
+        where=bbshifts_mask & predshift_mask,
+        out=bbshifts_arr,
+    )
+    return diff_arr, BBATNS, bbshifts_mask & predshift_mask
+
+
+def compute_running_offsets(diff_arr, mask, min_AIC=999.0):
+    weights = np.array([REFINED_WEIGHTS[atom_type] for atom_type in BBATNS])
+    weighted_diffs = diff_arr / weights
+    df = pd.DataFrame(weighted_diffs).mask(~mask)
+    # compute rolling standard deviation over detected shifts
+    # (missing values are ignored and stretched by rolling window)
+    per_atom_rolling_stds = []
+    per_atom_rolling_offsets = []
+    per_atom_rolling_stds_raw = []
+    for i in range(
+        7
+    ):  # TODO: only the selected position would suffice for per_atom_rolling_stds
         roll = df[i].dropna().rolling(9, center=True)
-        at_stdc.append(roll.std(ddof=0))
-        at_roff.append(roll.mean())
-        at_std0.append(roll.apply(lambda x : np.sqrt(x.pow(2).mean())))
-    runstds_ = pd.concat(at_stdc, axis=1).reindex(pd.Index([i for i in range(len(cmparr))]))
-    runoffs_ = pd.concat(at_roff, axis=1).reindex(pd.Index([i for i in range(len(cmparr))]))
-    runstd0s_ = pd.concat(at_std0, axis=1).reindex(pd.Index([i for i in range(len(cmparr))]))
-    # get index with the lowest mean rolling stddev for which all ats were detected (all that were detected anywhere for this sample)
-    runstds_val = runstds_[runstds_.columns[mask.any(axis=0)]].dropna(axis=0).mean(axis=1)
+        per_atom_rolling_stds.append(roll.std(ddof=0))
+        per_atom_rolling_offsets.append(roll.mean())
+        per_atom_rolling_stds_raw.append(
+            np.sqrt(per_atom_rolling_stds[-1] ** 2 + per_atom_rolling_offsets[-1] ** 2)
+        )
+    rolling_stds = pd.concat(per_atom_rolling_stds, axis=1).reindex(
+        pd.Index(list(range(len(diff_arr))))
+    )
+    rolling_offsets = pd.concat(per_atom_rolling_offsets, axis=1).reindex(
+        pd.Index(list(range(len(diff_arr))))
+    )
+    rolling_stds_raw = pd.concat(per_atom_rolling_stds_raw, axis=1).reindex(
+        pd.Index(list(range(len(diff_arr))))
+    )
+    # get index with the lowest mean rolling stddev
+    # for which all atom types were detected anywhere in this sample
+    mean_rolling_stds = (
+        rolling_stds[rolling_stds.columns[mask.any(axis=0)]].dropna(axis=0).mean(axis=1)
+    )
     try:
-        min_idx_ = runstds_val.idxmin()
+        best_idx = mean_rolling_stds.idxmin()
     except ValueError:
-        return None # still not found
-    
-    offdct_ = {}
-    for col in runstds_.dropna(how='all', axis=1).columns: # for all at shifts that were detected anywhere in this sample
-        at = BBATNS[col]
-        roff = runoffs_.loc[min_idx_][col]
-        std0 = runstd0s_.loc[min_idx_][col]
-        stdc = runstds_.loc[min_idx_][col]
-        dAIC = np.log(std0/stdc) * 9 - 1 # difference in Akaike’s information criterion, 9 is width of window
-        logging.getLogger('trizod.scoring').info(f'minimum running average: {at} {roff} {dAIC}')
-        if dAIC > minAIC:
-            logging.getLogger('trizod.scoring').info(f'using offset correction: {at} {roff} {dAIC}')
-            offdct_[at] = roff
+        return None  # still not found
+
+    offset_dict = {}
+    for col in rolling_stds.dropna(how="all", axis=1).columns:
+        atom_type = BBATNS[col]
+        rolling_offset = rolling_offsets.loc[best_idx][col]
+        std_raw = rolling_stds_raw.loc[best_idx][col]
+        std_corrected = rolling_stds.loc[best_idx][col]
+        # difference in Akaike's information criterion, 9 is width of window
+        delta_AIC = np.log(std_raw / std_corrected) * 9 - 1
+        logging.getLogger("trizod.scoring").info(
+            f"minimum running average: {atom_type} {rolling_offset} {delta_AIC}"
+        )
+        if delta_AIC > min_AIC:
+            logging.getLogger("trizod.scoring").info(
+                f"using offset correction: {atom_type} {rolling_offset} {delta_AIC}"
+            )
+            offset_dict[atom_type] = rolling_offset
         else:
-            logging.getLogger('trizod.scoring').info(f'rejecting offset correction due to low dAIC: {at} {roff} {dAIC}')
-            #offdct_[at] = 0.0
+            logging.getLogger("trizod.scoring").info(
+                f"rejecting offset correction due to low delta_AIC: {atom_type} {rolling_offset} {delta_AIC}"
+            )
 
-    return offdct_ #with the running offsets
+    return offset_dict
 
-def compute_offsets(shw_, accdct_, minAIC=999.):
-    anum_ = np.sum(accdct_, axis=0)
-    # I expect to see RuntimeWarnings in this block
-    # accdct_ can contain fully-False columns
+
+def compute_offsets(weighted_diffs, accepted_mask, min_AIC=999.0):
+    atom_counts = np.sum(accepted_mask, axis=0)
+    # RuntimeWarnings expected: accepted_mask can contain fully-False columns
     with warnings.catch_warnings():
         warnings.simplefilter("ignore", category=RuntimeWarning)
-        newoffdct_ = np.nanmean(shw_, axis=0, where=accdct_)
-        astd0_ = np.sqrt(np.nanmean(shw_ ** 2, axis=0, where=accdct_))
-        astdc_ = np.nanstd(shw_, axis=0, where=accdct_)
-        with np.errstate(divide='ignore'):
-            # for anum_ == 1, astdc_ is 0. resulting in an adAIC_ of inf as consequence of division-by-zero
-            # this would be problematic if not all offsets with anum_ < 4 were rejected anyways
-            adAIC_ = np.log(astd0_ / astdc_) * anum_ - 1
-    reject_mask = (adAIC_ < minAIC) | (anum_ < 4)
-    astdc_[reject_mask] = astd0_[reject_mask]
-    newoffdct_[reject_mask] = 0.
-    newoffdct_ = {at:val for at,val in zip(BBATNS,newoffdct_)}
-    return newoffdct_
-
-def get_outlier_mask(cdfs3_, cdfs_, ashwi_, mask, cdfthr=6.0):
-    oldct_ = ashwi_ > cdfthr
-    totnum_ = mask.sum(axis=1)
-    finaloutli_ = (cdfs_ > cdfthr) | ((cdfs3_ > cdfthr) & (cdfs_ > 0.0) & (totnum_ > 0))
-    ol_ = mask & (np.bitwise_or(np.expand_dims(finaloutli_, axis=1), oldct_)) # mask for the outliers
-    return ol_
-
-def get_std_norm_diffs(cmparr, mask, offdct={}):
-    w_ = np.array([REFINED_WEIGHTS[at] for at in BBATNS]) # ensure same order
-    off_ = np.array([offdct.get(at, 0.) for at in BBATNS])
-    shw_ = cmparr / w_
-    ashwi_ = shw_.copy() # need to do the copy here, because shw_ is reused later and would otherwise be partially overwritten due to the out=
-    ashwi_ = np.abs(np.subtract(shw_, off_, where=mask, out=ashwi_))
-    return shw_, ashwi_
-
-def compute_zscores(ashwi3, k3, mask, corr=False):
-    indices = np.where(np.any(mask,axis=1))
-    mini, maxi = indices[0][0], indices[0][-1]
-    tot3f_ = (np.minimum(ashwi3, 4.0) ** 2).sum(axis=1)
-    totn3f_ = k3
-    cdfs3_ = convChi2CDF(tot3f_, totn3f_)
+        new_offsets = np.nanmean(weighted_diffs, axis=0, where=accepted_mask)
+        std_uncorrected = np.sqrt(
+            np.nanmean(weighted_diffs**2, axis=0, where=accepted_mask)
+        )
+        std_corrected = np.nanstd(weighted_diffs, axis=0, where=accepted_mask)
+        with np.errstate(divide="ignore"):
+            # for atom_counts == 1, std_corrected is 0, resulting in inf delta_AIC;
+            # not problematic since all offsets with atom_counts < 4 are rejected anyway
+            delta_AIC = np.log(std_uncorrected / std_corrected) * atom_counts - 1
+    reject_mask = (delta_AIC < min_AIC) | (atom_counts < 4)
+    std_corrected[reject_mask] = std_uncorrected[reject_mask]
+    new_offsets[reject_mask] = 0.0
+    new_offsets = dict(zip(BBATNS, new_offsets))
+    return new_offsets
+
+
+def get_outlier_mask(
+    zscores_triplet, zscores, abs_weighted_diffs, mask, cdf_threshold=6.0
+):
+    atom_outliers = abs_weighted_diffs > cdf_threshold
+    shift_counts = mask.sum(axis=1)
+    final_outliers = (zscores > cdf_threshold) | (
+        (zscores_triplet > cdf_threshold) & (zscores > 0.0) & (shift_counts > 0)
+    )
+    outlier_mask = mask & (
+        np.bitwise_or(np.expand_dims(final_outliers, axis=1), atom_outliers)
+    )
+    return outlier_mask
+
+
+def compute_weighted_diffs(diff_arr, mask, offset_dict=None):
+    if offset_dict is None:
+        offset_dict = {}
+    weights = np.array([REFINED_WEIGHTS[atom_type] for atom_type in BBATNS])
+    offsets = np.array([offset_dict.get(atom_type, 0.0) for atom_type in BBATNS])
+    weighted_diffs = diff_arr / weights
+    # copy needed: weighted_diffs is reused later, subtract with out= would overwrite it
+    abs_weighted_diffs = weighted_diffs.copy()
+    abs_weighted_diffs = np.abs(
+        np.subtract(weighted_diffs, offsets, where=mask, out=abs_weighted_diffs)
+    )
+    return weighted_diffs, abs_weighted_diffs
+
+
+def compute_zscores(diffs, dof, mask, corr=False):
+    indices = np.where(np.any(mask, axis=1))
+    first_idx, last_idx = indices[0][0], indices[0][-1]
+    rss = (np.minimum(diffs, 4.0) ** 2).sum(axis=1)
+    zscores = chi2_cdf_approx(rss, dof)
     if corr:
         raise ValueError("Z_CORRECTION is not supported")
-        # for k in range(1,22):
-        #     m = totn3f_ == k
-        #     cdfs3_[m] += cdfs3_[m] * Z_CORRECTION[k]
-    cdfs3_[:mini] = np.nan
-    cdfs3_[maxi+1:] = np.nan
-    return cdfs3_
+    zscores[:first_idx] = np.nan
+    zscores[last_idx + 1 :] = np.nan
+    return zscores
 
-def compute_pscores(ashwi3, k3, mask, quotient=2.0, limit=4.0):
-    indices = np.where(np.any(mask,axis=1))
-    mini, maxi = indices[0][0], indices[0][-1]
+
+def compute_pscores(diffs, dof, mask, quotient=2.0, limit=4.0):
+    indices = np.where(np.any(mask, axis=1))
+    first_idx, last_idx = indices[0][0], indices[0][-1]
 
     if limit:
-        p = np.prod(scipy.stats.norm.pdf(np.minimum(ashwi3, limit) / quotient) / scipy.stats.norm.pdf(0.), axis=1)
-        with np.errstate(divide='ignore'): # zeros are to be expected; resulting NANs are ok
-            p = p ** (1/k3)
-        minimum = scipy.stats.norm.pdf(limit / quotient) / scipy.stats.norm.pdf(0.)
-        p = (p - minimum) / (1. - minimum)
+        p = np.prod(
+            scipy.stats.norm.pdf(np.minimum(diffs, limit) / quotient)
+            / scipy.stats.norm.pdf(0.0),
+            axis=1,
+        )
+        with np.errstate(
+            divide="ignore"
+        ):  # zeros are to be expected; resulting NANs are ok
+            p = p ** (1 / dof)
+        minimum = scipy.stats.norm.pdf(limit / quotient) / scipy.stats.norm.pdf(0.0)
+        p = (p - minimum) / (1.0 - minimum)
     else:
-        p = np.prod(scipy.stats.norm.pdf(ashwi3 / quotient) / scipy.stats.norm.pdf(0.), axis=1)
-        with np.errstate(divide='ignore'): # zeros are to be expected; resulting NANs are ok
-            p = p ** (1/k3)
-    p[k3 == 0] = np.nan
-    p[:mini] = np.nan
-    p[maxi+1:] = np.nan
+        p = np.prod(
+            scipy.stats.norm.pdf(diffs / quotient) / scipy.stats.norm.pdf(0.0), axis=1
+        )
+        with np.errstate(
+            divide="ignore"
+        ):  # zeros are to be expected; resulting NANs are ok
+            p = p ** (1 / dof)
+    p[dof == 0] = np.nan
+    p[:first_idx] = np.nan
+    p[last_idx + 1 :] = np.nan
     return p
 
-def convert_to_triplet_data(ashwi_, mask):
-    ashwi3 = ashwi_.copy()
-    ashwi3[~mask] = 0.
-    ashwi3 = np.column_stack([np.pad(ashwi3, ((1,1),(0,0)))[2:],   ashwi3,   np.pad(ashwi3, ((1,1),(0,0)))[:-2]])
-    k3     = np.column_stack([np.pad(mask, ((1,1),(0,0)))[2:],mask,np.pad(mask, ((1,1),(0,0)))[:-2]]).sum(axis=1)
-    return ashwi3, k3
 
-def get_offset_corrected_wSCS(seq, shifts, predshiftdct):
+def convert_to_triplet_data(abs_weighted_diffs, mask):
+    triplet_diffs = abs_weighted_diffs.copy()
+    triplet_diffs[~mask] = 0.0
+    triplet_diffs = np.column_stack(
+        [
+            np.pad(triplet_diffs, ((1, 1), (0, 0)))[2:],
+            triplet_diffs,
+            np.pad(triplet_diffs, ((1, 1), (0, 0)))[:-2],
+        ]
+    )
+    triplet_dof = np.column_stack(
+        [np.pad(mask, ((1, 1), (0, 0)))[2:], mask, np.pad(mask, ((1, 1), (0, 0)))[:-2]]
+    ).sum(axis=1)
+    return triplet_diffs, triplet_dof
+
+
+def get_offset_corrected_shifts(seq, shifts, predshiftdct):
     # get polymer sequence and chemical backbone shifts
     ret = bmrb.get_valid_bbshifts(shifts, seq)
     if ret is None:
-        logging.getLogger('trizod.scoring').error(f'retrieving backbone shifts failed')
+        logging.getLogger("trizod.scoring").error("retrieving backbone shifts failed")
         return
     bbshifts_arr, bbshifts_mask = ret
-    
+
     # compare predicted to actual shifts
-    cmparr, _, cmp_mask = comp2pred_arr(predshiftdct, bbshifts_arr, bbshifts_mask)
-    totbbsh = np.sum(cmp_mask)
-    if totbbsh == 0:
-        logging.getLogger('trizod.scoring').error(f'no comparable backbone shifts')
+    diff_arr, _, cmp_mask = compare_to_predicted(
+        predshiftdct, bbshifts_arr, bbshifts_mask
+    )
+    total_backbone_shifts = np.sum(cmp_mask)
+    if total_backbone_shifts == 0:
+        logging.getLogger("trizod.scoring").error("no comparable backbone shifts")
         return
-    logging.getLogger('trizod.scoring').info(f"total number of backbone shifts: {totbbsh}")
-
-    off0 = {at:0.0 for at in BBATNS}
-    shw0, ashwi0 = get_std_norm_diffs(cmparr, cmp_mask, off0)
-    cdfs0 = compute_zscores(ashwi0, cmp_mask.sum(axis=1), cmp_mask)
-    cdfs30 = compute_zscores(*convert_to_triplet_data(ashwi0, cmp_mask), cmp_mask)
-    ol0 = get_outlier_mask(cdfs30, cdfs0, ashwi0, cmp_mask, cdfthr=6.0)
-    noff0 = compute_offsets(shw0, cmp_mask & ~ol0, minAIC=6.0)
-    av0 = np.nanmean(cdfs30)
-    offf = noff0
-    olf = ol0
-
-    offr = compute_running_offsets(cmparr, cmp_mask, minAIC=6.0)
-    if offr is None:
-        logging.getLogger('trizod.scoring').warning(f'no running offset could be estimated')
-    elif np.any([v != 0. for v in offr.values()]):
-        shwc, ashwic = get_std_norm_diffs(cmparr, cmp_mask, offr)
-        cdfsc = compute_zscores(ashwic, cmp_mask.sum(axis=1), cmp_mask)
-        cdfs3c = compute_zscores(*convert_to_triplet_data(ashwic, cmp_mask), cmp_mask)
-        avc = np.nanmean(cdfs3c)
-        if av0 >= avc: # use offset correction only if it leads to, in average, better accordance with the POTENCI model (more disordered)
-            olc = get_outlier_mask(cdfs3c, cdfsc, ashwic, cmp_mask, cdfthr=6.0)
-            noffc = compute_offsets(shwc, cmp_mask & ~olc, minAIC=6.)
-            offf = noffc
-            olf = olc
-
-    shwf, ashwif = get_std_norm_diffs(cmparr, cmp_mask, offf)
-    return shwf, ashwif, cmp_mask, olf, offf, shw0, ashwi0, ol0, off0
+    logging.getLogger("trizod.scoring").info(
+        f"total number of backbone shifts: {total_backbone_shifts}"
+    )
+
+    offsets_initial = dict.fromkeys(BBATNS, 0.0)
+    weighted_diffs_initial, abs_weighted_diffs_initial = compute_weighted_diffs(
+        diff_arr, cmp_mask, offsets_initial
+    )
+    zscores_initial = compute_zscores(
+        abs_weighted_diffs_initial, cmp_mask.sum(axis=1), cmp_mask
+    )
+    zscores_triplet_initial = compute_zscores(
+        *convert_to_triplet_data(abs_weighted_diffs_initial, cmp_mask), cmp_mask
+    )
+    outlier_mask_initial = get_outlier_mask(
+        zscores_triplet_initial,
+        zscores_initial,
+        abs_weighted_diffs_initial,
+        cmp_mask,
+        cdf_threshold=6.0,
+    )
+    new_offsets_initial = compute_offsets(
+        weighted_diffs_initial, cmp_mask & ~outlier_mask_initial, min_AIC=6.0
+    )
+    mean_zscore_initial = np.nanmean(zscores_triplet_initial)
+    offsets_final = new_offsets_initial
+    outlier_mask_final = outlier_mask_initial
+
+    offsets_running = compute_running_offsets(diff_arr, cmp_mask, min_AIC=6.0)
+    if offsets_running is None:
+        logging.getLogger("trizod.scoring").warning(
+            "no running offset could be estimated"
+        )
+    elif np.any([v != 0.0 for v in offsets_running.values()]):
+        weighted_diffs_corrected, abs_weighted_diffs_corrected = compute_weighted_diffs(
+            diff_arr, cmp_mask, offsets_running
+        )
+        zscores_corrected = compute_zscores(
+            abs_weighted_diffs_corrected, cmp_mask.sum(axis=1), cmp_mask
+        )
+        zscores_triplet_corrected = compute_zscores(
+            *convert_to_triplet_data(abs_weighted_diffs_corrected, cmp_mask), cmp_mask
+        )
+        mean_zscore_corrected = np.nanmean(zscores_triplet_corrected)
+        if (
+            mean_zscore_initial >= mean_zscore_corrected
+        ):  # use offset correction only if it improves accordance with the POTENCI model
+            outlier_mask_corrected = get_outlier_mask(
+                zscores_triplet_corrected,
+                zscores_corrected,
+                abs_weighted_diffs_corrected,
+                cmp_mask,
+                cdf_threshold=6.0,
+            )
+            new_offsets_corrected = compute_offsets(
+                weighted_diffs_corrected,
+                cmp_mask & ~outlier_mask_corrected,
+                min_AIC=6.0,
+            )
+            offsets_final = new_offsets_corrected
+            outlier_mask_final = outlier_mask_corrected
+
+    weighted_diffs_final, abs_weighted_diffs_final = compute_weighted_diffs(
+        diff_arr, cmp_mask, offsets_final
+    )
+    return (
+        weighted_diffs_final,
+        abs_weighted_diffs_final,
+        cmp_mask,
+        outlier_mask_final,
+        offsets_final,
+        weighted_diffs_initial,
+        abs_weighted_diffs_initial,
+        outlier_mask_initial,
+        offsets_initial,
+    )
diff --git a/trizod/trizod.py b/trizod/trizod.py
index a45e7f4..0dc2fdd 100644
--- a/trizod/trizod.py
+++ b/trizod/trizod.py
@@ -1,409 +1,611 @@
 #!/usr/bin/env python3
-import os, sys
-import logging
 import argparse
-import trizod.potenci.potenci as potenci
+import hashlib
+import json
+import logging
+import pickle
+import re
+import sys
+import time
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+from pandarallel import pandarallel
+from tqdm import tqdm
+
 import trizod.bmrb.bmrb as bmrb
+import trizod.potenci.potenci as potenci
 import trizod.scoring.scoring as scoring
 from trizod.constants import BBATNS, CAN_TRANS
 from trizod.utils import ArgHelpFormatter
-import numpy as np
-import pandas as pd
-import traceback
-import re
-import pickle
-from tqdm import tqdm
-from pandarallel import pandarallel
-import time
 
-class Found(Exception): pass
-class ZscoreComputationError(Exception): pass
-class OffsetTooLargeException(Exception): pass
-class OffsetCausedFilterException(Exception): pass
-class FilterException(Exception): pass
-
-filter_defaults = pd.DataFrame({
-    'temperature-range' : [[-np.inf, +np.inf], [263.0, 333.0], [273.0, 323.0], [273.0, 313.0]],
-    'ionic-strength-range' : [[0., np.inf], [0., 7.], [0., 5.], [0., 3.]],
-    'pH-range' : [[-np.inf, np.inf], [2., 12.], [4., 10.], [6., 8.]],
-    'unit-assumptions' : [True, True, True, False],
-    'unit-corrections' : [True, True, False, False],
-    'default-conditions' : [True, True, True, False],
-    'peptide-length-range' : [[5], [5], [10], [15]],
-    'min-backbone-shift-types' : [1, 2, 3, 5],
-    'min-backbone-shift-positions' : [3, 3, 8, 12],
-    'min-backbone-shift-fraction' : [0., 0., 0.6, 0.8],
-    'max-noncanonical-fraction' : [1., 0.1, 0.025, 0.],
-    'max-x-fraction' : [1., 0.2, 0.05, 0.],
-    'keywords-blacklist' : [[], 
-                            ['denatur'], 
-                            ['denatur', 'unfold', 'misfold'], 
-                            ['denatur', 'unfold', 'misfold', 'interacti', 'bound']], # interacti[on/ng]
-    'chemical-denaturants' : [[], 
-                              ['guanidin', 'GdmCl', 'Gdn-Hcl','urea'], 
-                              ['guanidin', 'GdmCl', 'Gdn-Hcl','urea'], 
-                              ['guanidin', 'GdmCl', 'Gdn-Hcl','urea','BME','2-ME','mercaptoethanol', 
-                               'TFA', 'trifluoroethanol', 'Potassium Pyrophosphate', 'acetic acid', 'CD3COOH',
-                               'DTT', 'dithiothreitol', 'dss', 'deuterated sodium acetate']],
-    'exp-method-whitelist' : [['', '.'], ['','solution', 'structures'], ['','solution', 'structures'], ['solution','structures']],
-    'exp-method-blacklist' : [[], ['solid', 'state'], ['solid', 'state'], ['solid', 'state']],
-    'max-offset' : [np.inf, 3., 3., 2.],
-    'reject-shift-type-only' : [True, True, False, False],
-}, index=['unfiltered', 'tolerant', 'moderate', 'strict'])
+
+class Found(Exception):
+    pass
+
+
+class ZscoreComputationError(Exception):
+    pass
+
+
+class OffsetTooLargeException(Exception):
+    pass
+
+
+class OffsetCausedFilterException(Exception):
+    pass
+
+
+class FilterException(Exception):
+    pass
+
+
+filter_defaults = pd.DataFrame(
+    {
+        "temperature-range": [
+            [-np.inf, +np.inf],
+            [263.0, 333.0],
+            [273.0, 323.0],
+            [273.0, 313.0],
+        ],
+        "ionic-strength-range": [[0.0, np.inf], [0.0, 7.0], [0.0, 5.0], [0.0, 3.0]],
+        "pH-range": [[-np.inf, np.inf], [2.0, 12.0], [4.0, 10.0], [6.0, 8.0]],
+        "unit-assumptions": [True, True, True, False],
+        "unit-corrections": [True, True, False, False],
+        "default-conditions": [True, True, True, False],
+        "peptide-length-range": [[5], [5], [10], [15]],
+        "min-backbone-shift-types": [1, 2, 3, 4],
+        "min-backbone-shift-positions": [3, 3, 8, 12],
+        "min-backbone-shift-fraction": [0.0, 0.0, 0.6, 0.8],
+        "max-noncanonical-fraction": [1.0, 0.1, 0.025, 0.0],
+        "max-x-fraction": [1.0, 0.2, 0.05, 0.0],
+        "keywords-blacklist": [
+            [],
+            ["denatur"],
+            ["denatur", "unfold", "misfold"],
+            ["denatur", "unfold", "misfold", "interacti", "bound"],
+        ],  # interacti[on/ng]
+        "chemical-denaturants": [
+            [],
+            ["guanidin", "GdmCl", "Gdn-Hcl", "urea"],
+            ["guanidin", "GdmCl", "Gdn-Hcl", "urea"],
+            [
+                "guanidin",
+                "GdmCl",
+                "Gdn-Hcl",
+                "urea",
+                "TFA",
+                "trifluoroethanol",
+                "Potassium Pyrophosphate",
+            ],
+        ],
+        "exp-method-whitelist": [
+            ["", "."],
+            ["", "solution", "structures"],
+            ["", "solution", "structures"],
+            ["solution", "structures"],
+        ],
+        "exp-method-blacklist": [
+            [],
+            ["solid"],
+            ["solid"],
+            ["solid"],
+        ],
+        "exclude-paramagnetic": [False, True, True, True],
+        "max-offset": [np.inf, 3.0, 3.0, 2.0],
+        "reject-shift-type-only": [True, True, False, False],
+    },
+    index=["unfiltered", "tolerant", "moderate", "strict"],
+)
 
 
 def parse_args():
-    init_parser = argparse.ArgumentParser(description='', add_help=False)
-    
-    io_grp = init_parser.add_argument_group('Input/Output Options')
+    init_parser = argparse.ArgumentParser(description="", add_help=False)
+
+    io_grp = init_parser.add_argument_group("Input/Output Options")
     io_grp.add_argument(
-        '--input-dir', '-d', default='.',
-        help='Directory that is searched recursively for BMRB .str files.')
+        "--input-dir",
+        "-d",
+        default=".",
+        help="Directory that is searched recursively for BMRB .str files.",
+    )
     io_grp.add_argument(
-        '--output-prefix', default='./trizod_dataset',
-        help='Prefix (and path) of the created output file.')
+        "--output-prefix",
+        default="./trizod_dataset",
+        help="Prefix (and path) of the created output file.",
+    )
     io_grp.add_argument(
-        '--output-format', choices=['json', 'csv'], default='csv',
-        help='Output file format.')
+        "--output-format",
+        choices=["json", "csv"],
+        default="csv",
+        help="Output file format.",
+    )
     io_grp.add_argument(
-        '--cache-dir', default='./tmp',
-        help='Create and use cache files in the given directory to acelerate repeated execution.')
+        "--cache-dir",
+        default="./tmp",
+        help="Create and use cache files in the given directory to acelerate repeated execution.",
+    )
     io_grp.add_argument(
-        '--BMRB-file-pattern', default="bmr(\d+)_3\.str",
-        help="regular expression pattern for BMRB files.")
+        "--BMRB-file-pattern",
+        default=r"bmr(\d+)_3\.str",
+        help="regular expression pattern for BMRB files.",
+    )
     io_grp.add_argument(
-        '--include-shifts', action='store_true',
-        help='Add raw backbone atom shift data to the output.')
+        "--include-shifts",
+        action="store_true",
+        help="Add raw backbone atom shift data to the output.",
+    )
     io_grp.add_argument(
-        '--no-shift-averaging', action='store_true',
-        help='Do not average over Proton groups for HA and HB shifts.')
-    
-
-    filter_defaults_grp = init_parser.add_argument_group('Filter Default Settings')
-    filter_defaults_arg = filter_defaults_grp.add_argument(
-        '--filter-defaults', choices=list(filter_defaults.index), default='tolerant',
-        help='Sets defaults for all filter options, from unfiltered to strict.')
+        "--no-shift-averaging",
+        action="store_true",
+        help="Do not average over Proton groups for HA and HB shifts.",
+    )
+
+    filter_defaults_grp = init_parser.add_argument_group("Filter Default Settings")
+    filter_defaults_grp.add_argument(
+        "--filter-defaults",
+        choices=list(filter_defaults.index),
+        default="tolerant",
+        help="Sets defaults for all filter options, from unfiltered to strict.",
+    )
     args_init, remaining_argv = init_parser.parse_known_args()
 
     parser = argparse.ArgumentParser(
         parents=[init_parser],
         description=__doc__,
-        formatter_class=ArgHelpFormatter,#formatter_class=argparse.RawDescriptionHelpFormatter,
-        )
-    filter_grp = parser.add_argument_group('Filtering Options')
+        formatter_class=ArgHelpFormatter,  # formatter_class=argparse.RawDescriptionHelpFormatter,
+    )
+    filter_grp = parser.add_argument_group("Filtering Options")
+    filter_grp.add_argument(
+        "--temperature-range",
+        nargs=2,
+        type=float,
+        default=filter_defaults.loc[args_init.filter_defaults, "temperature-range"],
+        help="Minimum and maximum temperature in Kelvin.",
+    )
     filter_grp.add_argument(
-        '--temperature-range', nargs=2, type=float, 
-        default=filter_defaults.loc[args_init.filter_defaults, 'temperature-range'],
-        help='Minimum and maximum temperature in Kelvin.')
+        "--ionic-strength-range",
+        nargs=2,
+        type=float,
+        default=filter_defaults.loc[args_init.filter_defaults, "ionic-strength-range"],
+        help="Minimum and maximum ionic strength in Mol.",
+    )
     filter_grp.add_argument(
-        '--ionic-strength-range', nargs=2, type=float, 
-        default=filter_defaults.loc[args_init.filter_defaults, 'ionic-strength-range'],
-        help='Minimum and maximum ionic strength in Mol.')
+        "--pH-range",
+        nargs=2,
+        type=float,
+        default=filter_defaults.loc[args_init.filter_defaults, "pH-range"],
+        help="Minimum and maximum pH.",
+    )
     filter_grp.add_argument(
-        '--pH-range', nargs=2, type=float, 
-        default=filter_defaults.loc[args_init.filter_defaults, 'pH-range'],
-        help='Minimum and maximum pH.')
+        "--unit-assumptions",
+        action=argparse.BooleanOptionalAction,
+        default=filter_defaults.loc[args_init.filter_defaults, "unit-assumptions"],
+        help="Assume units for Temp., Ionic str. and pH if they are not given and exclude entries instead.",
+    )
     filter_grp.add_argument(
-        '--unit-assumptions', action=argparse.BooleanOptionalAction, 
-        default=filter_defaults.loc[args_init.filter_defaults, 'unit-assumptions'],
-        help='Assume units for Temp., Ionic str. and pH if they are not given and exclude entries instead.')
+        "--unit-corrections",
+        action=argparse.BooleanOptionalAction,
+        default=filter_defaults.loc[args_init.filter_defaults, "unit-corrections"],
+        help="Correct values for Temp., Ionic str. and pH if units are most likely wrong.",
+    )
     filter_grp.add_argument(
-        '--unit-corrections', action=argparse.BooleanOptionalAction, 
-        default=filter_defaults.loc[args_init.filter_defaults, 'unit-corrections'],
-        help='Correct values for Temp., Ionic str. and pH if units are most likely wrong.')
+        "--default-conditions",
+        action=argparse.BooleanOptionalAction,
+        default=filter_defaults.loc[args_init.filter_defaults, "default-conditions"],
+        help="Assume standard conditions if pH (7), ionic strength (0.1 M) or temperature (298 K) are missing and exclude entries instead.",
+    )
     filter_grp.add_argument(
-        '--default-conditions', action=argparse.BooleanOptionalAction, 
-        default=filter_defaults.loc[args_init.filter_defaults, 'default-conditions'],
-        help='Assume standard conditions if pH (7), ionic strength (0.1 M) or temperature (298 K) are missing and exclude entries instead.')
+        "--peptide-length-range",
+        nargs="+",
+        type=int,
+        default=filter_defaults.loc[args_init.filter_defaults, "peptide-length-range"],
+        help="Minimum (and optionally maximum) peptide sequence length.",
+    )
     filter_grp.add_argument(
-        '--peptide-length-range', nargs='+', type=int, 
-        default=filter_defaults.loc[args_init.filter_defaults, 'peptide-length-range'],
-        help='Minimum (and optionally maximum) peptide sequence length.')
+        "--min-backbone-shift-types",
+        type=int,
+        default=filter_defaults.loc[
+            args_init.filter_defaults, "min-backbone-shift-types"
+        ],
+        help="Minimum number of different backbone shift types (max 7).",
+    )
     filter_grp.add_argument(
-        '--min-backbone-shift-types', type=int, 
-        default=filter_defaults.loc[args_init.filter_defaults, 'min-backbone-shift-types'],
-        help='Minimum number of different backbone shift types (max 7).')
+        "--min-backbone-shift-positions",
+        type=int,
+        default=filter_defaults.loc[
+            args_init.filter_defaults, "min-backbone-shift-positions"
+        ],
+        help="Minimum number of positions with at least one backbone shift.",
+    )
     filter_grp.add_argument(
-        '--min-backbone-shift-positions', type=int, 
-        default=filter_defaults.loc[args_init.filter_defaults, 'min-backbone-shift-positions'],
-        help='Minimum number of positions with at least one backbone shift.')
+        "--min-backbone-shift-fraction",
+        type=float,
+        default=filter_defaults.loc[
+            args_init.filter_defaults, "min-backbone-shift-fraction"
+        ],
+        help="Minimum fraction of positions with at least one backbone shift.",
+    )
     filter_grp.add_argument(
-        '--min-backbone-shift-fraction', type=float, 
-        default=filter_defaults.loc[args_init.filter_defaults, 'min-backbone-shift-fraction'],
-        help='Minimum fraction of positions with at least one backbone shift.')
+        "--max-noncanonical-fraction",
+        type=float,
+        default=filter_defaults.loc[
+            args_init.filter_defaults, "max-noncanonical-fraction"
+        ],
+        help="Maximum fraction of non-canonical amino acids (X count as arbitrary canonical) in the amino acid sequence.",
+    )
     filter_grp.add_argument(
-        '--max-noncanonical-fraction', type=float, 
-        default=filter_defaults.loc[args_init.filter_defaults, 'max-noncanonical-fraction'],
-        help='Maximum fraction of non-canonical amino acids (X count as arbitrary canonical) in the amino acid sequence.')
+        "--max-x-fraction",
+        type=float,
+        default=filter_defaults.loc[args_init.filter_defaults, "max-x-fraction"],
+        help="Maximum fraction of X letters (arbitrary canonical amino acid) in the amino acid sequence.",
+    )
     filter_grp.add_argument(
-        '--max-x-fraction', type=float, 
-        default=filter_defaults.loc[args_init.filter_defaults, 'max-x-fraction'],
-        help='Maximum fraction of X letters (arbitrary canonical amino acid) in the amino acid sequence.')
+        "--keywords-blacklist",
+        nargs="*",
+        default=filter_defaults.loc[args_init.filter_defaults, "keywords-blacklist"],
+        help="Exclude entries with any of these keywords mentioned anywhere in the BMRB file, case ignored.",
+    )
     filter_grp.add_argument(
-        '--keywords-blacklist', nargs='*', 
-        default=filter_defaults.loc[args_init.filter_defaults, 'keywords-blacklist'],
-        help='Exclude entries with any of these keywords mentioned anywhere in the BMRB file, case ignored.')
+        "--chemical-denaturants",
+        nargs="*",
+        default=filter_defaults.loc[args_init.filter_defaults, "chemical-denaturants"],
+        help="Exclude entries with any of these chemicals as substrings of sample components, case ignored.",
+    )
     filter_grp.add_argument(
-        '--chemical-denaturants', nargs='*', 
-        default=filter_defaults.loc[args_init.filter_defaults, 'chemical-denaturants'],
-        help='Exclude entries with any of these chemicals as substrings of sample components, case ignored.')
+        "--exp-method-whitelist",
+        nargs="*",
+        default=filter_defaults.loc[args_init.filter_defaults, "exp-method-whitelist"],
+        help="Include only entries with any of these keywords as substring of the experiment subtype, case ignored.",
+    )
     filter_grp.add_argument(
-        '--exp-method-whitelist', nargs='*', 
-        default=filter_defaults.loc[args_init.filter_defaults, 'exp-method-whitelist'],
-        help='Include only entries with any of these keywords as substring of the experiment subtype, case ignored.')
+        "--exp-method-blacklist",
+        nargs="*",
+        default=filter_defaults.loc[args_init.filter_defaults, "exp-method-blacklist"],
+        help="Exclude entries with any of these keywords as substring of the experiment subtype, case ignored.",
+    )
     filter_grp.add_argument(
-        '--exp-method-blacklist', nargs='*', 
-        default=filter_defaults.loc[args_init.filter_defaults, 'exp-method-blacklist'],
-        help='Exclude entries with any of these keywords as substring of the experiment subtype, case ignored.')
-    
-    scores_grp = parser.add_argument_group('Scoring Options')
+        "--exclude-paramagnetic",
+        action=argparse.BooleanOptionalAction,
+        default=filter_defaults.loc[args_init.filter_defaults, "exclude-paramagnetic"],
+        help="Exclude entries flagged as paramagnetic in the BMRB assembly or entity metadata.",
+    )
+
+    scores_grp = parser.add_argument_group("Scoring Options")
     scores_grp.add_argument(
-        '--score-types', nargs='+', choices=['zscores', 'pscores', 'corrected'], 
-        default=['zscores','pscores'],
-        help='Which type of scores are created: Observation count-independent zscores (zscores), '
-        'original CheZOD zscores (chezod) or geometric mean of observation probabilities (pscores).')
+        "--score-types",
+        nargs="+",
+        choices=["zscores", "pscores", "corrected"],
+        default=["zscores", "pscores"],
+        help="Which type of scores are created: Observation count-independent zscores (zscores), "
+        "original CheZOD zscores (chezod) or geometric mean of observation probabilities (pscores).",
+    )
     scores_grp.add_argument(
-        '--offset-correction', action=argparse.BooleanOptionalAction, default=True,
-        help='Compute correction offsets for random coil chemical shifts')
+        "--offset-correction",
+        action=argparse.BooleanOptionalAction,
+        default=True,
+        help="Compute correction offsets for random coil chemical shifts",
+    )
     scores_grp.add_argument(
-        '--max-offset', type=float, 
-        default=filter_defaults.loc[args_init.filter_defaults, 'max-offset'],
-        help='Maximum valid offset correction for any random coil chemical shift type.')
+        "--max-offset",
+        type=float,
+        default=filter_defaults.loc[args_init.filter_defaults, "max-offset"],
+        help="Maximum valid offset correction for any random coil chemical shift type.",
+    )
     scores_grp.add_argument(
-        '--reject-shift-type-only', action=argparse.BooleanOptionalAction,
-        default=filter_defaults.loc[args_init.filter_defaults, 'reject-shift-type-only'],
-        help='Upon exceeding the maximal offset set by <--max-offset>, exclude only the backbone shifts exceeding the offset instead of the whole entry.')
+        "--reject-shift-type-only",
+        action=argparse.BooleanOptionalAction,
+        default=filter_defaults.loc[
+            args_init.filter_defaults, "reject-shift-type-only"
+        ],
+        help="Upon exceeding the maximal offset set by <--max-offset>, exclude only the backbone shifts exceeding the offset instead of the whole entry.",
+    )
     scores_grp.add_argument(
-        '--precision', type=int, default=4,
-        help='Number of decimal digits that are output to human readable files.')
+        "--precision",
+        type=int,
+        default=4,
+        help="Number of decimal digits that are output to human readable files.",
+    )
 
-    other_grp = parser.add_argument_group('Other Options')
+    other_grp = parser.add_argument_group("Other Options")
     other_grp.add_argument(
-        '--processes', default=8, type=int,
-        help='Number of processes to spawn in multiprocessing.')
+        "--processes",
+        default=8,
+        type=int,
+        help="Number of processes to spawn in multiprocessing.",
+    )
     other_grp.add_argument(
-        '--progress', action=argparse.BooleanOptionalAction,
+        "--progress",
+        action=argparse.BooleanOptionalAction,
         default=True,
-        help='Show progress bars.')
+        help="Show progress bars.",
+    )
 
-    parser.add_argument('--debug', action='store_true')
+    parser.add_argument("--debug", action="store_true")
     args = parser.parse_args(sys.argv[1:])
-    #args = argparse.Namespace(**vars(args_init), **vars(args))
+    # args = argparse.Namespace(**vars(args_init), **vars(args))
 
-
-    if not os.path.exists(args.input_dir):
-        logging.getLogger('trizod').error(f"Input directory {args.input_dir} does not exist.")
+    args.input_dir = Path(args.input_dir)
+    if not args.input_dir.exists():
+        logging.getLogger("trizod").error(
+            f"Input directory {args.input_dir} does not exist."
+        )
         exit(1)
-    if not os.path.isdir(args.input_dir):
-        logging.getLogger('trizod').error(f"Path {args.input_dir} is not a directory.")
+    if not args.input_dir.is_dir():
+        logging.getLogger("trizod").error(f"Path {args.input_dir} is not a directory.")
         exit(1)
-    args.input_dir = os.path.abspath(args.input_dir)
+    args.input_dir = args.input_dir.resolve()
 
-    args.output_prefix = os.path.abspath(args.output_prefix)
-    if not os.path.exists(os.path.dirname(args.output_prefix)):
-        logging.getLogger('trizod').error(f"Output directory {os.path.dirname(args.output_prefix)} does not exist.")
+    args.output_prefix = Path(args.output_prefix).resolve()
+    if not args.output_prefix.parent.exists():
+        logging.getLogger("trizod").error(
+            f"Output directory {args.output_prefix.parent} does not exist."
+        )
         exit(1)
 
     if len(args.peptide_length_range) == 1:
         args.peptide_length_range.append(np.inf)
 
-    args.cache_dir = os.path.abspath(args.cache_dir)
-    dirs = [args.cache_dir,
-            os.path.join(args.cache_dir, 'wSCS'),
-            os.path.join(args.cache_dir, 'bmrb_entries')]
-    if not np.all([os.path.exists(d) for d in dirs]):
-        if not os.path.exists(args.cache_dir):
-            logging.getLogger('trizod').debug(f"Directory {args.cache_dir} does not exist and is created.")
-        for d in dirs:
-            os.makedirs(d, exist_ok=True)
-    elif not os.path.isdir(args.cache_dir):
-        logging.getLogger('trizod').error(f"Path {args.cache_dir} is not a directory.")
+    args.cache_dir = Path(args.cache_dir).resolve()
+    subdirs = [
+        args.cache_dir,
+        args.cache_dir / "wSCS",
+        args.cache_dir / "bmrb_entries",
+        args.cache_dir / "potenci",
+    ]
+    if not all(d.exists() for d in subdirs):
+        if not args.cache_dir.exists():
+            logging.getLogger("trizod").debug(
+                f"Directory {args.cache_dir} does not exist and is created."
+            )
+        for d in subdirs:
+            d.mkdir(parents=True, exist_ok=True)
+    elif not args.cache_dir.is_dir():
+        logging.getLogger("trizod").error(f"Path {args.cache_dir} is not a directory.")
         exit(1)
 
     return args
 
-def find_bmrb_files(input_dir, pattern="bmr(\d+)_3\.str"):
+
+def find_bmrb_files(input_dir, pattern=r"bmr(\d+)_3\.str"):
     """
-    If the given path contains at least one bmr<id>_3.str file, only files in this directory are returned. 
+    If the given path contains at least one bmr<id>_3.str file, only files in this directory are returned.
     Else, all subdirectories are searched for bmr<id>_3.str files.
     """
     bmrb_files = {}
-    for p in os.listdir(input_dir):
-        m = re.fullmatch(pattern, p)
-        if m is not None:
-            bmrb_files[m.group(1)] = os.path.join(input_dir, m.group(0))
+    input_dir = Path(input_dir)
+    for p in input_dir.iterdir():
+        match = re.fullmatch(pattern, p.name)
+        if match is not None:
+            bmrb_files[match.group(1)] = input_dir / match.group(0)
     if not bmrb_files:
         # try finding BMRB files in subdirectories instead
-        for d in [os.path.join(input_dir, p) for p in os.listdir(input_dir) if os.path.isdir(os.path.join(input_dir, p))]:
-            for p in os.listdir(d):
-                m = re.fullmatch(pattern, p)
-                if m is not None:
-                    bmrb_files[m.group(1)] = os.path.join(d, m.group(0))
+        for d in [p for p in input_dir.iterdir() if p.is_dir()]:
+            for p in d.iterdir():
+                match = re.fullmatch(pattern, p.name)
+                if match is not None:
+                    bmrb_files[match.group(1)] = d / match.group(0)
     return bmrb_files
 
+
 def parse_bmrb_file(row, cache_dir=None):
     try:
         entry = bmrb.BmrbEntry(row.name, row.dir)
-    except:
+    except Exception:
         return None
     if cache_dir:
-        with open(row.cache_fp, 'wb') as f:
+        with open(row.cache_fp, "wb") as f:
             pickle.dump(entry, f)
     return entry
 
+
 def load_bmrb_entries(bmrb_files, cache_dir=None):
     entries, failed = {}, []
-    columns = ['entry', 'dir']
+    columns = ["entry", "dir"]
     # read cached data
     if cache_dir:
-        columns.append('cache_fp')
+        columns.append("cache_fp")
+        cache_base = Path(cache_dir) / "bmrb_entries"
         for id_, fp in bmrb_files.items():
-            cache_fp = os.path.join(cache_dir, "bmrb_entries", f"{id_}.pkl")
+            cache_fp = cache_base / f"{id_}.pkl"
             entry = None
-            if os.path.exists(cache_fp):
+            if cache_fp.exists():
                 try:
-                    with open(cache_fp, "rb") as f:
+                    with cache_fp.open("rb") as f:
                         entry = pickle.load(f)
-                except:
-                    logging.getLogger('trizod.bmrb').debug(f"cache file {cache_fp} corrupt or formatted wrong")
-            entries[id_] = (entry, os.path.dirname(fp), cache_fp)
+                except Exception:
+                    logging.getLogger("trizod.bmrb").debug(
+                        f"cache file {cache_fp} corrupt or formatted wrong"
+                    )
+            entries[id_] = (entry, fp.parent, cache_fp)
     else:
         for id_, fp in bmrb_files.items():
-            entries[id_] = (None, os.path.dirname(fp))
+            entries[id_] = (None, fp.parent)
     df = pd.DataFrame(entries.values(), index=entries.keys(), columns=columns)
     sel = pd.isna(df.entry)
     if not df.loc[sel].empty:
-        df.loc[sel, 'entry'] = df.loc[sel].parallel_apply(parse_bmrb_file, axis=1, cache_dir=cache_dir)
+        df.loc[sel, "entry"] = df.loc[sel].parallel_apply(
+            parse_bmrb_file, axis=1, cache_dir=cache_dir
+        )
     sel = pd.isna(df.entry)
     failed = df.loc[sel].index.to_list()
     return df.loc[~sel], failed
 
-def prefilter_dataframe(df,
-                        method_whitelist, method_blacklist,
-                        temperature_range,
-                        ionic_strength_range,
-                        pH_range,
-                        peptide_length_range,
-                        min_backbone_shift_types,
-                        min_backbone_shift_positions,
-                        min_backbone_shift_fraction,
-                        max_noncanonical_fraction,
-                        max_x_fraction,
-                        keywords,
-                        chemical_denaturants,
-                        ):
-    missing_vals = ~df[['exp_method', 'temperature', 'ionic_strength', 'pH', 'seq', 'total_bbshifts']].isna().any(axis=1)
-    method_sel = df.exp_method.str.lower().str.contains('nmr')
-    method_whitelist_ = [l.lower() for l in method_whitelist]
-    if method_whitelist_:
-        method_sel &= (df.exp_method_subtype.str.lower().str.contains("|".join(method_whitelist_), regex=True))
+
+def prefilter_dataframe(
+    df,
+    method_whitelist,
+    method_blacklist,
+    temperature_range,
+    ionic_strength_range,
+    pH_range,
+    peptide_length_range,
+    min_backbone_shift_types,
+    min_backbone_shift_positions,
+    min_backbone_shift_fraction,
+    max_noncanonical_fraction,
+    max_x_fraction,
+    keywords,
+    chemical_denaturants,
+    exclude_paramagnetic=False,
+):
+    missing_vals = ~df[
+        ["exp_method", "temperature", "ionic_strength", "pH", "seq", "total_bbshifts"]
+    ].isna().any(axis=1)
+    method_sel = df.exp_method.str.lower().str.contains("nmr")
+    whitelist_lower = [entry.lower() for entry in method_whitelist]
+    if whitelist_lower:
+        method_sel &= df.exp_method_subtype.str.lower().str.contains(
+            "|".join(whitelist_lower), regex=True
+        )
     else:
         method_sel = False
-    method_blacklist_ = [l.lower() for l in method_blacklist]
-    if method_blacklist_:
-        method_sel &= (~df.exp_method_subtype.str.lower().str.contains("|".join(method_blacklist_), regex=True))
-    if '' in method_whitelist_ and '' not in method_blacklist_:
-        method_sel |= df.exp_method.str.lower().str.contains('nmr') & pd.isna(df.exp_method_subtype)
+    blacklist_lower = [entry.lower() for entry in method_blacklist]
+    if blacklist_lower:
+        method_sel &= ~df.exp_method_subtype.str.lower().str.contains(
+            "|".join(blacklist_lower), regex=True
+        )
+    if "" in whitelist_lower and "" not in blacklist_lower:
+        method_sel |= df.exp_method.str.lower().str.contains("nmr") & pd.isna(
+            df.exp_method_subtype
+        )
     else:
-        #method_sel = sels_pre["method (sub-)type"].fillna(False)
+        # method_sel = sels_pre["method (sub-)type"].fillna(False)
         method_sel &= ~pd.isna(df.exp_method_subtype)
         missing_vals &= ~pd.isna(df.exp_method_subtype)
     sels_pre = {
-        #"missing values" : ~df[['ionic_strength', 'pH', 'temperature','seq','total_bbshifts', 'bbshift_types']].isna().any(axis=1),
-        ("method (sub-)type", "") : 
-            method_sel,
-        ("temperature", f"{list(temperature_range)}") : 
-            (df.temperature >= temperature_range[0]) & \
-            (df.temperature <= temperature_range[1]),
-        ("ionic strength", f"{list(ionic_strength_range)}") : 
-            (df.ionic_strength >= ionic_strength_range[0]) & \
-            (df.ionic_strength <= ionic_strength_range[1]),
-        ("pH", f"{list(pH_range)}") : 
-            (df.pH >= pH_range[0]) & \
-            (df.pH <= pH_range[1]),
-        ("peptide length", f"{list(peptide_length_range)}") : 
-            (df.seq.str.len() >= peptide_length_range[0]) & \
-            (df.seq.str.len() <= peptide_length_range[1]),
-        ("bb shift types", f"[{min_backbone_shift_types}, inf]") : 
-            (df.bbshift_types >= min_backbone_shift_types),
-        ("bb shift positions", f"[{min_backbone_shift_positions}, inf]") : 
-            (df.bbshift_positions >= min_backbone_shift_positions),
-        ("bb shift fraction", f"[{min_backbone_shift_fraction}, inf]") : 
-            ((df.bbshift_positions / df.seq.str.len()) >= min_backbone_shift_fraction),
-        ("non-canonical frac", f"[0, {max_noncanonical_fraction}]") : 
-            ((1. - df.seq.str.translate(CAN_TRANS).str.count('#') / df.seq.str.len()) <= max_noncanonical_fraction),
-        ("X fraction", f"[0, {max_x_fraction}]") : 
-            (df.seq.str.count('X') / df.seq.str.len() <= max_x_fraction),
-    }
-    sels_kws = {
-        kw : ~df[kw] for kw in keywords
-    }
-    sels_denat = {
-        cd : ~df[cd] for cd in chemical_denaturants
+        # "missing values" : ~df[['ionic_strength', 'pH', 'temperature','seq','total_bbshifts', 'bbshift_types']].isna().any(axis=1),
+        ("method (sub-)type", ""): method_sel,
+        ("temperature", f"{list(temperature_range)}"): (
+            df.temperature >= temperature_range[0]
+        )
+        & (df.temperature <= temperature_range[1]),
+        ("ionic strength", f"{list(ionic_strength_range)}"): (
+            df.ionic_strength >= ionic_strength_range[0]
+        )
+        & (df.ionic_strength <= ionic_strength_range[1]),
+        ("pH", f"{list(pH_range)}"): (df.pH >= pH_range[0]) & (df.pH <= pH_range[1]),
+        ("peptide length", f"{list(peptide_length_range)}"): (
+            df.seq.str.len() >= peptide_length_range[0]
+        )
+        & (df.seq.str.len() <= peptide_length_range[1]),
+        ("bb shift types", f"[{min_backbone_shift_types}, inf]"): (
+            df.bbshift_types >= min_backbone_shift_types
+        ),
+        ("bb shift positions", f"[{min_backbone_shift_positions}, inf]"): (
+            df.bbshift_positions >= min_backbone_shift_positions
+        ),
+        ("bb shift fraction", f"[{min_backbone_shift_fraction}, inf]"): (
+            (df.bbshift_positions / df.seq.str.len()) >= min_backbone_shift_fraction
+        ),
+        ("non-canonical frac", f"[0, {max_noncanonical_fraction}]"): (
+            (1.0 - df.seq.str.translate(CAN_TRANS).str.count("#") / df.seq.str.len())
+            <= max_noncanonical_fraction
+        ),
+        ("X fraction", f"[0, {max_x_fraction}]"): (
+            df.seq.str.count("X") / df.seq.str.len() <= max_x_fraction
+        ),
     }
-    sels_all_pre = {k[0]:v for k,v in sels_pre.items()} | sels_kws | sels_denat
-    
+    sels_kws = {keyword: ~df[keyword] for keyword in keywords}
+    sels_denat = {denaturant: ~df[denaturant] for denaturant in chemical_denaturants}
+    sels_paramag = {}
+    if exclude_paramagnetic:
+        sels_paramag = {"paramagnetic": ~df["paramagnetic"].astype(bool)}
+    sels_all_pre = (
+        {k[0]: v for k, v in sels_pre.items()} | sels_kws | sels_denat | sels_paramag
+    )
+
     passing = missing_vals.copy()
-    for filter, sel in sels_all_pre.items():
-        passing &= sel # passes all filters
-    
-    df['pass_pre'] = False
-    df.loc[passing, 'pass_pre'] = True
-    return df, missing_vals, sels_pre, sels_kws, sels_denat, sels_all_pre
-
-def postfilter_dataframe(df,
-                         min_backbone_shift_types,
-                         min_backbone_shift_positions,
-                         min_backbone_shift_fraction,
-                         reject_shift_type_only,
-                         score_types):
+    for _filter, sel in sels_all_pre.items():
+        passing &= sel  # passes all filters
+
+    df["pass_pre"] = False
+    df.loc[passing, "pass_pre"] = True
+    return df, missing_vals, sels_pre, sels_kws, sels_denat, sels_paramag, sels_all_pre
+
+
+def postfilter_dataframe(
+    df,
+    min_backbone_shift_types,
+    min_backbone_shift_positions,
+    min_backbone_shift_fraction,
+    reject_shift_type_only,
+    score_types,
+):
     comp_error = np.full((len(df),), False)
     for score_type in score_types:
         comp_error |= pd.isna(df[score_type])
     sels_post = {
-        ("bb shift types", f"[{min_backbone_shift_types}, inf]") : 
-            (df.bbshift_types_post >= min_backbone_shift_types),
-        ("bb shift positions", f"[{min_backbone_shift_positions}, inf]") : 
-            (df.bbshift_positions_post >= min_backbone_shift_positions),
-        ("bb shift fraction", f"[{min_backbone_shift_fraction}, inf]") : 
-            ((df.bbshift_positions_post / df.seq.str.len()) >= min_backbone_shift_fraction),
-        ("error in computation", "") : 
-            (~comp_error)
+        ("bb shift types", f"[{min_backbone_shift_types}, inf]"): (
+            df.bbshift_types_post >= min_backbone_shift_types
+        ),
+        ("bb shift positions", f"[{min_backbone_shift_positions}, inf]"): (
+            df.bbshift_positions_post >= min_backbone_shift_positions
+        ),
+        ("bb shift fraction", f"[{min_backbone_shift_fraction}, inf]"): (
+            (df.bbshift_positions_post / df.seq.str.len())
+            >= min_backbone_shift_fraction
+        ),
+        ("error in computation", ""): (~comp_error),
     }
     if not reject_shift_type_only:
         any_offsets_too_large = pd.Series(np.full((df.shape[0],), False))
-        for at in scoring.BBATNS:
-            any_offsets_too_large |= pd.isna(df[f"off_{at}"])
-        sels_post.update({("rejected due to any offset", "") : ~any_offsets_too_large})
+        for atom_type in scoring.BBATNS:
+            any_offsets_too_large |= pd.isna(df[f"off_{atom_type}"])
+        sels_post.update({("rejected due to any offset", ""): ~any_offsets_too_large})
 
     sels_off = {
-        f"off_{at}" : ~pd.isna(df[f"off_{at}"]) for at in BBATNS 
+        f"off_{atom_type}": ~pd.isna(df[f"off_{atom_type}"]) for atom_type in BBATNS
     }
-    sels_all_post = {k[0]:v for k,v in sels_post.items()} #| sels_off
+    sels_all_post = {k[0]: v for k, v in sels_post.items()}  # | sels_off
+
+    passing = df["pass_pre"].copy()
+    for _filter, sel in sels_all_post.items():
+        passing &= sel  # passes all filters
 
-    passing = df['pass_pre'].copy()
-    for filter, sel in sels_all_post.items():
-        passing &= sel # passes all filters
-    
-    df['pass_post'] = False
-    df.loc[passing, 'pass_post'] = True
+    df["pass_post"] = False
+    df.loc[passing, "pass_post"] = True
 
     return sels_post, sels_off, sels_all_post
 
-def print_filter_losses(df, missing_vals, sels_pre, sels_kws, sels_denat, sels_all_pre, sels_post, sels_off, sels_all_post):
-    w_str, w_num = np.max([len(key[0])+len(key[1])+5 for key in sels_all_pre] + [len(key[0])+len(key[1])+4 for key in sels_all_post] + [40]), 10
-    total_width = (w_str + 2*w_num + 7 + 8)
+
+def print_filter_losses(
+    df,
+    missing_vals,
+    sels_pre,
+    sels_kws,
+    sels_denat,
+    sels_paramag,
+    sels_all_pre,
+    sels_post,
+    sels_off,
+    sels_all_post,
+):
+    w_str, w_num = (
+        np.max(
+            [len(key[0]) + len(key[1]) + 5 for key in sels_all_pre]
+            + [len(key[0]) + len(key[1]) + 4 for key in sels_all_post]
+            + [40]
+        ),
+        10,
+    )
+    total_width = w_str + 2 * w_num + 7 + 8
     print("\nPre-computation filtering results")
     print("=" * total_width)
-    print(f"{'criterium':>{w_str}} : {'filtered':<{w_num}} {'unique':<{w_num}}")# {'missing':<{w_num}}")
-    for (filter,crit), sel in sels_pre.items():
+    print(
+        f"{'criterium':>{w_str}} : {'filtered':<{w_num}} {'unique':<{w_num}}"
+    )  # {'missing':<{w_num}}")
+    for (filter, crit), sel in sels_pre.items():
         uniq = pd.Series(np.full((len(sel),), False))
         for other_filter, other_sel in sels_all_pre.items():
             if other_filter != filter:
                 uniq |= ~other_sel
         uniq = ~sel & ~uniq
-        s = f"{filter}{'':<{w_str-(len(filter)+len(crit))}}{crit}" 
-        print(f"{s} : {(~sel).sum():>{w_num}} {uniq.sum():>{w_num}}")# {(uniq & ~missing_vals).sum():>{w_num}}")
+        s = f"{filter}{'':<{w_str - (len(filter) + len(crit))}}{crit}"
+        print(
+            f"{s} : {(~sel).sum():>{w_num}} {uniq.sum():>{w_num}}"
+        )  # {(uniq & ~missing_vals).sum():>{w_num}}")
     if sels_kws:
         print()
         print(f"{'keyword':>{w_str}} : {'filtered':<{w_num}} {'unique':<{w_num}}")
@@ -413,121 +615,192 @@ def print_filter_losses(df, missing_vals, sels_pre, sels_kws, sels_denat, sels_a
                 if other_filter != filter:
                     uniq |= ~other_sel
             uniq = ~sel & ~uniq
-            print(f"{'.*'+filter+'.*':<{w_str}} : {(~sel).sum():>{w_num}} {uniq.sum():>{w_num}}")
+            print(
+                f"{'.*' + filter + '.*':<{w_str}} : {(~sel).sum():>{w_num}} {uniq.sum():>{w_num}}"
+            )
     if sels_denat:
         print()
-        print(f"{'chemical denaturant':>{w_str}} : {'filtered':<{w_num}} {'unique':<{w_num}}")
+        print(
+            f"{'chemical denaturant':>{w_str}} : {'filtered':<{w_num}} {'unique':<{w_num}}"
+        )
         for filter, sel in sels_denat.items():
             uniq = pd.Series(np.full((len(sel),), False))
             for other_filter, other_sel in sels_all_pre.items():
                 if other_filter != filter:
                     uniq |= ~other_sel
             uniq = ~sel & ~uniq
-            print(f"{'.*'+filter+'.*':<{w_str}} : {(~sel).sum():>{w_num}} {uniq.sum():>{w_num}}")
-    
+            print(
+                f"{'.*' + filter + '.*':<{w_str}} : {(~sel).sum():>{w_num}} {uniq.sum():>{w_num}}"
+            )
+
+    if sels_paramag:
+        print()
+        for filter, sel in sels_paramag.items():
+            uniq = pd.Series(np.full((len(sel),), False))
+            for other_filter, other_sel in sels_all_pre.items():
+                if other_filter != filter:
+                    uniq |= ~other_sel
+            uniq = ~sel & ~uniq
+            print(f"{filter:<{w_str}} : {(~sel).sum():>{w_num}} {uniq.sum():>{w_num}}")
     print("-" * total_width)
-    passing_pre = df['pass_pre'].copy()
-    print(f"{'total filtered':<{w_str}} : {(~passing_pre).sum():>{w_num}} of {len(df):>{w_num-3}} ({(~passing_pre).sum() / len(df) * 100.:>{6}.2f} %)")
+    passing_pre = df["pass_pre"].copy()
+    print(
+        f"{'total filtered':<{w_str}} : {(~passing_pre).sum():>{w_num}} of {len(df):>{w_num - 3}} ({(~passing_pre).sum() / len(df) * 100.0:>{6}.2f} %)"
+    )
     print("=" * total_width)
     print()
-    print(f"{'remaining for scores computation':<{w_str}} : {(passing_pre).sum():>{w_num}} of {len(df):>{w_num-3}} ({(passing_pre).sum() / len(df) * 100.:>{6}.2f} %)")
+    print(
+        f"{'remaining for scores computation':<{w_str}} : {(passing_pre).sum():>{w_num}} of {len(df):>{w_num - 3}} ({(passing_pre).sum() / len(df) * 100.0:>{6}.2f} %)"
+    )
     print()
     print("\nRejected offsets stats")
     print("=" * total_width)
-    print(f"{'backbone atom identifier':>{w_str}} : {'rejected':<{w_num}} {'unique':<{w_num}}")
+    print(
+        f"{'backbone atom identifier':>{w_str}} : {'rejected':<{w_num}} {'unique':<{w_num}}"
+    )
     for filter, sel in sels_off.items():
         uniq = pd.Series(np.full((len(sel),), False))
         for other_filter, other_sel in sels_off.items():
             if other_filter != filter:
                 uniq |= ~other_sel
         uniq = ~sel & ~uniq & passing_pre
-        print(f"{filter[4:]:<{w_str}} : {(~sel & passing_pre).sum():>{w_num}} {uniq.sum():>{w_num}}")
+        print(
+            f"{filter[4:]:<{w_str}} : {(~sel & passing_pre).sum():>{w_num}} {uniq.sum():>{w_num}}"
+        )
     print("=" * total_width)
     print()
     print("\nPost-computation filtering results")
     print("=" * total_width)
     print(f"{'criterium':>{w_str}} : {'filtered':<{w_num}} {'unique':<{w_num}}")
-    for (filter,crit), sel in sels_post.items():
+    for (filter, crit), sel in sels_post.items():
         uniq = pd.Series(np.full((len(sel),), False))
         for other_filter, other_sel in sels_all_post.items():
             if other_filter != filter:
                 uniq |= ~other_sel
         uniq = ~sel & ~uniq & passing_pre
-        s = f"{filter}{'':<{w_str-(len(filter)+len(crit))}}{crit}" 
+        s = f"{filter}{'':<{w_str - (len(filter) + len(crit))}}{crit}"
         print(f"{s} : {(~sel & passing_pre).sum():>{w_num}} {uniq.sum():>{w_num}}")
     print("-" * total_width)
-    passing_post = df['pass_post']
-    print(f"{'total filtered':<{w_str}} : {(~passing_post & passing_pre).sum():>{w_num}} of {passing_pre.sum():>{w_num-3}} ({(~passing_post & passing_pre).sum() / passing_pre.sum() * 100.:>{6}.2f} %)")
+    passing_post = df["pass_post"]
+    print(
+        f"{'total filtered':<{w_str}} : {(~passing_post & passing_pre).sum():>{w_num}} of {passing_pre.sum():>{w_num - 3}} ({(~passing_post & passing_pre).sum() / passing_pre.sum() * 100.0:>{6}.2f} %)"
+    )
     print("=" * total_width)
     print()
-    print(f"{'final dataset entries':<{w_str}} : {(passing_post).sum():>{w_num}} of {len(df):>{w_num-3}} ({(passing_post).sum() / len(df) * 100.:>{6}.2f} %)")
+    print(
+        f"{'final dataset entries':<{w_str}} : {(passing_post).sum():>{w_num}} of {len(df):>{w_num - 3}} ({(passing_post).sum() / len(df) * 100.0:>{6}.2f} %)"
+    )
+
 
-def fill_row_data(row, chemical_denaturants, keywords,
-                  return_default=True, assume_si=True, fix_outliers=True,
-                  include_shifts=False, no_shift_averaging=False):
-    entry = bmrb_entries.loc[row['entryID'], 'entry'] #row['entry']
+def fill_row_data(
+    row,
+    chemical_denaturants,
+    keywords,
+    return_default=True,
+    assume_si=True,
+    fix_outliers=True,
+    include_shifts=False,
+    no_shift_averaging=False,
+):
+    entry = bmrb_entries.loc[row["entryID"], "entry"]  # row['entry']
     peptide_shifts = entry.get_peptide_shifts()
-    shifts, condID, assemID, sampleIDs = peptide_shifts[(row['stID'], row['entity_assemID'], row['entityID'])]
-    row['citation_title'] = entry.citation_title
-    row['citation_DOI'] = entry.citation_DOI
-    row['exp_method'] = entry.exp_method if entry.exp_method else pd.NA
-    row['exp_method_subtype'] = entry.exp_method_subtype if entry.exp_method_subtype else pd.NA
-    row['entity_name'] = entry.entities[row['entityID']].name
-    row['ionic_strength'] = entry.conditions[condID].get_ionic_strength(return_default=return_default, assume_si=assume_si, fix_outliers=fix_outliers)
-    row['pH'] = entry.conditions[condID].get_pH(return_default=return_default)
-    row['temperature'] = entry.conditions[condID].get_temperature(return_default=return_default, assume_si=assume_si, fix_outliers=fix_outliers)
-    seq = entry.entities[row['entityID']].seq
-    row['seq'] = seq
+    shifts, condID, assemID, sampleIDs = peptide_shifts[
+        (row["stID"], row["entity_assemID"], row["entityID"])
+    ]
+    row["citation_title"] = entry.citation_title
+    row["citation_DOI"] = entry.citation_DOI
+    row["exp_method"] = entry.exp_method if entry.exp_method else pd.NA
+    row["exp_method_subtype"] = (
+        entry.exp_method_subtype if entry.exp_method_subtype else pd.NA
+    )
+    row["entity_name"] = entry.entities[row["entityID"]].name
+    row["ionic_strength"] = entry.conditions[condID].get_ionic_strength(
+        return_default=return_default, assume_si=assume_si, fix_outliers=fix_outliers
+    )
+    row["pH"] = entry.conditions[condID].get_pH(return_default=return_default)
+    row["temperature"] = entry.conditions[condID].get_temperature(
+        return_default=return_default, assume_si=assume_si, fix_outliers=fix_outliers
+    )
+    seq = entry.entities[row["entityID"]].seq
+    row["seq"] = seq
     # retrieve # backbone shifts (H,HA,HB,C,CA,CB,N)
-    total_bbshifts, bbshift_types, bbshift_positions, bbshifts_arr = None, None, None, None
+    total_bbshifts, bbshift_types, bbshift_positions, bbshifts_arr = (
+        None,
+        None,
+        None,
+        None,
+    )
     if seq:
         ret = bmrb.get_valid_bbshifts(shifts, seq)
         if ret:
             bbshifts_arr, bbshifts_mask = ret
             if len(bbshifts_mask) >= 2:
                 # backbone shift of terminal amino acids are not counted
-                total_bbshifts = np.sum(bbshifts_mask[1:-1]) # total backbone shifts
-                bbshift_types = np.any(bbshifts_mask, axis=0).sum() # different backbone shifts
-                bbshift_positions = np.any(bbshifts_mask, axis=1).sum() # positions with backbone shifts
+                total_bbshifts = np.sum(bbshifts_mask[1:-1])  # total backbone shifts
+                bbshift_types = np.any(
+                    bbshifts_mask, axis=0
+                ).sum()  # different backbone shifts
+                bbshift_positions = np.any(
+                    bbshifts_mask, axis=1
+                ).sum()  # positions with backbone shifts
                 if include_shifts:
                     if no_shift_averaging:
-                        bbshifts_arr, bbshifts_mask = bmrb.get_valid_bbshifts(shifts, seq, averaging=False)
+                        bbshifts_arr, bbshifts_mask = bmrb.get_valid_bbshifts(
+                            shifts, seq, averaging=False
+                        )
                     bbshifts_arr[~bbshifts_mask] = np.nan
             else:
                 total_bbshifts = 0
                 bbshifts_arr = None
                 bbshift_types = 0
                 bbshift_positions = 0
-    row['total_bbshifts'] = total_bbshifts
-    row['bbshift_types'] = bbshift_types
-    row['bbshift_positions'] = bbshift_positions
+    row["total_bbshifts"] = total_bbshifts
+    row["bbshift_types"] = bbshift_types
+    row["bbshift_positions"] = bbshift_positions
     if include_shifts:
-        row['bbshifts'] = bbshifts_arr
+        row["bbshifts"] = bbshifts_arr
+    # check if entry is paramagnetic (assembly or entity level)
+    assembly = entry.assemblies[assemID]
+    entity = entry.entities[row["entityID"]]
+    row["paramagnetic"] = (
+        assembly.paramagnetic and assembly.paramagnetic.lower() == "yes"
+    ) or (entity.paramagnetic and entity.paramagnetic.lower() == "yes")
     # check if keywords are present
-    fields = [entry.title, entry.details, entry.citation_title,
-            entry.assemblies[assemID].name, entry.assemblies[assemID].details, 
-            entry.entities[row['entityID']].name, entry.entities[row['entityID']].details]
+    fields = [
+        entry.title,
+        entry.details,
+        entry.citation_title,
+        entry.assemblies[assemID].name,
+        entry.assemblies[assemID].details,
+        entry.entities[row["entityID"]].name,
+        entry.entities[row["entityID"]].details,
+    ]
     if entry.citation_keywords is not None:
-        if type(entry.citation_keywords) == list:
+        if isinstance(entry.citation_keywords, list):
             for el in entry.citation_keywords:
                 fields.extend(el)
         else:
             fields.extend(entry.citation_keywords)
     if entry.struct_keywords is not None:
-        if type(entry.struct_keywords) == list:
+        if isinstance(entry.struct_keywords, list):
             for el in entry.struct_keywords:
                 fields.extend(el)
         else:
             fields.extend(entry.struct_keywords)
     for sID in sampleIDs:
-        fields.extend([entry.samples[sID].name, entry.samples[sID].details, entry.samples[sID].framecode])
+        fields.extend(
+            [
+                entry.samples[sID].name,
+                entry.samples[sID].details,
+                entry.samples[sID].framecode,
+            ]
+        )
     for keyword in keywords:
         row[keyword] = False
         for field in fields:
-            if field: # can be None
-                if keyword.lower() in field.lower():
-                    row[keyword] = True
-                    break
+            if field and keyword.lower() in field.lower():  # field can be None
+                row[keyword] = True
+                break
     # check if chemical detergents are present
     for den_comp in chemical_denaturants:
         row[den_comp] = False
@@ -537,249 +810,518 @@ def fill_row_data(row, chemical_denaturants, keywords,
         try:
             for sID in sampleIDs:
                 for comp in entry.samples[sID].components:
-                    if comp[3] and not comp[2]: # if it has a name but no entity entry
-                        if den_comp.lower() in comp[3].lower():
-                            row[den_comp] = True
-                            raise Found
+                    if comp[3] and not comp[2] and den_comp.lower() in comp[3].lower():
+                        row[den_comp] = True
+                        raise Found
         except Found:
             pass
     # add columns that will be filled later
-    row['scores'] = None
-    row['k'] = None
-    row['total_bbshifts_post'] = np.nan
-    row['bbshift_types_post'] = np.nan
-    row['bbshift_positions_post'] = np.nan
-    for at in BBATNS:
-        row[f'off_{at}'] = pd.NA
+    row["scores"] = None
+    row["k"] = None
+    row["total_bbshifts_post"] = np.nan
+    row["bbshift_types_post"] = np.nan
+    row["bbshift_positions_post"] = np.nan
+    for atom_type in BBATNS:
+        row[f"off_{atom_type}"] = pd.NA
     return row
 
-def create_peptide_dataframe(bmrb_entries,
-                             chemical_denaturants, keywords,
-                             return_default=True, assume_si=True, fix_outliers=True,
-                             include_shifts=False,
-                             no_shift_averaging=False,
-                             progress=False
-                             ):
+
+def create_peptide_dataframe(
+    bmrb_entries,
+    chemical_denaturants,
+    keywords,
+    return_default=True,
+    assume_si=True,
+    fix_outliers=True,
+    include_shifts=False,
+    no_shift_averaging=False,
+    progress=False,
+):
     data = []
-    columns = ['entryID', #'entry', 
-               'stID', 'entity_assemID', 'entityID'
-            ]
-    it = tqdm(bmrb_entries.iterrows()) if progress else bmrb_entries.iterrows()
-    for id_,row in it:
+    columns = [
+        "entryID",  #'entry',
+        "stID",
+        "entity_assemID",
+        "entityID",
+    ]
+    row_iter = tqdm(bmrb_entries.iterrows()) if progress else bmrb_entries.iterrows()
+    for id_, row in row_iter:
         peptide_shifts = row.entry.get_peptide_shifts()
-        for (stID, entity_assemID, entityID) in peptide_shifts:
+        for stID, entity_assemID, entityID in peptide_shifts:
             data.append([])
             data[-1].append(id_)
-            #data[-1].append(entry)
+            # data[-1].append(entry)
             data[-1].extend([stID, entity_assemID, entityID])
     df = pd.DataFrame(data, columns=columns)
-    df = df.parallel_apply(fill_row_data, axis=1, args=(chemical_denaturants, keywords), 
-                           return_default=return_default, assume_si=assume_si, fix_outliers=fix_outliers,
-                           include_shifts=include_shifts, no_shift_averaging=no_shift_averaging)
-    df = df.astype({col : "string" for col in ['entryID', 'citation_title', 'citation_DOI', 'exp_method', 'exp_method_subtype', 'entity_name', 'seq']})
+    df = df.parallel_apply(
+        fill_row_data,
+        axis=1,
+        args=(chemical_denaturants, keywords),
+        return_default=return_default,
+        assume_si=assume_si,
+        fix_outliers=fix_outliers,
+        include_shifts=include_shifts,
+        no_shift_averaging=no_shift_averaging,
+    )
+    df = df.astype(
+        dict.fromkeys(
+            [
+                "entryID",
+                "citation_title",
+                "citation_DOI",
+                "exp_method",
+                "exp_method_subtype",
+                "entity_name",
+                "seq",
+            ],
+            "string",
+        )
+    )
     return df
 
-def compute_scores(entry, stID, entity_assemID, entityID,
-                   seq, ion, pH, temperature,
-                   score_types=['zscores'], offset_correction=True, 
-                   max_offset=np.inf, reject_shift_type_only=False,
-                   #min_backbone_shift_types=1, min_backbone_shift_positions=1, min_backbone_shift_fraction=0.,
-                   cache_dir=None):
+
+def _potenci_cache_key(seq, temperature, pH, ion):
+    """Content-based cache key for POTENCI predictions."""
+    raw = f"{seq}|{temperature}|{pH}|{ion}"
+    return hashlib.sha256(raw.encode()).hexdigest()[:16]
+
+
+def load_potenci_cache(cache_dir, seq, temperature, pH, ion):
+    """Load cached POTENCI predictions if available."""
+    if not cache_dir:
+        return None
+    cache_path = (
+        cache_dir / "potenci" / f"{_potenci_cache_key(seq, temperature, pH, ion)}.json"
+    )
+    if cache_path.exists():
+        try:
+            with cache_path.open() as f:
+                raw = json.load(f)
+            # JSON keys are strings — convert back to (int, str) tuples
+            return {(int(k.split(",")[0]), k.split(",")[1]): v for k, v in raw.items()}
+        except Exception:
+            logging.getLogger("trizod").debug(
+                f"POTENCI cache file {cache_path} corrupt, ignoring"
+            )
+    return None
+
+
+def save_potenci_cache(cache_dir, seq, temperature, pH, ion, predshiftdct):
+    """Save POTENCI predictions to cache."""
+    if not cache_dir:
+        return
+    cache_path = (
+        cache_dir / "potenci" / f"{_potenci_cache_key(seq, temperature, pH, ion)}.json"
+    )
+    # Convert (int, str) tuple keys to strings for JSON
+    raw = {f"{k[0]},{k[1]}": v for k, v in predshiftdct.items()}
+    with cache_path.open("w") as f:
+        json.dump(raw, f)
+
+
+def compute_scores(
+    entry,
+    stID,
+    entity_assemID,
+    entityID,
+    seq,
+    ion,
+    pH,
+    temperature,
+    score_types=None,
+    offset_correction=True,
+    max_offset=np.inf,
+    reject_shift_type_only=False,
+    # min_backbone_shift_types=1, min_backbone_shift_positions=1, min_backbone_shift_fraction=0.,
+    cache_dir=None,
+):
+    if score_types is None:
+        score_types = ["zscores"]
     exe_times = [np.nan, np.nan, np.nan]
-    wSCS_cache_fp = os.path.join(cache_dir, 'wSCS', f'{entry.id}_{stID}_{entity_assemID}_{entityID}.npz')
-    if cache_dir and os.path.exists(wSCS_cache_fp):
+    shifts_cache_path = (
+        cache_dir / "wSCS" / f"{entry.id}_{stID}_{entity_assemID}_{entityID}.npz"
+    )
+    if cache_dir and shifts_cache_path.exists():
         try:
-            z = np.load(wSCS_cache_fp)
-            shw, ashwi, cmp_mask, olf, offf, shw0, ashwi0, ol0, off0 = z['shw'], z['ashwi'], z['cmp_mask'], z['olf'], z['offf'], z['shw0'], z['ashwi0'], z['ol0'], z['off0']
-            offf, off0 = {at:off for at,off in zip(BBATNS, offf)}, {at:off for at,off in zip(BBATNS, off0)}
-        except:
-            logging.getLogger('trizod').debug(f"cache file {wSCS_cache_fp} corrupt or formatted wrong, delete and repeat computation")
-            os.remove(wSCS_cache_fp)
-    if not (cache_dir and os.path.exists(wSCS_cache_fp)):
+            # cache keys kept as-is for backward compatibility
+            cached = np.load(str(shifts_cache_path))
+            weighted_diffs_final = cached["shw"]
+            abs_weighted_diffs_final = cached["ashwi"]
+            cmp_mask = cached["cmp_mask"]
+            outlier_mask_final = cached["olf"]
+            offsets_final = cached["offf"]
+            weighted_diffs_initial = cached["shw0"]
+            abs_weighted_diffs_initial = cached["ashwi0"]
+            outlier_mask_initial = cached["ol0"]
+            offsets_initial = cached["off0"]
+            offsets_final = dict(zip(BBATNS, offsets_final))
+            offsets_initial = dict(zip(BBATNS, offsets_initial))
+        except Exception:
+            logging.getLogger("trizod").debug(
+                f"cache file {shifts_cache_path} corrupt or formatted wrong, delete and repeat computation"
+            )
+            shifts_cache_path.unlink()
+    if not (cache_dir and shifts_cache_path.exists()):
         peptide_shifts = entry.get_peptide_shifts()
-        shifts, condID, assemID, sampleIDs = peptide_shifts[(stID, entity_assemID, entityID)]
-        
+        shifts, condID, assemID, sampleIDs = peptide_shifts[
+            (stID, entity_assemID, entityID)
+        ]
+
         try:
             # predict random coil chemical shifts using POTENCI
-            usephcor = (pH != 7.0)
+            use_ph_corr = pH != 7.0
             start_time = time.time()
-            predshiftdct = potenci.getpredshifts(seq, temperature, pH, ion, usephcor, pkacsvfile=False)
+            predshiftdct = load_potenci_cache(cache_dir, seq, temperature, pH, ion)
+            if predshiftdct is None:
+                predshiftdct = potenci.get_pred_shifts(
+                    seq, temperature, pH, ion, use_ph_corr
+                )
+                save_potenci_cache(cache_dir, seq, temperature, pH, ion, predshiftdct)
             exe_times[0] = time.time() - start_time
-        except:
-            logging.getLogger('trizod').error(f"POTENCI failed for {(entry.id, stID, entity_assemID, entityID)} due to the following error:", exc_info=True)
-            raise ZscoreComputationError
+        except Exception as err:
+            logging.getLogger("trizod").error(
+                f"POTENCI failed for {(entry.id, stID, entity_assemID, entityID)} due to the following error:",
+                exc_info=True,
+            )
+            raise ZscoreComputationError from err
         start_time = time.time()
-        ret = scoring.get_offset_corrected_wSCS(seq, shifts, predshiftdct)
+        ret = scoring.get_offset_corrected_shifts(seq, shifts, predshiftdct)
         if ret is None:
-            logging.getLogger('trizod').error(f'TriZOD failed for {(entry.id, stID, entity_assemID, entityID)} due to an error in computation of corrected wSCSs.')
+            logging.getLogger("trizod").error(
+                f"TriZOD failed for {(entry.id, stID, entity_assemID, entityID)} due to an error in computation of corrected weighted shifts."
+            )
             raise ZscoreComputationError
         else:
             exe_times[1] = time.time() - start_time
-        shw, ashwi, cmp_mask, olf, offf, shw0, ashwi0, ol0, off0 = ret
+        (
+            weighted_diffs_final,
+            abs_weighted_diffs_final,
+            cmp_mask,
+            outlier_mask_final,
+            offsets_final,
+            weighted_diffs_initial,
+            abs_weighted_diffs_initial,
+            outlier_mask_initial,
+            offsets_initial,
+        ) = ret
         if cache_dir:
-            np.savez(wSCS_cache_fp, 
-                     shw=shw, ashwi=ashwi, cmp_mask=cmp_mask, 
-                     olf=olf, offf=np.array([offf[at] for at in BBATNS]),
-                     shw0=shw0, ashwi0=ashwi0, ol0=ol0, off0=np.array([off0[at] for at in BBATNS]))
-    offsets = offf
-    if offset_correction == False:
-        ashwi = ashwi0
-        offsets = off0
-    elif not (max_offset == None or np.isinf(max_offset)):
+            # cache keys kept as-is for backward compatibility
+            np.savez(
+                str(shifts_cache_path),
+                shw=weighted_diffs_final,
+                ashwi=abs_weighted_diffs_final,
+                cmp_mask=cmp_mask,
+                olf=outlier_mask_final,
+                offf=np.array([offsets_final[atom_type] for atom_type in BBATNS]),
+                shw0=weighted_diffs_initial,
+                ashwi0=abs_weighted_diffs_initial,
+                ol0=outlier_mask_initial,
+                off0=np.array([offsets_initial[atom_type] for atom_type in BBATNS]),
+            )
+    offsets = offsets_final
+    if not offset_correction:
+        abs_weighted_diffs_final = abs_weighted_diffs_initial
+        offsets = offsets_initial
+    elif not (max_offset is None or np.isinf(max_offset)):
         # check if any offsets are too large
-        for i,at in enumerate(BBATNS):
-            if np.abs(offf[at]) > max_offset:
-                offsets[at] = np.nan
+        for i, atom_type in enumerate(BBATNS):
+            if np.abs(offsets_final[atom_type]) > max_offset:
+                offsets[atom_type] = np.nan
                 if reject_shift_type_only:
-                    # mask data related to this backbone shift type, excluding it from scores computation
-                    cmp_mask[:,i] = False
+                    # mask this backbone shift type, excluding it from scores computation
+                    cmp_mask[:, i] = False
     if np.any(cmp_mask):
         start_time = time.time()
-        ashwi3, k3 = scoring.convert_to_triplet_data(ashwi, cmp_mask)
+        triplet_diffs, triplet_dof = scoring.convert_to_triplet_data(
+            abs_weighted_diffs_final, cmp_mask
+        )
         scores = []
         for score_type in score_types:
-            if 'corrected' == score_type:
-                scores.append(scoring.compute_zscores(ashwi3, k3, cmp_mask, corr=True))
-            elif 'zscores' == score_type:
-                scores.append(scoring.compute_zscores(ashwi3, k3, cmp_mask))
-            elif 'pscores' == score_type:
-                scores.append(scoring.compute_pscores(ashwi3, k3, cmp_mask))
+            if score_type == "corrected":
+                scores.append(
+                    scoring.compute_zscores(
+                        triplet_diffs, triplet_dof, cmp_mask, corr=True
+                    )
+                )
+            elif score_type == "zscores":
+                scores.append(
+                    scoring.compute_zscores(triplet_diffs, triplet_dof, cmp_mask)
+                )
+            elif score_type == "pscores":
+                scores.append(
+                    scoring.compute_pscores(triplet_diffs, triplet_dof, cmp_mask)
+                )
             else:
                 raise ValueError
-        k = k3
+        k = triplet_dof
         exe_times[2] = time.time() - start_time
     else:
-        scores, k = [np.full((cmp_mask.shape[0],), np.nan) for i in range(len(score_types))], np.full((cmp_mask.shape[0],), np.nan)
+        scores, k = (
+            [np.full((cmp_mask.shape[0],), np.nan) for i in range(len(score_types))],
+            np.full((cmp_mask.shape[0],), np.nan),
+        )
     return scores, k, cmp_mask, offsets, exe_times
 
-def compute_scores_row(row, score_types=['zscores'], offset_correction=True, 
-                       max_offset=np.inf, reject_shift_type_only=False,
-                       cache_dir=None):
-    if not row['pass_pre']:
+
+def compute_scores_row(
+    row,
+    score_types=None,
+    offset_correction=True,
+    max_offset=np.inf,
+    reject_shift_type_only=False,
+    cache_dir=None,
+):
+    if score_types is None:
+        score_types = ["zscores"]
+    if not row["pass_pre"]:
         return row
     try:
         start_time = time.time()
         scores, k, cmp_mask, offsets, exe_times = compute_scores(
-            bmrb_entries.loc[row['entryID'], 'entry'], row['stID'], row['entity_assemID'], row['entityID'],
-            row['seq'], row['ionic_strength'], row['pH'], row['temperature'],
-            score_types=score_types, offset_correction=offset_correction, 
-            max_offset=max_offset, reject_shift_type_only=reject_shift_type_only,
-            cache_dir=cache_dir)
-        for score_type, scores_ in zip(score_types, scores):
-            row[score_type] = scores_
-        row['k'] = k
-        #row['cmp_mask'] = cmp_mask
-        for at in BBATNS:
-            row[f'off_{at}'] = offsets[at]
-        row['total_bbshifts_post'] = np.sum(cmp_mask)
-        row['bbshift_types_post'] = np.any(cmp_mask, axis=0).sum()
-        row['bbshift_positions_post'] = np.any(cmp_mask, axis=1).sum()
-        row['tpotenci'] = exe_times[0]
-        row['ttrizod'] = exe_times[1]
-        row['tscores'] = exe_times[2]
-        row['ttotal'] = time.time() - start_time
+            bmrb_entries.loc[row["entryID"], "entry"],
+            row["stID"],
+            row["entity_assemID"],
+            row["entityID"],
+            row["seq"],
+            row["ionic_strength"],
+            row["pH"],
+            row["temperature"],
+            score_types=score_types,
+            offset_correction=offset_correction,
+            max_offset=max_offset,
+            reject_shift_type_only=reject_shift_type_only,
+            cache_dir=cache_dir,
+        )
+        for score_type, score_array in zip(score_types, scores):
+            row[score_type] = score_array
+        row["k"] = k
+        # row['cmp_mask'] = cmp_mask
+        for atom_type in BBATNS:
+            row[f"off_{atom_type}"] = offsets[atom_type]
+        row["total_bbshifts_post"] = np.sum(cmp_mask)
+        row["bbshift_types_post"] = np.any(cmp_mask, axis=0).sum()
+        row["bbshift_positions_post"] = np.any(cmp_mask, axis=1).sum()
+        row["tpotenci"] = exe_times[0]
+        row["ttrizod"] = exe_times[1]
+        row["tscores"] = exe_times[2]
+        row["ttotal"] = time.time() - start_time
     except ZscoreComputationError:
         pass
     return row
 
-def output_dataset(df, output_prefix, output_format, score_types, precision, include_shifts, no_shift_averaging):
-    df['ID'] = df['entryID']+'_'+df['stID']+'_'+df['entity_assemID']+'_'+df['entityID']
+
+def output_dataset(
+    df,
+    output_prefix,
+    output_format,
+    score_types,
+    precision,
+    include_shifts,
+    no_shift_averaging,
+):
+    df["ID"] = (
+        df["entryID"]
+        + "_"
+        + df["stID"]
+        + "_"
+        + df["entity_assemID"]
+        + "_"
+        + df["entityID"]
+    )
     for score_type in score_types:
-        df.loc[df.pass_post, score_type] = df.loc[df.pass_post, score_type].apply(np.round, args=(precision,))
+        df.loc[df.pass_post, score_type] = df.loc[df.pass_post, score_type].apply(
+            np.round, args=(precision,)
+        )
     shifts = []
     if include_shifts:
         if no_shift_averaging:
-            shifts = BBATNS + ['HA2', 'HA3' ,'HB1', 'HB2', 'HB3']
-        for i,at in enumerate(shifts):
-            df.loc[df.pass_post, at] = df.loc[df.pass_post, 'bbshifts'].apply(lambda x: x[:,i])
-            df.loc[df.pass_post, at] = df.loc[df.pass_post, at].apply(np.round, args=(precision,))
-    if output_format == 'csv':
-        df.loc[df.pass_post, 'seq'] = df[df.pass_post].seq.apply(lambda x: list(x))
-        dout = df.loc[df.pass_post].reset_index()[['ID', 'entryID', 'stID', 'entity_assemID', 'entityID', 'entity_name', 'seq', 'k'] + score_types + shifts]
-        dout['seq'] = dout.seq.apply(lambda x: list(x))
-        dout['seq_index'] = dout.seq.apply(lambda x: list(range(1,len(x)+1)))
-        dout = dout.explode(['seq_index', 'seq', 'k'] + score_types + shifts)
-        dout[['ID', 'entryID', 'stID', 'entity_assemID', 'entityID', 'entity_name', 'seq_index', 'seq', 'k'] + score_types + shifts]\
-            .to_csv(output_prefix + '.csv', 
-                    float_format='%.{}f'.format(precision))
-    elif output_format == 'json':
-        dout = df.loc[df.pass_post].reset_index()[['ID', 'entryID', 'stID', 'entity_assemID', 'entityID',
-                                                   'entity_name', 'exp_method', 'exp_method_subtype', 'citation_DOI', 'citation_title',
-                                                   'ionic_strength', 'pH', 'temperature',
-                                                   'off_C', 'off_CA', 'off_CB', 'off_H', 'off_HA', 'off_HB', 'off_N',
-                                                   'bbshift_positions_post', 'bbshift_types_post', 'total_bbshifts',
-                                                   'seq', 'k'] + score_types + shifts]
-        dout.to_json(output_prefix + '.json', orient='records', lines=True)
+            shifts = BBATNS + ["HA2", "HA3", "HB1", "HB2", "HB3"]
+        for i, atom_type in enumerate(shifts):
+            df.loc[df.pass_post, atom_type] = df.loc[df.pass_post, "bbshifts"].apply(
+                lambda x, i=i: x[:, i]
+            )
+            df.loc[df.pass_post, atom_type] = df.loc[df.pass_post, atom_type].apply(
+                np.round, args=(precision,)
+            )
+    if output_format == "csv":
+        df["seq"] = df["seq"].astype(object)
+        df.loc[df.pass_post, "seq"] = df[df.pass_post].seq.apply(lambda x: list(x))
+        dout = df.loc[df.pass_post].reset_index()[
+            [
+                "ID",
+                "entryID",
+                "stID",
+                "entity_assemID",
+                "entityID",
+                "entity_name",
+                "seq",
+                "k",
+            ]
+            + score_types
+            + shifts
+        ]
+        dout["seq"] = dout.seq.apply(lambda x: list(x))
+        dout["seq_index"] = dout.seq.apply(lambda x: list(range(1, len(x) + 1)))
+        dout = dout.explode(["seq_index", "seq", "k"] + score_types + shifts)
+        dout[
+            [
+                "ID",
+                "entryID",
+                "stID",
+                "entity_assemID",
+                "entityID",
+                "entity_name",
+                "seq_index",
+                "seq",
+                "k",
+            ]
+            + score_types
+            + shifts
+        ].to_csv(
+            output_prefix.parent / f"{output_prefix.name}.csv",
+            float_format=f"%.{precision}f",
+        )
+    elif output_format == "json":
+        dout = df.loc[df.pass_post].reset_index()[
+            [
+                "ID",
+                "entryID",
+                "stID",
+                "entity_assemID",
+                "entityID",
+                "entity_name",
+                "exp_method",
+                "exp_method_subtype",
+                "citation_DOI",
+                "citation_title",
+                "ionic_strength",
+                "pH",
+                "temperature",
+                "off_C",
+                "off_CA",
+                "off_CB",
+                "off_H",
+                "off_HA",
+                "off_HB",
+                "off_N",
+                "bbshift_positions_post",
+                "bbshift_types_post",
+                "total_bbshifts",
+                "seq",
+                "k",
+            ]
+            + score_types
+            + shifts
+        ]
+        dout.to_json(
+            output_prefix.parent / f"{output_prefix.name}.json",
+            orient="records",
+            lines=True,
+        )
     else:
         raise ValueError(f"Unknown output format: {output_format}")
 
+
 def main():
     args = parse_args()
     if args.processes is None:
         pandarallel.initialize(verbose=0, progress_bar=args.progress)
     else:
-        pandarallel.initialize(verbose=0, nb_workers=args.processes, progress_bar=args.progress)
+        pandarallel.initialize(
+            verbose=0, nb_workers=args.processes, progress_bar=args.progress
+        )
     level = logging.DEBUG if args.debug else logging.INFO
-    logging.basicConfig(level=level, format=f'%(levelname)s : %(message)s')
+    logging.basicConfig(level=level, format="%(levelname)s : %(message)s")
     # reject most logging messages for sub-routines like parsing database files:
-    logging.getLogger('trizod.bmrb').setLevel(logging.CRITICAL)
-    logging.getLogger('trizod.scoring').setLevel(logging.CRITICAL)
+    logging.getLogger("trizod.bmrb").setLevel(logging.CRITICAL)
+    logging.getLogger("trizod.scoring").setLevel(logging.CRITICAL)
 
-    logging.getLogger('trizod').info('Loading BMRB files.')
+    logging.getLogger("trizod").info("Loading BMRB files.")
     bmrb_files = find_bmrb_files(args.input_dir, args.BMRB_file_pattern)
     global bmrb_entries
     bmrb_entries, failed = load_bmrb_entries(bmrb_files, cache_dir=args.cache_dir)
     print()
     if failed:
-        logging.getLogger('trizod').warning(f"Failed loading {len(failed)} of {len(bmrb_files)} BMRB files")
-    logging.getLogger('trizod').info('Parsing and filtering relevant information.')
+        logging.getLogger("trizod").warning(
+            f"Failed loading {len(failed)} of {len(bmrb_files)} BMRB files"
+        )
+    logging.getLogger("trizod").info("Parsing and filtering relevant information.")
     df = create_peptide_dataframe(
-        bmrb_entries, 
-        chemical_denaturants=args.chemical_denaturants, 
-        keywords=args.keywords_blacklist, 
-        return_default=args.default_conditions, 
-        assume_si=args.unit_assumptions, 
+        bmrb_entries,
+        chemical_denaturants=args.chemical_denaturants,
+        keywords=args.keywords_blacklist,
+        return_default=args.default_conditions,
+        assume_si=args.unit_assumptions,
         fix_outliers=args.unit_corrections,
         include_shifts=args.include_shifts,
         no_shift_averaging=args.no_shift_averaging,
-        progress=args.progress)
-    df, missing_vals, sels_pre, sels_kws, sels_denat, sels_all_pre = prefilter_dataframe(
-        df,
-        method_whitelist=args.exp_method_whitelist, 
-        method_blacklist=args.exp_method_blacklist,
-        temperature_range=args.temperature_range,
-        ionic_strength_range=args.ionic_strength_range,
-        pH_range=args.pH_range,
-        peptide_length_range=args.peptide_length_range,
-        min_backbone_shift_types=args.min_backbone_shift_types,
-        min_backbone_shift_positions=args.min_backbone_shift_positions,
-        min_backbone_shift_fraction=args.min_backbone_shift_fraction,
-        max_noncanonical_fraction=args.max_noncanonical_fraction,
-        max_x_fraction=args.max_x_fraction,
-        keywords=args.keywords_blacklist,
-        chemical_denaturants=args.chemical_denaturants)
+        progress=args.progress,
+    )
+    df, missing_vals, sels_pre, sels_kws, sels_denat, sels_paramag, sels_all_pre = (
+        prefilter_dataframe(
+            df,
+            method_whitelist=args.exp_method_whitelist,
+            method_blacklist=args.exp_method_blacklist,
+            temperature_range=args.temperature_range,
+            ionic_strength_range=args.ionic_strength_range,
+            pH_range=args.pH_range,
+            peptide_length_range=args.peptide_length_range,
+            min_backbone_shift_types=args.min_backbone_shift_types,
+            min_backbone_shift_positions=args.min_backbone_shift_positions,
+            min_backbone_shift_fraction=args.min_backbone_shift_fraction,
+            max_noncanonical_fraction=args.max_noncanonical_fraction,
+            max_x_fraction=args.max_x_fraction,
+            keywords=args.keywords_blacklist,
+            chemical_denaturants=args.chemical_denaturants,
+            exclude_paramagnetic=args.exclude_paramagnetic,
+        )
+    )
     print()
-    logging.getLogger('trizod').info('Computing scores for each remaining entry.')
+    logging.getLogger("trizod").info("Computing scores for each remaining entry.")
     df = df.parallel_apply(
-        compute_scores_row, axis=1, 
-        score_types=args.score_types, 
-        offset_correction=args.offset_correction, 
-        max_offset=args.max_offset, 
+        compute_scores_row,
+        axis=1,
+        score_types=args.score_types,
+        offset_correction=args.offset_correction,
+        max_offset=args.max_offset,
         reject_shift_type_only=args.reject_shift_type_only,
-        cache_dir=args.cache_dir)
-    if args.progress : print() # prevents overwriting last line of progress bars
-    logging.getLogger('trizod').info('Filtering results.')
+        cache_dir=args.cache_dir,
+    )
+    if args.progress:
+        print()  # prevents overwriting last line of progress bars
+    logging.getLogger("trizod").info("Filtering results.")
     sels_post, sels_off, sels_all_post = postfilter_dataframe(
         df,
         min_backbone_shift_types=args.min_backbone_shift_types,
         min_backbone_shift_positions=args.min_backbone_shift_positions,
         min_backbone_shift_fraction=args.min_backbone_shift_fraction,
         reject_shift_type_only=args.reject_shift_type_only,
-        score_types=args.score_types)
-    logging.getLogger('trizod').info('Output filtering results.')
-    print_filter_losses(df, missing_vals, sels_pre, sels_kws, sels_denat, sels_all_pre, sels_post, sels_off, sels_all_post)
-    logging.getLogger('trizod').info('Writing dataset to file.')
-    output_dataset(df, args.output_prefix, args.output_format, args.score_types, args.precision, args.include_shifts, args.no_shift_averaging)
-
-if __name__ == '__main__':
-    main()
\ No newline at end of file
+        score_types=args.score_types,
+    )
+    logging.getLogger("trizod").info("Output filtering results.")
+    print_filter_losses(
+        df,
+        missing_vals,
+        sels_pre,
+        sels_kws,
+        sels_denat,
+        sels_paramag,
+        sels_all_pre,
+        sels_post,
+        sels_off,
+        sels_all_post,
+    )
+    logging.getLogger("trizod").info("Writing dataset to file.")
+    output_dataset(
+        df,
+        args.output_prefix,
+        args.output_format,
+        args.score_types,
+        args.precision,
+        args.include_shifts,
+        args.no_shift_averaging,
+    )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/trizod/utils.py b/trizod/utils.py
index 62f08b7..7740af3 100644
--- a/trizod/utils.py
+++ b/trizod/utils.py
@@ -1,16 +1,20 @@
 import argparse
 
+
 class ArgHelpFormatter(argparse.HelpFormatter):
-    '''
+    """
     Formatter adding default values to help texts.
-    '''
+    """
+
     def __init__(self, prog):
         super().__init__(prog)
 
     def _get_help_string(self, action):
         text = action.help
-        if  action.default is not None and \
-            action.default != argparse.SUPPRESS and \
-            'default:' not in text.lower():
-            text += ' (default: {})'.format(action.default)
-        return text
\ No newline at end of file
+        if (
+            action.default is not None
+            and action.default != argparse.SUPPRESS
+            and "default:" not in text.lower()
+        ):
+            text += f" (default: {action.default})"
+        return text
diff --git a/uv.lock b/uv.lock
new file mode 100644
index 0000000..683b5ae
--- /dev/null
+++ b/uv.lock
@@ -0,0 +1,2104 @@
+version = 1
+revision = 3
+requires-python = ">=3.9"
+resolution-markers = [
+    "python_full_version >= '3.14' and sys_platform == 'win32'",
+    "python_full_version >= '3.14' and sys_platform == 'emscripten'",
+    "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version >= '3.11' and python_full_version < '3.14' and sys_platform == 'win32'",
+    "python_full_version >= '3.11' and python_full_version < '3.14' and sys_platform == 'emscripten'",
+    "python_full_version >= '3.11' and python_full_version < '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version == '3.10.*'",
+    "python_full_version < '3.10'",
+]
+
+[[package]]
+name = "certifi"
+version = "2026.2.25"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/af/2d/7bf41579a8986e348fa033a31cdd0e4121114f6bce2457e8876010b092dd/certifi-2026.2.25.tar.gz", hash = "sha256:e887ab5cee78ea814d3472169153c2d12cd43b14bd03329a39a9c6e2e80bfba7", size = 155029, upload-time = "2026-02-25T02:54:17.342Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/9a/3c/c17fb3ca2d9c3acff52e30b309f538586f9f5b9c9cf454f3845fc9af4881/certifi-2026.2.25-py3-none-any.whl", hash = "sha256:027692e4402ad994f1c42e52a4997a9763c646b73e4096e4d5d6db8af1d6f0fa", size = 153684, upload-time = "2026-02-25T02:54:15.766Z" },
+]
+
+[[package]]
+name = "charset-normalizer"
+version = "3.4.5"
+source = { registry = "https://pypi.org/simple" }
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