| """Reader for the published MagNET-Zero/MagNET-PCM linear-scaling tables in this folder. |
| |
| These two CSV files (one per nucleus) ARE Supporting Information Tables S10 (proton) and S11 (carbon), |
| verbatim: the reflection-symmetrized (n_passes=10, symmetrize=True) per-solvent coefficients that turn |
| a MagNET-Zero gas-phase shielding plus a MagNET-PCM chloroform correction into a predicted shift. |
| analysis/code/scaling_factors.py derives them; see its module docstring for the method. |
| |
| Each row is one solvent; columns are intercept, stationary (the MagNET-Zero slope), and pcm (the |
| MagNET-PCM-correction slope). Predicted shift = intercept + stationary * shielding + pcm * correction. |
| """ |
| import os |
|
|
| import pandas as pd |
|
|
| HERE = os.path.dirname(os.path.abspath(__file__)) |
| NUCLEI = ("H", "C") |
|
|
|
|
| def _csv_path(nucleus): |
| if nucleus not in NUCLEI: |
| raise ValueError(f"nucleus must be one of {NUCLEI}, got {nucleus!r}") |
| return os.path.join(HERE, f"scaling_factors_symmetrized_{nucleus}.csv") |
|
|
|
|
| def load_symmetrized_tables(): |
| """Returns {"H": DataFrame, "C": DataFrame}, each indexed by solvent with columns |
| intercept / stationary / pcm.""" |
| return {n: pd.read_csv(_csv_path(n)).set_index("solvent") for n in NUCLEI} |
|
|
|
|
| def predict_shift(table, solvent, shielding, correction): |
| """Chemical shift for one site: intercept + stationary * shielding + pcm * correction, using |
| `table` (one nucleus' DataFrame from load_symmetrized_tables) and its `solvent` row.""" |
| row = table.loc[solvent] |
| return float(row["intercept"] + row["stationary"] * shielding + row["pcm"] * correction) |
|
|
