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64c992d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 | """Figure 1A data and analysis: informative 1H shift differences are too small for DFT to resolve.
Loads per-site 1H shift spread across the Goodman CP3 stereoisomers and classifies each site against
the experimental noise floor (0.02 ppm) and DFT's resolving power (0.10 ppm); ~36% fall in between.
Data: `goodman2009_cp3.xlsx` (Goodman, J. Org. Chem. 2009, 74, 4597), shipped alongside. Plotting is
in the figure notebook.
"""
import os
import numpy as np
import pandas as pd
HERE = os.path.dirname(os.path.abspath(__file__))
CP3_XLSX = os.path.join(HERE, "..", "..", "data", "cp3", "goodman2009_cp3.xlsx")
# accuracy thresholds in ppm: the experimental noise floor, the DFT resolving limit, and the cutoff
# above which a variation counts as large. These define the zones a site's variation falls into.
EXPERIMENTAL_LIMIT = 0.02
DFT_LIMIT = 0.10
LARGE_VARIATION = 0.30
def load_variations(path=CP3_XLSX, nucleus="H"):
"""The per-site chemical-shift standard deviations across the CP3 stereoisomers for one nucleus
("H" or "C"), as a 1D array of finite values (ppm)."""
df = pd.read_excel(path)
x = df[df["nucleus"] == nucleus]["stdev"].to_numpy(dtype=float)
return x[np.isfinite(x)]
def _zone(value):
"""Classify one variation (ppm) into its accuracy zone: within experimental noise, informative
but below the DFT resolving limit, resolvable by DFT, or large."""
if value < EXPERIMENTAL_LIMIT:
return "below_experimental"
if value < DFT_LIMIT:
return "below_dft"
if value < LARGE_VARIATION:
return "dft_zone"
return "large"
def fraction_below_dft(variations, lo=EXPERIMENTAL_LIMIT, hi=DFT_LIMIT, bins=30):
"""The fraction of the variation histogram's area between the experimental floor and the DFT
limit: the sites whose shift variation is informative but too small for DFT to resolve (the
paper's 36%). Computed as histogram area in [lo, hi] over total area, splitting the bins that
straddle a boundary, exactly as the figure does."""
edges = np.linspace(0.0, float(variations.max()), bins + 1)
counts, edge = np.histogram(variations, bins=edges)
total = window = 0.0
for height, left, right in zip(counts, edge[:-1], edge[1:]):
total += height * (right - left)
overlap = max(0.0, min(right, hi) - max(left, lo))
window += height * overlap
return window / total if total > 0 else float("nan")
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