sp-fitting-models

English README

超分子ポリマーのフィッティングモデルライブラリ

A Python library for fitting supramolecular polymerization data with various thermodynamic models.

概要 / Overview

このライブラリは、超分子ポリマー形成データを解析するための数理モデルを提供します。特に温度依存的な会合挙動を定量的に解析し、熱力学パラメータ（エンタルピー、エントロピー）を推定することができます。

This library provides mathematical models for analyzing supramolecular polymerization data. It enables quantitative analysis of temperature-dependent aggregation behavior and estimation of thermodynamic parameters (enthalpy, entropy).

特徴 / Features

  • 複数のモデルに対応

    • Isodesmicモデル: すべての会合定数が等しい単純な会合モデル
    • Cooperativeモデル: 核形成と伸長で異なる定数を持つ協同的会合モデル
    • Mixedモデル: IsodesmicとCooperativeの2経路が競合するモデル

  • 温度依存性の解析

    • van't Hoff式に基づく温度依存的な会合定数の計算
    • ΔH（エンタルピー変化）とΔS（エントロピー変化）の推定

  • フィッティング機能

    • lmfitライブラリを使用した実験データへのフィッティング
    • 複数濃度データの同時フィッティング(グローバルフィット)に対応

インストール / Installation

uv add https://github.com/IndigoCarmine/sp_fitting_models.git

使用方法 / Usage

基本的な使用例

import numpy as np
import matplotlib.pyplot as plt
from sp_fitting_models.models import temp_cooperative_model

# Temperature range
temps = np.linspace(280, 400, 200)  # 280-400 K

# Thermodynamic parameters
deltaH = -96000      # Enthalpy change (J/mol)
deltaS = -180        # Entropy change (J/(mol·K))
deltaHnuc = 100000   # Nucleation penalty (J/mol)
c_tot = 5e-6         # Total concentration (M)

# Calculate aggregation
agg = temp_cooperative_model(
    Temp=temps,
    deltaH=deltaH,
    deltaS=deltaS,
    deltaHnuc=deltaHnuc,
    c_tot=c_tot,
    scaler=1.0
)

# Plot
plt.plot(temps - 273.15, agg)
plt.xlabel('Temperature (°C)')
plt.ylabel('Aggregation')
plt.show()

データフィッティング

import lmfit as lm
from sp_fitting_models.data import TempVsAggData
from sp_fitting_models.fitting import objective_temp_cooperative

# Prepare your experimental data
data_list = [
    TempVsAggData(temp=temps1, agg=agg1, concentration=c1),
    TempVsAggData(temp=temps2, agg=agg2, concentration=c2),
]

# Set up parameters
params = lm.Parameters()
params.add('deltaH', value=-100000, min=-200000, max=0)
params.add('deltaS', value=-180, min=-400, max=0)
params.add('deltaHnuc', value=50000, min=0, max=200000)
params.add('scaler', value=1.0, min=0.5, max=1.5)

# Fit
minner = lm.Minimizer(objective_temp_cooperative, params, fcn_args=(data_list,))
result = minner.minimize()

print(lm.fit_report(result))

インタラクティブな可視化

# Run the interactive mixed model example
python examples/interactive_mixed.py

スライダーを使用してパラメータを変更し、リアルタイムで会合曲線の変化を観察できます。

Windowsアプリとしてビルド (uv + PyInstaller)

examples/interactive_mixed.py をコンソールなしの Windows GUI アプリとしてビルドできます。

./scripts/build_interactive_mixed.ps1

または cmd.exe から:

build_interactive_mixed.bat

生成物:

  • dist/interactive_mixed/interactive_mixed.exe

このスクリプトは次を自動で行います。

  1. uv sync で依存関係とローカルパッケージを同期
  2. uv run --with pyinstaller ... で GUI アプリをビルド

プロジェクト構造 / Project Structure

sp_fitting_models/
├── src/
│   └── sp_fitting_models/
│       ├── __init__.py
│       ├── data.py              # Data structures
│       ├── models/              # Model implementations
│       │   ├── __init__.py
│       │   ├── isodesmic.py     # Isodesmic models
│       │   ├── cooperative.py   # Cooperative models
│       │   ├── mixed.py         # Mixed models
│       │   └── utils.py         # Utility functions
│       └── fitting/             # Fitting utilities
│           ├── __init__.py
│           └── objective.py     # Objective functions for lmfit
├── tests/                       # Test files
│   ├── test_isodesmic.py
│   ├── test_cooperative.py
│   ├── test_mixed.py
│   └── test_fitting.py
├── examples/                    # Example scripts
│   ├── basic_usage.py
│   └── interactive_mixed.py
├── pyproject.toml
└── README.md

モデルの説明 / Model Description

Isodesmicモデル

すべての会合ステップが同じ平衡定数Kを持つモデルです。シグモイド型の会合曲線を示します。

$$K = \exp\left(-\frac{\Delta H}{RT} + \frac{\Delta S}{R}\right)$$

次のような平衡状態です。 供給されるモノマーは省略してあります。： $$M \stackrel{K}{\rightleftarrows} M_2\stackrel{K}{\rightleftarrows} M_3 \stackrel{K}{\rightleftarrows} ...$$

Cooperativeモデル

核形成と伸長で異なる平衡定数を持つモデルです。非シグモイド型の会合曲線を示します。核形成ペナルティσにより協同性が表現されます。

$$\sigma = \exp\left(-\frac{\Delta H_{nuc}}{RT}\right)$$

$$ K = \exp\left(-\frac{\Delta H}{RT} + \frac{\Delta S}{R}\right)$$

$$ K_{nuc} = \sigma K $$ 次のような平衡状態です。 $$M \stackrel{K_{nuc}}{\rightleftarrows} M_2\stackrel{K}{\rightleftarrows} M_3 \stackrel{K}{\rightleftarrows} ...$$

Mixedモデル

IsodesmicとCooperativeの2つの経路が同じモノマープールを共有して競合するモデルです。実験系で複数の会合機構が同時に起こる場合に適用できます。

次のような平衡状態を考えています。 $$M \stackrel{K_{nuc}}{\rightleftarrows} M_2\stackrel{K}{\rightleftarrows} M_3 \stackrel{K}{\rightleftarrows} ...$$ $$ \searrow \nwarrow^{K_{iso}} M_2\stackrel{K_{iso}}{\rightleftarrows} M_3 \stackrel{K_{iso}}{\rightleftarrows} ...$$ （MDではこれ以上きれいに書けませんでした...）

テスト / Testing

# Run all tests
python -m pytest tests/

# Run specific test
python tests/test_cooperative.py

サンプル / Examples

# Basic usage examples
python examples/basic_usage.py

# Interactive mixed model visualization
python examples/interactive_mixed.py

uvでの実行

uv run --with maturin maturin develop --release
uv run pytest

依存関係 / Dependencies

  • Python >= 3.13
  • numpy >= 2.4.2
  • numba >= 0.64.0
  • lmfit >= 1.3.4
  • matplotlib >= 3.10.8

引用　/Citation

書いていただけるなら嬉しいですが、必ずしも論文で言及する必要はありません。 ご自由にお使いください。

I would be grateful if you could cite this library in your publications, but it is not mandatory. Please feel free to use it as you see fit.

作成者 / Author

山田悠平　(Yuhei Yamada, Orcid: 0009-0003-9780-4135, google scholar: Yuhei Yamada)

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (1.3.8)

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for IndigoCarmine from gravatar.com IndigoCarmine

Credits

Author: Yuhei Yamada

License expression

MIT AND (Apache-2.0 OR BSD-2-Clause)
View SPDX License List

Requires

Python >=3.13

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