fidapy 0.1.0

A package to handle and analyse data from the FIDA 1 instrument.

fidapy

A Python package for analyzing data from Flow-Induced Dispersion Analysis (FIDA) instruments. Supports Taylor Dispersion Analysis (TDA), Taylor Dispersion-Induced Phase Separation (TDIPS), and Capillary Flow Experiments (Capflex).

Installation

pip install fidapy

Optional extras:

# Fitting libraries (lmfit, emcee, corner)
pip install "fidapy[fitting]"

# Analysis libraries (seaborn, scikit-learn, scikit-image)
pip install "fidapy[analysis]"

For development:

git clone https://gitlab.gbar.dtu.dk/dtu_biophysics/fidapy
cd fidapy
pip install -e ".[dev]"

Quick Start

TDA — hydrodynamic radius

from fidapy import TDAData

data = TDAData(
    path='sample.txt',
    measurement_pressure=400e2,   # Pa
    capillary_temperature=25,     # °C
    analyte_components={'NaCl': 0.1},  # M
)

print(f"rH = {data.hydrodynamic_radius * 1e9:.2f} nm")
print(f"D  = {data.diffusion_coefficient:.2e} m²/s")

Binding affinity — K_d from a titration series

from fidapy.experiments.binding_experiment import BindingExperiment

exp = BindingExperiment(
    path='data/titration/',
    analyte_components={'NaCl': 0.1},
    measurement_pressure=400e2,
)

print(f"K_d = {exp.kd * 1e9:.2f} nM  (95% CI: "
      f"{exp.kd_95ci[0]*1e9:.2f}–{exp.kd_95ci[1]*1e9:.2f} nM)")
print(f"dG  = {exp.dG:.1f} ± {exp.dG_std:.1f} kJ/mol")

By default the Langmuir isotherm is used. For tight binders (K_d ≈ indicator concentration) swap to the quadratic model:

from fidapy.fit_functions import quadratic_binding
exp.fit(model=quadratic_binding, indicator_concentration=1e-9)

TDIPS — phase separation screen

from fidapy.experiments.TDIPS_experiment import TDIPSExperiment

exp = TDIPSExperiment(
    path='data/screen/',
    filename_regex_function=my_parser,
    measurement_pressure=8e4,
    norm_conc=True,
)

df = exp.summary()     # DataFrame: one row per sample, LLPS metrics + buffer components
df.sort_values('llps_score', ascending=False)

summary() columns always present: name, time, analyte_concentration, spike_num, spike_sum_prominences, mid_int, front_integral, llps_score. Additional analyte_* / indicator_* columns are added for every buffer component extracted from filenames.

CapFlex — droplet characterization

from fidapy import CapflexData

data = CapflexData(
    path='sample.txt',
    measurement_pressure=4e4,
    capillary_temperature=20,
    indicator_components={'NaCl': 0.1},
)

print(f"Baseline fluorescence: {data.baseline_fluorescence:.3f}")
print(f"Detected droplets: {len(data.spikes[0])}")

spike_heights = data.spikes[1]['prominences']
spike_widths  = data.spikes[1]['widths'] / data.measurement_frequency  # s

Experiment classes

The TDIPSExperiment and BindingExperiment classes extend the base Experiment to handle batch loading with experiment-specific parameters.

Class	Data class	Key method
Experiment	any FIDAData subclass	base loader
TDIPSExperiment	TDIPSData	summary() → DataFrame
BindingExperiment	TDAData	fit(), summary() → dict

For FidaNeo files with a sample map, the base Experiment class reads buffer compositions directly from instrument metadata:

from fidapy.experiments import Experiment
from fidapy import TDIPSData

# sample_map.xlsx defines what is in each vial
exp = Experiment(
    path='data/experiment_folder/',
    sample_map_path='data/experiment_folder/sample_map.xlsx',
    data_object_class=TDIPSData,
)

The sample map Excel file needs two sheets (Tray 1, Tray 2). Each sheet has Vial pos, Vial name, and any number of component columns. protein_concentration is a special column used for signal normalization.

Vial pos    Vial name   NaCl    PEG4000   protein_concentration
1           A1
2           B1          0.5     2
3           C1                            0.000010

Custom filename parsing

When buffer conditions are encoded in the filename rather than in FidaNeo metadata, provide a parser function:

import re
from pathlib import Path

def my_parser(filename: str):
    """Return (indicator_components, analyte_components)."""
    indicator, analyte = {}, {}
    basename = Path(filename).name

    # Example: '120uM_TDP16_0mM_urea_100mM_NaCl-03-05-2025-13-19.txt'
    m = re.match(r'(\d+)uM_([A-Za-z0-9]+)_(.*?)-\d{2}-\d{2}-\d{4}', basename)
    if m:
        indicator['protein_concentration'] = float(m.group(1)) * 1e-6
        indicator['protein_name'] = m.group(2)
        # parse buffer section ...

    return indicator, analyte

data = TDIPSData(
    path='sample.txt',
    filename_regex_function=my_parser,
    measurement_pressure=5e4,
    capillary_length=0.5,
)

Titrant concentration for TDA/binding experiments is read from the filename brackets automatically: [27 uM] protein_buffer-date.txt → data.analyte_concentration = 27e-6.

Viscosity calculations

Buffer viscosity is calculated automatically from analyte_components / indicator_components. It can also be called directly:

from fidapy.viscosity import calculate_buffer_viscosity, list_compatible_components

visc, not_used = calculate_buffer_viscosity(
    components={'NaCl': 0.15, 'PEG4000': 5, 'urea': 1.0},
    temp=25,  # °C
)
print(f"η = {visc * 1e3:.3f} mPa·s")

# List all supported components
print(list_compatible_components())

Salts use the Laliberté (2007) correlation; PEG uses the Huggins equation (Kirinčič & Klofutar 1999); urea and GdmHCl use empirical correlations. Unrecognised components are returned in not_used with a warning.

Binding models

fit_functions.py provides:

Function	Use case
langmuir_isotherm	Simple 1:1 binding; K_d ≫ [indicator]
quadratic_binding	Tight binding; K_d ≈ [indicator]
competitive_displacement	Competitive assays (Wang 1995)
observable_fast_exchange	Weighted-average observable from populations

All binding functions take dG_diss (kJ/mol) and return [frac_unbound, frac_bound]. K_d = exp(-dG / RT).

Requirements

• Python ≥ 3.11
• numpy ≥ 1.26.4
• scipy ≥ 1.14.0
• pandas ≥ 2.2.0
• matplotlib ≥ 3.8.0
• openpyxl ≥ 3.1.0

License

MIT

Citation

If you use fidapy in your research, please cite the upcoming paper. Reference will be added when available.

Contact

Rasmus Krogh Norrild — [e-mail]
Repository: https://gitlab.gbar.dtu.dk/dtu_biophysics/fidapy
Developed at the DTU Biophysics group.