pranzo 0.1.2

couple experimental calo and rheo data

Pranzo

Pranzo is a Python library designed to study structural build-up at rest in cementitious materials by coupling data from isothermal calorimetry and rheometry experiments (Michel et al. 2024). Its simplicity allows for quick data analysis, even during your lunch break.

Table of Contents

• License
• Installation
• Basic Usage

License

Copyright © 2024 ETH Zurich (Luca Michel, Flavio Lorez & David S. Kammer)

Pranzo is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

Pranzo is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details.

You should have received a copy of the GNU Lesser General Public License along with Pranzo. If not, see https://www.gnu.org/licenses/.

Installation

Requirements

• Python 3.7+
• numpy
• pandas
• h5py
• bamboost < 0.9

Installation using pip

pip install pranzo

Installation in editable mode

git clone https://gitlab.ethz.ch/cmbm-public/toolboxes/pranzo
cd pranzo
pip install -e .

Basic Usage

Input

The input to Pranzo includes:

• Calorimetry data: Machine output file (calo_fileid.csv)
• Rheometry data: Machine output file (rheo_fileid.csv)
• Meta data: Written by the user (meta_fileid.toml)

An experiments consists of different measurements (calo, rheo). Ensure that each experiment has a unique identifier (fileid).

The structure for meta_fileid.toml is given in examples/meta_example.toml.

While Pranzo is built to couple calorimetry and rheometry data, it can also be used for standalone calorimetry or rheometry experiments.

Saving Data

Pranzo saves data in a bamboost database. Sample code to add an experiment to a database is given in the notebook examples/example_usage.ipynb.

Alternatively, save an experiment from the command line:

save_exp db_name data_dir file_id

• db_name: Name of the bamboost database
• data_dir: Directory containing the data files
• file_id: Identifier of the experiment

Analyzing data

Below are basic examples of data analysis with pranzo. Further examples are given in the notebook examples/example_usage.ipynb.

from bamboost import Manager
from pranzo import Analyzer

db = Manager('db_name')
exp = db['file_id']
a = Analyzer(exp)

# to quickly plot time evolutions
a.plot_Gt()
a.plot_ht()
a.plot_Ht()

# to access data arrays
rheo = a.rheo
t = rheo['time_s'][:]
G = rheo['storage_modulus_Pa'][:]

calo = a.calo
t = calo['time_s'][:]
h = calo['heat_flow_W'][:]
H = calo['heat_J'][:]

# to couple heat and storage modulus data
import numpy as np
import matplotlib.pyplot as plt

time = np.linspace(0, 4*3600, 10000)
G = rheo.interpolate('storage_modulus_Pa', time)
H = calo.interpolate('norm_heat_Jpgbinder', time)

plt.semilogy(H, G)
plt.show()