PyCpep

PyCpep package predicts the deviation in the isobaric heat capacity measurement (at 298~K) due to the improper amount of the sample or/and calibration standard in Tian-Calvet microDSC. PyCpep package works on the well-trained artificial neural network (ANN) model.

Estimated PyCpep prediction accuracy over the test data is 99.83[%] and R2-score 99.4

Direction

1. Open terminal and install the PyCpep package by the following pip command.

pip install pycpep

2. To check the pkg download and importing the pkg in python. Python 3.8 or higher version is required.

$ python

## DeviationPredictor
DeviationPredictor is a class from PyCpep package to predict a deviation in the heat capacity measurement (at 298~K) due to the improper amount of the sample or/and calibration standard in Tian-Calvet microDSC. PyCpep package works on the well-trained artificial neural network (ANN) model.

## useage:
## importing module
from pycpep import DeviationPredictor
deviation = DeviationPredictor(Ref, Sam)
## calling help
help(deviation)
## quick info
deviation.info()
## downloading trained model locally
deviation.load_locally()
## deviation prediction
deviation.deviation_prediction()

Minimum Working Example

# to load pkg
from PyCpep import DeviationPredictor
deviation = DeviationPredictor()

# help
help(deviation)

# info
deviation.info()

# Minimum working example download for a quick start
deviation.laod_locally()

# deviation prediction
R = 1 # Reference amount
S = 1 # Sample amount
deviation.deviation_prediction(R,S)

# NOTE: enter the sample and reference material amount as mentioned below
    ## Full cell:               1.0     [0.80 to 1.00 ml]
    ## Two Third full cell:     0.66    [0.40 to 0.80 ml]
    ## One half full cell:      0.5     [0.26 to 0.40 ml]
    ## One third full cell:     0.33    [0.10 to 0.26 ml]
# prediction of the deviation in heat capacity measurement