Dali (Data Analysis and Laboratory Instrumentation) is a Python library created for my laboratory courses. Dali was made to speed up tasks like plotting, data analysis and more, and was designed to be quite versatile as well as fast and reliable. Feel free to contribute by forking and opening pull requests.
Dali is still a very basic project with little core functionality, however it can speed up redundant and boring tasks by ~200%. Here is a list of all the features implemented so far:
- Basic plotting (plot, errorbar and histogram)
- Support for multiple plots (max 3)
- Multiple color palettes and designed to be stylish
- Organized output folder for images
- Basic algorithms for data analysis (Clusterization, Data cleaner)
- LaTeX support for exporting measurements (Measurements and data tables)
- Deployment as a Python package
- Support for other types of plots (Spectrogram and more)
To install Dali, simply use:
pip install dali_lib
To use it in your own project, use:
import Dali
# Your code...The following code will produce this output:
from Dali import *
def main():
sigma = 0.3
x = np.linspace(0, 10, 1000)
y = LinearMap(x, 2, 0.2)
y += np.random.randn(len(x)) * sigma
p, cov = CurveFit(LinearMap, x, y, sigma=[sigma] * len(x), absolute_sigma=True)
PlotErrorData(x, y, None, [sigma]*len(y), 'Data and best fit', 'Time [s]', 'Distance [cm]', legend = 'Data')
PlotFit(x, LinearMap(x, *p))
ChangePlotIndex(1)
PlotHistogram(EvalSquareResidues(y, LinearMap(x, *p), len(x) - 2), 'Squared residues histogram', 'Squared distances [$\\sigma^2$]', 'Occurencies', bins = 20, legend=f'df = {len(x) - 2}')
ChangePlotIndex(2)
PlotHistogram(EvalResidues(y, LinearMap(x, *p), len(x) - 2), 'Residues histogram', 'Distances [$\\sigma$]', 'Frequencies', legend=f'df = {len(x) - 2}')
ShowPlot('Trimosaic.png')
if __name__ == '__main__':
InitPlotMode(mode = 'trimosaic', figSize = (16, 8), palette = 'Funky', path = 'Images/')
main()