PyNanoLab is aimed to create a GUI based software to integrate the Python-based ecosystem of open-source software for mathematics, science, and engineering.
Initially, PyNanoLab is designed for the purpose of nanopore data analysis. Now, it is also a general data analysis and data visualization software for students, scientists and engineers in academia and laboratories. PyNanoLab offers an easy-to-use interface for beginners to use the functions in pandas and matplotlib by integrate the scientific computing packages of Python and Qt framework.
PyNanoLab is a free alternatives software for OrignaLab and similar software.
Project Data Structure
PyNanoLab use the folder tree to manager the project. The project could save all the information of the data and rebuild the figure and reset the parameters when next opened. Ther have three sub folder in the root folder.
- Data Folder
The Data Folder is used to manage the sampling data, such as electrophysiology signal, and Ni LabVIEW TDMS data. These kinds of datas usually need large memory. The pyqtgraph is used to visualize the sampling data. Recently, the .abf, .tdms, .xdat and .spe file format is supported.
- Table Folder
The Table Folder is used to manage the wo dimensional tabular data, just like the excel. And you could complete most of the operations and analysis in MicroSoft Excel and Pandas.
Supported file Format: all ASCII text file, .csv, .xls, .xlsx, .mat, .npy,.npz.
- Figure Folder
The Figure Folder is used to manage the figure object created by matplotlib. At present, PyNanoLab have already supported all matplotlib basic graph type. You could setup any figure parms, interactively. And easy save and rebuild the figure.
Up to Version 2.3.0，PyNanoLab have the Curves Fitting, Clustering, and the Nanopore analysis toolbox.
This Toolbox is used to fit the data to a defined functions, such as Gaussian, lorentz,exponential and polynomial. The basic package of this toolbox is used the lmfit. Combined with the matplotlib, this toolbox can use to fit any data, interactively.
This toolbox is used that automatic grouping of similar objects into sets by scikit-learn. You colud set the algorithm and parameter with a well-designed gui. And preview the result, interactively.
The nanopore toolbox is used to extract the nanopore signal automaticly. We have integrated various methods to adapt different signal.
More detailed uasage please refer the document.