Metadata-Version: 2.1
Name: psd
Version: 1.5.2
Summary: Python tools for data visualization and spectral analysis.
Home-page: https://gitlab.in2p3.fr/j2b.bayle/psd
Author: Jean-Baptiste Bayle (APC/CNRS/CNES)
Author-email: bayle@apc.in2p3.fr
License: GNU GPLv3
Description: # PSD Module
        
        
        **This module provides easy-to-use tools for quick data visualization and spectral
        analysis.**
        
        Data must be stored on text, Numpy or HDF5 files, and all formats compatible with
        the standard `numpy.loadtxt` and `numpy.load` are accepted. First dimension, or
        rows, is used for time and second dimension, or columns, for series. The first
        column is always assumed to represent the times associated with each row.
        
        ## Installation
        
        Make sure that Python 3 is available on your machine, and run
        
        ```shell
        pip3 install psd
        ```
        
        The package is also available at https://pypi.org/project/psd/.
        
        ## Getting Started
        
        ### Time-Series Visualization
        
        You can visualize time-series from Numpy or text files using
        
        ```shell
        psd --time-series my_file.npy another_file.txt ...
        ```
        
        You can read HDF5 files as well by specifying the path to the dataset inside
        your file using
        
        ```shell
        psd --time-series my_hdf5_file.hdf5:mygroup/mydataset ...
        ```
        
        ### Power Spectrum Estimation
        
        To compute Power Spectral Density (PSD) estimates for each series using the
        [Welch method](https://en.wikipedia.org/wiki/Welch%27s_method), simply use
        
        ```shell
        psd my_file.npy
        ```
        
        You can specify the number of rows at the top of the files you want to skip
        using `-s SKIPROWS` option, the number of points per segment you want to use
        with `-n NPERSEF` option, or the windowing function using `--window WINDOW`.
        
        For time-series visualization and spectral analysis, you can hide the legend
        with the `--no-legend` option, specify a title with `--title TITLE`, or save
        the output as a text file, a Numpy file or an image using `-o OUTPUT`. You
        can specify line and marker aspect using matplotlib notation with `--aspect`.
        
        ```shell
        psd -s 500 -n 10000 --window nuttall my_file.npy --title "This is an example"
        ```
        
        ### Format Conversion
        
        You can easily convert from text files to Numpy binary files using the quick
        `convert` command-line tool included in this package, i.e.
        
        ```shell
        convert my_file1.txt my_file2.text
        ```
        
        To reverse the conversion and get a text file from a Numpy file, use the `-r`
        option. You can specify the output file name using `-o OUTPUT`.
        
        ```shell
        convert -r my_numpy.npy -o my_text_file.txt
        ```
        
        The tool can also remove original files as soon as they are converted if you
        specify the `--delete` or `-d` option.
        
        ```shell
        convert -d file*.txt
        ```
        
        ## Documentation
        
        Other options are available, use `psd --help` or `convert --help` to show
        documentation.
        
        Developped by Jean-Baptiste Bayle (APC/CNES/CNRS), bayle@apc.in2p3.fr.
        
Platform: UNKNOWN
Description-Content-Type: text/markdown
