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        "description": "# PSD Module\n\n\n**This module provides easy-to-use tools for quick data visualization and spectral\nanalysis.**\n\nData must be stored on text, Numpy or HDF5 files, and all formats compatible with\nthe standard `numpy.loadtxt` and `numpy.load` are accepted. First dimension, or\nrows, is used for time and second dimension, or columns, for series. The first\ncolumn is always assumed to represent the times associated with each row.\n\n## Installation\n\nMake sure that Python 3 is available on your machine, and run\n\n```shell\npip3 install psd\n```\n\nThe package is also available at https://pypi.org/project/psd/.\n\n## Getting Started\n\n### Time-Series Visualization\n\nYou can visualize time-series from Numpy or text files using\n\n```shell\npsd --time-series my_file.npy another_file.txt ...\n```\n\nYou can read HDF5 files as well by specifying the path to the dataset inside\nyour file using\n\n```shell\npsd --time-series my_hdf5_file.hdf5:mygroup/mydataset ...\n```\n\n### Power Spectrum Estimation\n\nTo compute Power Spectral Density (PSD) estimates for each series using the\n[Welch method](https://en.wikipedia.org/wiki/Welch%27s_method), simply use\n\n```shell\npsd my_file.npy\n```\n\nYou can specify the number of rows at the top of the files you want to skip\nusing `-s SKIPROWS` option, the number of points per segment you want to use\nwith `-n NPERSEF` option, or the windowing function using `--window WINDOW`.\n\nFor time-series visualization and spectral analysis, you can hide the legend\nwith the `--no-legend` option, specify a title with `--title TITLE`, or save\nthe output as a text file, a Numpy file or an image using `-o OUTPUT`. You\ncan specify line and marker aspect using matplotlib notation with `--aspect`.\n\n```shell\npsd -s 500 -n 10000 --window nuttall my_file.npy --title \"This is an example\"\n```\n\n### Format Conversion\n\nYou can easily convert from text files to Numpy binary files using the quick\n`convert` command-line tool included in this package, i.e.\n\n```shell\nconvert my_file1.txt my_file2.text\n```\n\nTo reverse the conversion and get a text file from a Numpy file, use the `-r`\noption. You can specify the output file name using `-o OUTPUT`.\n\n```shell\nconvert -r my_numpy.npy -o my_text_file.txt\n```\n\nThe tool can also remove original files as soon as they are converted if you\nspecify the `--delete` or `-d` option.\n\n```shell\nconvert -d file*.txt\n```\n\n## Documentation\n\nOther options are available, use `psd --help` or `convert --help` to show\ndocumentation.\n\nDevelopped by Jean-Baptiste Bayle (APC/CNES/CNRS), bayle@apc.in2p3.fr.",
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