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        "author": "Kevin Kramer",
        "author_email": "kevin.kramer@uzh.ch",
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            "License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3"
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        "description": "# ARPYS: python module for ARPES (**A**ngle **R**esolved **P**hoto**E**mission **S**pectroscopy) data analysis \n\n![](https://raw.githubusercontent.com/kuadrat/arpys/master/screenshots/pit_demo.gif)\n\nThis repository consists of libraries, programs and scripts related to ARPES \ndata analysis.\nThe software contained in this repository is distributed under the GNU \nGeneral Public License v3+. See file 'COPYING' for more information.\nThe file 'LICENSE-3RD-PARTY.txt' covers the different licenses of libraries \nand other programs used by ARPYS.\n\n## Requirements\n\nThe requirements are listed in `requirements.txt`. Most notable are \n`pyqtgraph`, a nice library that's built on PyQt and allows fast real-time \ndata visualization. Consequently, `arpys` requires `PyQt5` and its dependency \n`SIP`. Confer `INSTALLING.md` for more info.\n\n## Installation\n\nPlease refer to the file `INSTALLING.md`\n\n## Rough description of contents\n\nThe recommended way of using `arpys` currently is to make use of the classes \nin `dataloaders.py` (if the beamline in question has already been implemented) \nto get the relevant data into a usable format in python. Then, one can use the\nfunctions provided in `postprocessing.py` (normalizations, background \nsubtractions, etc.) on the so loaded data. Here's a simple example:\n```\n# Import the dataloaders and postprocessings\nfrom arpys import dl, pp \n\n# Load the data (this requires an appropriate dataloader to be defined in \n# dataloaders.py. If it isn't, check the file to see how you should define it\n# in your case.\nD = dl.load_data('your_arpes_data_file.suffix')\n\n# D is a Namespace object which stores the data array and some meta-data.\n# In this example we're assuming the data to contain a single energy-k cut.\n# arpys always loads data as 3d-arrays, however, so we need to take D.data[0]\n# here.\ndata = D.data[0]\nenergies = D.xscale\nangles = D.yscale\n\n# Apply some background subtraction (use at your own discretion):\nbg_subtracted = pp.subtract_bg_matt(data)\n\n# Try taking the second derivative to make the bands more visible. This often\n# requires smoothing first and is very susceptible to the various parameters.\nfrom scipy.ndimage import filters\nsmoothened = filters.gaussian_filter(bg_subtracted, sigma=10)\ndx = energies[1] - energies[0]\ndy = angles[1] - angles[0]\nsecond_derivative = pp.laplacian(smoothened, dx, dy)\n```\n\nThe tools that ship with `arpys` should be considered to be in an untested \nstage and used at your own discretion.\n\npostprocessing.py\n-----------------\n\nLibrary-like module that contains functions to process ARPES data, like \nnormalizations, bg subtractions, derivative methods, etc.\n\ndataloaders.py\n--------------\n\nContains classes which handle reading of ARPES data from different beamlines \n(i.e. different data format and conventions) and passing it in a fixed, \npython-friendly format for use by other tools and scripts in this module.\n\narpes_plot.py\n-------------\n\nImplements a commad-line interpreter that allows quick visualization of data \nand provides some basic postprocessing options (like cropping, normalization, \nangle-to-k conversion,...)\nCan be used as a program from the command line (possibly after a `chmod 755 \narpes_plot.py`) by\n```Bash\n$ arpes_plot.py <path-to-data>\n```\n\npit\n---\n\nThe **P**ython **I**mage **T**ool: A graphical data analysis tool (in the \nmaking) based on the `pyqtgraph` module.\n\ntools/\n------\n\nA set of little scripts and command-line tools for specific jobs.\nConfer each tools respective documentation for more info.\n\n#### gui.py\n\n[Deprecated] A GUI which allows to take quick looks at cuts and maps. This is \nbuilt with tkinter and matplotlib and, consequently, is rather slow.\nIn most cases `arpes_plot.py` should be used instead.\n\n#### apc\n\nA link to `arpes_plot.py`\n\n#### bandcharacters.py\n\nA tool to plot the band characters from a wien2k DFT calculation.\n\nutilities/\n----------\n\nA submodule that contains some custom python code that the original author \nused on his system and got incorporated into arpys.\narpys mostly needs the axes subclasses and some small helper functions from \nthere.\nThis is actually just a copy of another module that is hosted at \n<git@github.com:kuadrat/kustom.git>.\n\n================================================================================\nCopyright (c) 2018 Kevin Kramer, Universit\u00e4t Z\u00fcrich (kevin.kramer@uzh.ch)\n\n\n\n",
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