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        "description": "<h1>autogpy - AutoGnuplot.py</h1>\n\n<h2>Automatic generation of gnuplot figures/scripts/data from python.</h2>\n\n\n**Author:** [Alessandro Corbetta](http://corbetta.phys.tue.nl/), 2019  \n**Documentation:** https://acorbe.github.io/autogpy/  \n\n![build status](https://travis-ci.org/acorbe/autogpy.svg?branch=master)\n\n\n### Which problem does it solve?\n\n`autogpy` eliminates annoying duplications of code/data when doing data analytics in python and publication figures in gnuplot. Using a syntax close to gnuplot, it automatically generates gnuplot scripts and dumps suitably the data.  \n\nIn the scientific community, [gnuplot](http://www.gnuplot.info/) is a gold standard for publication-quality plots. Python is quickly becoming a tool of choice for data analytics. While it comes with several options for plotting, often gnuplot is preferred in production.\n\n\n### Features\n+ anything that be obtained by the gnuplot command `plot` can be produced\n+ any gnuplot state modification can be achieved\n+ multiplots\n+ `plt.hist`-like gnuplot histogram figures generator\n+ jupyter notebook figure preview\n+ jupyter notebook gnuplot script inspection\n+ gnuplot terminals epslatex, tikz/pgfplot and jpg\n+ output figures are shipped in a folder that includes scripts, data and makefile\n+ easy scp-based synchronization between a machine in which the figures are generated (e.g. from even larger datasets) and the \"paper writing\" machine.\n\n**Works on**\n+ Linux/MacOs\n+ Python 3\n\n**KWONW ISSUES**\n+ Certain features require imagemagick and a working `gnuplot-tikz.lua`. Some versions of these might have bugs. Do `figure.display_fixes()` to show known fixes.\n\n\n\n## In a nutshell\n\n### Getting autogpy\n\nFrom `pip`\n\n```bash\npip install autogpy\n\n```\n\nFrom source\n\n```bash\ngit clone git@github.com:acorbe/autogpy.git\npip install ./autogpy\n```\n\n### Usage\n\n```python\nimport autogpy\nimport numpy as np\n\nxx = np.linspace(0,6,100)\nyy = np.sin(xx)\nzz = np.cos(xx)\n\nfigure = autogpy.AutogpyFigure(\"test_figure\",\"test1\")\n\nfigure.p_generic(r'u 1:2 with lines t \"sin\"',xx,yy)\nfigure.p_generic(r'u 1:2 with lines t \"cos\"',xx,zz)\nfigure.generate_gnuplot_file()\n\nfigure.jupyter_show_pdflatex() # only in jupyter\n\n```\n\n\nwill generate the following figure (also appearing in jupyter)\n\n<img src=\"https://github.com/acorbe/autogpy/raw/master/example_fig.jpeg\" alt=\"example figure\" width=\"500px\" >\n\n\n**Most importantly**, the following source and data will be created in the folder `test_figure` \n\n```bash\n$ ls test_figure\n\nMakefile\nsync_me.sh\ntest1__0__.dat\ntest1__1__.dat\ntest1__.core.gnu\ntest1__.jpg.gnu\ntest1__.pdflatex_compile.sh\ntest1__.pdflatex.gnu\ntest1__.tikz_compile.sh\ntest1__.tikz.gnu\n```\n\nWith `make` one can obtain jpg, epslatex, and tikz/pgfplot versions of the figure.\nNotice that the input data has been formatted automatically.\n\nInspecting `test1__.pdflatex.gnu`, responsible of the epslatex version of the figure, one gets:\n```gnuplot\nset terminal epslatex size 9.9cm,8.cm color colortext standalone      'phv,12 '  linewidth 2\nset output 'fig.latex.nice/plot_out.tex'\n\nload \"test1__.core.gnu\"; \n```\nwhile `test1__.core.gnu` reads:\n```gnuplot\np \"test1__0__.dat\" u 1:2 with lines t \"sin\",\\\n\"test1__1__.dat\" u 1:2 with lines t \"cos\"\n\n```",
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