{ "info": { "author": "Jo\u00e3o Faria", "author_email": "joao.faria@astro.up.pt", "bugtrack_url": null, "classifiers": [ "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", "Programming Language :: Python :: 3" ], "description": "pySWEETCat\n==========\n\nA pure-Python package to download data from SWEET-Cat_.\n\nThis small package was developed as an exercise in a Python course (see CONTRIBUTORS_).\nIt downloads all the data from the SWEET-Cat online catalogue of stellar parameters \nand builds a (custom) dictionary with each column. \nIt is a pure-Python package with no extra dependencies (see below).\n\n\n|License MIT| |Travis build| |PyPI version|\n\nHow to use\n----------\n\nInstall it from pip (**pySWEETCat** has no extra depencies)\n\n::\n\n pip install pySWEETCat\n\nand it's ready to use from Python\n\n.. code:: python\n\n import pysweetcat\n\n\n**pySWEETCat** has one simple function, ``get_data()``,\nwhich downloads the data from the online archive and returns it in a dictionary.\n\n.. code:: python\n\n >>> data = pysweetcat.get_data()\n\n.. code::\n\n Downloading SWEET-Cat data\n Saved SWEET-Cat data to $HOME/.pysweetcat/SWEET_cat.tsv\n Data in `SWEET_cat.tsv` is recent.\n There are 25 columns with 2627 entries each in `SWEET_cat.tsv`\n\nwhere ``$HOME`` will be your home directory.\nThe second time you call ``get_data()`` it will check if the data was downloaded recently, \nand only conditionally download it again.\n\n.. code:: python\n\n >>> data = pysweetcat.get_data()\n\n.. code::\n\n Data in `SWEET_cat.tsv` is recent.\n There are 25 columns with 2627 entries each in `SWEET_cat.tsv`\n\nNow, `data` is (basically) a Python dictionary with the each column as keys.\nBut it has a couple extra methods and properties. For example\n\n.. code:: python\n\n data.size\n 2627\n\nreturns the number of values in each column.\n\nThe columns can be accessed as in a normal dictionary,\nas in \n\n.. code:: python\n\n data['feh'] # stellar metallicity\n data['name'] # name of the star\n\n\nand both of these will work\n\n.. code:: python\n\n data['\u03c3_vmag'] \n data['sigma_vmag']\n\nAlso, to drop the NaN values in a column (for some columns there will be quite a few)\nwe can use\n\n.. code:: python\n\n data['teff_nonan']\n\n np.isnan(data['teff']).any() # True\n np.isnan(data['teff_nonan']).any() # False\n\n\nwhich allows us to more easily do histograms of the values.\n\nFinnally, the ``.to_numpy(inplace=True)`` method converts all the columns to numpy arrays, either in place or not\n(this is the only function in **pySWEETCat** that requires numpy).\n\n\nLicense\n-------\n\nCopyright 2018 Jo\u00e3o Faria.\n\n**pySWEETCat** is free software made available under the MIT License. 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