{
    "info": {
        "author": "astropenguin",
        "author_email": "taniguchi@a.phys.nagoya-u.ac.jp",
        "bugtrack_url": null,
        "classifiers": [
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7"
        ],
        "description": "# ndRADEX\n\n[![](https://img.shields.io/pypi/v/ndradex.svg?label=PyPI&style=flat-square)](https://pypi.org/pypi/ndradex/)\n[![](https://img.shields.io/pypi/pyversions/ndradex.svg?label=Python&color=yellow&style=flat-square)](https://pypi.org/pypi/ndradex/)\n[![Travis](https://img.shields.io/travis/astropenguin/ndradex/master.svg?label=Travis%20CI&style=flat-square)](https://travis-ci.org/astropenguin/ndradex)\n[![License](https://img.shields.io/badge/license-MIT-blue.svg?label=License&style=flat-square)](LICENSE)\n[![DOI](https://img.shields.io/badge/DOI-10.5281/zenodo.3384095-blue?style=flat-square)](https://doi.org/10.5281/zenodo.3384095)\n\n:zap: Python package for RADEX grid calculation\n\n## TL;DR\n\nndRADEX is a Python package which can run [RADEX], non-LTE molecular radiative transfer code, with parameters of multiple values (i.e., RADEX with grid parameters).\nThe output will be multi-dimensional arrays, which may be useful for parameter search of physical conditions in comparison with observed values.\n\n## Features\n\n- **Grid calculation:** ndRADEX has a simple `run()` function, where all parameters of RADEX can be griddable (i.e., they can be list-like with length of more than one).\n- **Builtin RADEX:** ndRADEX provides builtin RADEX binaries in the package, which are automatically downloaded and built during the package installation. You don't need any additional setups.\n- **Multiprocessing:** ndRADEX supports multiprocessing RADEX run by default. At least twice speedup is expected compared to single processing.\n- **Handy I/O:** The output of ndRADEX is a [xarray]'s Dataset, a standard multi-dimensional data structure as well as [pandas]. You can handle it in the same manner as NumPy and pandas (i.e., element-wise operation, save/load data, plotting, etc).\n\n## Requirements\n\n- Python 3.6 or 3.7\n- gfortran (necessary to build RADEX)\n\n## Installation\n\nYou can install ndRADEX with pip:\n\n```shell\n$ pip install ndradex\n```\n\nPlease make sure that all requirements are met before the installation.\n\n## Usages\n\nWithin Python, import the package like:\n\n```python\n>>> import ndradex\n```\n\n### Single RADEX run\n\nThe main funtion of ndRADEX is `ndradex.run()`.\nFor example, to get RADEX results of CO(1-0) with kinetic temperature of 100 K, CO column density of 1e15 cm^-2, and H2 density of 1e3 cm^-3:\n\n```python\n>>> ds = ndradex.run('co.dat', '1-0', 100, 1e15, 1e3)\n```\n\nwhere `'co.dat'` is a name of [LAMDA] datafile and `'1-0'` is a name of transition.\nThe available values are listed in [List of available LAMDA datafiles and transitions](https://github.com/astropenguin/ndradex/wiki/List-of-available-LAMDA-datafiles-and-transitions).\nNote that you don't need to any download datafiles:\nndRADEX automatically manage this.\n\nIn this case, other parameters like line width, background temperature are default values defined in the function.\nThe geometry of escape probability is uniform (`'uni'`) by default.\nYou can change these values with custom config (see customizations below).\n\nThe output is a [xarray]'s Dataset with no dimension:\n\n```python\n>>> print(ds)\n<xarray.Dataset>\nDimensions:      ()\nCoordinates:\n    QN_ul        <U3 '1-0'\n    T_kin        int64 100\n    N_mol        float64 1e+15\n    n_H2         float64 1e+03\n    T_bg         float64 2.73\n    dv           float64 1.0\n    geom         <U3 'uni'\n    description  <U9 'LAMDA(CO)'\nData variables:\n    E_u          float64 5.5\n    freq         float64 115.3\n    wavel        float64 2.601e+03\n    T_ex         float64 132.5\n    tau          float64 0.009966\n    T_r          float64 1.278\n    pop_u        float64 0.4934\n    pop_l        float64 0.1715\n    I            float64 1.36\n    F            float64 2.684e-08\n```\n\nYou can access each result value like:\n\n```python\n>>> flux = ds['F'].values\n```\n\n### Grid RADEX run\n\nAs a natural extension, you can run grid RADEX calculation like:\n\n```python\n>>> ds = ndradex.run('co.dat', ['1-0', '2-1'], T_kin=[100, 200, 300],\n                     N_mol=1e15, n_H2=[1e3, 1e4, 1e5, 1e6, 1e7])\n```\n\nThere are 13 parameters which can be griddable:\n`QN_ul` (transition name), `T_kin` (kinetic temeperature), `N_mol` (column density), `n_H2` (H2 density), `n_pH2` (para-H2 density), `n_oH2` (ortho-H2 density), `n_e` (electron density), `n_H` (atomic hydrogen density), `n_He` (Helium density), `n_Hp` (ionized hydrogen density), `T_bg` (background temperature), `dv` (line width), and `geom` (photon escape geometry).\n\nThe output of this example is a [xarray]'s Dataset with three dimensions of (`QN_ul`, `T_kin`, `n_H2`):\n\n```python\n>>> print(ds)\n<xarray.Dataset>\nDimensions:      (QN_ul: 2, T_kin: 3, n_H2: 5)\nCoordinates:\n  * QN_ul        (QN_ul) <U3 '1-0' '2-1'\n  * T_kin        (T_kin) int64 100 200 300\n    N_mol        float64 1e+15\n  * n_H2         (n_H2) float64 1e+03 1e+04 1e+05 1e+06 1e+07\n    T_bg         float64 2.73\n    dv           float64 1.0\n    geom         <U3 'uni'\n    description  <U9 'LAMDA(CO)'\nData variables:\n    E_u          (QN_ul, T_kin, n_H2) float64 5.5 5.5 5.5 5.5 ... 16.6 16.6 16.6\n    freq         (QN_ul, T_kin, n_H2) float64 115.3 115.3 115.3 ... 230.5 230.5\n    wavel        (QN_ul, T_kin, n_H2) float64 2.601e+03 2.601e+03 ... 1.3e+03\n    T_ex         (QN_ul, T_kin, n_H2) float64 132.5 -86.52 127.6 ... 316.6 301.6\n    tau          (QN_ul, T_kin, n_H2) float64 0.009966 -0.005898 ... 0.0009394\n    T_r          (QN_ul, T_kin, n_H2) float64 1.278 0.5333 ... 0.3121 0.2778\n    pop_u        (QN_ul, T_kin, n_H2) float64 0.4934 0.201 ... 0.04972 0.04426\n    pop_l        (QN_ul, T_kin, n_H2) float64 0.1715 0.06286 ... 0.03089 0.02755\n    I            (QN_ul, T_kin, n_H2) float64 1.36 0.5677 ... 0.3322 0.2957\n    F            (QN_ul, T_kin, n_H2) float64 2.684e-08 1.12e-08 ... 4.666e-08\n```\n\nFor more information, run `help(ndradex.run)` to see the docstrings.\n\n### Save/load results\n\nYou can save and load the dataset like:\n\n```python\n# save results to a netCDF file\n>>> ndradex.save_dataset(ds, 'results.nc')\n\n# load results from a netCDF file\n>>> ds = ndradex.load_dataset('results.nc')\n```\n\n## Customizations\n\nFor the first time you import ndRADEX, the custom configuration file is created as `~/.config/ndradex/config.toml`.\nBy editing this, you can customize the following two settings of ndRADEX.\nNote that you can change the path of configuration file by setting an environment variable, `NDRADEX_PATH`.\n\n### Changing default values\n\nAs mentioned above, you can change the default values of the `run()` function like:\n\n```toml\n# config.toml\n\n[grid]\nT_bg = 10 # change default background temp to 10 K\ngeom = \"lvg\" # change default geometry to LVG\ntimeout = 30\nn_procs = 2\n```\n\nYou can also change the number of multiprocesses (`n_procs`) and timeout (`timeout`) here.\n\n### Setting datafile aliases\n\nSometimes datafile names are not intuitive (for example, name of CS datafile is `cs@lique.dat`).\nFor convenience, you can define aliases of datafile names like:\n\n```toml\n# config.toml\n\n[lamda]\nCO = \"co.dat\"\nCS = \"cs@lique.dat\"\nH13CN = \"https://home.strw.leidenuniv.nl/~moldata/datafiles/h13cn@xpol.dat\"\n```\n\nAs shown in the third example, you can also specify URL or local file path on the right hand.\nAfter the customization, you can use these aliases in the `run()` function:\n\n```python\n>>> ds = ndradex.run('CS', '1-0', ...) # equiv to cs@lique.dat\n```\n\n## References\n\n- [RADEX]\n- [LAMDA]\n- [xarray]\n- [pandas]\n\n[xarray]: http://xarray.pydata.org/en/stable/\n[RADEX]: https://home.strw.leidenuniv.nl/~moldata/radex.html\n[LAMDA]: https://home.strw.leidenuniv.nl/~moldata/\n[pandas]: https://pandas.pydata.org/",
        "description_content_type": "text/markdown",
        "docs_url": null,
        "download_url": "",
        "downloads": {
            "last_day": -1,
            "last_month": -1,
            "last_week": -1
        },
        "home_page": "https://github.com/astropenguin/ndradex",
        "keywords": "radio-astronomy,python,radex,xarray",
        "license": "MIT",
        "maintainer": "",
        "maintainer_email": "",
        "name": "ndradex",
        "package_url": "https://pypi.org/project/ndradex/",
        "platform": "any",
        "project_url": "https://pypi.org/project/ndradex/",
        "project_urls": {
            "Homepage": "https://github.com/astropenguin/ndradex"
        },
        "release_url": "https://pypi.org/project/ndradex/0.2.0/",
        "requires_dist": null,
        "requires_python": ">=3.6",
        "summary": "Python package for RADEX grid calculation",
        "version": "0.2.0"
    },
    "last_serial": 5771539,
    "releases": {
        "0.1.1": [
            {
                "comment_text": "",
                "digests": {
                    "md5": "b5752f58a4e48617af574ddfc36fc306",
                    "sha256": "fa0b0a39291012e4bf93c60eead43a543e989c97b300841aa1e02ddc0b3d3857"
                },
                "downloads": -1,
                "filename": "ndradex-0.1.1.tar.gz",
                "has_sig": false,
                "md5_digest": "b5752f58a4e48617af574ddfc36fc306",
                "packagetype": "sdist",
                "python_version": "source",
                "requires_python": ">=3.6",
                "size": 10476,
                "upload_time": "2019-05-06T04:35:45",
                "url": "https://files.pythonhosted.org/packages/79/ae/9763b7505d098a73388e775056dbef1c04bdd9a5830a23f17d2783daa508/ndradex-0.1.1.tar.gz"
            }
        ],
        "0.1.2": [
            {
                "comment_text": "",
                "digests": {
                    "md5": "7e7a3daa746ae9cb2eca5da2d4ea8144",
                    "sha256": "5f8e4e1adda127d27f7f00cb8b4a0b9f3eab844f1a1ab981248e6d47b1a26f12"
                },
                "downloads": -1,
                "filename": "ndradex-0.1.2.tar.gz",
                "has_sig": false,
                "md5_digest": "7e7a3daa746ae9cb2eca5da2d4ea8144",
                "packagetype": "sdist",
                "python_version": "source",
                "requires_python": ">=3.6",
                "size": 10507,
                "upload_time": "2019-05-06T11:30:20",
                "url": "https://files.pythonhosted.org/packages/61/95/2416770fa881df3cc6487adc718f8c30f45c51d1bf8fba490023f5cc2867/ndradex-0.1.2.tar.gz"
            }
        ],
        "0.1.3": [
            {
                "comment_text": "",
                "digests": {
                    "md5": "7c7eac19c44f6a229f77531c8c622a37",
                    "sha256": "0d9f43c726958f5bdab0d72c266918535815a88503b0ce8fa6cef5308415c976"
                },
                "downloads": -1,
                "filename": "ndradex-0.1.3.tar.gz",
                "has_sig": false,
                "md5_digest": "7c7eac19c44f6a229f77531c8c622a37",
                "packagetype": "sdist",
                "python_version": "source",
                "requires_python": ">=3.6",
                "size": 10514,
                "upload_time": "2019-05-06T11:45:05",
                "url": "https://files.pythonhosted.org/packages/2d/34/895749f13b51803d7ffcd7c1b0a6b23db82f9f80412df6c4f278a3bc7df2/ndradex-0.1.3.tar.gz"
            }
        ],
        "0.1.4": [
            {
                "comment_text": "",
                "digests": {
                    "md5": "38cd56d74866bdafb5270326135e12e1",
                    "sha256": "d66f090f8702b53cba005cb522579b5e8ebc6aab2c0bc94cbee7fa6142f0125b"
                },
                "downloads": -1,
                "filename": "ndradex-0.1.4.tar.gz",
                "has_sig": false,
                "md5_digest": "38cd56d74866bdafb5270326135e12e1",
                "packagetype": "sdist",
                "python_version": "source",
                "requires_python": ">=3.6",
                "size": 15808,
                "upload_time": "2019-05-06T17:42:48",
                "url": "https://files.pythonhosted.org/packages/5d/1b/3fa9d132705aae629c8d63133f8784c30a4bbade514715e7fbc5e2e5377f/ndradex-0.1.4.tar.gz"
            }
        ],
        "0.1.5": [
            {
                "comment_text": "",
                "digests": {
                    "md5": "665bd87a50659321beb5d7280a2eb9cd",
                    "sha256": "aec655c025e4c38f4d9b8ddbf4bf56a2ee4690a0e05bf8c6a34133fddfdb7353"
                },
                "downloads": -1,
                "filename": "ndradex-0.1.5.tar.gz",
                "has_sig": false,
                "md5_digest": "665bd87a50659321beb5d7280a2eb9cd",
                "packagetype": "sdist",
                "python_version": "source",
                "requires_python": ">=3.6",
                "size": 15875,
                "upload_time": "2019-05-07T08:44:50",
                "url": "https://files.pythonhosted.org/packages/7e/40/e37b1ef3a79f8be16b52998f5f2412f3855bf2bb75f759471c3e22c3386b/ndradex-0.1.5.tar.gz"
            }
        ],
        "0.1.6": [
            {
                "comment_text": "",
                "digests": {
                    "md5": "ae9400d6e55457450217b73d6cd2fd31",
                    "sha256": "e03d09255bf25cd297c92b841b354daa51f715425d89f9b7fdf678ef0551c0e9"
                },
                "downloads": -1,
                "filename": "ndradex-0.1.6.tar.gz",
                "has_sig": false,
                "md5_digest": "ae9400d6e55457450217b73d6cd2fd31",
                "packagetype": "sdist",
                "python_version": "source",
                "requires_python": ">=3.6",
                "size": 15901,
                "upload_time": "2019-05-08T11:59:40",
                "url": "https://files.pythonhosted.org/packages/a6/db/bed2efd4a58f783477f6d1cc0082f0ed76a3a2cd57dc5b341f81464f1131/ndradex-0.1.6.tar.gz"
            }
        ],
        "0.1.7": [
            {
                "comment_text": "",
                "digests": {
                    "md5": "ea0cd10d15074b3681ae1057f6978a90",
                    "sha256": "d4296dfb4ff5d028fe3fcc1ee76d7ab442f50a5e59c4b7781e7c5704c7800587"
                },
                "downloads": -1,
                "filename": "ndradex-0.1.7.tar.gz",
                "has_sig": false,
                "md5_digest": "ea0cd10d15074b3681ae1057f6978a90",
                "packagetype": "sdist",
                "python_version": "source",
                "requires_python": ">=3.6",
                "size": 15923,
                "upload_time": "2019-05-12T12:34:27",
                "url": "https://files.pythonhosted.org/packages/28/12/c38124266fe88fabe3f637487066342df0b3dfe59be71fdec2e171054be3/ndradex-0.1.7.tar.gz"
            }
        ],
        "0.1.8": [
            {
                "comment_text": "",
                "digests": {
                    "md5": "df2eff31a53b35173a62df140c6b7b9e",
                    "sha256": "364ee07976f9ff56bc1bd49708edd6d27590532026041d508ab612698ced550b"
                },
                "downloads": -1,
                "filename": "ndradex-0.1.8.tar.gz",
                "has_sig": false,
                "md5_digest": "df2eff31a53b35173a62df140c6b7b9e",
                "packagetype": "sdist",
                "python_version": "source",
                "requires_python": ">=3.6",
                "size": 16106,
                "upload_time": "2019-05-16T09:40:36",
                "url": "https://files.pythonhosted.org/packages/0a/32/6f9ce23a1e3b375dfaa501ddbf3e84299382e3ecb675fc863cd5f60b9407/ndradex-0.1.8.tar.gz"
            }
        ],
        "0.1.9": [
            {
                "comment_text": "",
                "digests": {
                    "md5": "6be629f97c7158ec2a70be99e6c73fe6",
                    "sha256": "9246f8a72625e550ad99a5b42390f8477927a82fa9677951e41a95732c95ac9b"
                },
                "downloads": -1,
                "filename": "ndradex-0.1.9.tar.gz",
                "has_sig": false,
                "md5_digest": "6be629f97c7158ec2a70be99e6c73fe6",
                "packagetype": "sdist",
                "python_version": "source",
                "requires_python": ">=3.6",
                "size": 16105,
                "upload_time": "2019-09-02T13:51:17",
                "url": "https://files.pythonhosted.org/packages/98/05/69050d842f45cb886628a31735c57c1ebff31e3719fea329e9344bbc7f0d/ndradex-0.1.9.tar.gz"
            }
        ],
        "0.2.0": [
            {
                "comment_text": "",
                "digests": {
                    "md5": "6851f0b6d53569ada499e884e59c31a9",
                    "sha256": "a74640bcbfa4faf20d3975e990166e1954c745eb02536cbd310c1b0c18f689dc"
                },
                "downloads": -1,
                "filename": "ndradex-0.2.0.tar.gz",
                "has_sig": false,
                "md5_digest": "6851f0b6d53569ada499e884e59c31a9",
                "packagetype": "sdist",
                "python_version": "source",
                "requires_python": ">=3.6",
                "size": 16076,
                "upload_time": "2019-09-02T15:48:11",
                "url": "https://files.pythonhosted.org/packages/33/a3/7ad4eddb8661b1313046aa9136154da68cc57ccf4f0a288ae3e3c63ff006/ndradex-0.2.0.tar.gz"
            }
        ]
    },
    "urls": [
        {
            "comment_text": "",
            "digests": {
                "md5": "6851f0b6d53569ada499e884e59c31a9",
                "sha256": "a74640bcbfa4faf20d3975e990166e1954c745eb02536cbd310c1b0c18f689dc"
            },
            "downloads": -1,
            "filename": "ndradex-0.2.0.tar.gz",
            "has_sig": false,
            "md5_digest": "6851f0b6d53569ada499e884e59c31a9",
            "packagetype": "sdist",
            "python_version": "source",
            "requires_python": ">=3.6",
            "size": 16076,
            "upload_time": "2019-09-02T15:48:11",
            "url": "https://files.pythonhosted.org/packages/33/a3/7ad4eddb8661b1313046aa9136154da68cc57ccf4f0a288ae3e3c63ff006/ndradex-0.2.0.tar.gz"
        }
    ]
}