{
    "info": {
        "author": "Drazen Lucanin",
        "author_email": "kermit666@gmail.com",
        "bugtrack_url": null,
        "classifiers": [],
        "description": "Installation\n=======\n\nUbuntu Linux (or other Debian-based distro)\n-----------------------------------------------------------\nOpen a terminal and change directory to the location of this file.\n\nRun the install_dependencies script:\n\n$ sh install_dependencies.sh\n\nWindows\n------------\nBest to install the python(x,y) bundle (http://code.google.com/p/pythonxy/).\n\nAfterwards all the packages mentioned in the install_dependencies.sh file can be installed via pip. For example to install xlrd one would issue the following command inside Command Prompt:\n\n$ pip install xlrd\n\nRunning\n=======\nSet the desired preferences in the configuration file:\n\n./common/conf.py\n\nWrite down the crisis/normal years in the XLS file:\n\n./odabir_uzoraka.xls\n\nPosition yourself inside the irb.foc.forecaster folder (pwd output just to show an example of the correct path):\n\n$ cd irb.foc.forecaster\n$ pwd\n/media/Data/Drazen/Dropbox/dev/eclipse/w2/irb.foc.forecaster\n\nRun the Python interpreter with the entry script run.py as an argument:\n\n$ python run.py\n\n\nLay of the code\n===============\n\nforcaster - main module, use it to start the program\n\ncommon\n------\n|- conf - configuration file with all the preferences\n\\- exceptions - all the custom exceptions are defined here\n\nmodel - contains the data structures\n-----\n|- country - code and list of indicators\n\\- indicator - internal representation: list of dates, list of values\n\nsources - represents the data sources available online.\n-------\n\\- wb - extracts data from the World Bank\n\nai - classes regarding pattern recognition, train and test building etc.\n--\n|- input - parses XLS files to get crisis and normal period years\n|- output - writes the dataset into a text file in a subgroup-discovery-friendly format\n|- samples_set - the representation of the train and test datasets that can build samples based on the crisis/normal years input and indicators and countries specified in the conf file; fetches the data live from the World Bank API\n|- preprocessor - processes the samples to extract useful features (min, max, slope...)\n\\- metadata - column labels and data type marks used when writing the dataset\n\ntests - unit tests for individual modules\n-----",
        "description_content_type": null,
        "docs_url": null,
        "download_url": "UNKNOWN",
        "downloads": {
            "last_day": -1,
            "last_month": -1,
            "last_week": -1
        },
        "home_page": "http://kermit.epska.org/",
        "keywords": null,
        "license": "UNKNOWN",
        "maintainer": null,
        "maintainer_email": null,
        "name": "foc-forecaster",
        "package_url": "https://pypi.org/project/foc-forecaster/",
        "platform": "UNKNOWN",
        "project_url": "https://pypi.org/project/foc-forecaster/",
        "project_urls": {
            "Download": "UNKNOWN",
            "Homepage": "http://kermit.epska.org/"
        },
        "release_url": "https://pypi.org/project/foc-forecaster/0.1/",
        "requires_dist": null,
        "requires_python": null,
        "summary": "UNKNOWN",
        "version": "0.1"
    },
    "last_serial": 2762176,
    "releases": {
        "0.1": [
            {
                "comment_text": "",
                "digests": {
                    "md5": "cba5e2a09fe3898793e73cbc983284e0",
                    "sha256": "48e6948a71034f24e7ce3f6a15da37f1218967b3bb7c6c0e2ffa8db4393c8b54"
                },
                "downloads": -1,
                "filename": "foc_forecaster-0.1-py2.7.egg",
                "has_sig": false,
                "md5_digest": "cba5e2a09fe3898793e73cbc983284e0",
                "packagetype": "bdist_egg",
                "python_version": "2.7",
                "requires_python": null,
                "size": 41732,
                "upload_time": "2012-03-16T11:07:09",
                "url": "https://files.pythonhosted.org/packages/bd/8a/2c754854f8f5f63c6695ece34d361f7b1a15415a5313cccc73e633d6ad75/foc_forecaster-0.1-py2.7.egg"
            }
        ]
    },
    "urls": [
        {
            "comment_text": "",
            "digests": {
                "md5": "cba5e2a09fe3898793e73cbc983284e0",
                "sha256": "48e6948a71034f24e7ce3f6a15da37f1218967b3bb7c6c0e2ffa8db4393c8b54"
            },
            "downloads": -1,
            "filename": "foc_forecaster-0.1-py2.7.egg",
            "has_sig": false,
            "md5_digest": "cba5e2a09fe3898793e73cbc983284e0",
            "packagetype": "bdist_egg",
            "python_version": "2.7",
            "requires_python": null,
            "size": 41732,
            "upload_time": "2012-03-16T11:07:09",
            "url": "https://files.pythonhosted.org/packages/bd/8a/2c754854f8f5f63c6695ece34d361f7b1a15415a5313cccc73e633d6ad75/foc_forecaster-0.1-py2.7.egg"
        }
    ]
}