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        "author": "Johannes Sahlmann",
        "author_email": "jsahlmann@stsci.edu",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 2 - Pre-Alpha",
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: GNU Lesser General Public License v3 or later (LGPLv3+)",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Topic :: Scientific/Engineering :: Astronomy"
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        "description": "\nlinearfit\n======\n\n**python class that implements a general least-squares fit of a linear model using numpy matrix inversion.**\n\nUncertainties in the dependent variables (but not in the independent\nvariables) can be taken into account. All inputs have to be numpy matrices.\n\nMath is based on Press'  \nNumerical Receipes p661 : Section 15.2 Fitting Data to a Straight Line  \nNumerical Receipes p671 : Section 15.4 General Linear Least Squares  \n\nCode is based on an early yorick implementation by Damien Segransan\n(University of Geneva)\n\nPython implementation and tools by Johannes Sahlmann 2009-2017 (University of Geneva, European Space Agency, STScI/AURA)\n\nPlease see the test in test_linearfit.py for an example usage.\n\n\nBasic example usage (Fitting a straight line to data with uncertainties in y)\n-------------\n\n```\n# independent variable\nx = np.array([0.0, 0.9, 1.8, 2.6, 3.3, 4.4, 5.2, 6.1, 6.5,7.4])\n\n# dependent variable\t\ny = np.array([5.9, 5.4, 4.4, 4.6, 3.5, 3.7, 2.8, 2.8, 2.4, 1.5])\n\n# weights of dependent variable\t\nwy = np.array([1,1.8,4,8,20,20,70,70,100,500])\n\n# prepare matrices\nM = np.mat(y);\n#       diagonal covariance matrix of dependent variables\nS = np.mat(np.diag(wy));\n# matrix of independent variables, here only ones\nC = np.mat(np.vstack( (np.ones(len(x)) , x)))\n\n# initialise object\nres = linearfit.LinearFit(M,S,C);\n\n# do the fit\nres.fit()\nres.display_results()\n```\n\n\nDocumentation\n-------------\n\nAll classes and methods/functions include basic documentation. \n\n\nInstallation notes\n------------------\n\nThis package was developed in a python 2.7 environment, but was also\nsuccessfully tested using python 3.5.\n\nThe following python packages are required:\n\n* numpy\n\n\nHow to run the example script\n-----------\n\nYou may use pip for installation:  \n```pip install linearfit```\n\nOr get the source files, e.g.:   \n```git clone https://github.com/johannes-sahlmann/linearfit```\n\nInstall pygacs:  \n```cd linearfit```  \n```python setup.py install --user```\n\nTo run the test, do:  \n```python test_linearfit.py```\n\n\nLicense\n-------\n\nCopyright (c) 2017 Johannes Sahlmann, STScI/AURA\n\nlinearfit is open source and free software: you can redistribute it and/or modify\nit under the terms of the GNU Lesser General Public License as published by the\nFree Software Foundation, either version 3 of the License, or (at your option)\nany later version.\n\nThis program is distributed in the hope that it will be useful, but WITHOUT ANY\nWARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A\nPARTICULAR PURPOSE.  See the GNU General Public License for more details.\n\nYou should have received a copy of the GNU General Public License along with\nthis program. If not, see http://www.gnu.org/licenses/.\n\n\nChangelog\n---------\n\n.. :changelog:\n\n0.1 (May 2017)\n++++++++++++++++++\n\n- First posting on github\n\n  \n",
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