Metadata-Version: 2.1
Name: pdffit
Version: 0.1
Summary: Fit a Lognormal + Power law distribution to data
Home-page: https://github.com/shivankhullar/PDF_Fit
Author: Shivan Khullar
Author-email: shivankhullar@gmail.com
License: UNKNOWN
Description: # PDF_Fit
        A code to fit the LN+PL or LN+2PL form to the density PDF of star forming regions.
        To use this:
        
        ```python
        pip install pdffit
        ````
        
        Once you have installed, you can use the following sample script - 
        ```python
        from fitter import *
        import numpy as np
        
        
        sample_data = np.load('./sample_data.npz')
        
        #xdata and ydata are just two arrays. 
        xdata = sample_data['arr_0']
        ydata = sample_data['arr_1']
        sink = float(sample_data['arr_2'])
        
        params = Params(s_cut_off = sink)
        
        p0 = [1.7, 1.6, 0.8, 7.1]
        PLPLresult = PLPLFit(xdata, ydata, p0, params, use_K21=True)
        
        p0 = [1.85, 1.57]
        PLresult = PLFit(xdata, ydata, p0, params)
        
        print (PLresult.sigma_err, PLresult.alpha_err, PLPLresult.sigma_err, PLPLresult.sb)
        #To know more about how to access the result: help(Result)
        
        ```
        
        In case you want to dig deeper or just use the function for plotting:
        ```python
        from LNPLPL_functions import *
        from LNPL_functions import *
        ```
        or whichever way you wish to import the modules and the functions within them. 
        
        
        To get help on any function:
        ```python
        help(function_name)
        ```
        
        If you're on the pypi page, please checkout the github version to get the sample data if you need. 
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.8
Classifier: License :: OSI Approved :: MIT License
Description-Content-Type: text/markdown
