Original 0.2.2 release

CHANGES.txt

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+Version 0.1.0:
+Initial Release
+
+Version 0.1.1:
+Corrected Small Errors and Typos
+
+Version 0.2.0: (13/01/14)
+
+Added Probability Density Functions (PDF)
+Added Reverse Lmoment Estimation Functions (LMOM)
+Added Negative Log Likelhood Function (NlogL)
+Included Unit Tests
+Implimented better version of PELWAK function
+Support for lists as x inputs for all CDF functions
+Bugfixes
+Now licensed under the GPLv3
+
+VERSION 0.2.1: (15/01/14)
+Support for lists as F inputs for all QUA functions
+Added Random Number Generator (rand) for all functions
+Split the main lmoments.py file into several files, as the
+project is getting to large to maintain as one single file.
+
+VERSION 0.2.2: (23/01/14)
+Improved samlmu function to support any value of nmom
+Added Bayesian Information Criterion (BIC) function
+Fixed import glitch that allowed users to import container files
+General Bugfixes

PKG-INFO

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+Metadata-Version: 1.0
+Name: lmoments
+Version: 0.2.2
+Summary: L-Moment Algorithms in Python
+Home-page: http://pypithon.org/pypi/lmoments/
+Author: Sam Gillespie
+Author-email: sam.gillespie@my.jcu.edu.au
+License: GPLv3 License
+Description: """
+        This library was designed to use L-moments to predict optimal parameters
+        for a number of distributions.  Distributions supported in this file are
+        listed below, with their distribution suffix:
+            *Exponential (EXP)
+            *Gamma (GAM)
+            *Generalised Extreme Value (GEV)
+            *Generalised Logistic (GLO)
+            *Generalised Normal (GNO)
+            *Generalised Pareto (GPA)
+            *Gumbel (GUM)
+            *Kappa (KAP)
+            *Normal (NOR)
+            *Pearson III (PE3)
+            *Wakeby (WAK)
+            *Weibull (WEI)
+
+        The primary function in this file is the samlmu(x,nmom) function, which takes
+        an input dataset x and  input of the number of moments to produce the log
+        moments of that dataset.
+
+        For Instance, given a list "Data", if 5 l-moments are needed, the function
+        would be called by lmoments.samlmu(Data,5)
+
+        In this file contains four different functions for using each distribution.
+        Each function can be called by the prefix FUN with the suffix DIS.
+
+        *PEL: (x,nmom):
+              Parameter Estimates.  This takes the L-Moments calculated by samlmu()
+              and predicts the parameter estimates for that function.
+
+              EXAMPLE: Find Wakeby distribution that best fits dataset DATA:
+
+                  import lmoments
+                  para = lmoments.pelwak(lmoments.samlmu(DATA,5))
+
+        *QUA: (f,para)
+              Quantile Estimates.  This takes the parameter estimates for a
+              distribution, and a given Quantile value to calculate the quantile for the
+              given function.
+
+              EXAMPLE: Find the Upper Quantile (75%) of the Kappa distribution that
+              best fits dataset DATA:
+
+                  import lmoments
+                  para = lmoments.pelkap(lmoments.samlmu(DATA,5))
+                  UQ = lmoments.quakap(0.75,para)
+
+        *LMR: (para,nmom):
+              L-Moment Ratios.  This takes the parameter estimates for a distribution
+              and calculates nmom L-Moment ratios.
+
+              EXAMPLE: Find 4 lmoment ratios for the Gumbel distribution that
+              best fits dataset DATA:
+
+                  import lmoments
+                  para = lmoments.pelgum(lmoments.samlmu(DATA,5))
+                  LMR = lmoments.lmrgum(para,4)
+
+        *CDF: (x,para):
+              Cumulative Distribution Function.  This takes the parameter estimates
+              for a distribution and calculates the quantile for a given value x.
+
+              EXAMPLE: Find the quantile of the datapoint 6.4 for the Weibull
+              Distribution that best fits the dataset DATA:
+
+                  import lmoments
+                  para = lmoments.pelwei(lmoments.samlmu(DATA,5))
+                  quantile = lmoments.cdfwei(6.4,para)
+
+
+        *PDF: (x,para):
+              Probability Distribution Function.  This takes the parameter estimates
+              for a distribution and calculates the p value for a given value x.
+
+              EXAMPLE: Find the p-value of the datapoint 6.4 for the Weibull
+              Distribution that best fits the dataset DATA:
+
+                  import lmoments
+                  para = lmoments.pelwei(lmoments.samlmu(DATA,5))
+                  quantile = lmoments.pdfwei(6.4,para)
+
+        *LMOM: (para):
+              L-Moment Estimation from Parameters.  This function takes the input
+              parameters for a given distribution, and attempt to calculate the
+              L-Moments that would correspond to this distribution.
+
+              EXAMPLE: Estimate the L-Moments of the Weibull Distribution that has
+              parameters (2.5,1.5,0.5)
+
+                  import lmoments
+                  Lmoments = lmoments.lmomwei([2.5,1.5,0.5])
+
+        #RAND:  (n,para)
+              Random Number Generator for a given function.  This takes a curve fit
+              and returns a list of random numbers generated from that distribution
+
+              EXAMPLE: Generate 10 numbers from the weibull distribution that makes
+              up the dataset "data"
+
+                  import lmoments
+                  weibullfit = lmoments.pelwei(lmoments.samlmu(data))
+                  randnums = lmoments.randwei(10,weibullfit)
+
+        *NlogL: (data,dist,peldist):
+              Calculates the Negative Log Likelihood for use in AIC/AICc/BIC calculations.
+              Provide data, and distribution to calculate NlogL.  Can also provide curve
+              fitting parameters, but if they aren't provided, then the function will
+              generate them via pelxxx(samlmu(data)).  To specify the distribution, use the
+              three letters typically assigned.
+
+              EXAMPLE: Calculate the Negative Log Likelihood of a Gamma distribution
+              fitted to Data.
+
+                  import lmoments
+                  NLL = lmoments.NlogL(data,"GAM")
+
+              EXAMPLE:  Calculate the Negative Log Likelihood of a Gamma distribution
+              with parameters [2.5,1.0] when fitted to Data.
+
+                  import lmoments
+                  NLL = lmoments.NlogL(data,"GAM",[2.5,1.0])
+
+        *AIC: (data,dist,*distfit):
+              Calculate the Akaike Information Criterion (AIC) using the chosen dataset
+              and distribution
+
+              EXAMPLE:  Calculate the Akaike Information Criterion for the weibull
+              distribution using the input dataset data:
+
+                  import lmoments
+                  Akaike = AIC(data,"WEI")
+
+
+        *BIC: (data,dist,*distfit):
+              Calculate the Bayesian Information Criterion (AIC) using the chosen dataset
+              and distribution
+
+              EXAMPLE:  Calculate the Bayesian Information Criterion for the weibull
+              distribution using the input dataset data:
+
+                  import lmoments
+                  Akaike = AIC(data,"WEI")
+
+        This file contains a Python implimentation of the lmoments.f library created by
+        J. R. M. HOSKING.                                                                                 
+
+        The base Fortran code is copyright of the IBM Corperation, and the licensing
+        information is shown below:
+
+        %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+        IBM software disclaimer
+
+        LMOMENTS: Fortran routines for use with the method of L-moments
+        Permission to use, copy, modify and distribute this software for any purpose
+        and without fee is hereby granted, provided that this copyright and permission
+        notice appear on all copies of the software. The name of the IBM Corporation
+        may not be used in any advertising or publicity pertaining to the use of the
+        software. IBM makes no warranty or representations about the suitability of the
+        software for any purpose. It is provided "AS IS" without any express or implied
+        warranty, including the implied warranties of merchantability, fitness for a
+        particular purpose and non-infringement. IBM shall not be liable for any direct,
+        indirect, _special or consequential damages resulting from the loss of use,
+        data or projects, whether in an action of contract or tort, arising out of or
+        in connection with the use or performance of this software.
+        %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+        Additional code from the R library "lmomco" has been converted into Python.
+        This library was developed by WILLIAM ASQUITH, and was released under the GPL-3
+        License. Copyright (C) 2012 WILLIAM ASQUITH
+
+        The Python translation was conducted by:
+            Sam Gillespie
+            Numerical Analyst
+            C&R Consulting
+            Townsville Australia
+            September 2013
+
+        For more information, or to report bugs, contact:
+            sam.gillespie@my.jcu.edu.au
+
+        Licensing for Python Translation:
+        ####################################################
+            Copyright (C) 2014 Sam Gillespie
+
+            This program is free software: you can redistribute it and/or modify
+            it under the terms of the GNU General Public License as published by
+            the Free Software Foundation, either version 3 of the License, or
+            (at your option) any later version.
+
+            This program is distributed in the hope that it will be useful,
+            but WITHOUT ANY WARRANTY; without even the implied warranty of
+            MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+            GNU General Public License for more details.
+
+            You should have received a copy of the GNU General Public License
+            along with this program.  If not, see <http://www.gnu.org/licenses/>.Version 0.1.0:
+        ####################################################
+
+        Initial Release
+
+        Version 0.1.1:
+        Corrected Small Errors and Typos
+
+        Version 0.2.0:
+        Added Probability Density Functions (PDF)
+        Added Reverse Lmoment Estimation Functions (LMOM)
+        Added Negative Log Likelhood Function (NlogL)
+        Included Unit Tests
+        Implimented better version of PELWAK function
+        Support for lists as x inputs for all CDF functions
+        Bugfixes
+        Now licensed under the GPLv3
+
+        VERSION 0.2.1:
+        Support for lists as F inputs for all QUA functions
+        Added Random Number Generator (rand) for all functions
+        Split the main lmoments.py file into several files, as the
+        project is getting to large to maintain as one single file.
+
+
+        VERSION 0.2.2:
+        Improved samlmu function to support any value of nmom
+        Added Bayesian Information Criterion (BIC) function
+        Fixed import glitch that allowed users to import container files
+        General Bugfixes
+
+        """
+Platform: UNKNOWN

lmoments3/__init__.py

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+from lmoments import *