http://code.activestate.com/recipes/tags/calculus-gradient/2014-03-08T12:58:26-08:00ActiveState Code Recipes2014-03-08T12:58:26-08:00Johnhttp://code.activestate.com/recipes/users/4189390/http://code.activestate.com/recipes/578847-simple-derivative-solver-in-python/

<p style="color: grey"> Python recipe 578847 by <a href="/recipes/users/4189390/">John</a> (<a href="/recipes/tags/basic/">basic</a>, <a href="/recipes/tags/beginner/">beginner</a>, <a href="/recipes/tags/calculator/">calculator</a>, <a href="/recipes/tags/calculus/">calculus</a>, <a href="/recipes/tags/derivative/">derivative</a>, <a href="/recipes/tags/python/">python</a>). </p> <p>Solves monomials, might try to make one that solves polynomials in the future.</p>

2010-01-15T06:08:56-08:00Eric-Olivier LE BIGOThttp://code.activestate.com/recipes/users/2672032/http://code.activestate.com/recipes/576721-calculations-with-error-propagation-and-semi-forma/

<p style="color: grey"> Python recipe 576721 by <a href="/recipes/users/2672032/">Eric-Olivier LE BIGOT</a> (<a href="/recipes/tags/calculus/">calculus</a>, <a href="/recipes/tags/error_propagation/">error_propagation</a>, <a href="/recipes/tags/expressions/">expressions</a>, <a href="/recipes/tags/formal_calculations/">formal_calculations</a>, <a href="/recipes/tags/lazy_evaluation/">lazy_evaluation</a>, <a href="/recipes/tags/uncertainties/">uncertainties</a>). Revision 22. </p> <p><strong>Do not use this module</strong>, but use instead the more powerful <a href="http://pypi.python.org/pypi/uncertainties/">uncertainties.py module</a>.</p> <p>Module for performing calculations with error propagation, such as (1 +- 0.1) * 2 = 2 +- 0.2. Mathematical operations (addition, etc.), operations defined in the math module (sin, atan,...) and logical operations (&lt;, &gt;, etc.) can be used.</p> <p>Correlations between parts of an expression are correctly taken into account (for instance, the error on "x-x" is strictly zero).</p> <p>Code written for floats should directly work with the numbers with uncertainty defined here, without much need for modifications.</p> <p>The module also contains a class that represents non-evaluated mathematical expressions. This class is used for performing the differentiation required by the error propagation calculation, but can be used on its own, for manipulating "semi-formal" expressions whose variables can be accessed.</p>