Solves automatic numerical differentiation problems in one or more variables.
Project description
numdifftools
The numdifftools library is a suite of tools written in _Python to solve automatic numerical differentiation problems in one or more variables. Finite differences are used in an adaptive manner, coupled with a Richardson extrapolation methodology to provide a maximally accurate result. The user can configure many options like; changing the order of the method or the extrapolation, even allowing the user to specify whether complex-step, central, forward or backward differences are used.
The methods provided are:
Derivative: Compute the derivatives of order 1 through 10 on any scalar function.
directionaldiff: Compute directional derivative of a function of n variables
Gradient: Compute the gradient vector of a scalar function of one or more variables.
Jacobian: Compute the Jacobian matrix of a vector valued function of one or more variables.
Hessian: Compute the Hessian matrix of all 2nd partial derivatives of a scalar function of one or more variables.
Hessdiag: Compute only the diagonal elements of the Hessian matrix
All of these methods also produce error estimates on the result.
Numdifftools also provide an easy to use interface to derivatives calculated with in _AlgoPy. Algopy stands for Algorithmic Differentiation in Python. The purpose of AlgoPy is the evaluation of higher-order derivatives in the forward and reverse mode of Algorithmic Differentiation (AD) of functions that are implemented as Python programs.
Getting Started
Visualize high order derivatives of the tanh function
>>> import numpy as np >>> import numdifftools as nd >>> import matplotlib.pyplot as plt >>> x = np.linspace(-2, 2, 100) >>> for i in range(10): ... df = nd.Derivative(np.tanh, n=i) ... y = df(x) ... h = plt.plot(x, y/np.abs(y).max())plt.show()
Compute 1’st and 2’nd derivative of exp(x), at x == 1:
>>> fd = nd.Derivative(np.exp) # 1'st derivative >>> fdd = nd.Derivative(np.exp, n=2) # 2'nd derivative >>> np.allclose(fd(1), 2.7182818284590424) True >>> np.allclose(fdd(1), 2.7182818284590424) True
Nonlinear least squares:
>>> xdata = np.reshape(np.arange(0,1,0.1),(-1,1)) >>> ydata = 1+2*np.exp(0.75*xdata) >>> fun = lambda c: (c[0]+c[1]*np.exp(c[2]*xdata) - ydata)**2 >>> Jfun = nd.Jacobian(fun) >>> np.allclose(np.abs(Jfun([1,2,0.75])), 0) # should be numerically zero True
Compute gradient of sum(x**2):
>>> fun = lambda x: np.sum(x**2) >>> dfun = nd.Gradient(fun) >>> np.allclose(dfun([1,2,3]), [ 2., 4., 6.]) True
Compute the same with the easy to use interface to AlgoPy:
>>> import numdifftools.nd_algopy as nda >>> import numpy as np >>> fd = nda.Derivative(np.exp) # 1'st derivative >>> fdd = nda.Derivative(np.exp, n=2) # 2'nd derivative >>> np.allclose(fd(1), 2.7182818284590424) True >>> np.allclose(fdd(1), 2.7182818284590424) True
Nonlinear least squares:
>>> xdata = np.reshape(np.arange(0,1,0.1),(-1,1)) >>> ydata = 1+2*np.exp(0.75*xdata) >>> fun = lambda c: (c[0]+c[1]*np.exp(c[2]*xdata) - ydata)**2 >>> Jfun = nda.Jacobian(fun, method='reverse') >>> np.allclose(np.abs(Jfun([1,2,0.75])), 0) # should be numerically zero True
Compute gradient of sum(x**2):
>>> fun = lambda x: np.sum(x**2) >>> dfun = nda.Gradient(fun) >>> np.allclose(dfun([1,2,3]), [ 2., 4., 6.]) True
See also
scipy.misc.derivative
Documentation and code
Numdifftools works on Python 2.7+ and Python 3.0+.
Official releases available at: http://pypi.python.org/pypi/numdifftools
Official documentation available at: http://numdifftools.readthedocs.io/en/latest/
Bleeding edge: https://github.com/pbrod/numdifftools.
Installation
If you have pip installed, then simply type:
$ pip install numdifftools
to get the lastest stable version. Using pip also has the advantage that all requirements are automatically installed.
Unit tests
To test if the toolbox is working paste the following in an interactive python session:
import numdifftools as nd nd.test('--doctest-modules', '--disable-warnings')
Acknowledgement
The numdifftools package for Python was written by Per A. Brodtkorb based on the adaptive numerical differentiation toolbox written in Matlab by John D’Errico [DErrico2006].
Numdifftools has as of version 0.9 been extended with some of the functionality found in the statsmodels.tools.numdiff module written by Josef Perktold [Perktold2014] and in the project report of [Verheyleweghen2014].
References
D’Errico, J. R. (2006), Adaptive Robust Numerical Differentiation http://www.mathworks.com/matlabcentral/fileexchange/13490-adaptive-robust-numerical-differentiation
Perktold, J (2014), numdiff package http://statsmodels.sourceforge.net/0.6.0/_modules/statsmodels/tools/numdiff.html
Gregory Lantoine (2010), A methodology for robust optimization of low-thrust trajectories in multi-body environments, Phd thesis, Georgia Institute of Technology
Gregory Lantoine, R.P. Russell, and T. Dargent (2012) Using multicomplex variables for automatic computation of high-order derivatives, ACM Transactions on Mathematical Software, Vol. 38, No. 3, Article 16, April 2012, 21 pages, http://doi.acm.org/10.1145/2168773.2168774
M.E. Luna-Elizarraras, M. Shapiro, D.C. Struppa1, A. Vajiac (2012), Bicomplex Numbers and Their Elementary Functions, CUBO A Mathematical Journal, Vol. 14, No 2, (61-80). June 2012.
Adriaen Verheyleweghen, (2014) Computation of higher-order derivatives using the multi-complex step method, Project report, NTNU
Changelog
Version 0.9.39 Jun 10, 2019
- Robert Parini (1):
Fix issue #43: numpy future warning
Version 0.9.38 Jun 10, 2019
- Andrew Nelson (1):
MAINT: special.factorial instead of misc.factorial
- Dougal J. Sutherland (1):
include LICENSE.txt in distributions
- Per A Brodtkorb (140):
Adjusted runtime for hypothesis tests to avoid failure and fixed pep8 failures.
Fixed a bug in setup.cfg
Replaced valarray function with numpy.full in step_generators.py
Added try except on import of algopy
Updated the badges used in the README.rst
Replaced numpy.testing.Tester with pytest.
Removed dependence on pyscaffold.
Simplified setup.py and setup.cfg
Updated .travis.yml configuration.
Reorganized the documentation.
Ongoing work to simplify the classes.
Replaced unittest with pytest.
Added finite_difference.py
replaced , with .
Reverted to coverage=4.3.4
New attempt
Fixed conflicting import
Missing import of EPS
Added missing FD_RULES = {}
Removed pinned coverage, removed dependence on pyscaffold
Updated .travis.yml and .appveyor.yml
Replaced conda channel omnia with conda-forge
Removed commented out code. Set pyqt=5 in appveyor.yml
Updated codeclimate checks
Dropped support for python 3.3 and 3.4. Added support for python 3.6, 3.7
Simplified code.
Pinned IPython==5.0 in order to make the testserver not crash.
Added line_profiler to appveyor.yml
Removed line_profiler from requirements.txt
Fix issue #37: Unable to install on Python 2.7
Added method=’backward’ to nd_statsmodels.py
Skip test_profile_numdifftools_profile_hessian and TestDoProfile
Added missing import of warnings
Added tests for the scripts from profile_numdifftools.py, _find_default_scale.py and run_benchmark.py.
Added reason to unittest.skipIf
Added line_profiler to requirements.
misssing import of warnings fixed.
Renamed test so it comes last, because I suspect this test mess up the coverage stats.
Reordered the tests.
Added more tests.
Cleaned up _find_default_scale.py
Removed link to depsy
Reverted: install of cython and pip install setuptools
Disabled sonar-scanner -X for python 3.5 because it crashes.
Reverted [options.packages.find] to exclude tests again
Added cython and reverted to pip install setuptools
Updated sphinx to 1.6.7
Try to install setuptools with conda instead.
Added hypothesis and pytest to requirements.readthedocs.txt
Set version of setuptools==37.0
Added algopy, statsmodels and numpy to requirements.readthedocs.txt
Restricted sphinx in the hope that the docs will be generated.
Removed exclusion of tests/ directory from test coverage.
Added dependencies into setup.cfg
Readded six as dependency
Refactored and removed commented out code.
Fixed a bug in the docstring example: Made sure the shape passed on to zeros is an integer.
Fixed c_abs so it works with algopy on python 3.6.
Fixed flaky test and made it more robust.
Fixed bug in .travis.yml
Refactored the taylor function into the Taylor class in order to simplify the code.
Fixed issue #35 and added tests
Attempt to simplify complexity
Made doctests more robust
Updated project path
Changed install of algopy
Fixed small bugs
Updated docstrings
Changed Example and Reference to Examples and References in docstrings to comply with numpydoc-style.
Renamed CHANGES.rst to CHANGELOG.rst
Renamed source path
Hack due to a bug in algopy or changed behaviour.
Small fix.
Try to reduce complexity
Reduced cognitive complexity of min_num_steps
Simplified code in Jacobian
Merge branch ‘master’ of https://github.com/pbrod/numdifftools
Fixed issue #34 Licence clarification.
Locked coverage=4.3.4 due to a bug in coverage that cause code-climate test-reporter to fail.
Added script for finding default scale
updated from sonarcube to sonarcloud
Made sure shape is an integer.
Refactored make_step_generator into a step property
Issue warning message to the user when setting the order to something different than 1 or 2 in Hessian.
Updated example in Gradient.
Reverted –timid option to coverage because it took too long time to run.
Reverted –pep8 option
pep8 + added –timid in .travis.yml coverage run in order to to increase missed coverage.
Refactored taylor to reduce complexity
No support for python 3.3. Added python 3.6
Fixed a small bug and updated test.
Removed unneccasarry perenthesis. Reduced the complexity of do_profile
Made python3 compatible
Removed assert False
Made unittests more forgiving.
updated doctest in nd_scipy.py and profiletools.py install line_profiler on travis
Made python 3 compatible
Updated tests
Added test_profiletools.py and capture_stdout_and_stderr in testing.py
Optimized numdifftools.core.py for speed: fd_rules are now only computed once.
Only keeping html docs in the distribution.
Added doctest and updated .pylintrc and requirements.txt
Reduced time footprint on tests in the hope that it will pass on Travis CI.
Prefer static methods over instance methods
Better memory handling: This fixes issue #27
Added statsmodels to requirements.txt
Added nd_statsmodels.py
Simplified input
Merge branch ‘master’ of https://github.com/pbrod/numdifftools
Updated link to the documentation.
- Robert Parini (4):
Avoid RuntimeWarning in _get_logn
Allow fd_derivative to take complex valued functions
- solarjoe (1):
doc: added nd.directionaldiff example
Version 0.9.20, Jan 11, 2017
- Per A Brodtkorb (1):
Updated the author email address in order for twine to be able to upload to pypi.
Version 0.9.19, Jan 11, 2017
- Per A Brodtkorb (1):
Updated setup.py in a attempt to get upload to pypi working again.
Version 0.9.18, Jan 11, 2017
- Per A Brodtkorb (38):
Updated setup
Added import statements in help header examples.
Added more rigorous tests using hypothesis.
Forced to use wxagg backend
Moved import of matplotlib.pyplot to main in order to avoid import error on travis.
Added fd_derivative function
Updated references.
Attempt to automate sonarcube analysis
Added testcoverage to sonarqube and codeclimate
Simplified code
Added .pylintrc + pep8
Major change in api: class member variable self.f changed to self.fun
Fixes issue #25 (Jacobian broken since 0.9.15)
Version 0.9.17, Sep 8, 2016
- Andrew Fowlie (1):
Fix ReadTheDocs link as mentioned in #21
- Per A Brodtkorb (79):
Added test for MinMaxStepgenerator
Removed obsolete docs from core.py
Updated appveyor.yml
Fixed sign in inverse matrix
Simplified code
Added appveyor badge + synchronised info.py with README.rst.
Removed plot in help header
Added Programming Language :: Python :: 3.5
Simplified code
Renamed bicomplex to Bicomplex
Simplified example_functions.py
- Moved MinStepGenerator, MaxStepGeneretor and MinMaxStepGenerator to step_generators.py
Unified the step generators
Moved step_generator tests to test_step_generators.py
Major simplification of step_generators.py
Removed duplicated code + pep8
Moved fornberg_weights to fornberg.py + added taylor and derivative
Fixed print statement
Replace xrange with range
Added examples + made computation more robust.
Made ‘backward’ and alias for ‘reverse’ in nd_algopy.py
Expanded the tests + added test_docstrings to testing.py
Replace string interpolation with format()
Removed obsolete parameter
Smaller start radius for Fornberg method
Simplified “n” and “order” properties
Simplified default_scale
Removed unecessary parenthesis and code. pep8
Fixed a bug in Dea + small refactorings.
Added test for EpsAlg
Avoid mutable default args and prefer static methods over instance-meth.
Refactored to reduce cyclomatic complexity
Changed some instance methods to static methods
Renamed non-pythonic variable names
Turned on xvfb (X Virtual Framebuffer) to imitate a display.
Added extra test for Jacobian
Replace lambda function with a def
Removed unused import
Added test for epsalg
Fixed test_scalar_to_vector
Updated test_docstrings
Version 0.9.15, May 10, 2016
- Cody (2):
Migrated % string formating
Migrated % string formating
- Per A Brodtkorb (28):
Updated README.rst + setup.cfg
Replaced instance methods with static methods +pep8
Merge branch ‘master’ of https://github.com/pbrod/numdifftools
Fixed a bug: replaced missing triple quote
Added depsy badge
added .checkignore for quantificode
Added .codeclimate.yml
Fixed failing tests
Changed instance methods to static methods
Made untyped exception handlers specific
Replaced local function with a static method
Simplified tests
Removed duplicated code Simplified _Derivative._get_function_name
exclude tests from testclimate
Renamed test_functions.py to example_functions.py Added test_example_functions.py
- Per A. Brodtkorb (2):
Merge pull request #17 from pbrod/autofix/wrapped2_to3_fix
Merge pull request #18 from pbrod/autofix/wrapped2_to3_fix-0
- pbrod (17):
updated conf.py
added numpydoc>=0.5, sphinx_rtd_theme>=0.1.7 to setup_requires if sphinx
updated setup.py
added requirements.readthedocs.txt
Updated README.rst with info about how to install it using conda in an anaconda package.
updated conda install description
Fixed number of arguments so it does not differs from overridden ‘_default_base_step’ method
Added codecov to .travis.yml
Attempt to remove coverage of test-files
Added directionaldiff function in order to calculate directional derivatives. Fixes issue #16. Also added supporting tests and examples to the documentation.
Fixed isssue #19 multiple observations mishandled in Jacobian
Moved rosen function into numdifftools.testing.py
updated import of rosen function from numdifftools.testing
Simplified code + pep8 + added TestResidue
Updated readme.rst and replaced string interpolation with format()
Cleaned Dea class + pep8
Updated references for Wynn extrapolation method.
Version 0.9.14, November 10, 2015
- pbrod (53):
Updated documentation of setup.py
Updated README.rst
updated version
Added more documentation
Updated example
Added .landscape.yml updated .coveragerc, .travis.yml
Added coverageall to README.rst.
updated docs/index.rst
Removed unused code and added tests/test_extrapolation.py
updated tests
Added more tests
Readded c_abs c_atan2
Removed dependence on wheel, numpydoc>=0.5 and sphinx_rtd_theme>=0.1.7 (only needed for building documentation)
updated conda path in .travis.yml
added omnia channel to .travis.yml
Added conda_recipe files Filtered out warnings in limits.py
Version 0.9.13, October 30, 2015
- pbrod (21):
Updated README.rst and CHANGES.rst.
updated Limits.
Made it possible to differentiate complex functions and allow zero’th order derivative.
BUG: added missing derivative order, n to Gradient, Hessian, Jacobian.
Made test more robust.
Updated structure in setup according to pyscaffold version 2.4.2.
Updated setup.cfg and deleted duplicate tests folder.
removed unused code.
Added appveyor.yml.
Added required appveyor install scripts
Fixed bug in appveyor.yml.
added wheel to requirements.txt.
updated appveyor.yml.
Removed import matplotlib.
- Justin Lecher (1):
Fix min version for numpy.
- kikocorreoso (1):
fix some prints on run_benchmark.py to make it work with py3
Version 0.9.12, August 28, 2015
pbrod (12):
Updated documentation.
Updated version in conf.py.
Updated CHANGES.rst.
Reimplemented outlier detection and made it more robust.
Added limits.py with tests.
Updated main tests folder.
Moved Richardson and dea3 to extrapolation.py.
Making a new release in order to upload to pypi.
Version 0.9.11, August 27, 2015
- pbrod (2):
Fixed sphinx-build and updated docs.
Fixed issue #9 Backward differentiation method fails with additional parameters.
Version 0.9.10, August 26, 2015
- pbrod (7):
Fixed sphinx-build and updated docs.
Added more tests to nd_algopy.
Dropped support for Python 2.6.
Version 0.9.4, August 26, 2015
- pbrod (7):
Fixed sphinx-build and updated docs.
Version 0.9.3, August 23, 2015
- Paul Kienzle (1):
more useful benchmark plots.
- pbrod (7):
Fixed bugs and updated docs.
Major rewrite of the easy to use interface to Algopy.
Added possibility to calculate n’th order derivative not just for n=1 in nd_algopy.
Added tests to the easy to use interface to algopy.
Version 0.9.2, August 20, 2015
- pbrod (3):
Updated documentation
Added parenthesis to a call to the print function
Made the test less strict in order to pass the tests on Travis for python 2.6 and 3.2.
Version 0.9.1, August 20,2015
- Christoph Deil (1):
Fix Sphinx build
- pbrod (47):
- Total remake of numdifftools with slightly different call syntax.
Can compute derivatives of order up to 10-14 depending on function and method used.
Updated documentation and tests accordingly.
Fixed a bug in dea3.
Added StepsGenerator as an replacement for the adaptive option.
Added bicomplex class for testing the complex step second derivative.
Added fornberg_weights_all for computing optimal finite difference rules in a stable way.
Added higher order complex step derivative methods.
Version 0.7.7, December 18, 2014
- pbrod (35):
Got travis-ci working in order to run the tests automatically.
Fixed bugs in Dea class.
Fixed better error estimate for the Hessian.
Fixed tests for python 2.6.
Adding tests as subpackage.
Restructerd folders of numdifftools.
Version 0.7.3, December 17, 2014
- pbrod (5):
Small cosmetic fixes.
pep8 + some refactorings.
Simplified code by refactoring.
Version 0.6.0, February 8, 2014
- pbrod (20):
Update and rename README.md to README.rst.
Simplified call to Derivative: removed step_fix.
Deleted unused code.
Simplified and Refactored. Now possible to choose step_num=1.
Changed default step_nom from max(abs(x0), 0.2) to max(log2(abs(x0)), 0.2).
pep8ified code and made sure that all tests pass.
Version 0.5.0, January 10, 2014
- pbrod (9):
Updated the examples in Gradient class and in info.py.
Added test for vec2mat and docstrings + cosmetic fixes.
Refactored code into private methods.
Fixed issue #7: Derivative(fun)(numpy.ones((10,5)) * 2) failed.
Made print statements compatible with python 3.
Version 0.4.0, May 5, 2012
- pbrod (1)
Fixed a bug for inf and nan values.
Version 0.3.5, May 19, 2011
- pbrod (1)
Fixed a bug for inf and nan values.
Version 0.3.4, Feb 24, 2011
- pbrod (11)
Made automatic choice for the stepsize more robust.
Added easy to use interface to the algopy and scientificpython modules.
Version 0.3.1, May 20, 2009
- pbrod (4)
First version of numdifftools published on google.code
Copyright (c) 2009-2018, Per A. Brodtkorb, John D’Errico All rights reserved.
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
Neither the name of the copyright holders nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS “AS IS” AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for numdifftools-0.9.39-py2.py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | e3a1912a6e4d0fd655d4e2683245c385fa4a021be387781a44bc2b8c7ca60b41 |
|
MD5 | 103bb84d5d77c274dd04510cf6edef86 |
|
BLAKE2b-256 | abc0b0d967160ecc8db52ae34e063937d85e8d386f140ad4826aae2086245a5e |