Contents¶
Overview¶
Mixtures of Independently Collected Samples
- Free software: MIT license
Installation¶
pip install mics
Documentation¶
Development¶
To run the all tests run:
tox
Note, to combine the coverage data from all the tox environments run:
Windows | set PYTEST_ADDOPTS=--cov-append
tox
|
---|---|
Other | PYTEST_ADDOPTS=--cov-append tox
|
Reference¶
mics¶
mixtures¶
-
class
mics.mixtures.
mixture
[source]¶ A mixture of independently collected samples (MICS)
Parameters: - samples (list or tuple) – a list of samples.
- title (str, optional) – a title.
- verbose (bool, optional) – a verbosity tag.
- tol (float, optional) – a tolerance.
-
free_energies
(reference=0)[source]¶ Returns a data frame containing the relative free energies of the datasetd samples of a mixture, as well as their standard errors.
-
reweighting
(potential, properties={}, derivatives={}, combinations={}, conditions=Empty DataFrame Columns: [] Index: [], reference=0, **kwargs)[source]¶ Performs reweighting of the properties computed by functions from the mixture to the samples determined by the provided potential with all parameter values.
Parameters: - potential (string)
- properties (dict of strings)
- combinations (dict of strings)
- derivatives (dict of tuples)
- conditions (pandas.DataFrame)
- verbose (boolean)
- **kwargs
samples¶
-
class
mics.samples.
pool
(label='', verbose=False)[source]¶ A pool of independently collected samples.
-
class
mics.samples.
sample
(dataset, potential, autocorr=None, label=None, batchsize=None, verbose=False, **kwargs)[source]¶ A sample of configurations collected at a specific equilibrium state, aimed to be part of a mixture of independently collected samples (MICS).
- Args:
- dataset (pandas.DataFrame):
- a data frame whose rows represent configurations datasetd according to a given probability distribution and whose columns contain a number of properties evaluated for such configurations.
- potential (function):
- the reduced potential that defines the equilibrium sample. This function might for instance receive x and return the result of an element-wise calculation involving x[“a”], x[“b”], etc, with “a”, “b”, etc being names of properties in dataset.
- autocorr (function, optional):
- a function similar to potential, but whose result is an autocorrelated property to be used for determining the effective dataset size. If omitted, potential will be used to for this purpose.
- Note:
- Formally, functions potential and autocorr must receive x and return y, where length(y) == nrow(x).
utils¶
-
mics.utils.
covariance
(y, ym, b)[source]¶ Computes the covariance matrix of the rows of matrix y among themselves. The method of Overlap Batch Mean (OBM) is employed with blocks of size b.
-
mics.utils.
cross_covariance
(y, ym, z, zm, b)[source]¶ Computes the cross-covariance matrix between the rows of matrix y with those of matrix z. The method of Overlap Batch Mean (OBM) is employed with blocks of size b.
-
mics.utils.
genfunc
(function, variables, constants)[source]¶ Returns a function based on the passed argument.
-
mics.utils.
multimap
(functions, sample)[source]¶ Applies a list of
functions
to DataFrame sample and returns a numpy matrix whose number of rows is equal to the length of list functions and whose number of columns is equal to the number of rows in sample.Note
Each function of the array might for instance receive x and return the result of an element-wise calculation involving x[“A”], x[“B”], etc, with “A”, “B”, etc being names of properties in DataFrame sample.
Contributing¶
Contributions are welcome, and they are greatly appreciated! Every little bit helps, and credit will always be given.
Bug reports¶
When reporting a bug please include:
- Your operating system name and version.
- Any details about your local setup that might be helpful in troubleshooting.
- Detailed steps to reproduce the bug.
Documentation improvements¶
MICS could always use more documentation, whether as part of the official MICS docs, in docstrings, or even on the web in blog posts, articles, and such.
Feature requests and feedback¶
The best way to send feedback is to file an issue at https://github.com/craabreu/mics/issues.
If you are proposing a feature:
- Explain in detail how it would work.
- Keep the scope as narrow as possible, to make it easier to implement.
- Remember that this is a volunteer-driven project, and that code contributions are welcome :)
Development¶
To set up mics for local development:
Fork mics (look for the “Fork” button).
Clone your fork locally:
git clone git@github.com:your_name_here/mics.git
Create a branch for local development:
git checkout -b name-of-your-bugfix-or-feature
Now you can make your changes locally.
When you’re done making changes, run all the checks, doc builder and spell checker with tox one command:
tox
Commit your changes and push your branch to GitHub:
git add . git commit -m "Your detailed description of your changes." git push origin name-of-your-bugfix-or-feature
Submit a pull request through the GitHub website.
Pull Request Guidelines¶
If you need some code review or feedback while you’re developing the code just make the pull request.
For merging, you should:
- Include passing tests (run
tox
) [1]. - Update documentation when there’s new API, functionality etc.
- Add a note to
CHANGELOG.rst
about the changes. - Add yourself to
AUTHORS.rst
.
[1] | If you don’t have all the necessary python versions available locally you can rely on Travis - it will run the tests for each change you add in the pull request. It will be slower though … |
Tips¶
To run a subset of tests:
tox -e envname -- py.test -k test_myfeature
To run all the test environments in parallel (you need to pip install detox
):
detox
Authors¶
- Charlles R. A. Abreu - http://atoms.peq.coppe.ufrj.br