Contents:

# MMF Utils¶

Small set of utilities: containers and interfaces.

This package provides some utilities that I tend to rely on during development. Presently it includes some convenience containers, plotting tools, and a patch for including zope.interface documentation in a notebook.

(Note: If this file does not render properly, try viewing it through nbviewer.org)

Source: * https://alum.mit.edu/www/mforbes/hg/forbes-group/mmfutils: Permalink (will forward). * https://hg.iscimath.org/forbes-group/mmfutils: Current, in case the permalink fails. * https://github.com/forbes-group/mmfutils: Public read-only mirror.

Build Status:

• 1  MMF Utils

• 1.1  Installing

• 2  Usage

• 2.1  Containers

• 2.1.1  ObjectBase and Object

• 2.1.1.1  Object Example

• 2.1.2  Container

• 2.1.2.1  Container Examples

• 2.2  Contexts

• 2.3  Interfaces

• 2.3.1  Interface Documentation

• 2.4  Parallel

• 2.5  Performance

• 2.6  Plotting

• 2.6.1  Fast Filled Contour Plots

• 2.7  Angular Variables

• 2.8  Debugging

• 2.9  Mathematics

• 3  Developer Instructions

• 3.1  Releases

• 4  Change Log

• 4.1  REL: 0.5.4

• 4.2  REL: 0.5.3

• 4.3  REL: 0.5.1

• 4.4  REL: 0.5.0

• 4.5  REL: 0.4.13

• 4.6  REL: 0.4.10

• 4.7  REL: 0.4.9

• 4.8  REL: 0.4.7

## Installing¶

This package can be installed from PyPI:

python3 -m pip install mmfutils


or, if you need to install from source, you can get it from one of the repositories:

python3 -m pip install hg+https://alum.mit.edu/www/mforbes/hg/forbes-group/mmfutils
python3 -m pip install git+https://github.com/forbes-group/mmfutils


# Usage¶

## Containers¶

### ObjectBase and Object¶

The ObjectBase and Object classes provide some useful features described below. Consider a problem where a class is defined through a few parameters, but requires extensive initialization before it can be properly used. An example is a numerical simulation where one passes the number of grid points $$N$$ and a length $$L$$, but the initialization must generate large grids for efficient use later on. These grids should be generated before computations begin, but should not be re-generated every time needed. They also should not be pickled when saved to disk.

Deferred initialization via the init() method: The idea here changes the semantics of __init__() slightly by deferring any expensive initialization to init(). Under this scheme, __init__() should only set and check what we call picklable attributes: these are parameters that define the object (they will be pickled in Object below) and will be stored in a list self.picklable_attributes which is computed at the end of ObjectBase.__init__() as the list of all keys in __dict__. Then, ObjectBase.__init__() will call init() where all remaining attributes should be calculated.

This allows users to change various attributes, then reinitialize the object once with an explicit call to init() before performing expensive computations. This is an alternative to providing complete properties (getters and setters) for objects that need to trigger computation. The use of setters is safer, but requires more work on the side of the developer and can lead to complex code when different properties depend on each other. The approach here puts all computations in a single place. Of course, the user must remember to call init() before working with the object.

To facilitate this, we provide a mild check in the form of an initialized flag that is set to True at the end of the base init() chain, and set to False if any variables are in pickleable_attributes are set.

Serialization and Deferred Initialization: The base class ObjectBase does not provide any pickling services but does provide a nice representation. Additional functionality is provided by Object which uses the features of ObjectBase to define __getstate__() and __setstate__() methods for pickling which pickle only the picklable_attributes. Note: unpickling an object will not call __init__() but will call init() giving objects a chance to restore the computed attributes from pickles.

• Note: Before using, consider if these features are really needed – with all such added functionality comes additional potential failure modes from side-interactions. The ObjectBase class is quite simple, and therefore quite safe, while Object adds additional functionality with potential side-effects. For example, a side-effect of support for pickles is that copy.copy() will also invoke init() when copying might instead be much faster. Thus, we recommend only using ObjectBase for efficient code.

#### Object Example¶

ROOTDIR = !hg root
ROOTDIR = ROOTDIR[0]
import sys;sys.path.insert(0, ROOTDIR)

import numpy as np

from mmfutils.containers import ObjectBase, ObjectMixin

class State(ObjectBase):
_quiet = False
def __init__(self, N, L=1.0, **kw):
"""Set all of the picklable parameters, in this case, N and L."""
self.N = N
self.L = L

# Now register these and call init()
super().__init__(**kw)
if not self._quiet:
print("__init__() called")

def init(self):
if not self._quiet:
print("init() called")
dx = self.L / self.N
self.x = np.arange(self.N, dtype=float) * dx - self.L/2.0
self.k = 2*np.pi * np.fft.fftfreq(self.N, dx)

# Set highest momentum to zero if N is even to
# avoid rapid oscillations
if self.N % 2 == 0:
self.k[self.N//2] = 0.0

# Calls base class which sets self.initialized
super().init()

def compute_derivative(self, f):
"""Return the derivative of f."""
return np.fft.ifft(self.k*1j*np.fft.fft(f)).real

s = State(256)
print(s)  # No default value for L

init() called
__init__() called
State(L=1.0, N=256)

s.L = 2.0
print(s)

State(L=2.0, N=256)


One feature is that a nice repr() of the object is produced. Now let’s do a calculation:

f = np.exp(3*np.cos(2*np.pi*s.x/s.L)) / 15
df = -2.*np.pi/5.*np.exp(3*np.cos(2*np.pi*s.x/s.L))*np.sin(2*np.pi*s.x/s.L)/s.L
np.allclose(s.compute_derivative(f), df)

False


Oops! We forgot to reinitialize the object… (The formula is correct, but the lattice is no longer commensurate so the FFT derivative has huge errors).

print(s.initialized)
s.init()
assert s.initialized
f = np.exp(3*np.cos(2*np.pi*s.x/s.L)) / 15
df = -2.*np.pi/5.*np.exp(3*np.cos(2*np.pi*s.x/s.L))*np.sin(2*np.pi*s.x/s.L)/s.L
np.allclose(s.compute_derivative(f), df)

False
init() called

True


Here we demonstrate pickling. Note that using Object makes the pickles very small, and when unpickled, init() is called to re-establish s.x and s.k. Generally one would inherit from Object, but since we already have a class, we can provide pickling functionality with ObjectMixin:

class State1(ObjectMixin, State):
pass

s = State(N=256, _quiet=True)
s1 = State1(N=256, _quiet=True)

import pickle, copy
s_repr = pickle.dumps(s)
s1_repr = pickle.dumps(s1)
print(f"ObjectBase pickle:  {len(s_repr)} bytes")
print(f"ObjectMixin pickle: {len(s1_repr)} bytes")

ObjectBase pickle:  4396 bytes
ObjectMixin pickle: 103 bytes


Note, however, that the speed of copying is significantly impacted:

%timeit copy.copy(s)
%timeit copy.copy(s1)

2.58 µs ± 34.3 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
27.1 µs ± 404 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)


Another use case applies when init() is expensive. If $$x$$ and $$k$$ were computed in __init__(), then using properties to change both $$N$$ and $$L$$ would trigger two updates. Here we do the updates, then call init(). Good practice is to call init() automatically before any serious calculation to ensure that the object is brought up to date before the computation.

s.N = 64
s.L = 2.0
s.init()


Finally, we demonstrate that Object instances can be archived using the persist package:

import persist.archive
a = persist.archive.Archive(check_on_insert=True)
a.insert(s=s)

d = {}
exec(str(a), d)

d['s']

State(L=2.0, N=64, _quiet=True)


### Container¶

The Container object is a slight extension of Object that provides a simple container for storing data with attribute and iterative access. These implement some of the Collections Abstract Base Classes from the python standard library. The following containers are provided:

• Container: Bare-bones container extending the Sized, Iterable, and Container abstract ase classes (ABCs) from the standard containers library.

• ContainerList: Extension that acts like a tuple/list satisfying the Sequence ABC from the containers library (but not the MutableSequence ABC. Although we allow setting and deleting items, we do not provide a way for insertion, which breaks this interface.)

• ContainerDict: Extension that acts like a dict satisfying the MutableMapping ABC from the containers library.

These were designed with the following use cases in mind:

• Returning data from a function associating names with each data. The resulting ContainerList will act like a tuple, but will support attribute access. Note that the order will be lexicographic. One could use a dictionary, but attribute access with tab completion is much nicer in an interactive session. The containers.nametuple generator could also be used, but this is somewhat more complicated (though might be faster). Also, named tuples are immutable - here we provide a mutable object that is picklable etc. The choice between ContainerList and ContainerDict will depend on subsequent usage. Containers can be converted from one type to another.

#### Container Examples¶

from mmfutils.containers import Container

c = Container(a=1, c=2, b='Hi there')
print(c)
print(tuple(c))

Container(a=1, b='Hi there', c=2)
(1, 'Hi there', 2)

# Attributes are mutable
c.b = 'Ho there'
print(c)

Container(a=1, b='Ho there', c=2)

# Other attributes can be used for temporary storage but will not be pickled.
import numpy as np

c.large_temporary_array = np.ones((256,256))
print(c)
print(c.large_temporary_array)

Container(a=1, b='Ho there', c=2)
[[1. 1. 1. ... 1. 1. 1.]
[1. 1. 1. ... 1. 1. 1.]
[1. 1. 1. ... 1. 1. 1.]
...
[1. 1. 1. ... 1. 1. 1.]
[1. 1. 1. ... 1. 1. 1.]
[1. 1. 1. ... 1. 1. 1.]]

import pickle
print(c1)
c1.large_temporary_array

Container(a=1, b='Ho there', c=2)

---------------------------------------------------------------------------

AttributeError                            Traceback (most recent call last)

<ipython-input-13-bd53d5116502> in <module>
3 print(c1)
----> 4 c1.large_temporary_array

AttributeError: 'Container' object has no attribute 'large_temporary_array'


## Contexts¶

The mmfutils.contexts module provides two useful contexts:

NoInterrupt: This can be used to susspend KeyboardInterrupt exceptions until they can be dealt with at a point that is convenient. A typical use is when performing a series of calculations in a loop. By placing the loop in a NoInterrupt context, one can avoid an interrupt from ruining a calculation:

from mmfutils.contexts import NoInterrupt

complete = False
n = 0
with NoInterrupt() as interrupted:
while not complete and not interrupted:
n += 1
if n > 10:
complete = True


Note: One can nest NoInterrupt contexts so that outer loops are also interrupted. Another use-case is mapping. See doc/Animation.ipynb for more examples.

res = NoInterrupt().map(abs, range(-100, 100))
np.sign(res)

array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1])


## Interfaces¶

The interfaces module collects some useful zope.interface tools for checking interface requirements. Interfaces provide a convenient way of communicating to a programmer what needs to be done to used your code. This can then be checked in tests.

from mmfutils.interface import Interface, Attribute, verifyClass, verifyObject, implementer

"""Interface for objects that support addition."""

value = Attribute('value', "Current value of object")

# No self here since this is the "user" interface
"""Return self + other."""


Here is a broken implementation. We muck up the arguments to add:

@implementer(IAdder)
# There should only be one argument!
return one + another

try:
except Exception as e:
print("{0.__class__.__name__}: {0}".format(e))

BrokenMethodImplementation: The object <class '__main__.AdderBroken'> has failed to implement interface __main__.IAdder: The contract of __main__.IAdder.add(other) is violated because 'AdderBroken.add(self, one, another)' requires too many arguments.


Now we get add right, but forget to define value. This is only caught when we have an object since the attribute is supposed to be defined in __init__():

@implementer(IAdder)
return one + other

# The class validates...

# ... but objects are missing the value Attribute
try:
except Exception as e:
print("{0.__class__.__name__}: {0}".format(e))

BrokenImplementation: The object <__main__.AdderBroken object at 0x11693cac0> has failed to implement interface __main__.IAdder: The __main__.IAdder.value attribute was not provided.


Finally, a working instance:

@implementer(IAdder)
def __init__(self, value=0):
self.value = value
return one + other


True


### Interface Documentation¶

We also monkeypatch zope.interface.documentation.asStructuredText() to provide a mechanism for documentating interfaces in a notebook.

from mmfutils.interface import describe_interface

<string>

Interface for objects that support addition.

Attributes:

value -- Current value of object

Methods:

add(other) -- Return self + other.

## Parallel¶

The mmfutils.parallel module provides some tools for launching and connecting to IPython clusters. The parallel.Cluster class represents and controls a cluster. The cluster is specified by the profile name, and can be started or stopped from this class:

import logging
logger = logging.getLogger()
logger.setLevel(logging.INFO)
import numpy as np
from mmfutils import parallel
cluster = parallel.Cluster(profile='default', n=3, sleep_time=1.0)
cluster.start()
cluster.wait()  # Instance of IPython.parallel.Client
x = np.linspace(-6, 6, 100)
y = view.map(lambda x:x**2, x)
print(np.allclose(y, x**2))
cluster.stop()

Waiting for connection file: ~/.ipython/profile_default/security/ipcontroller-client.json

INFO:root:Starting cluster: ipcluster start --daemonize --quiet --profile=default --n=3

Waiting for connection file: ~/.ipython/profile_default/security/ipcontroller-client.json

INFO:root:waiting for 3 engines
INFO:root:0 of 3 running
INFO:root:3 of 3 running
INFO:root:Stopping cluster: ipcluster stop --profile=default

True
Waiting for connection file: ~/.ipython/profile_default/security/ipcontroller-client.json


If you only need a cluster for a single task, it can be managed with a context. Be sure to wait for the result to be computed before exiting the context and shutting down the cluster!

with parallel.Cluster(profile='default', n=3, sleep_time=1.0) as client:
x = np.linspace(-6, 6, 100)
y = view.map(lambda x:x**2, x, block=True)  # Make sure to wait for the result!
print(np.allclose(y, x**2))

Waiting for connection file: ~/.ipython/profile_default/security/ipcontroller-client.json

INFO:root:Starting cluster: ipcluster start --daemonize --quiet --profile=default --n=3

Waiting for connection file: ~/.ipython/profile_default/security/ipcontroller-client.json

INFO:root:waiting for 3 engines
INFO:root:0 of 3 running
INFO:root:3 of 3 running
INFO:root:Stopping cluster: ipcluster stop --profile=default

Waiting for connection file: ~/.ipython/profile_default/security/ipcontroller-client.json
True


If you just need to connect to a running cluster, you can use parallel.get_client().

## Performance¶

The mmfutils.performance module provides some tools for high performance computing. Note: this module requires some additional packages including numexp, pyfftw, and the mkl package installed by anaconda. Some of these require building system libraries (i.e. the FFTW). However, the various components will not be imported by default.

Here is a brief description of the components:

• mmfutils.performance.blas: Provides an interface to a few of the scipy BLAS wrappers. Very incomplete (only things I currently need).

• mmfutils.performance.fft: Provides an interface to the FFTW using pyfftw if it is available. Also enables the planning cache and setting threads so you can better control your performance.

• mmfutils.performance.numexpr: Robustly imports numexpr and disabling the VML. (If you don’t do this carefully, it will crash your program so fast you won’t even get a traceback.)

• mmfutils.performance.threads: Provides some hooks for setting the maximum number of threads in a bunch of places including the MKL, numexpr, and fftw.

## Plotting¶

Several tools are provided in mmfutils.plot:

### Fast Filled Contour Plots¶

mmfutils.plot.imcontourf is similar to matplotlib’s plt.contourf function, but uses plt.imshow which is much faster. This is useful for animations and interactive work. It also supports my idea of saner array-shape processing (i.e. if x and y have different shapes, then it will match these to the shape of z). Matplotlib now provies plt.pcolourmesh which is similar, but has the same interface issues.

%matplotlib inline
from matplotlib import pyplot as plt
import time
import numpy as np
from mmfutils import plot as mmfplt
x = np.linspace(-1, 1, 100)[:, None]**3
y = np.linspace(-0.1, 0.1, 200)[None, :]**3
z = np.sin(10*x)*y**2
plt.figure(figsize=(12,3))
plt.subplot(141)
%time mmfplt.imcontourf(x, y, z, cmap='gist_heat')
plt.subplot(142)
%time plt.contourf(x.ravel(), y.ravel(), z.T, 50, cmap='gist_heat')
plt.subplot(143)
%time plt.pcolor(x.ravel(), y.ravel(), z.T, cmap='gist_heat', shading='auto')
plt.subplot(144)
%time plt.pcolormesh(x.ravel(), y.ravel(), z.T, cmap='gist_heat', shading='auto')

CPU times: user 9.48 ms, sys: 3.72 ms, total: 13.2 ms
Wall time: 16.5 ms
CPU times: user 38.2 ms, sys: 2.97 ms, total: 41.2 ms
Wall time: 45 ms
CPU times: user 126 ms, sys: 5.86 ms, total: 132 ms
Wall time: 133 ms
CPU times: user 4.43 ms, sys: 209 µs, total: 4.64 ms
Wall time: 4.65 ms

<matplotlib.collections.QuadMesh at 0x124ec2130>


## Angular Variables¶

A couple of tools are provided to visualize angular fields, such as the phase of a complex wavefunction.

%matplotlib inline
from matplotlib import pyplot as plt
import time
import numpy as np
from mmfutils import plot as mmfplt
x = np.linspace(-1, 1, 100)[:, None]
y = np.linspace(-1, 1, 200)[None, :]
z = x + 1j*y

plt.figure(figsize=(9,2))
ax = plt.subplot(131)
mmfplt.phase_contour(x, y, z, colors='k', linewidths=0.5)
ax.set_aspect(1)

# This is a little slow but allows you to vary the luminosity.
ax = plt.subplot(132)
mmfplt.imcontourf(x, y, mmfplt.colors.color_complex(z))
mmfplt.phase_contour(x, y, z, linewidths=0.5)
ax.set_aspect(1)

# This is faster if you just want to show the phase and allows
# for a colorbar via a registered colormap
ax = plt.subplot(133)
mmfplt.imcontourf(x, y, np.angle(z), cmap='huslp')
ax.set_aspect(1)
plt.colorbar()
mmfplt.phase_contour(x, y, z, linewidths=0.5);


## Debugging¶

A couple of debugging tools are provided. The most useful is the debug decorator which will store the local variables of a function in a dictionary or in your global scope.

from mmfutils.debugging import debug

@debug(locals())
def f(x):
y = x**1.5
z = 2/x
return z

print(f(2.0), x, y, z)

1.0 2.0 2.8284271247461903 1.0


## Mathematics¶

We include a few mathematical tools here too. In particular, numerical integration and differentiation. Check the API documentation for details.

# Developer Instructions¶

If you are a developer of this package, there are a few things to be aware of.

1. If you modify the notebooks in docs/notebooks then you may need to regenerate some of the .rst files and commit them so they appear on bitbucket. This is done automatically by the pre-commit hook in .hgrc if you include this in your .hg/hgrc file with a line like:

# %include ../.hgrc


Security Warning: if you do this, be sure to inspect the .hgrc file carefully to make sure that no one inserts malicious code.

This runs the following code:

!cd $ROOTDIR; jupyter nbconvert --to=rst --output=README.rst doc/README.ipynb  [NbConvertApp] Converting notebook doc/README.ipynb to rst [NbConvertApp] Support files will be in README_files/ [NbConvertApp] Making directory doc/README_files [NbConvertApp] Making directory doc/README_files [NbConvertApp] Writing 52142 bytes to doc/README.rst  We also run a comprehensive set of tests, and the pre-commit hook will fail if any of these do not pass, or if we don’t have complete code coverage. We run these tests in a conda environment that can be made using the makefile: make test # nox  To run these manually in a specific environment, you could do: cond activate _mmfutils pytest  Here is an example: !cd$ROOTDIR; conda activate _mmfutils; pytest -n4


Complete code coverage information is provided in build/_coverage/index.html.

from IPython.display import HTML
with open(os.path.join(ROOTDIR, 'build/_coverage/index.html')) as f:
HTML(coverage)

Coverage report

n s m x c   change column sorting

## Releases¶

We try to keep the repository clean with the following properties:

1. The default branch is stable: i.e. if someone runs hg clone, this will pull the latest stable release.

2. Each release has its own named branch so that e.g. hg up 0.5.0 will get the right thing. Note: this should update to the development branch, not the default branch so that any work committed will not pollute the development branch (which would violate the previous point).

To do this, we advocate the following proceedure.

1. Update to Correct Branch: Make sure this is the correct development branch, not the default branch by explicitly updating:

hg up <version>


(Compare with hg up default which should take you to the default branch instead.)

2. Work: Do your work, committing as required with messages as shown in the repository with the following keys:

• DOC: Documentation changes.

• API: Changes to the exising API. This could break old code.

• EHN: Enhancement or new functionality. Without an API tag, these should not break existing codes.

• BLD: Build system changes (setup.py, requirements.txt etc.)

• TST: Update tests, code coverage, etc.

• BUG: Address an issue as filed on the issue tracker.

• BRN: Start a new branch (see below).

• REL: Release (see below).

• WIP: Work in progress. Do not depend on these! They will be stripped. This is useful when testing things like the rendering of documentation on bitbucket etc. where you need to push an incomplete set of files. Please collapse and strip these eventually when you get things working.

• CHK: Checkpoints. These should not be pushed to bitbucket!

3. Tests: Make sure the tests pass. Comprehensive tests should be run with nox:

nox


Quick tests while developing can be run with the _mmfutils environment:

conda env update --file environment.yml
conda activate _mmfutils; pytest


(hg com will automatically run tests after pip-installing everything in setup.py if you have linked the .hgrc file as discussed above, but the use of independent environments is preferred now.)

4. Update Docs: Update the documentation if needed. To generate new documentation run:

cd doc
sphinx-apidoc -eTE ../mmfutils -o source
rm source/mmfutils.*tests*

• Include any changes at the bottom of this file (doc/README.ipynb).

• You may need to copy new figures to README_files/ if the figure numbers have changed, and then hg add these while hg rm the old ones.

Edit any new files created (titles often need to be added) and check that this looks good with

make html
open build/html/index.html


Look especially for errors of the type “WARNING: document isn’t included in any toctree”. This indicates that you probably need to add the module to an upper level .. toctree::. Also look for “WARNING: toctree contains reference to document u’…’ that doesn’t have a title: no link will be generated”. This indicates you need to add a title to a new file. For example, when I added the mmf.math.optimize module, I needed to update the following:

.. doc/source/mmfutils.rst
mmfutils
========

.. toctree::
...
mmfutils.optimize
...

.. doc/source/mmfutils.optimize.rst
mmfutils.optimize
=================

.. automodule:: mmfutils.optimize
:members:
:undoc-members:
:show-inheritance:

1. Clean up History: Run hg histedit, hg rebase, or hg strip as needed to clean up the repo before you push. Branches should generally be linear unless there is an exceptional reason to split development.

2. Release: First edit mmfutils/__init__.py to update the version number by removing the dev part of the version number. Commit only this change and then push only the branch you are working on:

bash hg com -m "REL: <version>" hg push -b .

3. Pull Request: Create a pull request on the development fork from your branch to default on the release project bitbucket. Review it, fix anything, then accept the PR and close the branch.

4. Publish on PyPI: Publish the released version on PyPI using twine

# Build the package.
python setup.py sdist bdist_wheel

# Test that everything looks right:


5. Build Conda Package: This will run all the tests in a fresh environment as specified by meta.yaml. Make sure that the dependencies in meta.yaml, environment.yml, and setup.py are consistent. Note that the list of versions to be built is specified in conda_build_config.yaml.

conda build .
conda build . --output   # Use this below

6. Start new branch: On the same development branch (not default), increase the version number in mmfutils/__init__.py and add dev: i.e.:

__version__ = '0.5.1dev'


Then create this branch and commit this:

hg branch "0.5.1"
hg com -m "BRN: Started branch 0.5.1"

7. Optional: Update any setup.py files that depend on your new features/fixes etc.

# Change Log¶

## REL: 0.5.4¶

• Drop support for Python 3.5.

• Use Nox for testing (see Notes.md)

## REL: 0.5.3¶

Allow Python 3.8. Previous version required python <= 3.7 due to an issue with ipyparallel. This has been resolved with revision 6.2.5 which is available with conda.

## REL: 0.5.1¶

API changes:

• Split mmfutils.containers.Object into ObjectBase which is simple and ObjectMixin which provides the picking support. Demonstrate in docs how the pickling can be useful, but slows copying.

## REL: 0.5.0¶

API changes:

• Python 3 support only.

• mmfutils.math.bases.interface renamed to mmfutils.math.bases.interfaces.

• Added default class-variable attribute support to emmfutils.containers.Object.

• Minor enhancements to mmfutils.math.bases.PeriodicBasis to enhance GPU support.

• Added mmfutils.math.bases.interfaces.IBasisLz and support in mmfutils.math.bases.bases.PeriodicBasis for rotating frames.

• Cleanup of build environment and tests.

• Single environment _mmfutils now used for testing and documentation.

## REL: 0.4.13¶

API changes:

• Use @implementer() class decorator rather than classImplements or implements in all interfaces.

• Improve NoInterrupt context. Added NoInterrupt.unregister(): this allows NoInterrupt to work in a notebook cell even when the signal handlers are reset. (But only works in that one cell.)

• Added Abel transform integrate2 to Cylindrical bases.

Issues:

• Resolved issue #22: Masked arrays work with imcontourf etc.

• Resolved issue #23: NoInterrupt works well except in notebooks due to ipykernel issue #328.

• Resolved issue #24: Python 3 is now fully supported and tested.

## REL: 0.4.10¶

API changes:

• Added contourf, error_line, and ListCollections to mmfutils.plot.

• Added Python 3 support (still a couple of issues such as mmfutils.math.integrate.ssum_inline.)

• Added mmf.math.bases.IBasisKx and update lagrangian in bases to accept k2 and kx2 for modified dispersion control (along x).

• Added math.special.ellipkinv.

• Added some new mmfutils.math.linalg tools.

Issues:

• Resolved issue #20: DyadicSum and scipy.optimize.nonlin.Jacobian

• Resolved issue #22: imcontourf now respects masked arrays.

• Resolved issue #24: Support Python 3.

< incomplete >

## REL: 0.4.7¶

API changes:

• Added mmfutils.interface.describe_interface() for inserting interfaces into documentation.

• Added some DVR basis code to mmfutils.math.bases.

• Added a diverging colormap and some support in mmfutils.plot.

• Added a Wigner Ville distribution computation in mmfutils.math.wigner

• Added mmfutils.optimize.usolve and ubrentq for finding roots with uncertanties <https://pythonhosted.org/uncertainties/>__ support.

Issues:

• Resolve issue #8: Use ipyparallel <https://github.com/ipython/ipyparallel>__ now.

• Resolve issue #9: Use pytest rather than nose (which is no longer supported).

• Resolve issue #10: PYFFTW wrappers now support negative axis and axes arguments.

• Address issue #11: Preliminary version of some DVR basis classes.

• Resolve issue #12: Added solvers with uncertanties <https://pythonhosted.org/uncertainties/>__ support.