"https://breakingbytes.github.io/".For the how and why I am making this move from Google Blogger to Pelican and GitHub Pages, please read my first post.
Moving to Pelican at GitHub PagesGood bye Google Blogger, you've served me well. And for anyone following Poquito Picante, I hope to see you soon at Breaking Bytes.
I'm super excited to announce the Cotton Candy release of Carousel, version 0.3 on PyPI and GitHub. There were a few more issues I really wanted to close with this release, but I decided to push it forward anyway. So the remaining milestones for v0.3 will get pushed to v0.3.1.
Meta class for all layers - currently usage is spotty and inconsistent. I wanted to keep the number of commits to close PR #68 to a minimum (following the winning workflow) so I only implemented Meta classes where I had to. In fact it's not even implemented in the DataSource example below.dependencies to an attribute of each parameter, the DAG shows which calculations are orthogonal, so we can run them simultaneously. According to issue #22, I had this idea already, but when I saw this presentation at PyData SF 2016 on Airflow by Matt Davis I was even more motivated to make it happen.Calculator class whose job it is to crunch through the calculations, somewhat similar to what DataReaders and FormulaImporter do for their layers. Then the boiler plate uncertainty propagation code in the static class could be applied to any calculator such as a dynamic calculator, a linear system solver for an acyclic DAG of linear equations or a non-linear system solver for an acyclic DAG of non-linear equations.Parameter classWhat's new in Carousel-0.3 (Cotton Candy)? The biggest difference is the introduction of the Parameter class which is now used to specify parameters for data, formulas, outputs, calculations and simulation settings. For example, previously data parameters would be entered as a dictionary of attributes.
class PVPowerData(DataSource):
"""
Data sources for PV Power demo.
"""
data_reader = ArgumentReader
latitude = {"units": "degrees", "uncertainty": 1.0}
longitude = {"units": "degrees", "uncertainty": 1.0}
elevation = {"units": "meters", "uncertainty": 1.0}
class PVPowerData(DataSource):
"""
Data sources for PV Power demo.
"""
latitude = DataParameter(units="degrees", uncertainty=1.0)
longitude = DataParameter(units="degrees", uncertainty=1.0)
elevation = DataParameter(units="meters", uncertainty=1.0)
class Meta:
data_reader = ArgumentReader
Why the change? The Bicycle Bear version did not have any way to distinguish parameters, like latitude from attributes of the DataSource like data_reader. This had two unfortunate side-effects:
The Cotton Candy version makes it easy for the metaclass to determine which class attributes are parameters, which are attributes of the layer and then leaves everything else alone. Every layer now has a corresponding Parameter subclass which also defines some base attributes corresponding to that layer. Any extra parameter attributes are saved in extras. Attributes that apply to the entire layer are now specified in the `Meta` class attribute, similar to Django, Marshmallow and DRF. The similarities are completely intentional as I have been strongly inspired by those project code bases. Unfortunately, the Meta class is only partially implemented, but will be the major focus of v0.3.1.
The Formula class is also improved. Now each formulas is a Parameter with attributes, rather than a giant dictionary. This improvement is still on the roadmap for the Calculation class. As I said above, it was a stretch goal for this release.
There are many blog posts on the topic of effective Git workflows, SO questions and answers, BitBucket tutorials and GitHub guides and an article that has been archived by former BBC Mark McDonnell. So why another post on git workflow? None of these workflows seemed right for us, but recently it's just clicked, and I feel like we've finally found the process that works for us. The key was finding the simplest workflow that included the most valuable best practices. In particular, we found that complicated multi-branch strategies were unnecessary, but test driven development (TDD) and continuous integration (CI) were a must.
We start with the assumption that all of collaborators fork the upstream repository to their personal profile. Then each person clones their profile to their laptop as origin and adds another remote pointing to the upstream repository. For convenience, they may also create remotes to the forks of their most frequent collaborators.
[myusername@mycomputer ~/Projects] $ git clone git@github.com:myusername/myrepo.git [myusername@mycomputer ~/Projects] $ cd myrepo [myusername@mycomputer ~/Projects/myrepo] $ git remote add upstream git@github.com:mycompany/myrepo.git [myusername@mycomputer ~/Projects/myrepo] $ git remote add mycollaborator git@github.com:mycollaborator/myrepo.git [myusername@mycomputer ~/Projects/myrepo] $ git remote show origin upstream mycollaborator
The next assumption is that we all keep our version of master synchronized with upstream master. And we never work out of our own master branch! Basically this means at the start of any new work we do the following:
git fetch --all to get the lay of the land. This combined with git log --all --graph --date=short --pretty=format:"%ad %h %s%d [%an]"
I'm also going to assume that everyone is following the recommended project layout. This means that their project has all dependencies listed in requirements.txt, is developed and deployed in its own virtual environment, includes testing and documentation that aims for >80% coverage, has a boilerplate design that allows testing, documentation and package data to be bundled into a distribution and enables use with a test runner with self discovery, and is written with docstrings for autodocumentation. Nothing is ever perfect, so being diligent of path clashes, aware of the arcana of Mac OS X1 or Windows2 and able to use Stack Overflow to find answers is still important.
This is key. Set up Travis, Circle, AppVeyor or Jenkins on upstream master to test and build every commit, every commit to an open PR and to deploy on every tag. Easy!
There are some features of this style that stand out:
PYTHONHOME=/home/you/path/to/project/venv/ python instead of using source venv/bin/activate.nosetests script will use your system Python and therefore the Python path will not include your virtual environment. The solution is to never use system site packages and install all dependencies directly in your virtual environment.The trick to this is creating a custom field and overloading pre_save. Pay special attention to the self.attname member that is set to the value. The source for DateField is a good example. Make sure that if you add any new attributes to the field in it's __init__ method you also add a corresponding deconstruct method.
I'm sure this is completely wrong. I needed a Django model for testing, but I don't have a Django app or even a Django project. I'm developing a Django model reader for Carousel, and so I needed a model to test it out with. Sure I could have created a quick django project, but that seemed silly, and my first instinct was to import django.db.models, make a model and use it, but this raised:
ImproperlyConfigured: Requested setting DEFAULT_INDEX_TABLESPACE, but settings are not
configured. You must either define the environment variable
DJANGO_SETTINGS_MODULE or call settings.configure() before accessing
settings.
Most normal people would turn back now, but instead I imported django.conf.settings and called settings.configure() just like it said to do. Now I got this error:
AppRegistryNotReady: Apps aren't loaded yet.
So now I felt like I was getting somewhere. But where? Googling told me to import django and run setup which I did and that raised:
RuntimeError: Model class __main__.MyModel doesn't declare an explicit app_label and isn't
in an application in INSTALLED_APPS.
Wow! Normally RuntimeError is a scary warning, like you dumped your core, but this just said I needed to add the app to settings.INSTALLED_APPS, which makes perfect sense, and it also complained that my model wasn't actually part of an app and even explained how to explicitly declare it. Some more Googling and I discovered that app_label is a model option that can be set in class Meta. So I did as told, and it worked!
from django.db import models
from django.conf import settings
import django
MYAPP = 'myapp.MyApp'
settings.configure()
django.setup()
settings.INSTALLED_APPS.append(MYAPP)
class MyModel(models.Model):
air_temp = models.FloatField()
latitude = models.FloatField()
longitude = models.FloatField()
timezone = models.FloatField()
pvmodule = models.CharField(max_length=20)
class Meta:
app_label = MYAPP
mymodel = MyModel(air_temp=25.0, latitude=38.0, longitude=-122.0,
timezone=-8.0, pvmodule='SPR E20-327')
mymodel.__dict__
#{'_state': <django.db.models.base.ModelState at 0x496b2b0>,
# 'air_temp': 25.0,
# 'id': None,
# 'latitude': 38.0,
# 'longitude': -122.0,
# 'pvmodule': 'SPR E20-327',
# 'timezone': -8.0}
So I should stop here and point out that that evidently the order of these commands matters, because if I add the fake app to INSTALLED_APPS before calling django.setup() then I get this:
ImportError: No module named myapp
And unfortunately, I just figured this out now, in this post. But this isn't what I originally did. Yes, I'm completely crazy. First I added a fake module called 'myapp' to sys.modules setting it to a mock object, but that didn't work. I got back TypeError: 'Mock' object is not iterable because, as I found out later, there has to be an AppConfig subclass in the app module. But since I didn't know that yet, I did the only logical thing and put the module in a list. What? Yes, did I mention I'm an idiot? This nonsense yielded the following stern warning:
ImproperlyConfigured: The app module [] has no filesystem location, you must configure this app with an AppConfig subclass with a 'path' class attribute.
But this is where I found out about AppConfig in the Django docs which is covered quite nicely. Following the nice directions, I did as told and subclassed AppConfig, added path and also name which I learned from the docs, monkeypatched my mock module with it, and used the dotted name of the app myapp.MyApp now. I felt like I was getting closer, since I only got: AttributeError: __name__ which seemed like a problem with my pretend module. Another monkeypatch and we have my final ludicrously ridiculous hack.
from django.db import models
from django.conf import settings
import django
from django.apps import AppConfig
import sys
import mock
class MyApp(AppConfig):
"""
Apps subclass ``AppConfig`` and define ``name`` and ``path``
"""
path = '.' # path to app
name = 'myapp' # name of app
# make a mock module with ``__name__`` and ``MyApp`` member
myapp_module = mock.Mock(__name__='myapp', MyApp=MyApp)
MYAPP = 'myapp.MyApp' # full path to app
sys.modules['myapp'] = myapp_module # register module
settings.configure()
settings.INSTALLED_APPS.append(MYAPP)
django.setup()
class MyModel(models.Model):
air_temp = models.FloatField()
latitude = models.FloatField()
longitude = models.FloatField()
timezone = models.FloatField()
pvmodule = models.CharField(max_length=20)
class Meta:
app_label = MYAPP
mymodel = MyModel(air_temp=25.0, latitude=38.0, longitude=-122.0,
timezone=-8.0, pvmodule='SPR E20-327')
mymodel.__dict__
#{'_state': <django.db.models.base.ModelState at 0x496b2b0>,
# 'air_temp': 25.0,
# 'id': None,
# 'latitude': 38.0,
# 'longitude': -122.0,
# 'pvmodule': 'SPR E20-327',
# 'timezone': -8.0}
Yay?
I want to introduce Carousel, a project that my employer, SunPower has been supporting for use in prediction models. Carousel is an extensible framework for mathematical models that handles generic routines such as loading and saving data, generating reports, converting units, propagating uncertainty and running simulations so developers can focus on creating complex algorithms that are easy to share and maintain.
Mathematical models consist of algorithms glued together with generic routines. While the algorithms may sometimes be unique and complex, the rest of the code is often simple and routine. Sometimes mathematical models developed by teams of developers over time become difficult to update because there is no framework for how new data, calculations and outputs are integrated into the existing models. Carousel allows developers to focus on creating complex mathematical models that are robust and easy to maintain by abstracting generic routines and establishing a simple but extensible framework.
A Carousel basic model consists of 5 built in layers:
Carousel is extensible by creating more advanced models and layers. A Carousel model is a collection of layers. Carousel layers share a common base class. Each layer also has a corresponding object and a registry where objects are stored. All layers have a load method that loads all of the layer objects specified into the model. When a model is loaded it loads the objects specified for each layer.
Consider a load shifting algorithm for residential or commercial rooftop solar power. The model might have a performance calculation, a load calculation, a cost calculation and an optimization algorithm that determines how home or business appliances are operated to minimize overall yearly cost of the system.
The performance calculation contains several formulas which require input data from an internal database of solar panel parameters and an online API of weather conditions, so the user creates a data source and reader for each of these. There are some data readers already included in Carousel and once a data reader is created it can be reused in many different projects. Maybe the user submits a pull request to Carousel to add the new API and database reader. The load calculation contains formulas for how the appliances are used. The input data for the appliances are entered into a generic worksheet so the user creates a data source for appliances and uses the XLRDReader to collect the data for each appliance from their worksheets.
The user organizes the formulas into 4 modules, that correspond to each of the calculations, but some formulas are reused since they are generic. For example, data frame summation formulas are used with different time series to create daily, monthly and annual outputs. The user maps out the calculations and specifies their interdependence to other formulas. For example, the cost calculation depends on the load and performance calculations and the optimization algorithm depends on the cost.
The user specifies each output name, initial value and other attributes. Specifications for each layer can be in a JSON parameter file or directly in the code as class attributes; Carousel will interpret either at runtime when it creates the model. Finally the user creates a simulation which in this case is unique because instead of marching through time or space, the simulation iterates over potential load shifting solutions from the algorithm. The user decides which data or outputs to log during the simulation and which to save in reports. Now that the model is created, the user loads the model and sends it the "start" command. After the simulation is complete, the user can examine the outputs and their variances. The outputs will have been automatically converted to the units specified in the model.
The data layer handles all inputs to the models. The data layer object is a data source. Each data source has a data reader. A data source is a document, API, database or other place from which input data for the model can be obtained. The data source and reader provide a framework for specifying how data is acquired. For example a data source for stock market prices might be a public API. An implementation of the stock market API data source specifies the names and attributes of each input data that will be read from the API and how the data reader should read them. The data source is similar to a deserializer because it describes how the data from the source should be interpreted by the model and creates an object in the data registry.
The formula layer handles operations on input data that generate new outputs. It differs from the calculation layer which handles how formulas are combined together. The formula layer object is a formula, and each formula has a formula importer. For example the Python formula importer can import formulas that are written as Python functions.
Calculations are combinations of formulas. Each calculation also has a map of what data and output are used as arguments and what outputs the return values will be. Calculations also implement calculators. Currently there is a static calculator and a dynamic calculator, but new calculators can be implemented that can be reused in other models. The calculation also implements indexing into data and output registries in order retrieve items by index or at a specific time.
Outputs are just like data except they don't need a reader because they are only generated from calculations. Each output is like a serializer because it determines how output objects will be reported or displayed to the user.
The simulation layer determines the order of calculations based on the calculation dependencies. It first executes all of the static calculations and then loops over dynamic calculations, displaying logs and saving periodic reports as specified.
The model is a low level class that can be extended to add new layers or implement new simulation commands. Currently only the basic model is implemented. A new model might contain a post processing layer that generates plots and reports from outputs.
Every layer has a dictionary called a registry that contains all of the layer objects and metadata corresponding to the layer attributes. The registry implements a register method that doesn't allow an item to registered more than once. Each layer registry is subclassed from the base registry so that specific layer attributes can be associated to each key. For example, data sources and outputs have a variance attribute while formulas have an args attribute.
After a model has been described using the framework, it can be loaded. Then any or all of the model simulations can be executed from the model. The simulation specifies commands to the model that user sends using the models command method. Currently the basic model can execute the simulation start command.
Carousel uses the Pint units wrapper to convert units as specified. Uncertainty is propagated using the UncertaintyWrapper package which was developed for Carousel. It can wrap Python extensions and non-linear algorithms without changing any code. It propagates covariance and sensitivity across all formulas.
A basic version of Carousel is ready now. There is an example of a photovoltaic module performance model in the documentation online at GitHub. Some ideas for new features are listed in the Carousel wiki on GitHub.
Carousel was presented at the 5th Sandia PVPMC Workshop hosted by EPRI in Santa Clara in May 2016
Carousel and UncertaintyWrapper were developed with the support of SunPower Corp. They are distributed with a BSD 3-clause license.
[UPDATE 2018-09-04] Links to Cookiecutter and Bootstrap a Scientific Python Library from the National Synchrotron Light Source II (NLSL-II).
[UPDATE 2016-07-19] Lately I've preferred using core instead of lib for the main package modules.
[UPDATE 2015-06-04] Create top level package to bundle all sub-packages and package-data together for install.
Been looking for a good, comprehensive, credible guide:
This is a tutorial with a template for packaging, testing, documenting, and publishing scientific Python code.
A command-line utility that creates projects from cookiecutters (project templates), e.g. creating a Python package project from a Python package project template.
It's hard to pin a standard style down. Here’s mine:
MyProject/ <- git repository
|
+- .gitignore <- *.pyc, IDE files, venv/, build/, dist/, doc/_build, etc.
|
+- requirements.txt <- to install into a virtualenv
|
+- setup.py <- use setuptools, include packages, extensions, scripts and data
|
+- MANIFEST.in <- files to include in or exclude from sdist
|
+- readme.rst <- incorporate into setup.py and docs
|
+- changes.rst <- release notes, incorporate into setup.py and docs
|
+- myproject_script.py <- script to run myproject from command line, use Python
| argparse for command line arguments put shebang
| `#! /usr/bin/env python` on 1st line and end with a
| `if __name__ == "__main__":` section, include in
| setup.py scripts section for install
|
+- any_other_scripts.py <- scripts for configuration, documentation generation
| or downloading assets, etc., include in setup.py
|
+- venv/ <- virtual environment to run tests, validate setup.py, development
|
+- myproject/ <- top level package keeps sub-packages and package-data together
| for install
|
+- __init__.py <- contains __version__, an API by importing key modules,
| classes, functions and constants, __all__ for easy import
|
+- docs/ <- use Sphinx to auto-generate documentation
|
+- tests/ <- use nose to perform unit tests
|
+- other_package_data/ <- images, data files, include in setup.py
|
+- core/ <- main source code for myproject, sometimes called `lib`
| |
| +- __init__.py <- necessary to make mypoject_lib a sub-package
| |
| +- … <- the rest of the folders and files in myproject
|
+- related_project/ <- a GUI library that uses myproject_lib or tools that
| myproject_lib depends on that's bundled together, etc.
|
+- __init__.py <- necessary to make related_project a sub-package
|
+- … <- the rest of the folders and files in your the related project
stop reading now!if you have strong views on religion. What I am about to express is my opinion, and is not meant to influence or offend anyone. If you are already offended then I apologize, and hope that by turning back now you can avert any more offense.
Equality increases self esteem.Right now my son loves wearing pink, and says today, "I'm wearing a dress." Next week it will be a different color. He is completely innocent. Theses issues seem trivial to us today in the socially enlightening 21st century. In fact we view it as a victory over the absurd and old-fashioned dogma about distinct gender roles.
