General-purpose library for engineering computations
Project description
encomp
General-purpose library for engineering computations, with focus on clean and consistent interfaces.
Package documentation at https://encomp.readthedocs.io/en/latest/
Features
- Consistent interfaces to commonly used engineering tools:
pint
for units and conversionsCoolProp
for fluid properties (including IAPWS)fluids
andthermo
for process engineering calculations- Integrates with the rest of the Python scientific stack
- Strong type system that integrates physical units and their dimensionalities
- Leverages the standard library
typing
module,pint
andtypeguard
to ensure that inputs and outputs to functions and classes match the specified dimensionalities - Uses
pydantic
to create self-validating objects
- Leverages the standard library
This library is under work: all features are not yet implemented.
Installation
Install with pip
:
pip install encomp
This will install encomp
along with its dependencies into the currently active Python environment.
CoolProp
might not be installable withpip
for Python 3.9. Install manually withconda
for now:
conda install conda-forge::coolprop
Development environment
Install Miniconda or Anconda if not already installed.
Clone this repository, open a terminal and navigate to the root directory.
Setup a new environment using conda
:
conda env create -f environment.yml
This will install the necessary dependencies into a new conda
environment named encomp-env
.
The dependencies (except for scipy
and jupyter
) are installed with pip
.
Install encomp
into the new environment:
conda activate encomp-env
pip install .
Removing the conda
environment
To completely remove the conda
environment for encomp
:
conda remove -y --name encomp-env --all
Getting started
To use encomp
from a Jupyter Notebook, import the encomp.notebook
module:
# imports commonly used functions, registers Notebook magics
from encomp.notebook import *
This will import commonly used functions and classes.
It also registers the %read
and %%write
Jupyter magics for reading and writing custom objects from and to JSON.
Some examples:
# converts 1 bar to kPa, displays it in case it's the cell output
Q(1, 'bar').to('kPa')
# creates an object that represents water at a certain temperature and pressure
Water(T=Q(25, 'degC'), P=Q(2, 'bar'))
The Quantity
class
The main part of encomp
is the encomp.units.Quantity
class (shorthand Q
), which is an extension of pint.Quantity
.
This class is used to construct objects with a magnitude and unit.
It can also be used to restrict function and class attribute types.
Each dimensionality (for example pressure, length, time) is represented by a subclass of Quantity
.
Use type annotations to restrict the dimensionalities of a function's parameters and return value.
The typeguard.typechecked
decorator is automatically applied to all functions and methods inside the main encomp
library.
To use it on your own functions, apply the decorator explicitly:
from typeguard import typechecked
from encomp.api import Quantity
@typechecked
def some_func(T: Quantity['Temperature']) -> Quantity['Length']:
return T * Quantity(12.4, 'm/K')
some_func(Q(12, 'delta_degC')) # the dimensionalities check out
some_func(Q(26, 'kW')) # raises an exception
# TypeError: type of argument "T" must be Quantity[Temperature]; got Quantity[Power] instead
The dimensionality of a quantity can be specified with string values like 'Temperature'
or pint.UnitsContainer
objects.
To create a new dimensionality (for example temperature difference per length), combine the pint.UnitsContainer
objects defined in encomp.utypes
using *
and /
:
from encomp.api import Quantity
from encomp.utypes import Temperature, Length
qty = Quantity[Temperature / Length](1, 'delta_degC / km')
# raises an exception since liter is Length**3 and the Quantity expects Length**2
another_qty = Quantity[Temperature / Length**2](1, 'delta_degC / liter')
TODO
- Combine EPANET for pressure / flow simulation with energy systems simulations (
omeof
) - Make a web interface to draw circuits (using a JS node-graph editor) and visualize results.
Ensure compatibility with
-
numpy
-
pandas
-
Excel (via df.to_excel, both with
openpyxl
andxlsxwriter
- parse units from Excel (header name like "Pressure [bar]" etc...)
-
nbconvert (HTML and Latex/PDF output)
- figure out how to typeset using SIUNITX
- look into JupyterBook and similar projects
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