encomp 0.2.12

General-purpose library for engineering computations

encomp

General-purpose library for engineering computations, with focus on clean and consistent interfaces.

Documentation at https://encomp.readthedocs.io/en/latest/

Features

Main functionality of the encomp library:

• Handles physical quantities with magnitude(s), dimensionality and units

  • Modules encomp.units, encomp.utypes
  • Extends the pint library
  • Uses Python's type system to validate dimensionalities
  • Integrates with np.ndarray and pd.Series
  • Automatic JSON serialization and decoding

• Implements a flexible interface to CoolProp

  • Module encomp.fluids
  • Uses quantities for all inputs and outputs
  • Fluids are represented as class instances, the properties are class attributes

• Extends Sympy

  • Module encomp.sympy
  • Adds convenience methods for creating symbols with sub- and superscripts
  • Additional functions to convert (algebraic) expressions and systems to Python code that supports Numpy arrays

• Jupyter Notebook integration

  • Module encomp.notebook
  • Imports commonly used functions and classes
  • Defines custom Jupyter magics

The other modules implement calculations related to process engineering and thermodynamics. The module encomp.serialize implements custom JSON serialization and decoding for classes used elsewhere in the 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 is not installable with pip for Python 3.9. Install manually with conda for now:

conda install conda-forge::coolprop

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.

The Quantity class

The main part of encomp is the encomp.units.Quantity class (shorthand alias Q), which is an extension of pint.Quantity. This class is used to construct objects with a magnitude and unit.

Some examples:

from encomp.units import Q

# converts 1 bar to kPa, displays it in case it's the cell output
Q(1, 'bar').to('kPa')

# a single string with one numerical value can also be given as input
Q('0.1 MPa').to('bar')

# list and tuple inputs are converted to np.ndarray
Q([1, 2, 3], 'bar') * 2 # [2, 4, 6] bar

# in case no unit is specified, the quantity is dimensionless
Q(0.1) == Q(10, '%')

The Quantity class can also be used to restrict function and class attribute types. Each dimensionality (for example pressure, length, time, dimensionless) is represented by a subclass of Quantity. It is possible to use type annotations to restrict the dimensionalities of function parameters and return values.

In case the ENCOMP_TYPE_CHECKING environment variable is set to True, 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

# the full class name is used for annotations, for the sake of clarity
from encomp.units import Q, Quantity

@typechecked
def some_func(T: Quantity['Temperature']) -> tuple[Quantity['Length'], Quantity['Pressure']]:
    return T * Q(12.4, 'm/K'), Q(1, 'bar')

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.units import Q, Quantity
from encomp.utypes import Temperature, Length, Volume

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')

# create a new subclass of Quantity with restricted input units
CustomCoolingCapacity = Quantity[Temperature / Volume]

# Quantity handles a wide range of input formats and unit names
assert CustomCoolingCapacity(3, '°F per yard³') == Q('3 degree_Fahrenheit per yard cubed')

The Fluid class

The class encomp.fluids.Fluid is a wrapper around the CoolProp library. The class takes two input points (three for humid air) that fix the state of the fluid. Other fluid parameters can be evaluated using attribute access. The outputs and inputs are Quantity objects. CoolProp property names and codes are used throughout. Use the .search() method to find the correct name.

from encomp.units import Q
from encomp.fluids import Fluid

air = Fluid('air', T=Q(25, 'degC'), P=Q(2, 'bar'))

air.D # 2.338399526231983 kilogram/meter3

air.search('density')
# ['DELTA, Delta: Reduced density (rho/rhoc) [dimensionless]',
#  'DMOLAR, Dmolar: Molar density [mol/m³]',
#  'D, DMASS, Dmass: Mass density [kg/m³]', ...

# any of the names are valid attributes (case-sensitive)
air.Dmolar # 80.73061937328056 mole/meter3

The fluid name 'water' (or the alias class Water) uses IAPWS to evaluate steam and water properties.

from encomp.units import Q
from encomp.fluids import Fluid, Water

Fluid('water', P=Q(25, 'bar'), T=Q(550, 'C'))
# <Fluid "water", P=2500 kPa, T=550.0 °C, D=6.7 kg/m³, V=0.031 cP>

# note that the CoolProp property "Q" (vapor quality) has the same name as the class
# the Water class has a slightly different string representation
Water(Q=Q(0.5), T=Q(170, 'degC'))
# <Water (Two-phase), P=792 kPa, T=170.0 °C, D=8.2 kg/m³, V=0.0 cP>

Water(H=Q(2800, 'kJ/kg'), S=Q(7300, 'J/kg/K'))
# <Water (Gas), P=225 kPa, T=165.8 °C, D=1.1 kg/m³, V=0.0 cP>

The HumidAir class requires three input points (R means relative humidity):

from encomp.units import Q
from encomp.fluids import HumidAir

HumidAir(P=Q(1, 'bar'), T=Q(100, 'degC'), R=Q(0.5))
# <HumidAir, P=100 kPa, T=100.0 °C, R=0.50, Vda=2.2 m³/kg, Vha=1.3 m³/kg, M=0.017 cP>

Settings

The attributes in the encomp.settings.Settings class can be modified with an .env-file. Place a file named .env in the current working directory to override the default settings. The attribute names are prefixed with ENCOMP_. See the file .env.example in the base of this repository for examples.

TODO

• Would be nice to see issues with dimensionality directly in the IDE
  • Might not be possible since the subclass Quantity['Temperature'] is constructed at runtime
• Combine EPANET (wntr) for pressure / flow simulation with energy systems simulations (omeof)

Ensure compatibility with

• numpy

• pandas

• nbconvert (HTML and Latex/PDF output)

  • figure out how to typeset using SIUNITX
  • look into JupyterBook and similar projects

• http://www.thermocycle.net/

• https://github.com/topics/process-engineering

• https://github.com/oemof/tespy

• https://github.com/oemof

• https://python-control.readthedocs.io/en/0.9.0/index.html

• https://ruralwater.readthedocs.io/en/dev/readme.html