Skip to main content

calculations with physical quantities

Project description

Author: Ken Kundert
Version: 0.3.0

A companion to QuantiPhy, quantiphy_eval evaluates strings containing simple algebraic expressions that involve quantities. It returns a quantity. For example:

>>> from quantiphy_eval import evaluate

>>> average = evaluate('($1.2M + $1.3M)/2', '$')
>>> print(f'{average}')

>>> f_avg = evaluate('(122.317MHz + 129.349MHz)/2', 'Hz')
>>> print(f'{f_avg}')
125.83 MHz

QuantiPhy Eval is used in networth to allow you to give your estimated values using expressions that include numbers that have units, SI scale factors, and commas. That allows you the convenience of copy-and-pasting your numbers from websites without being forced to reformat them.

With QuantiPhy the units do not survive operations, so you can specify the resolved units using the second argument. In fact, the second argument is passed to QuantiPhy as the model, which allows you to give the return value a name and description along with units, as demonstrated in the next example.

By default QuantiPhy Eval provides no built-in constants. However, you can add your own constants:

>>> from quantiphy import Quantity
>>> from quantiphy_eval import evaluate, initialize
>>> import math

>>> my_constants = dict(
...     k = Quantity('k'),
...     q = Quantity('q'),
...     T = Quantity('25°C', scale='K'),
...     π = Quantity(math.pi),
...     τ = Quantity(math.tau),
... )
>>> initialize(variables=my_constants)

>>> Vt = evaluate('k*T/q', 'Vt V thermal voltage')
>>> print(Vt.render(show_label='f'))
Vt = 25.693 mV -- thermal voltage

Or, you can use evaluate to assign values to names directly, QuantiPhy Eval remembers these values between calls to evaluate:

>>> f_0 = evaluate('f₀ = 1MHz')
>>> omega_0 = evaluate('ω₀ = τ*f₀', 'rads/s')
>>> print(omega_0.render(show_label='f'))
ω₀ = 6.2832 Mrads/s

Similarly, QuantiPhy Eval provides no built-in functions by default, but you can add any you need.

>>> def median(*args):
...    args = sorted(args)
...    l = len(args)
...    m = l//2
...    if l % 2:
...        return args[m]
...    return (args[m] + args[m-1])/2
>>> initialize(functions = dict(median=median))
>>> median_price = evaluate('median($636122, $749151, $706781)', '$')
>>> print(median_price.fixed(show_commas=True))

initialize takes three arguments, variables, functions and quantity. Both arguments and functions take dictionaries that overwrite any previously saved values. quantity takes a quantiphy Quantity class. The return value of evaluate will be an object of this class.

rm_commas function for removing commas from an expression. This is used if your number contain commas. Simply stripping the commas it would prevent you from using multi-argument functions, however after removing the commas rm_commas also converts semicolons to commas. So the previous example could be rewritten as:

>>> from quantiphy_eval import evaluate, rm_commas

>>> median_price = evaluate(
...     rm_commas('median($636,122; $749,151; $706,781)'),
...     '$',
... )
>>> print(median_price.fixed(show_commas=True))

Finally, QuantiPhy Eval supports comments. A # and anything that follows it to the end of the line is ignored:

>>> average_price = evaluate(
...     rm_commas('''
...         median(
...             $636,122 +   # Zillow
...             $749,151 +   # Redfin
...             $706,781     # Trulia
...         )/3
...     '''),
...     '$'
... )
>>> print(average_price.fixed(show_commas=True, prec=2, strip_zeros=False))


Latest development release:
Version: 0.3.0
Released: 2020-08-12
0.3 (2020-08-12):
  • complete re-write, parser now implemented with ply rather than pyparsing.
  • all built-in constants and functions have been removed.
  • split evaluate into two: evaluate and initialize.
0.2 (2020-03-06):
  • rm_commas now converts semicolons to commas
  • support comments
0.1 (2020-03-05):
  • Add support for user-defined constants and functions.
  • add rm_commas function.
0.0 (2020-02-14):
Initial version.

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

quantiphy_eval-0.3.0.tar.gz (39.9 kB view hashes)

Uploaded source

Built Distribution

quantiphy_eval-0.3.0-py3-none-any.whl (50.6 kB view hashes)

Uploaded py3

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor NVIDIA NVIDIA PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page