formulas 1.1.0

Install extras

Some additional functionality is enabled installing the following extras:

• excel: enables to compile Excel workbooks to python and execute

  using: ExcelModel.

• plot: enables to plot the formula ast and the Excel model.

To install formulas and all extras, do:

$ pip install formulas[all]

Development version

To help with the testing and the development of formulas, you can install the development version:

$ pip install https://github.com/vinci1it2000/formulas/archive/dev.zip

Parsing formula

An example how to parse and execute an Excel formula is the following:

>>> import formulas
>>> func = formulas.Parser().ast('=(1 + 1) + B3 / A2')[1].compile()

To visualize formula model and get the input order you can do the following:

>>> list(func.inputs)
['A2', 'B3']
>>> func.plot(view=False)  # Set view=True to plot in the default browser.
SiteMap([(=((1 + 1) + (B3 / A2)), SiteMap())])

[graph]

Finally to execute the formula and plot the workflow:

>>> func(1, 5)
Array(7.0, dtype=object)
>>> func.plot(workflow=True, view=False)  # Set view=True to plot in the default browser.
SiteMap([(=((1 + 1) + (B3 / A2)), SiteMap())])

[graph]

Excel workbook

An example how to load, calculate, and write an Excel workbook is the following:

>>> import formulas
>>> fpath, dir_output = 'excel.xlsx', 'output'
>>> xl_model = formulas.ExcelModel().loads(fpath).finish()
>>> xl_model.calculate()
Solution(...)
>>> xl_model.write(dirpath=dir_output)
{'EXCEL.XLSX': {Book: <openpyxl.workbook.workbook.Workbook ...>}}

Tip: If you have or could have circular references, add

circular=True to finish method.

To plot the dependency graph that depict relationships between Excel cells:

>>> dsp = xl_model.dsp
>>> dsp.plot(view=False)  # Set view=True to plot in the default browser.
SiteMap([(ExcelModel, SiteMap(...))])

[graph]

To overwrite the default inputs that are defined by the excel file or to impose some value to a specific cell:

>>> xl_model.calculate(
...     inputs={
...         "'[excel.xlsx]DATA'!A2": 3,  # To overwrite the default value.
...         "'[excel.xlsx]DATA'!B3": 1  # To impose a value to B3 cell.
...     },
...     outputs=[
...        "'[excel.xlsx]DATA'!C2", "'[excel.xlsx]DATA'!C4"
...     ] # To define the outputs that you want to calculate.
... )
Solution([("'[excel.xlsx]DATA'!A2", <Ranges>('[excel.xlsx]DATA'!A2)=[[3]]),
          ("'[excel.xlsx]DATA'!A3", <Ranges>('[excel.xlsx]DATA'!A3)=[[6]]),
          ("'[excel.xlsx]DATA'!B3", <Ranges>('[excel.xlsx]DATA'!B3)=[[1]]),
          ("'[excel.xlsx]DATA'!D2", <Ranges>('[excel.xlsx]DATA'!D2)=[[1]]),
          ("'[excel.xlsx]DATA'!B2", <Ranges>('[excel.xlsx]DATA'!B2)=[[9.0]]),
          ("'[excel.xlsx]DATA'!D3", <Ranges>('[excel.xlsx]DATA'!D3)=[[2.0]]),
          ("'[excel.xlsx]DATA'!C2", <Ranges>('[excel.xlsx]DATA'!C2)=[[10.0]]),
          ("'[excel.xlsx]DATA'!D4", <Ranges>('[excel.xlsx]DATA'!D4)=[[3.0]]),
          ("'[excel.xlsx]DATA'!C4", <Ranges>('[excel.xlsx]DATA'!C4)=[[4.0]])])

To build a single function out of an excel model with fixed inputs and outputs, you can use the compile method of the ExcelModel that returns a DispatchPipe. This is a function where the inputs and outputs are defined by the data node ids (i.e., cell references).

>>> func = xl_model.compile(
...     inputs=[
...         "'[excel.xlsx]DATA'!A2",  # First argument of the function.
...         "'[excel.xlsx]DATA'!B3"   # Second argument of the function.
...     ], # To define function inputs.
...     outputs=[
...         "'[excel.xlsx]DATA'!C2", "'[excel.xlsx]DATA'!C4"
...     ] # To define function outputs.
... )
>>> func
<schedula.utils.dsp.DispatchPipe object at ...>
>>> [v.value[0, 0] for v in func(3, 1)]  # To retrieve the data.
[10.0, 4.0]
>>> func.plot(view=False)  # Set view=True to plot in the default browser.
SiteMap([(ExcelModel, SiteMap(...))])

[graph]

Alternatively, to load a partial excel model from the output cells, you can use the from_ranges method of the ExcelModel:

>>> xl = formulas.ExcelModel().from_ranges(
...     "'[%s]DATA'!C2:D2" % fpath,  # Output range.
...     "'[%s]DATA'!B4" % fpath,  # Output cell.
... )
>>> dsp = xl.dsp
>>> sorted(dsp.data_nodes)
["'[excel.xlsx]DATA'!A2",
 "'[excel.xlsx]DATA'!A3",
 "'[excel.xlsx]DATA'!A3:A4",
 "'[excel.xlsx]DATA'!A4",
 "'[excel.xlsx]DATA'!B2",
 "'[excel.xlsx]DATA'!B3",
 "'[excel.xlsx]DATA'!B4",
 "'[excel.xlsx]DATA'!C2",
 "'[excel.xlsx]DATA'!D2"]

[graph]

>>> import json
>>> xl_dict = xl_model.to_dict()  # To JSON-able dict.
>>> xl_dict  # Exported format.
{
 "'[excel.xlsx]DATA'!A1": "inputs",
 "'[excel.xlsx]DATA'!B1": "Intermediate",
 "'[excel.xlsx]DATA'!C1": "outputs",
 "'[excel.xlsx]DATA'!D1": "defaults",
 "'[excel.xlsx]DATA'!A2": 2,
 "'[excel.xlsx]DATA'!D2": 1,
 "'[excel.xlsx]DATA'!A3": 6,
 "'[excel.xlsx]DATA'!A4": 5,
 "'[excel.xlsx]DATA'!B2": "=('[excel.xlsx]DATA'!A2 + '[excel.xlsx]DATA'!A3)",
 "'[excel.xlsx]DATA'!C2": "=(('[excel.xlsx]DATA'!B2 / '[excel.xlsx]DATA'!B3) + '[excel.xlsx]DATA'!D2)",
 "'[excel.xlsx]DATA'!B3": "=('[excel.xlsx]DATA'!B2 - '[excel.xlsx]DATA'!A3)",
 "'[excel.xlsx]DATA'!C3": "=(('[excel.xlsx]DATA'!C2 * '[excel.xlsx]DATA'!A2) + '[excel.xlsx]DATA'!D3)",
 "'[excel.xlsx]DATA'!D3": "=(1 + '[excel.xlsx]DATA'!D2)",
 "'[excel.xlsx]DATA'!B4": "=MAX('[excel.xlsx]DATA'!A3:A4, '[excel.xlsx]DATA'!B2)",
 "'[excel.xlsx]DATA'!C4": "=(('[excel.xlsx]DATA'!B3 ^ '[excel.xlsx]DATA'!C2) + '[excel.xlsx]DATA'!D4)",
 "'[excel.xlsx]DATA'!D4": "=(1 + '[excel.xlsx]DATA'!D3)"
}
>>> xl_json = json.dumps(xl_dict, indent=True)  # To JSON.
>>> xl_model = formulas.ExcelModel().from_dict(json.loads(xl_json))  # From JSON.

Custom functions

An example how to add a custom function to the formula parser is the following:

>>> import formulas
>>> FUNCTIONS = formulas.get_functions()
>>> FUNCTIONS['MYFUNC'] = lambda x, y: 1 + y + x
>>> func = formulas.Parser().ast('=MYFUNC(1, 2)')[1].compile()
>>> func()
4