excel-modelling-helper 0.2.7

Use Excel to define your model parameters.

Given an excel file with rows similar to the below

variable	scenario	module	distribution	param 1	param 2	param 3	unit	start date	end date	CAGR	ref date	label	comment	source
a		numpy.random	choice	1			kg	01/01/2009	01/04/2009	0.10	01/01/2009	test var 1
b		numpy.random	uniform	2	4							label
c		numpy.random	triangular	3	6	10						label
d		bottom_up_comparision.sampling_core_router s	Distribution	core_routers.csv			J/Gb					label
a	s1	numpy.random	choice	2								test var 1
multiple choice		numpy.random	choice	1,2,3			kg	01/01/2007	01/01/2009			test var 1

You can run python/ numpy code that references these variables and generates random distributions.

For example, the following will initialise a variable c with a vector of size 2 with random values from a triangular distribution.

    np.random.seed(123)

    data = ParameterLoader.from_excel('test.xlsx', size=2, sheet_index=0)
    c = data['c']
>>> [ 7.08471918  5.45131111]

Other types of distributions include choice and normal. However you can specify any distribution from numpy that takes up to three parameters to init.

You can also specify a .csv file with samples and an empiricial distribution function is generated and variable values will be sampled from that.

Scenarios

It is possible to define scenarios and have paramter values for a variable change with each scenario.

data = ParameterLoader.from_excel('test.xlsx', size=1, sheet_index=0)
res = data['a'][0]

assert res == 1.

data.select_scenario('s1')
res = data['a'][0]

assert res == 2.

use data.unselect_scenario() to return to the default value.

Pandas Dataframes

It is possible to define a time frame for distributions and have sample values change over time.

# the time axis of our dataset
times = pd.date_range('2009-01-01', '2009-04-01', freq='MS')
# the sample axis our dataset
samples = 2

dfl = DataSeriesLoader.from_excel('test.xlsx', times, size=samples, sheet_index=0)
res = dfl['a']

assert res.loc[[datetime(2009, 1, 1)]][0] == 1
assert np.abs(res.loc[[datetime(2009, 4, 1)]][0] - pow(1.1, 3. / 12)) < 0.00001

Reload

Reloading data sources is useful when underlying excel files change.

times = pd.date_range('2009-01-01', '2009-04-01', freq='MS')
samples = 2

data = MultiSourceLoader()
data.add_source(ExcelSeriesLoaderDataSource('test.xlsx', times, size=samples, sheet_index=0))

res = data['a'][0]
assert res == 1.

wb = load_workbook(filename='test.xlsx')
ws = wb.worksheets[0]
ws['E2'] = 4.
wb.save(filename='test.xlsx')

data.reload_sources()

res = data['a'][0]
assert res == 4.

wb = load_workbook(filename='test.xlsx')
ws = wb.worksheets[0]
ws['E2'] = 1.
wb.save(filename='test.xlsx')

data.reload_sources()

data.set_scenario('s1')
res = data['a'][0]

assert res == 2.

data.reset_scenario()
res = data['a'][0]

assert res == 1.

Metadata

The contents of the rows is also contained in the metadata

# the time axis of our dataset
times = pd.date_range('2009-01-01', '2009-04-01', freq='MS')
# the sample axis our dataset
samples = 3

dfl = DataSeriesLoader.from_excel('test.xlsx', times, size=samples, sheet_index=0)
res = dfl['a']

print(res._metadata)

15.5.2015 0.1.1 Renamed class to ParameterLoader 22.5.2015 0.1.2 Add sheet index as parameter to loader 11.1.2016 0.2.2 Added support to generate pandas dataframes, update to python 3 18.4.2016 0.2.7 Added new flag ‘single_var’ to freeze all variables except one to their mean value - use in sensitivity analysis.