swolfpy_inputdata

MC

class swolfpy_inputdata.MC(input_dict, process_name)

This class generates random number for Monte-Carlo simulations. This class is the interface to stats_arrays package.

The example below is showing the usage of stats_arrays.

Example:

>>> from stats_arrays import *
>>> my_variables = UncertaintyBase.from_dicts(
...     {'loc': 2, 'scale': 0.5, 'uncertainty_type': NormalUncertainty.id},
...     {'loc': 1.5, 'minimum': 0, 'maximum': 10, 'uncertainty_type': TriangularUncertainty.id}
... )
>>> my_variables
array([(2.0, 0.5, nan, nan, nan, False, 3),
       (1.5, nan, nan, 0.0, 10.0, False, 5)],
    dtype=[('loc', '<f8'), ('scale', '<f8'), ('shape', '<f8'),
           ('minimum', '<f8'), ('maximum', '<f8'), ('negative', '?'),
           ('uncertainty_type', 'u1')])
>>> my_rng = MCRandomNumberGenerator(my_variables)
>>> my_rng.next()
array([ 2.74414022,  3.54748507])

Parameters:

input_dict (list) – list of dictionaries that include input data (see the example)

>>> from swolfpy_inputdata import MC
>>> input_dict={'Cat1': {'Par1': {'Name': 'Name1','amount': 1.0,'unit': 'Unit1',
...                                 'uncertainty_type': 3,'loc': 1,'scale':0.2 ,'shape': None,
...                                 'minimum': None,'maximum': None,
...                                 'Reference': None,'Comment': None},
...                     'Par2': {'Name': 'Name2','amount': 1.5,'unit': 'Unit2',
...                                 'uncertainty_type': 3,'loc': 1.5,'scale': 0.4,'shape': None,
...                                 'minimum': None,'maximum': None,
...                                 'Reference': None,'Comment': None}}}
>>> test_MC = MC(input_dict)
>>> test_MC.setup_MC()
>>> test_MC.gen_MC()
[(('Cat1', 'Par1'), 1.0554408376879747),
 (('Cat1', 'Par2'), 1.9366617123732333)]

setup_MC(seed=None)

Creates MCRandomNumberGenerator and store it in MC.rand attribute.

Parameters:

seed (int, optional) – seed for random number generation

gen_MC()

Generate random numbers and update data. It also returns a list of tuples include the name of parameter and generted number.

Returns:

List of tuples include the name of parameter and generted number: [((parameter_category,parameter),generated_number)]

Return type:

list

InputData

class swolfpy_inputdata.InputData(input_data_path, process_name, eval_parameter=False)

Bases: MC

InputData class reads the input data from the csv file and load them as class attributes. This class is inherited from the MC class.

Main functionalities include: loading data, updating data and generating random number for data based on the defined probability distributions.

Parameters:

• input_data_path (str) – absolute path to the input data file

• eval_parameter (bool, optional) – If the parameters are tuple instead of str, it will evaluate their real value.

add_process_data(process_data_path, index)

Update_input(NewData)

Get a new DataFrame and update the data in InputData class.

Parameters:

NewData ('pandas.DataFrame') –

setup_MC(seed=None)

Initialize the parent class (MC) and create MCRandomNumberGenerator based on the data for uncertainty distributions via calling MC.setupMC() method.

Parameters:

seed (int, optional) – seed for random number generation

See also

Class_MC

reset_static_vals()

CommonData

class swolfpy_inputdata.CommonData(input_data_path=None, process_name='CommonData')

Bases: InputData

Reprocessing_Index = ['Al', 'Fe', 'OCC', 'Mixed_Paper', 'ONP', 'OFF', 'Fiber_Other', 'Brown_glass', 'Clear_glass', 'Green_glass', 'Mixed_Glass', 'PET', 'HDPE_P', 'HDPE_T', 'LDPE_Film']

Collection_Index = ['RWC', 'SSR', 'DSR', 'MSR', 'LV', 'SSYW', 'SSO', 'SSO_AnF', 'SSO_HC', 'ORG', 'DryRes', 'REC', 'WetRes', 'MRDO', 'SSYWDO', 'MSRDO']

Waste_Pr_Index = ['Bottom_Ash', 'Fly_Ash', 'Unreacted_Ash', 'Separated_Organics', 'Other_Residual', 'Separated_Recyclables', 'RDF']

All_Waste_Pr_Index = ['Bottom_Ash', 'Fly_Ash', 'Unreacted_Ash', 'Separated_Organics', 'Other_Residual', 'Separated_Recyclables', 'RDF', 'RWC', 'SSR', 'DSR', 'MSR', 'LV', 'SSYW', 'SSO', 'SSO_AnF', 'SSO_HC', 'ORG', 'DryRes', 'REC', 'WetRes', 'MRDO', 'SSYWDO', 'MSRDO', 'Al', 'Fe', 'OCC', 'Mixed_Paper', 'ONP', 'OFF', 'Fiber_Other', 'Brown_glass', 'Clear_glass', 'Green_glass', 'Mixed_Glass', 'PET', 'HDPE_P', 'HDPE_T', 'LDPE_Film']

Index = ['Yard_Trimmings_Leaves', 'Yard_Trimmings_Grass', 'Yard_Trimmings_Branches', 'Food_Waste_Vegetable', 'Food_Waste_Non_Vegetable', 'Wood', 'Wood_Other', 'Textiles', 'Rubber_Leather', 'Newsprint', 'Corr_Cardboard', 'Office_Paper', 'Magazines', 'Third_Class_Mail', 'Folding_Containers', 'Paper_Bags', 'Mixed_Paper', 'Paper_Non_recyclable', 'HDPE_Translucent_Containers', 'HDPE_Pigmented_Containers', 'PET_Containers', 'Plastic_Other_1_Polypropylene', 'Plastic_Other_2', 'Mixed_Plastic', 'Plastic_Film', 'Plastic_Non_Recyclable', 'Ferrous_Cans', 'Ferrous_Metal_Other', 'Aluminum_Cans', 'Aluminum_Foil', 'Aluminum_Other', 'Ferrous_Non_recyclable', 'Al_Non_recyclable', 'Glass_Brown', 'Glass_Green', 'Glass_Clear', 'Mixed_Glass', 'Glass_Non_recyclable', 'Misc_Organic', 'Misc_Inorganic', 'E_waste', 'Bottom_Ash', 'Fly_Ash', 'Diapers_and_sanitary_products']