Code Document¶
IncomFlow¶
Project¶
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class
PFAS_SAT.
Project
(Inventory, CommonData, ProcessModels=None, pop_up=None)[source]¶ -
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static
calc
(product, source, processmodel, FlowParams, Inventory, CuttOff, ProcessNameRef, Treatment_options, pop_up=None)[source]¶
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static
MC¶
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class
PFAS_SAT.
MC
(input_dict)[source]¶ - Parameters
input_dict (list) – list of dictionaries that include input data (see the example)
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])
>>> from PFAS_SAT 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)]
InputData¶
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class
PFAS_SAT.
InputData
(input_data_path, eval_parameter=False)[source]¶ Bases:
PFAS_SAT.MC.MC
InputData
class reads the input data from the csv file and load them as class attributes. This class is inherited from theMC
class.Main functionalities include: loading data, updating data and generating random number for data based on the defined probabiliy 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 evalute their real value.
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Update_input
(NewData)[source]¶ Get a new DataFrame and update the
data
inInputData
class.- Parameters
NewData ('pandas.DataFrame') –
CommonData¶
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class
PFAS_SAT.
CommonData
(input_data_path=None)[source]¶ Bases:
PFAS_SAT.InputData.InputData
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WasteMaterials
= ['FoodWaste', 'Compost', 'ADLiquids', 'ADSolids', 'MSW', 'CombustionResiduals', 'CompostResiduals', 'ContaminatedSoil', 'C_DWaste', 'AFFF', 'LFLeachate', 'ContaminatedWater', 'ContactWater', 'WWTEffluent', 'RawWWTSolids', 'DewateredWWTSolids', 'DriedWWTSolids', 'WWTScreenRejects', 'SCWOSteam', 'SCWOSlurry', 'SpentGAC', 'ROConcentrate', 'StabilizedSoil', 'SpentIER']¶
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PFAS_Index
= ['PFOA', 'PFOS', 'PFBA', 'PFPeA', 'PFHxA', 'PFHpA', 'PFNA', 'PFDA', 'PFBS', 'PFHxS']¶
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Process Model¶
Flow¶
Sub-Processes¶
Created on Thu Jul 23 19:18:05 2020
@author: msmsa
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PFAS_SAT.SubProcesses.
aerobic_composting
(mixture, ProcessData, LogPartCoef_data, PrecipitationData)[source]¶
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PFAS_SAT.SubProcesses.
landfil_sorption
(mixture, LogPartCoef_data, LF_Leachate_Vol, Leachate_Vol)[source]¶