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<!doctype html>
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<title>modules.ODYM_Classes API documentation</title>
<meta name="description" content="Created on Thu Mar
2 17:29:41 2017 …" />
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<h1 class="title">Module <code>modules.ODYM_Classes</code></h1>
</header>
<section id="section-intro">
<p>Created on Thu Mar
2 17:29:41 2017</p>
<p>@author: spauliuk</p>
<details class="source">
<summary>Source code</summary>
<pre><code class="python"># -*- coding: utf-8 -*-
"""
Created on Thu Mar 2 17:29:41 2017
@author: spauliuk
"""
"""
File ODYM_Classes
Check https://github.com/IndEcol/ODYM for latest version.
Contains class definitions for ODYM
standard abbreviation: msc (material-system-classes)
dependencies:
numpy >= 1.9
scipy >= 0.14
Repository for this class, documentation, and tutorials: https://github.com/IndEcol/ODYM
"""
import os
import logging
import numpy as np
import pandas as pd
import xlrd, xlwt
####################################
# Define classes for ODYM #
####################################
def __version__():
return str('1.0') # version number of this file
class Obj(object):
"""
Class with the object definition for a data object (system, process, flow, ...) in ODYM
"""
def __init__(self, Name=None, ID=None, UUID=None):
""" Basic initialisation of Obj."""
self.Name = Name # object name
self.ID = ID # object ID
self.UUID = UUID # object UUID
self.Aspects = {'Time': 'Model time','Cohort': 'Age-cohort','OriginProcess':'Process where flow originates','DestinationProcess':'Destination process of flow','OriginRegion': 'Region where flow originates from','DestinationRegion': 'Region where flow is bound to', 'Good': 'Process, good, or commodity', 'Material': 'Material: ore, alloy, scrap type, ...','Element': 'Chemical element' } # Define the aspects of the system variables
self.Dimensions = {'Time': 'Time', 'Process':'Process', 'Region': 'Region', 'Good': 'Process, good, or commodity', 'Material': 'Material: ore, alloy, scrap type, ...','Element': 'Chemical element' } # Define the dimensions of the system variables
class Classification(Obj):
"""
Class for aspect classification
"""
def __init__(self, Name = None, ID = None, UUID = None, Dimension = None, Items = None, IDs = None, AdditionalProporties = {}):
""" Basic initialisation of an item list for alloys, materials, etc."""
Obj.__init__(self, Name = Name, ID = ID, UUID = UUID) # Hand over parameters to parent class init
self.Dimension = Dimension # Dimension of classification: Time, Region, process, material, goods, ...
self.Items = Items # list with names of items
self.IDs = IDs # list with IDs of items
self.AdditionalProps = AdditionalProporties # Like population for regions, element composition for alloys, ...
class MFAsystem(Obj):
"""
Class with the definition and methods for a system in ODYM
"""
def __init__(self, Name, Time_Start, Time_End, Geogr_Scope, Unit, IndexTable, Elements, ProcessList = [], FlowDict = {}, StockDict = {}, ParameterDict = {}, Graphical = None, ID = None, UUID = None, ):
""" Initialisation of MFAsystem."""
Obj.__init__(self, Name = Name, ID = ID, UUID = UUID) # Hand over parameters to parent class init
self.Time_Start = Time_Start # start time of model (year: int)
self.Time_End = Time_End # end time of model (year: int)
self.Geogr_Scope = Geogr_Scope # geographical boundary (string)
self.Elements = Elements # list of chemical elements considered, indicated by atomic numbers
self.Unit = Unit # flow and stock base unit, without 'per yr'
self.ProcessList = ProcessList # list of processes, processes are referred to by their number
self.FlowDict = FlowDict # Dictionary of flows, are indexed by tuples of process they are attached to (p1,p2)
self.StockDict = StockDict # Dictionary of stocks, are indexed by process they are located at (p)
self.ParameterDict = ParameterDict # Dictionary of of parameters: lifetime, yield rates, etc.
self.IndexTable = IndexTable # Dictionary of abbreviations for aspect-classification tuples
self.Graphical = Graphical # Dictionary of graphical properties (size in pixel, background color, etc.)
@property
def Time_V(self):
""" Array of all model years"""
return np.arange(self.Time_Start,self.Time_End +1,1)
@property
def Time_L(self):
""" List of all model years"""
return np.arange(self.Time_Start,self.Time_End +1,1).tolist()
def IndexTableCheck(self):
""" Check whether chosen classifications fit to dimensions of index table."""
for indx in self.IndexTable.index:
if self.IndexTable.ix[indx]['Dimension'] != self.IndexTable.ix[indx]['Classification'].Dimension:
raise ValueError('Dimension mismatch. Dimension of classifiation needs to fit to dimension of flow or parameter index. Found a mismatch for the following index: {foo}. Check your index table definition!'.format(foo = indx))
if 'Time' not in self.IndexTable.index:
raise ValueError(' "Time" aspect must be present in IndexTable. Please check your index table definition!')
if 'Element' not in self.IndexTable.index:
raise ValueError(' "Element" aspect must be present in IndexTable. Please check your index table definition!')
if len(self.IndexTable.ix['Element'].Classification.Items) == 0:
raise ValueError('Need at least one element in element list, please check your classification definition!')
if len(self.IndexTable.ix['Time'].Classification.Items) == 0:
raise ValueError('Need at least one element in Time list, please check your classification definition!')
return True
def Initialize_FlowValues(self):
""" This method will construct empty numpy arrays (zeros) for all flows where the value is None and wheree the indices are given."""
for key in self.FlowDict:
if self.FlowDict[key].Values is None:
self.FlowDict[key].Values = np.zeros(tuple([len(self.IndexTable.set_index('IndexLetter').ix[x]['Classification'].Items) for x in self.FlowDict[key].Indices.split(',')]))
# Raw code, for development
# Indices = 't,Ro,a,e'
# IndList = Indices.split(',')
# Dimensions = [len(IndexTable.ix[x]['Classification'].Items) for x in IndList]
# Values = np.zeros(tuple(Dimensions))
def Initialize_StockValues(self):
""" This method will construct empty numpy arrays (zeros) for all stocks where the value is None and wheree the indices are given."""
for key in self.StockDict:
if self.StockDict[key].Values is None:
self.StockDict[key].Values = np.zeros(tuple([len(self.IndexTable.set_index('IndexLetter').ix[x]['Classification'].Items) for x in self.StockDict[key].Indices.split(',')]))
def Initialize_ParameterValues(self):
""" This method will construct empty numpy arrays (zeros) for all parameters where the value is None and wheree the indices are given."""
for key in self.ParameterDict:
if self.ParameterDict[key].Values is None:
self.ParameterDict[key].Values = np.zeros(tuple([len(self.IndexTable.set_index('IndexLetter').ix[x]['Classification'].Items) for x in self.ParameterDict[key].Indices.split(',')]))
def Consistency_Check(self):
""" Method that check a readily defined system for consistency of dimensions, Value setting, etc. See detailed comments."""
# 1) Check dimension consistency in index table:
A = self.IndexTableCheck()
# 2) Check whether all process indices that the flows refer to are in the process list:
for key in self.FlowDict:
if self.FlowDict[key].P_Start > len(self.ProcessList) -1:
raise ValueError('Start process of flow {foo} not present. Check your flow definition!'.format(foo = key))
if self.FlowDict[key].P_End > len(self.ProcessList) -1:
raise ValueError('End process of flow {foo} not present. Check your flow definition!'.format(foo = key))
# 3) Check whethe all flow valua arrays match with the index structure:
for key in self.FlowDict:
if tuple([len(self.IndexTable.set_index('IndexLetter').ix[x]['Classification'].Items) for x in self.FlowDict[key].Indices.split(',')]) != self.FlowDict[key].Values.shape:
raise ValueError('Dimension mismatch. Dimension of flow value array does not fit to flow indices for flow {foo}. Check your flow and flow value definition!'.format(foo = key))
return A, True, True
def Flow_Sum_By_Element(self,FlowKey):
"""
Reduce flow values to a Time x Elements matrix and return as t x e array.
We take the indices of each flow, e.g., 't,O,D,G,m,e', strip off the ',' to get 'tODGme',
add a '->' and the index letters for time and element (here, t and e),
and call the Einstein sum function np.einsum with the string 'tODGme->te',
and apply it to the flow values.
"""
return np.einsum(self.FlowDict[FlowKey].Indices.replace(',','') + '->'+ self.IndexTable.ix['Time'].IndexLetter + self.IndexTable.ix['Element'].IndexLetter ,self.FlowDict[FlowKey].Values)
def Stock_Sum_By_Element(self,StockKey):
"""
Reduce stock values to a Time x Elements matrix and return as t x e array.
We take the indices of each stock, e.g., 't,c,G,m,e', strip off the ',' to get 'tcGme',
add a '->' and the index letters for time and element (here, t and e),
and call the Einstein sum function np.einsum with the string 'tcGme->te',
and apply it to the stock values.
"""
return np.einsum(self.StockDict[StockKey].Indices.replace(',','') + '->'+ self.IndexTable.ix['Time'].IndexLetter + self.IndexTable.ix['Element'].IndexLetter ,self.StockDict[StockKey].Values)
def MassBalance(self, Element = None):
"""
Determines mass balance of MFAsystem
We take the indices of each flow, e.g., 't,O,D,G,m,e', strip off the ',' to get 'tODGme',
add a '->' and the index letters for time and element (here, t and e),
and call the Einstein sum function np.einsum with the string 'tODGme->te',
and apply it to the flow values.
Sum to t and e is subtracted from process where flow is leaving from and added to destination process.
"""
Bal = np.zeros((len(self.Time_L),len(self.ProcessList),len(self.Elements))) # Balance array: years x process x element:
#process position 0 is the balance for the system boundary, the other positions are for the processes,
#element position 0 is the balance for the entire mass, the other are for the balance of the individual elements
for key in self.FlowDict: # Add all flows to mass balance
Bal[:,self.FlowDict[key].P_Start,:] -= self.Flow_Sum_By_Element(key) # Flow leaving a process
Bal[:,self.FlowDict[key].P_End,:] += self.Flow_Sum_By_Element(key) # Flow entering a process
for key in self.StockDict: # Add all stock changes to the mass balance
if self.StockDict[key].Type == 1:
Bal[:,self.StockDict[key].P_Res,:] -= self.Stock_Sum_By_Element(key) # 1: net stock change or addition to stock
elif self.StockDict[key].Type == 2:
Bal[:,self.StockDict[key].P_Res,:] += self.Stock_Sum_By_Element(key) # 2: removal/release from stock
#add stock changes to process with number 0 ('system boundary, environment of system')
for key in self.StockDict:
if self.StockDict[key].Type == 1:
Bal[:,0,:] += self.Stock_Sum_By_Element(key) # 1: net stock change or addition to stock
elif self.StockDict[key].Type == 2:
Bal[:,0,:] -= self.Stock_Sum_By_Element(key) # 2: removal/release from stock
return Bal
def Check_If_All_Chem_Elements_Are_present(self,FlowKey,AllElementsIndex):
"""
This method is applicable to systems where the chemical element list contains both 0 ('all' chemical elements) and individual elements.
It checks whether the sum of the system variable of the other elements equals the entry for element 0.
This means that the breakdown of the system variable into individual elements has the same mass as the total for all elements.
AllElementsindex is the position of the element 0 in the element list, typically, it is also 0.
"""
txe = self.Flow_Sum_By_Element(FlowKey)
txe_0 = txe[:,AllElementsIndex]
txe_o = np.delete(txe,AllElementsIndex,axis=1).sum(axis=1)
if np.allclose(txe_0,txe_o):
Check = True
else:
Check = False
return Check, txe_0, txe_o # Check flag, time series for element 'all', time series for all 'other' elements.
def SankeyExport(self,Year, Path, Element): # Export data for given year in excel format for the D3.js Circular Sankey method
""" Exports MFAsystem to xls Template for the Circular Sankey method."""
TimeIndex = Year - self.Time_Start
myfont = xlwt.Font()
myfont.bold = True
mystyle = xlwt.XFStyle()
mystyle.font = myfont
Result_workbook = xlwt.Workbook(encoding = 'ascii')
Result_worksheet = Result_workbook.add_sheet('Nodes')
Result_worksheet.write(0, 0, label = 'Name', style = mystyle)
Result_worksheet.write(0, 1, label = 'Color', style = mystyle)
Result_worksheet.write(0, 2, label = 'Orientation', style = mystyle)
Result_worksheet.write(0, 3, label = 'Width', style = mystyle)
Result_worksheet.write(0, 4, label = 'Height', style = mystyle)
Result_worksheet.write(0, 5, label = 'x_position', style = mystyle)
Result_worksheet.write(0, 6, label = 'y_position', style = mystyle)
for m in range(0,len(self.ProcessList)):
if self.ProcessList[m].Graphical is None:
raise ValueError('Graphical properties of process number {foo} are not set. No export to Sankey possible, as position of process on canvas etc. needs is not specified.'.format(foo = m))
Result_worksheet.write(m +1, 0, label = self.ProcessList[m].Graphical['Name'])
Result_worksheet.write(m +1, 1, label = self.ProcessList[m].Graphical['Color'])
Result_worksheet.write(m +1, 2, label = self.ProcessList[m].Graphical['Angle'])
Result_worksheet.write(m +1, 3, label = self.ProcessList[m].Graphical['Width'])
Result_worksheet.write(m +1, 4, label = self.ProcessList[m].Graphical['Height'])
Result_worksheet.write(m +1, 5, label = self.ProcessList[m].Graphical['xPos'])
Result_worksheet.write(m +1, 6, label = self.ProcessList[m].Graphical['yPos'])
Result_worksheet = Result_workbook.add_sheet('Flows')
Result_worksheet.write(0, 0, label = 'StartNode', style = mystyle)
Result_worksheet.write(0, 1, label = 'EndNode', style = mystyle)
Result_worksheet.write(0, 2, label = 'Value', style = mystyle)
Result_worksheet.write(0, 3, label = 'Color', style = mystyle)
for key in self.FlowDict:
Result_worksheet.write(m +1, 0, label = self.FlowDict[key].P_Start)
Result_worksheet.write(m +1, 1, label = self.FlowDict[key].P_End)
Result_worksheet.write(m +1, 2, label = float(self.Flow_Sum_By_Element(key)[TimeIndex,Element]))
Result_worksheet.write(m +1, 3, label = self.FlowDict[key].Color)
Result_workbook.save(Path + self.Name + '_' + str(TimeIndex) + '_' + str(Element) + '_Sankey.xls')
class Process(Obj):
"""
Class with the definition and methods for a process in ODYM
"""
def __init__(self, Name = None, ID = None, UUID = None, Bipartite = None, Graphical = None, Extensions = None, Parameters = None):
""" Basic initialisation of a process."""
Obj.__init__(self, Name = Name, ID = ID, UUID = UUID) # Hand over parameters to parent class init
self.Bipartite = Bipartite # For bipartite system graphs, a string with value 't' or 'd' for transformation and distribution process indicates which group the process belongs to.
self.Extensions= Extensions # Dictionary of
self.Graphical = Graphical # # Dictionary of graphical properties: xPos = None, yPos = None, Orientation = None, Color=None, Width = None, Height=None,
def add_extension(self,Time = None, Name = None, Value=None, Unit = None, Uncert=None): # Extensions flows that are not part of the system-wide mass balance!
if self.Extensions is None:
self.Extensions = []
self.Extensions.append(Flow(P_Start = self.ID, P_End = None, Time = Time, Name = Name, Unit = Unit, Value = Value, Uncert = Uncert))
def add_parameter(self,Name = None):
if self.Parameters is None:
self.Parameters = []
self.Parameters.append(Parameter(Value = None))
class Flow(Obj): # Flow needs to at least have dimension time x element
"""
Class with the definition and methods for a flow in ODYM
"""
def __init__(self, Name = None, ID = None, UUID = None, P_Start = None, P_End = None, Indices = None, Values=None, Uncert=None, Unit = None, Color = None):
""" Basic initialisation of a flow."""
Obj.__init__(self, Name = Name, ID = ID, UUID = UUID) # Hand over parameters to parent class init
self.P_Start = P_Start # id of start process of flow (id: int)
self.P_End = P_End # id of end process of flow (id: int)
self.Indices = Indices # String with indices as defined in IndexTable, separated by ,: 't,c,p,s,e'
self.Values = Values # flow values, np.array, multidimensional, unit is system-wide unit
self.Uncert = Uncert # uncertainty of value in %
self.Unit = Unit # Unit string
self.Color = Color # color as string 'R,G,B', where each of R, G, B has a value of 0...255
class Stock(Obj): # Flow needs to at least have dimension time x element
"""
Class with the definition and methods for a stock in ODYM
"""
def __init__(self, Name = None, ID = None, UUID = None, P_Res = None, Indices = None, Type = None, Values=None, Uncert=None, Unit = None, Color = None):
""" Basic initialisation of a stock."""
Obj.__init__(self, Name = Name, ID = ID, UUID = UUID) # Hand over parameters to parent class init
self.P_Res = P_Res # id of process where stock resides (id: int)
self.Indices = Indices # String with indices as defined in IndexTable, separated by ,: 't,c,p,s,e'
self.Type = Type # Type is an int value, indicating: 0: stock, 1: (net) stock change or addition to stock, 2: removal from stock
self.Values = Values # flow values, np.array, multidimensional, unit is system-wide unit
self.Uncert = Uncert # uncertainty of value in %
self.Unit = Unit # Unit string
self.Color = Color # color as string 'R,G,B', where each of R, G, B has a value of 0...255
class Parameter(Obj):
"""
Class with the definition and methods for parameters
"""
def __init__(self, Name = None, ID = None, UUID = None, P_Res = None, MetaData = None, Indices = None, Values=None, Uncert=None, Unit = None):
""" Basic initialisation of a parameter."""
Obj.__init__(self, Name = Name, ID = ID, UUID = UUID) # Hand over parameters to parent class init
self.P_Res = P_Res # id of process to which parameter is assigned (id: int)
self.Indices = Indices # String with indices as defined in IndexTable, separated by ,: 't,c,p,s,e'
self.MetaData = MetaData # Dictionary with additional metadata
self.Values = Values # parameter values, np.array, multidimensional, unit is Unit
self.Uncert = Uncert # uncertainty of value in %
self.Unit = Unit # Unit of parameter values
#
#
# </code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="modules.ODYM_Classes.Classification"><code class="flex name class">
<span>class <span class="ident">Classification</span></span>
<span>(</span><span>Name=None, ID=None, UUID=None, Dimension=None, Items=None, IDs=None, AdditionalProporties={})</span>
</code></dt>
<dd>
<section class="desc"><p>Class for aspect classification</p>
<p>Basic initialisation of an item list for alloys, materials, etc.</p></section>
<details class="source">
<summary>Source code</summary>
<pre><code class="python">class Classification(Obj):
"""
Class for aspect classification
"""
def __init__(self, Name = None, ID = None, UUID = None, Dimension = None, Items = None, IDs = None, AdditionalProporties = {}):
""" Basic initialisation of an item list for alloys, materials, etc."""
Obj.__init__(self, Name = Name, ID = ID, UUID = UUID) # Hand over parameters to parent class init
self.Dimension = Dimension # Dimension of classification: Time, Region, process, material, goods, ...
self.Items = Items # list with names of items
self.IDs = IDs # list with IDs of items
self.AdditionalProps = AdditionalProporties # Like population for regions, element composition for alloys, ...</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li><a title="modules.ODYM_Classes.Obj" href="#modules.ODYM_Classes.Obj">Obj</a></li>
</ul>
</dd>
<dt id="modules.ODYM_Classes.Flow"><code class="flex name class">
<span>class <span class="ident">Flow</span></span>
<span>(</span><span>Name=None, ID=None, UUID=None, P_Start=None, P_End=None, Indices=None, Values=None, Uncert=None, Unit=None, Color=None)</span>
</code></dt>
<dd>
<section class="desc"><p>Class with the definition and methods for a flow in ODYM</p>
<p>Basic initialisation of a flow.</p></section>
<details class="source">
<summary>Source code</summary>
<pre><code class="python">class Flow(Obj): # Flow needs to at least have dimension time x element
"""
Class with the definition and methods for a flow in ODYM
"""
def __init__(self, Name = None, ID = None, UUID = None, P_Start = None, P_End = None, Indices = None, Values=None, Uncert=None, Unit = None, Color = None):
""" Basic initialisation of a flow."""
Obj.__init__(self, Name = Name, ID = ID, UUID = UUID) # Hand over parameters to parent class init
self.P_Start = P_Start # id of start process of flow (id: int)
self.P_End = P_End # id of end process of flow (id: int)
self.Indices = Indices # String with indices as defined in IndexTable, separated by ,: 't,c,p,s,e'
self.Values = Values # flow values, np.array, multidimensional, unit is system-wide unit
self.Uncert = Uncert # uncertainty of value in %
self.Unit = Unit # Unit string
self.Color = Color # color as string 'R,G,B', where each of R, G, B has a value of 0...255</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li><a title="modules.ODYM_Classes.Obj" href="#modules.ODYM_Classes.Obj">Obj</a></li>
</ul>
</dd>
<dt id="modules.ODYM_Classes.MFAsystem"><code class="flex name class">
<span>class <span class="ident">MFAsystem</span></span>
<span>(</span><span>Name, Time_Start, Time_End, Geogr_Scope, Unit, IndexTable, Elements, ProcessList=[], FlowDict={}, StockDict={}, ParameterDict={}, Graphical=None, ID=None, UUID=None)</span>
</code></dt>
<dd>
<section class="desc"><p>Class with the definition and methods for a system in ODYM</p>
<p>Initialisation of MFAsystem.</p></section>
<details class="source">
<summary>Source code</summary>
<pre><code class="python">class MFAsystem(Obj):
"""
Class with the definition and methods for a system in ODYM
"""
def __init__(self, Name, Time_Start, Time_End, Geogr_Scope, Unit, IndexTable, Elements, ProcessList = [], FlowDict = {}, StockDict = {}, ParameterDict = {}, Graphical = None, ID = None, UUID = None, ):
""" Initialisation of MFAsystem."""
Obj.__init__(self, Name = Name, ID = ID, UUID = UUID) # Hand over parameters to parent class init
self.Time_Start = Time_Start # start time of model (year: int)
self.Time_End = Time_End # end time of model (year: int)
self.Geogr_Scope = Geogr_Scope # geographical boundary (string)
self.Elements = Elements # list of chemical elements considered, indicated by atomic numbers
self.Unit = Unit # flow and stock base unit, without 'per yr'
self.ProcessList = ProcessList # list of processes, processes are referred to by their number
self.FlowDict = FlowDict # Dictionary of flows, are indexed by tuples of process they are attached to (p1,p2)
self.StockDict = StockDict # Dictionary of stocks, are indexed by process they are located at (p)
self.ParameterDict = ParameterDict # Dictionary of of parameters: lifetime, yield rates, etc.
self.IndexTable = IndexTable # Dictionary of abbreviations for aspect-classification tuples
self.Graphical = Graphical # Dictionary of graphical properties (size in pixel, background color, etc.)
@property
def Time_V(self):
""" Array of all model years"""
return np.arange(self.Time_Start,self.Time_End +1,1)
@property
def Time_L(self):
""" List of all model years"""
return np.arange(self.Time_Start,self.Time_End +1,1).tolist()
def IndexTableCheck(self):
""" Check whether chosen classifications fit to dimensions of index table."""
for indx in self.IndexTable.index:
if self.IndexTable.ix[indx]['Dimension'] != self.IndexTable.ix[indx]['Classification'].Dimension:
raise ValueError('Dimension mismatch. Dimension of classifiation needs to fit to dimension of flow or parameter index. Found a mismatch for the following index: {foo}. Check your index table definition!'.format(foo = indx))
if 'Time' not in self.IndexTable.index:
raise ValueError(' "Time" aspect must be present in IndexTable. Please check your index table definition!')
if 'Element' not in self.IndexTable.index:
raise ValueError(' "Element" aspect must be present in IndexTable. Please check your index table definition!')
if len(self.IndexTable.ix['Element'].Classification.Items) == 0:
raise ValueError('Need at least one element in element list, please check your classification definition!')
if len(self.IndexTable.ix['Time'].Classification.Items) == 0:
raise ValueError('Need at least one element in Time list, please check your classification definition!')
return True
def Initialize_FlowValues(self):
""" This method will construct empty numpy arrays (zeros) for all flows where the value is None and wheree the indices are given."""
for key in self.FlowDict:
if self.FlowDict[key].Values is None:
self.FlowDict[key].Values = np.zeros(tuple([len(self.IndexTable.set_index('IndexLetter').ix[x]['Classification'].Items) for x in self.FlowDict[key].Indices.split(',')]))
# Raw code, for development
# Indices = 't,Ro,a,e'
# IndList = Indices.split(',')
# Dimensions = [len(IndexTable.ix[x]['Classification'].Items) for x in IndList]
# Values = np.zeros(tuple(Dimensions))
def Initialize_StockValues(self):
""" This method will construct empty numpy arrays (zeros) for all stocks where the value is None and wheree the indices are given."""
for key in self.StockDict:
if self.StockDict[key].Values is None:
self.StockDict[key].Values = np.zeros(tuple([len(self.IndexTable.set_index('IndexLetter').ix[x]['Classification'].Items) for x in self.StockDict[key].Indices.split(',')]))
def Initialize_ParameterValues(self):
""" This method will construct empty numpy arrays (zeros) for all parameters where the value is None and wheree the indices are given."""
for key in self.ParameterDict:
if self.ParameterDict[key].Values is None:
self.ParameterDict[key].Values = np.zeros(tuple([len(self.IndexTable.set_index('IndexLetter').ix[x]['Classification'].Items) for x in self.ParameterDict[key].Indices.split(',')]))
def Consistency_Check(self):
""" Method that check a readily defined system for consistency of dimensions, Value setting, etc. See detailed comments."""
# 1) Check dimension consistency in index table:
A = self.IndexTableCheck()
# 2) Check whether all process indices that the flows refer to are in the process list:
for key in self.FlowDict:
if self.FlowDict[key].P_Start > len(self.ProcessList) -1:
raise ValueError('Start process of flow {foo} not present. Check your flow definition!'.format(foo = key))
if self.FlowDict[key].P_End > len(self.ProcessList) -1:
raise ValueError('End process of flow {foo} not present. Check your flow definition!'.format(foo = key))
# 3) Check whethe all flow valua arrays match with the index structure:
for key in self.FlowDict:
if tuple([len(self.IndexTable.set_index('IndexLetter').ix[x]['Classification'].Items) for x in self.FlowDict[key].Indices.split(',')]) != self.FlowDict[key].Values.shape:
raise ValueError('Dimension mismatch. Dimension of flow value array does not fit to flow indices for flow {foo}. Check your flow and flow value definition!'.format(foo = key))
return A, True, True
def Flow_Sum_By_Element(self,FlowKey):
"""
Reduce flow values to a Time x Elements matrix and return as t x e array.
We take the indices of each flow, e.g., 't,O,D,G,m,e', strip off the ',' to get 'tODGme',
add a '->' and the index letters for time and element (here, t and e),
and call the Einstein sum function np.einsum with the string 'tODGme->te',
and apply it to the flow values.
"""
return np.einsum(self.FlowDict[FlowKey].Indices.replace(',','') + '->'+ self.IndexTable.ix['Time'].IndexLetter + self.IndexTable.ix['Element'].IndexLetter ,self.FlowDict[FlowKey].Values)
def Stock_Sum_By_Element(self,StockKey):
"""
Reduce stock values to a Time x Elements matrix and return as t x e array.
We take the indices of each stock, e.g., 't,c,G,m,e', strip off the ',' to get 'tcGme',
add a '->' and the index letters for time and element (here, t and e),
and call the Einstein sum function np.einsum with the string 'tcGme->te',
and apply it to the stock values.
"""
return np.einsum(self.StockDict[StockKey].Indices.replace(',','') + '->'+ self.IndexTable.ix['Time'].IndexLetter + self.IndexTable.ix['Element'].IndexLetter ,self.StockDict[StockKey].Values)
def MassBalance(self, Element = None):
"""
Determines mass balance of MFAsystem
We take the indices of each flow, e.g., 't,O,D,G,m,e', strip off the ',' to get 'tODGme',
add a '->' and the index letters for time and element (here, t and e),
and call the Einstein sum function np.einsum with the string 'tODGme->te',
and apply it to the flow values.
Sum to t and e is subtracted from process where flow is leaving from and added to destination process.
"""
Bal = np.zeros((len(self.Time_L),len(self.ProcessList),len(self.Elements))) # Balance array: years x process x element:
#process position 0 is the balance for the system boundary, the other positions are for the processes,
#element position 0 is the balance for the entire mass, the other are for the balance of the individual elements
for key in self.FlowDict: # Add all flows to mass balance
Bal[:,self.FlowDict[key].P_Start,:] -= self.Flow_Sum_By_Element(key) # Flow leaving a process
Bal[:,self.FlowDict[key].P_End,:] += self.Flow_Sum_By_Element(key) # Flow entering a process
for key in self.StockDict: # Add all stock changes to the mass balance
if self.StockDict[key].Type == 1:
Bal[:,self.StockDict[key].P_Res,:] -= self.Stock_Sum_By_Element(key) # 1: net stock change or addition to stock
elif self.StockDict[key].Type == 2:
Bal[:,self.StockDict[key].P_Res,:] += self.Stock_Sum_By_Element(key) # 2: removal/release from stock
#add stock changes to process with number 0 ('system boundary, environment of system')
for key in self.StockDict:
if self.StockDict[key].Type == 1:
Bal[:,0,:] += self.Stock_Sum_By_Element(key) # 1: net stock change or addition to stock
elif self.StockDict[key].Type == 2:
Bal[:,0,:] -= self.Stock_Sum_By_Element(key) # 2: removal/release from stock
return Bal
def Check_If_All_Chem_Elements_Are_present(self,FlowKey,AllElementsIndex):
"""
This method is applicable to systems where the chemical element list contains both 0 ('all' chemical elements) and individual elements.
It checks whether the sum of the system variable of the other elements equals the entry for element 0.
This means that the breakdown of the system variable into individual elements has the same mass as the total for all elements.
AllElementsindex is the position of the element 0 in the element list, typically, it is also 0.
"""
txe = self.Flow_Sum_By_Element(FlowKey)
txe_0 = txe[:,AllElementsIndex]
txe_o = np.delete(txe,AllElementsIndex,axis=1).sum(axis=1)
if np.allclose(txe_0,txe_o):
Check = True
else:
Check = False
return Check, txe_0, txe_o # Check flag, time series for element 'all', time series for all 'other' elements.
def SankeyExport(self,Year, Path, Element): # Export data for given year in excel format for the D3.js Circular Sankey method
""" Exports MFAsystem to xls Template for the Circular Sankey method."""
TimeIndex = Year - self.Time_Start
myfont = xlwt.Font()
myfont.bold = True
mystyle = xlwt.XFStyle()
mystyle.font = myfont
Result_workbook = xlwt.Workbook(encoding = 'ascii')
Result_worksheet = Result_workbook.add_sheet('Nodes')
Result_worksheet.write(0, 0, label = 'Name', style = mystyle)
Result_worksheet.write(0, 1, label = 'Color', style = mystyle)
Result_worksheet.write(0, 2, label = 'Orientation', style = mystyle)
Result_worksheet.write(0, 3, label = 'Width', style = mystyle)
Result_worksheet.write(0, 4, label = 'Height', style = mystyle)
Result_worksheet.write(0, 5, label = 'x_position', style = mystyle)
Result_worksheet.write(0, 6, label = 'y_position', style = mystyle)
for m in range(0,len(self.ProcessList)):
if self.ProcessList[m].Graphical is None:
raise ValueError('Graphical properties of process number {foo} are not set. No export to Sankey possible, as position of process on canvas etc. needs is not specified.'.format(foo = m))
Result_worksheet.write(m +1, 0, label = self.ProcessList[m].Graphical['Name'])
Result_worksheet.write(m +1, 1, label = self.ProcessList[m].Graphical['Color'])
Result_worksheet.write(m +1, 2, label = self.ProcessList[m].Graphical['Angle'])
Result_worksheet.write(m +1, 3, label = self.ProcessList[m].Graphical['Width'])
Result_worksheet.write(m +1, 4, label = self.ProcessList[m].Graphical['Height'])
Result_worksheet.write(m +1, 5, label = self.ProcessList[m].Graphical['xPos'])
Result_worksheet.write(m +1, 6, label = self.ProcessList[m].Graphical['yPos'])
Result_worksheet = Result_workbook.add_sheet('Flows')
Result_worksheet.write(0, 0, label = 'StartNode', style = mystyle)
Result_worksheet.write(0, 1, label = 'EndNode', style = mystyle)
Result_worksheet.write(0, 2, label = 'Value', style = mystyle)
Result_worksheet.write(0, 3, label = 'Color', style = mystyle)
for key in self.FlowDict:
Result_worksheet.write(m +1, 0, label = self.FlowDict[key].P_Start)
Result_worksheet.write(m +1, 1, label = self.FlowDict[key].P_End)
Result_worksheet.write(m +1, 2, label = float(self.Flow_Sum_By_Element(key)[TimeIndex,Element]))
Result_worksheet.write(m +1, 3, label = self.FlowDict[key].Color)
Result_workbook.save(Path + self.Name + '_' + str(TimeIndex) + '_' + str(Element) + '_Sankey.xls') </code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li><a title="modules.ODYM_Classes.Obj" href="#modules.ODYM_Classes.Obj">Obj</a></li>
</ul>
<h3>Instance variables</h3>
<dl>
<dt id="modules.ODYM_Classes.MFAsystem.Time_L"><code class="name">var <span class="ident">Time_L</span></code></dt>
<dd>
<section class="desc"><p>List of all model years</p></section>
<details class="source">
<summary>Source code</summary>
<pre><code class="python">@property
def Time_L(self):
""" List of all model years"""
return np.arange(self.Time_Start,self.Time_End +1,1).tolist()</code></pre>
</details>
</dd>
<dt id="modules.ODYM_Classes.MFAsystem.Time_V"><code class="name">var <span class="ident">Time_V</span></code></dt>
<dd>
<section class="desc"><p>Array of all model years</p></section>
<details class="source">
<summary>Source code</summary>
<pre><code class="python">@property
def Time_V(self):
""" Array of all model years"""
return np.arange(self.Time_Start,self.Time_End +1,1)</code></pre>
</details>
</dd>
</dl>
<h3>Methods</h3>
<dl>
<dt id="modules.ODYM_Classes.MFAsystem.Check_If_All_Chem_Elements_Are_present"><code class="name flex">
<span>def <span class="ident">Check_If_All_Chem_Elements_Are_present</span></span>(<span>self, FlowKey, AllElementsIndex)</span>
</code></dt>
<dd>
<section class="desc"><p>This method is applicable to systems where the chemical element list contains both 0 ('all' chemical elements) and individual elements.
It checks whether the sum of the system variable of the other elements equals the entry for element 0.
This means that the breakdown of the system variable into individual elements has the same mass as the total for all elements.
AllElementsindex is the position of the element 0 in the element list, typically, it is also 0.</p></section>
<details class="source">
<summary>Source code</summary>
<pre><code class="python">def Check_If_All_Chem_Elements_Are_present(self,FlowKey,AllElementsIndex):
"""
This method is applicable to systems where the chemical element list contains both 0 ('all' chemical elements) and individual elements.
It checks whether the sum of the system variable of the other elements equals the entry for element 0.
This means that the breakdown of the system variable into individual elements has the same mass as the total for all elements.
AllElementsindex is the position of the element 0 in the element list, typically, it is also 0.
"""
txe = self.Flow_Sum_By_Element(FlowKey)
txe_0 = txe[:,AllElementsIndex]
txe_o = np.delete(txe,AllElementsIndex,axis=1).sum(axis=1)
if np.allclose(txe_0,txe_o):
Check = True
else:
Check = False
return Check, txe_0, txe_o # Check flag, time series for element 'all', time series for all 'other' elements.</code></pre>
</details>
</dd>
<dt id="modules.ODYM_Classes.MFAsystem.Consistency_Check"><code class="name flex">
<span>def <span class="ident">Consistency_Check</span></span>(<span>self)</span>
</code></dt>
<dd>
<section class="desc"><p>Method that check a readily defined system for consistency of dimensions, Value setting, etc. See detailed comments.</p></section>
<details class="source">
<summary>Source code</summary>
<pre><code class="python">def Consistency_Check(self):
""" Method that check a readily defined system for consistency of dimensions, Value setting, etc. See detailed comments."""
# 1) Check dimension consistency in index table:
A = self.IndexTableCheck()
# 2) Check whether all process indices that the flows refer to are in the process list:
for key in self.FlowDict:
if self.FlowDict[key].P_Start > len(self.ProcessList) -1:
raise ValueError('Start process of flow {foo} not present. Check your flow definition!'.format(foo = key))
if self.FlowDict[key].P_End > len(self.ProcessList) -1:
raise ValueError('End process of flow {foo} not present. Check your flow definition!'.format(foo = key))
# 3) Check whethe all flow valua arrays match with the index structure:
for key in self.FlowDict:
if tuple([len(self.IndexTable.set_index('IndexLetter').ix[x]['Classification'].Items) for x in self.FlowDict[key].Indices.split(',')]) != self.FlowDict[key].Values.shape:
raise ValueError('Dimension mismatch. Dimension of flow value array does not fit to flow indices for flow {foo}. Check your flow and flow value definition!'.format(foo = key))
return A, True, True</code></pre>
</details>
</dd>
<dt id="modules.ODYM_Classes.MFAsystem.Flow_Sum_By_Element"><code class="name flex">
<span>def <span class="ident">Flow_Sum_By_Element</span></span>(<span>self, FlowKey)</span>
</code></dt>
<dd>
<section class="desc"><p>Reduce flow values to a Time x Elements matrix and return as t x e array.
We take the indices of each flow, e.g., 't,O,D,G,m,e', strip off the ',' to get 'tODGme',
add a '->' and the index letters for time and element (here, t and e),
and call the Einstein sum function np.einsum with the string 'tODGme->te',
and apply it to the flow values.</p></section>
<details class="source">
<summary>Source code</summary>
<pre><code class="python">def Flow_Sum_By_Element(self,FlowKey):
"""
Reduce flow values to a Time x Elements matrix and return as t x e array.
We take the indices of each flow, e.g., 't,O,D,G,m,e', strip off the ',' to get 'tODGme',
add a '->' and the index letters for time and element (here, t and e),
and call the Einstein sum function np.einsum with the string 'tODGme->te',
and apply it to the flow values.
"""
return np.einsum(self.FlowDict[FlowKey].Indices.replace(',','') + '->'+ self.IndexTable.ix['Time'].IndexLetter + self.IndexTable.ix['Element'].IndexLetter ,self.FlowDict[FlowKey].Values) </code></pre>
</details>
</dd>
<dt id="modules.ODYM_Classes.MFAsystem.IndexTableCheck"><code class="name flex">
<span>def <span class="ident">IndexTableCheck</span></span>(<span>self)</span>
</code></dt>
<dd>
<section class="desc"><p>Check whether chosen classifications fit to dimensions of index table.</p></section>
<details class="source">
<summary>Source code</summary>
<pre><code class="python">def IndexTableCheck(self):
""" Check whether chosen classifications fit to dimensions of index table."""
for indx in self.IndexTable.index:
if self.IndexTable.ix[indx]['Dimension'] != self.IndexTable.ix[indx]['Classification'].Dimension:
raise ValueError('Dimension mismatch. Dimension of classifiation needs to fit to dimension of flow or parameter index. Found a mismatch for the following index: {foo}. Check your index table definition!'.format(foo = indx))
if 'Time' not in self.IndexTable.index:
raise ValueError(' "Time" aspect must be present in IndexTable. Please check your index table definition!')
if 'Element' not in self.IndexTable.index:
raise ValueError(' "Element" aspect must be present in IndexTable. Please check your index table definition!')
if len(self.IndexTable.ix['Element'].Classification.Items) == 0:
raise ValueError('Need at least one element in element list, please check your classification definition!')
if len(self.IndexTable.ix['Time'].Classification.Items) == 0:
raise ValueError('Need at least one element in Time list, please check your classification definition!')
return True</code></pre>
</details>
</dd>
<dt id="modules.ODYM_Classes.MFAsystem.Initialize_FlowValues"><code class="name flex">
<span>def <span class="ident">Initialize_FlowValues</span></span>(<span>self)</span>
</code></dt>
<dd>
<section class="desc"><p>This method will construct empty numpy arrays (zeros) for all flows where the value is None and wheree the indices are given.</p></section>
<details class="source">
<summary>Source code</summary>
<pre><code class="python">def Initialize_FlowValues(self):
""" This method will construct empty numpy arrays (zeros) for all flows where the value is None and wheree the indices are given."""
for key in self.FlowDict:
if self.FlowDict[key].Values is None:
self.FlowDict[key].Values = np.zeros(tuple([len(self.IndexTable.set_index('IndexLetter').ix[x]['Classification'].Items) for x in self.FlowDict[key].Indices.split(',')])) </code></pre>
</details>
</dd>
<dt id="modules.ODYM_Classes.MFAsystem.Initialize_ParameterValues"><code class="name flex">
<span>def <span class="ident">Initialize_ParameterValues</span></span>(<span>self)</span>
</code></dt>
<dd>
<section class="desc"><p>This method will construct empty numpy arrays (zeros) for all parameters where the value is None and wheree the indices are given.</p></section>
<details class="source">
<summary>Source code</summary>
<pre><code class="python">def Initialize_ParameterValues(self):
""" This method will construct empty numpy arrays (zeros) for all parameters where the value is None and wheree the indices are given."""
for key in self.ParameterDict:
if self.ParameterDict[key].Values is None:
self.ParameterDict[key].Values = np.zeros(tuple([len(self.IndexTable.set_index('IndexLetter').ix[x]['Classification'].Items) for x in self.ParameterDict[key].Indices.split(',')])) </code></pre>
</details>
</dd>
<dt id="modules.ODYM_Classes.MFAsystem.Initialize_StockValues"><code class="name flex">
<span>def <span class="ident">Initialize_StockValues</span></span>(<span>self)</span>
</code></dt>
<dd>
<section class="desc"><p>This method will construct empty numpy arrays (zeros) for all stocks where the value is None and wheree the indices are given.</p></section>
<details class="source">
<summary>Source code</summary>
<pre><code class="python">def Initialize_StockValues(self):
""" This method will construct empty numpy arrays (zeros) for all stocks where the value is None and wheree the indices are given."""
for key in self.StockDict:
if self.StockDict[key].Values is None:
self.StockDict[key].Values = np.zeros(tuple([len(self.IndexTable.set_index('IndexLetter').ix[x]['Classification'].Items) for x in self.StockDict[key].Indices.split(',')])) </code></pre>
</details>
</dd>
<dt id="modules.ODYM_Classes.MFAsystem.MassBalance"><code class="name flex">
<span>def <span class="ident">MassBalance</span></span>(<span>self, Element=None)</span>
</code></dt>
<dd>
<section class="desc"><p>Determines mass balance of MFAsystem
We take the indices of each flow, e.g., 't,O,D,G,m,e', strip off the ',' to get 'tODGme',
add a '->' and the index letters for time and element (here, t and e),
and call the Einstein sum function np.einsum with the string 'tODGme->te',
and apply it to the flow values.
Sum to t and e is subtracted from process where flow is leaving from and added to destination process.</p></section>
<details class="source">
<summary>Source code</summary>
<pre><code class="python">def MassBalance(self, Element = None):
"""
Determines mass balance of MFAsystem
We take the indices of each flow, e.g., 't,O,D,G,m,e', strip off the ',' to get 'tODGme',
add a '->' and the index letters for time and element (here, t and e),
and call the Einstein sum function np.einsum with the string 'tODGme->te',
and apply it to the flow values.
Sum to t and e is subtracted from process where flow is leaving from and added to destination process.
"""
Bal = np.zeros((len(self.Time_L),len(self.ProcessList),len(self.Elements))) # Balance array: years x process x element:
#process position 0 is the balance for the system boundary, the other positions are for the processes,
#element position 0 is the balance for the entire mass, the other are for the balance of the individual elements
for key in self.FlowDict: # Add all flows to mass balance
Bal[:,self.FlowDict[key].P_Start,:] -= self.Flow_Sum_By_Element(key) # Flow leaving a process
Bal[:,self.FlowDict[key].P_End,:] += self.Flow_Sum_By_Element(key) # Flow entering a process
for key in self.StockDict: # Add all stock changes to the mass balance
if self.StockDict[key].Type == 1:
Bal[:,self.StockDict[key].P_Res,:] -= self.Stock_Sum_By_Element(key) # 1: net stock change or addition to stock
elif self.StockDict[key].Type == 2:
Bal[:,self.StockDict[key].P_Res,:] += self.Stock_Sum_By_Element(key) # 2: removal/release from stock
#add stock changes to process with number 0 ('system boundary, environment of system')
for key in self.StockDict:
if self.StockDict[key].Type == 1:
Bal[:,0,:] += self.Stock_Sum_By_Element(key) # 1: net stock change or addition to stock
elif self.StockDict[key].Type == 2:
Bal[:,0,:] -= self.Stock_Sum_By_Element(key) # 2: removal/release from stock
return Bal</code></pre>
</details>
</dd>
<dt id="modules.ODYM_Classes.MFAsystem.SankeyExport"><code class="name flex">
<span>def <span class="ident">SankeyExport</span></span>(<span>self, Year, Path, Element)</span>
</code></dt>
<dd>
<section class="desc"><p>Exports MFAsystem to xls Template for the Circular Sankey method.</p></section>
<details class="source">
<summary>Source code</summary>
<pre><code class="python">def SankeyExport(self,Year, Path, Element): # Export data for given year in excel format for the D3.js Circular Sankey method
""" Exports MFAsystem to xls Template for the Circular Sankey method."""
TimeIndex = Year - self.Time_Start
myfont = xlwt.Font()
myfont.bold = True
mystyle = xlwt.XFStyle()
mystyle.font = myfont
Result_workbook = xlwt.Workbook(encoding = 'ascii')
Result_worksheet = Result_workbook.add_sheet('Nodes')
Result_worksheet.write(0, 0, label = 'Name', style = mystyle)
Result_worksheet.write(0, 1, label = 'Color', style = mystyle)
Result_worksheet.write(0, 2, label = 'Orientation', style = mystyle)
Result_worksheet.write(0, 3, label = 'Width', style = mystyle)
Result_worksheet.write(0, 4, label = 'Height', style = mystyle)
Result_worksheet.write(0, 5, label = 'x_position', style = mystyle)
Result_worksheet.write(0, 6, label = 'y_position', style = mystyle)
for m in range(0,len(self.ProcessList)):
if self.ProcessList[m].Graphical is None:
raise ValueError('Graphical properties of process number {foo} are not set. No export to Sankey possible, as position of process on canvas etc. needs is not specified.'.format(foo = m))
Result_worksheet.write(m +1, 0, label = self.ProcessList[m].Graphical['Name'])
Result_worksheet.write(m +1, 1, label = self.ProcessList[m].Graphical['Color'])
Result_worksheet.write(m +1, 2, label = self.ProcessList[m].Graphical['Angle'])
Result_worksheet.write(m +1, 3, label = self.ProcessList[m].Graphical['Width'])
Result_worksheet.write(m +1, 4, label = self.ProcessList[m].Graphical['Height'])
Result_worksheet.write(m +1, 5, label = self.ProcessList[m].Graphical['xPos'])
Result_worksheet.write(m +1, 6, label = self.ProcessList[m].Graphical['yPos'])
Result_worksheet = Result_workbook.add_sheet('Flows')
Result_worksheet.write(0, 0, label = 'StartNode', style = mystyle)
Result_worksheet.write(0, 1, label = 'EndNode', style = mystyle)
Result_worksheet.write(0, 2, label = 'Value', style = mystyle)
Result_worksheet.write(0, 3, label = 'Color', style = mystyle)
for key in self.FlowDict:
Result_worksheet.write(m +1, 0, label = self.FlowDict[key].P_Start)
Result_worksheet.write(m +1, 1, label = self.FlowDict[key].P_End)
Result_worksheet.write(m +1, 2, label = float(self.Flow_Sum_By_Element(key)[TimeIndex,Element]))
Result_worksheet.write(m +1, 3, label = self.FlowDict[key].Color)
Result_workbook.save(Path + self.Name + '_' + str(TimeIndex) + '_' + str(Element) + '_Sankey.xls') </code></pre>
</details>
</dd>
<dt id="modules.ODYM_Classes.MFAsystem.Stock_Sum_By_Element"><code class="name flex">
<span>def <span class="ident">Stock_Sum_By_Element</span></span>(<span>self, StockKey)</span>
</code></dt>
<dd>
<section class="desc"><p>Reduce stock values to a Time x Elements matrix and return as t x e array.
We take the indices of each stock, e.g., 't,c,G,m,e', strip off the ',' to get 'tcGme',
add a '->' and the index letters for time and element (here, t and e),
and call the Einstein sum function np.einsum with the string 'tcGme->te',
and apply it to the stock values.</p></section>
<details class="source">
<summary>Source code</summary>
<pre><code class="python">def Stock_Sum_By_Element(self,StockKey):
"""
Reduce stock values to a Time x Elements matrix and return as t x e array.
We take the indices of each stock, e.g., 't,c,G,m,e', strip off the ',' to get 'tcGme',
add a '->' and the index letters for time and element (here, t and e),
and call the Einstein sum function np.einsum with the string 'tcGme->te',
and apply it to the stock values.
"""
return np.einsum(self.StockDict[StockKey].Indices.replace(',','') + '->'+ self.IndexTable.ix['Time'].IndexLetter + self.IndexTable.ix['Element'].IndexLetter ,self.StockDict[StockKey].Values) </code></pre>
</details>
</dd>
</dl>
</dd>
<dt id="modules.ODYM_Classes.Obj"><code class="flex name class">
<span>class <span class="ident">Obj</span></span>
<span>(</span><span>Name=None, ID=None, UUID=None)</span>
</code></dt>
<dd>
<section class="desc"><p>Class with the object definition for a data object (system, process, flow, …) in ODYM</p>
<p>Basic initialisation of Obj.</p></section>
<details class="source">
<summary>Source code</summary>
<pre><code class="python">class Obj(object):
"""
Class with the object definition for a data object (system, process, flow, ...) in ODYM
"""
def __init__(self, Name=None, ID=None, UUID=None):
""" Basic initialisation of Obj."""
self.Name = Name # object name
self.ID = ID # object ID
self.UUID = UUID # object UUID
self.Aspects = {'Time': 'Model time','Cohort': 'Age-cohort','OriginProcess':'Process where flow originates','DestinationProcess':'Destination process of flow','OriginRegion': 'Region where flow originates from','DestinationRegion': 'Region where flow is bound to', 'Good': 'Process, good, or commodity', 'Material': 'Material: ore, alloy, scrap type, ...','Element': 'Chemical element' } # Define the aspects of the system variables
self.Dimensions = {'Time': 'Time', 'Process':'Process', 'Region': 'Region', 'Good': 'Process, good, or commodity', 'Material': 'Material: ore, alloy, scrap type, ...','Element': 'Chemical element' } # Define the dimensions of the system variables</code></pre>
</details>
<h3>Subclasses</h3>
<ul class="hlist">
<li><a title="modules.ODYM_Classes.Classification" href="#modules.ODYM_Classes.Classification">Classification</a></li>
<li><a title="modules.ODYM_Classes.MFAsystem" href="#modules.ODYM_Classes.MFAsystem">MFAsystem</a></li>
<li><a title="modules.ODYM_Classes.Process" href="#modules.ODYM_Classes.Process">Process</a></li>
<li><a title="modules.ODYM_Classes.Flow" href="#modules.ODYM_Classes.Flow">Flow</a></li>