Merge branch 'develop' into 'master'

Hotfix for tests See merge request iek-3/shared-code/fine!315
FZJ-IEK3-VSA · Jan 29, 2024 · a8f4a11 · a8f4a11
2 parents f45fdbe + 6c05c41
commit a8f4a11
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Showing 24 changed files with 149 additions and 128 deletions.
diff --git a/fine/IOManagement/__init__.py b/fine/IOManagement/__init__.py
@@ -3,6 +3,7 @@
 
 |br| @author: FINE Developer Team (FZJ IEK-3)
 """
+
 from .standardIO import *
 from .dictIO import *
 from .exploitOutput import *

diff --git a/fine/IOManagement/utilsIO.py b/fine/IOManagement/utilsIO.py
@@ -274,9 +274,9 @@ def addDFVariablesToXarray(xr_ds, component_dict, df_iteration_dict):
         df_variable.index.set_names("component", level=0, inplace=True)
 
         ds_component = xr.Dataset()
-        ds_component[
-            f"ts_{variable_description}"
-        ] = df_variable.sort_index().to_xarray()
+        ds_component[f"ts_{variable_description}"] = (
+            df_variable.sort_index().to_xarray()
+        )
 
         for comp in df_variable.index.get_level_values(0).unique():
             this_class = comp.split("; ")[0]
@@ -372,9 +372,9 @@ def addSeriesVariablesToXarray(xr_ds, component_dict, series_iteration_dict, loc
             df_variable = pd.concat(space_space_dict)
             df_variable.index.set_names("component", level=0, inplace=True)
             ds_component = xr.Dataset()
-            ds_component[
-                f"2d_{variable_description}"
-            ] = df_variable.sort_index().to_xarray()
+            ds_component[f"2d_{variable_description}"] = (
+                df_variable.sort_index().to_xarray()
+            )
 
             for comp in df_variable.index.get_level_values(0).unique():
                 this_class = comp.split("; ")[0]
@@ -396,9 +396,9 @@ def addSeriesVariablesToXarray(xr_ds, component_dict, series_iteration_dict, loc
             df_variable = pd.concat(space_dict)
             df_variable.index.set_names("component", level=0, inplace=True)
             ds_component = xr.Dataset()
-            ds_component[
-                f"1d_{variable_description}"
-            ] = df_variable.sort_index().to_xarray()
+            ds_component[f"1d_{variable_description}"] = (
+                df_variable.sort_index().to_xarray()
+            )
 
             for comp in df_variable.index.get_level_values(0).unique():
                 this_class = comp.split("; ")[0]
@@ -420,9 +420,9 @@ def addSeriesVariablesToXarray(xr_ds, component_dict, series_iteration_dict, loc
             df_variable = pd.concat(time_dict)
             df_variable.index.set_names("component", level=0, inplace=True)
             ds_component = xr.Dataset()
-            ds_component[
-                f"ts_{variable_description}"
-            ] = df_variable.sort_index().to_xarray()
+            ds_component[f"ts_{variable_description}"] = (
+                df_variable.sort_index().to_xarray()
+            )
 
             for comp in df_variable.index.get_level_values(0).unique():
                 this_class = comp.split("; ")[0]

diff --git a/fine/__init__.py b/fine/__init__.py
@@ -3,6 +3,7 @@
 
 |br| @author: FINE Developer Team (FZJ IEK-3)
 """
+
 from .energySystemModel import EnergySystemModel
 from .sourceSink import Source, Sink
 from .conversion import Conversion

diff --git a/fine/aggregations/__init__.py b/fine/aggregations/__init__.py
@@ -3,5 +3,6 @@
 
 |br| @author: FINE Developer Team (FZJ IEK-3)
 """
+
 from .spatialAggregation import *
 from .technologyAggregation import *
diff --git a/fine/aggregations/spatialAggregation/__init__.py b/fine/aggregations/spatialAggregation/__init__.py
@@ -3,6 +3,7 @@
 
 |br| @author: FINE Developer Team (FZJ IEK-3)
 """
+
 from .aggregation import *
 from .grouping import *
 from .groupingUtils import *

diff --git a/fine/aggregations/spatialAggregation/aggregation.py b/fine/aggregations/spatialAggregation/aggregation.py
@@ -130,10 +130,9 @@ def aggregate_time_series_spatially(
 
             weighted_sub_region_da = sub_region_da * sub_region_weight_da
 
-            xr_data_array_out.loc[
-                dict(space=sup_region_id)
-            ] = weighted_sub_region_da.sum(dim="space") / sub_region_weight_da.sum(
-                dim="space"
+            xr_data_array_out.loc[dict(space=sup_region_id)] = (
+                weighted_sub_region_da.sum(dim="space")
+                / sub_region_weight_da.sum(dim="space")
             )
 
         elif mode == "mean":

diff --git a/fine/aggregations/spatialAggregation/groupingUtils.py b/fine/aggregations/spatialAggregation/groupingUtils.py
@@ -1,5 +1,6 @@
 """Functions to assist spatial grouping algorithms. 
 """
+
 import warnings
 import numpy as np
 from scipy.cluster import hierarchy
@@ -452,9 +453,9 @@ def get_connectivity_matrix(xarray_datasets):
         for comp, comp_ds in comp_dict.items():
             for varname, da in comp_ds.data_vars.items():
                 if varname[:3] == "2d_":
-                    connectivity_matrix[
-                        da.values > 0
-                    ] = 1  # if a pos, non-zero value exits, make a connection!
+                    connectivity_matrix[da.values > 0] = (
+                        1  # if a pos, non-zero value exits, make a connection!
+                    )
 
     return connectivity_matrix
 

diff --git a/fine/aggregations/spatialAggregation/manager.py b/fine/aggregations/spatialAggregation/manager.py
@@ -1,5 +1,6 @@
 """Manager function that calls spatial grouping and aggregation algorithm. 
 """
+
 import os
 import logging
 import warnings

diff --git a/fine/aggregations/spatialAggregation/managerUtils.py b/fine/aggregations/spatialAggregation/managerUtils.py
@@ -1,6 +1,7 @@
 """
 Functions to assist spatial aggregation 
 """
+
 import warnings
 import numpy as np
 import pandas as pd

diff --git a/fine/aggregations/technologyAggregation/__init__.py b/fine/aggregations/technologyAggregation/__init__.py
@@ -3,5 +3,6 @@
 
 |br| @author: FINE Developer Team (FZJ IEK-3)
 """
+
 from .techAggregation import *
 from .techAggregationUtils import *
diff --git a/fine/aggregations/technologyAggregation/techAggregation.py b/fine/aggregations/technologyAggregation/techAggregation.py
@@ -1,6 +1,7 @@
 """
 Aggregation of RE technologies in every region.
 """
+
 import logging
 import numpy as np
 import xarray as xr

diff --git a/fine/aggregations/technologyAggregation/techAggregationUtils.py b/fine/aggregations/technologyAggregation/techAggregationUtils.py
@@ -1,6 +1,7 @@
 """
 Functions to assist technology aggregation algorithm.
 """
+
 import warnings
 import os
 import numpy as np

diff --git a/fine/component.py b/fine/component.py
@@ -3089,10 +3089,11 @@ def getEconomicsDesign(
                 # write costs into dataframe
                 # a) costs for complete intervals
                 for i in range(commisYear, commisYear + intervalsWithCompleteCosts):
-                    costContribution[(loc, compName)][
-                        (commisYear, i)
-                    ] = annuity * utils.annuityPresentValueFactor(
-                        esM, compName, loc, esM.investmentPeriodInterval
+                    costContribution[(loc, compName)][(commisYear, i)] = (
+                        annuity
+                        * utils.annuityPresentValueFactor(
+                            esM, compName, loc, esM.investmentPeriodInterval
+                        )
                     )
 
                 # b) costs for last economic interval
@@ -3159,14 +3160,15 @@ def getEconomicsDesign(
                             ]
                         )
                         if getOptValueCostType == "NPV":
-                            cost_results[ip].loc[
-                                compName, loc
-                            ] = cContrSum * utils.discountFactor(esM, ip, compName, loc)
+                            cost_results[ip].loc[compName, loc] = (
+                                cContrSum * utils.discountFactor(esM, ip, compName, loc)
+                            )
                         elif getOptValueCostType == "TAC":
-                            cost_results[ip].loc[
-                                compName, loc
-                            ] = cContrSum / utils.annuityPresentValueFactor(
-                                esM, compName, loc, esM.investmentPeriodInterval
+                            cost_results[ip].loc[compName, loc] = (
+                                cContrSum
+                                / utils.annuityPresentValueFactor(
+                                    esM, compName, loc, esM.investmentPeriodInterval
+                                )
                             )
                 return cost_results
             else:
@@ -3458,18 +3460,18 @@ def getEconomicsOperation(
 
             locCompIpCombinations = list(set([(x[0], x[1], x[2]) for x in var]))
             for loc, compName, year in locCompIpCombinations:
-                costContribution[(loc, compName)][
-                    (year, year)
-                ] = self.getLocEconomicsOperation(
-                    pyM,
-                    esM,
-                    fncType,
-                    factorNames,
-                    varName,
-                    loc,
-                    compName,
-                    year,
-                    getOptValue,
+                costContribution[(loc, compName)][(year, year)] = (
+                    self.getLocEconomicsOperation(
+                        pyM,
+                        esM,
+                        fncType,
+                        factorNames,
+                        varName,
+                        loc,
+                        compName,
+                        year,
+                        getOptValue,
+                    )
                 )
 
             # create dictionary with ip as key and a dataframe with
@@ -3737,12 +3739,14 @@ def setOptimalValues(self, esM, pyM, indexColumns, plantUnit, unitApp=""):
         tuples = list(
             map(
                 lambda x: (
-                    x[0],
-                    x[1],
-                    "[" + getattr(compDict[x[0]], plantUnit) + unitApp + "]",
-                )
-                if x[1] in ["capacity", "commissioning", "decommissioning"]
-                else x,
+                    (
+                        x[0],
+                        x[1],
+                        "[" + getattr(compDict[x[0]], plantUnit) + unitApp + "]",
+                    )
+                    if x[1] in ["capacity", "commissioning", "decommissioning"]
+                    else x
+                ),
                 tuples,
             )
         )
@@ -3871,9 +3875,9 @@ def setOptimalValues(self, esM, pyM, indexColumns, plantUnit, unitApp=""):
             commisOptVal_ = utils.formatOptimizationOutput(
                 commisValues, "designVariables", self.dimension, ip, compDict=compDict
             )
-            self._commissioningVariablesOptimum[
-                esM.investmentPeriodNames[ip]
-            ] = commisOptVal_
+            self._commissioningVariablesOptimum[esM.investmentPeriodNames[ip]] = (
+                commisOptVal_
+            )
             # Get and set optimal variable values for decommissioning
             decommisValues = decommisVar.get_values()
             decommisOptVal = utils.formatOptimizationOutput(
@@ -3882,9 +3886,9 @@ def setOptimalValues(self, esM, pyM, indexColumns, plantUnit, unitApp=""):
             decommisOptVal_ = utils.formatOptimizationOutput(
                 decommisValues, "designVariables", self.dimension, ip, compDict=compDict
             )
-            self._decommissioningVariablesOptimum[
-                esM.investmentPeriodNames[ip]
-            ] = decommisOptVal_
+            self._decommissioningVariablesOptimum[esM.investmentPeriodNames[ip]] = (
+                decommisOptVal_
+            )
 
             if capOptVal is not None:
                 # Check if the installed capacities are close to a bigM val

diff --git a/fine/conversion.py b/fine/conversion.py
@@ -447,13 +447,13 @@ def setAggregatedTimeSeriesData(self, data, ip):
             if self.fullCommodityConversionFactors[ip] != {}:
                 self.aggregatedCommodityConversionFactors[ip] = {}
                 for commod in self.fullCommodityConversionFactors[ip]:
-                    self.aggregatedCommodityConversionFactors[ip][
-                        commod
-                    ] = self.getTSAOutput(
-                        self.fullCommodityConversionFactors[ip][commod],
-                        "_commodityConversionFactorTimeSeries" + str(commod) + "_",
-                        data,
-                        ip,
+                    self.aggregatedCommodityConversionFactors[ip][commod] = (
+                        self.getTSAOutput(
+                            self.fullCommodityConversionFactors[ip][commod],
+                            "_commodityConversionFactorTimeSeries" + str(commod) + "_",
+                            data,
+                            ip,
+                        )
                     )
         else:
             # if depending on the commissioning year, iterate over the relevant commissioning years for the
@@ -1243,12 +1243,14 @@ def setOptimalValues(self, esM, pyM):
             tuples = list(
                 map(
                     lambda x: (
-                        x[0],
-                        x[1],
-                        x[2].replace("-", compDict[x[0]].physicalUnit),
-                    )
-                    if x[1] == "operation"
-                    else x,
+                        (
+                            x[0],
+                            x[1],
+                            x[2].replace("-", compDict[x[0]].physicalUnit),
+                        )
+                        if x[1] == "operation"
+                        else x
+                    ),
                     tuples,
                 )
             )

diff --git a/fine/expansionModules/transformationPath.py b/fine/expansionModules/transformationPath.py
@@ -215,10 +215,10 @@ def getStock(esM, mileStoneYear, nbOfRepresentedYears):
                     if getattr(stockComp, "capacityFix") is None:
                         if isinstance(compValues.loc[comp], pd.DataFrame):
                             stockComp.processedCapacityFix = {}
-                            stockComp.processedCapacityFix[
-                                0
-                            ] = utils.preprocess2dimData(
-                                compValues.loc[comp].fillna(value=-1), discard=False
+                            stockComp.processedCapacityFix[0] = (
+                                utils.preprocess2dimData(
+                                    compValues.loc[comp].fillna(value=-1), discard=False
+                                )
                             )
                         else:
                             # NOTE: Values of capacityMin and capacityMax are not overwritten.

diff --git a/fine/sourceSink.py b/fine/sourceSink.py
@@ -1271,12 +1271,14 @@ def setOptimalValues(self, esM, pyM):
             tuples = list(
                 map(
                     lambda x: (
-                        x[0],
-                        x[1],
-                        x[2].replace("-", compDict[x[0]].commodityUnit),
-                    )
-                    if x[1] == "operation"
-                    else x,
+                        (
+                            x[0],
+                            x[1],
+                            x[2].replace("-", compDict[x[0]].commodityUnit),
+                        )
+                        if x[1] == "operation"
+                        else x
+                    ),
                     tuples,
                 )
             )