Update filter usage to be more efficient (#618)

TulipaEnergy · Apr 30, 2024 · 179f064 · 179f064
1 parent 1e267eb
commit 179f064
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Showing 6 changed files with 26 additions and 16 deletions.
diff --git a/src/constraints/consumer.jl b/src/constraints/consumer.jl
@@ -22,7 +22,7 @@ function add_consumer_constraints!(
 )
 
     # - Balance constraint (using the lowest temporal resolution)
-    df = filter(row -> row.asset ∈ Ac, dataframes[:highest_in_out]; view = true)
+    df = filter(:asset => ∈(Ac), dataframes[:highest_in_out]; view = true)
     model[:consumer_balance] = [
         @constraint(
             model,

diff --git a/src/constraints/conversion.jl b/src/constraints/conversion.jl
@@ -20,7 +20,7 @@ function add_conversion_constraints!(
 )
 
     # - Balance constraint (using the lowest temporal resolution)
-    df = filter(row -> row.asset ∈ Acv, dataframes[:lowest]; view = true)
+    df = filter(:asset => ∈(Acv), dataframes[:lowest]; view = true)
     model[:conversion_balance] = [
         @constraint(
             model,

diff --git a/src/constraints/hub.jl b/src/constraints/hub.jl
@@ -20,7 +20,7 @@ function add_hub_constraints!(
 )
 
     # - Balance constraint (using the lowest temporal resolution)
-    df = filter(row -> row.asset ∈ Ah, dataframes[:highest_in_out]; view = true)
+    df = filter(:asset => ∈(Ah), dataframes[:highest_in_out]; view = true)
     model[:hub_balance] = [
         @constraint(
             model,

diff --git a/src/constraints/transport.jl b/src/constraints/transport.jl
@@ -70,7 +70,7 @@ function add_transport_constraints!(model, graph, df_flows, flow, Ft, flows_inve
     ]
 
     ## Constraints that define bounds for a transport flow Ft
-    df = filter(row -> (row.from, row.to) ∈ Ft, df_flows)
+    df = filter([:from, :to] => (from, to) -> (from, to) ∈ Ft, df_flows; view = true)
 
     # - Max transport flow limit
     model[:max_transport_flow_limit] = [

diff --git a/src/create-model.jl b/src/create-model.jl
@@ -194,15 +194,22 @@ function add_expression_terms_inter_rp_constraints!(
 
     # Incoming, outgoing flows, and profile aggregation
     for row_inter in eachrow(df_inter)
-        sub_df_map = filter(row -> row.period in row_inter.periods_block, df_map)
+        sub_df_map = filter(:period => in(row_inter.periods_block), df_map; view = true)
 
         for row_map in eachrow(sub_df_map)
-            sub_df_flows =
-                filter(row -> row.to == row_inter.asset && row.rp == row_map.rep_period, df_flows)
+            sub_df_flows = filter(
+                [:to, :rp] => (to, rp) -> to == row_inter.asset && rp == row_map.rep_period,
+                df_flows;
+                view = true,
+            )
             row_inter.incoming_flow +=
                 LinearAlgebra.dot(sub_df_flows.flow, sub_df_flows.efficiency) * row_map.weight
-            sub_df_flows =
-                filter(row -> row.from == row_inter.asset && row.rp == row_map.rep_period, df_flows)
+            sub_df_flows = filter(
+                [:from, :rp] =>
+                    (from, rp) -> from == row_inter.asset && rp == row_map.rep_period,
+                df_flows;
+                view = true,
+            )
             row_inter.outgoing_flow +=
                 LinearAlgebra.dot(sub_df_flows.flow, sub_df_flows.efficiency) * row_map.weight
             row_inter.inflows_profile_aggregation +=

diff --git a/src/io.jl b/src/io.jl
@@ -240,8 +240,9 @@ function create_internal_structures(table_tree::TableTree)
     for asset_profile_row in eachrow(table_tree.profiles.assets["rep-periods"]) # row = asset, profile_type, profile_name
         gp = DataFrames.groupby( # 3. group by RP
             filter(
-                row -> row.profile_name == asset_profile_row.profile_name, # 2. Filter profile_name
-                table_tree.profiles.data["rep-periods"][asset_profile_row.profile_type], # 1. Get the profile of given type
+                :profile_name => ==(asset_profile_row.profile_name), # 2. Filter profile_name
+                table_tree.profiles.data["rep-periods"][asset_profile_row.profile_type]; # 1. Get the profile of given type
+                view = true,
             ),
             :rep_period,
         )
@@ -256,10 +257,11 @@ function create_internal_structures(table_tree::TableTree)
     for flow_profile_row in eachrow(table_tree.profiles.flows)
         gp = DataFrames.groupby(
             filter(
-                row -> row.profile_name == flow_profile_row.profile_name,
-                table_tree.profiles.data["rep-periods"][flow_profile_row.profile_type],
+                :profile_name => ==(flow_profile_row.profile_name),
+                table_tree.profiles.data["rep-periods"][flow_profile_row.profile_type];
+                view = true,
             ),
-            :rep_period,
+            :rep_period;
         )
         for ((rp,), df) in pairs(gp)
             graph[flow_profile_row.from_asset, flow_profile_row.to_asset].rep_periods_profiles[(
@@ -271,8 +273,9 @@ function create_internal_structures(table_tree::TableTree)
 
     for asset_profile_row in eachrow(table_tree.profiles.assets["timeframe"]) # row = asset, profile_type, profile_name
         df = filter(
-            row -> row.profile_name == asset_profile_row.profile_name, # 2. Filter profile_name
-            table_tree.profiles.data["timeframe"][asset_profile_row.profile_type], # 1. Get the profile of given type
+            :profile_name => ==(asset_profile_row.profile_name), # 2. Filter profile_name
+            table_tree.profiles.data["timeframe"][asset_profile_row.profile_type]; # 1. Get the profile of given type
+            view = true,
         )
         graph[asset_profile_row.asset].timeframe_profiles[asset_profile_row.profile_type] = df.value
     end