Rename base periods to appropriate names (#556)

benchmark/EU/profiles-rep-periods-availability.csv

@@ -1,5 +1,5 @@
 ,,,p.u.
-profile_name,rep_period,time_step,value
+profile_name,rep_period,timestep,value
 NL_Wind_Onshore,1,1,0.007717831
 NL_Wind_Onshore,1,2,0.006234769
 NL_Wind_Onshore,1,3,0.007409822

benchmark/EU/profiles-rep-periods-demand.csv

@@ -1,5 +1,5 @@
 ,,,p.u.
-profile_name,rep_period,time_step,value
+profile_name,rep_period,timestep,value
 NL_E_Demand,1,1,0.68144623
 NL_E_Demand,1,2,0.666893812
 NL_E_Demand,1,3,0.656786995

benchmark/EU/profiles-rep-periods-inflows.csv

@@ -1,5 +1,5 @@
 ,,,p.u.
-profile_name,rep_period,time_step,value
+profile_name,rep_period,timestep,value
 BE_Hydro,1,1,0.4007930950734051
 BE_Hydro,1,2,0.4007930950734051
 BE_Hydro,1,3,0.4007930950734051

benchmark/EU/rep-periods-data.csv

@@ -1,3 +1,3 @@
 ,hours,,hours
-id,weight,num_time_steps,resolution
+id,weight,num_timesteps,resolution
 1,1,8760,1.0

benchmark/profiling.jl

@@ -36,7 +36,7 @@ end
 
 #%%
 
-@time graph, representative_periods, base_periods =
+@time graph, representative_periods, timeframe =
     create_graph_and_representative_periods_from_csv_folder(input_dir);
 @benchmark create_graph_and_representative_periods_from_csv_folder($input_dir)
 # @profview create_graph_and_representative_periods_from_csv_folder(input_dir);
@@ -50,17 +50,17 @@ end
 #%%
 
 @time dataframes =
-    construct_dataframes(graph, representative_periods, constraints_partitions, base_periods)
+    construct_dataframes(graph, representative_periods, constraints_partitions, timeframe)
 @benchmark construct_dataframes(
     $graph,
     $representative_periods,
     $constraints_partitions,
-    $base_periods,
+    $timeframe,
 )
-# @profview construct_dataframes($graph, $representative_periods, $constraints_partitions, $base_periods)
+# @profview construct_dataframes($graph, $representative_periods, $constraints_partitions, $timeframe)
 
 #%%
 
-@time model = create_model(graph, representative_periods, dataframes, base_periods);
-@benchmark create_model($graph, $representative_periods, $dataframes, $base_periods)
-# @profview create_model(graph, representative_periods, dataframes, base_periods);
+@time model = create_model(graph, representative_periods, dataframes, timeframe);
+@benchmark create_model($graph, $representative_periods, $dataframes, $timeframe)
+# @profview create_model(graph, representative_periods, dataframes, timeframe);

docs/src/how-to-use.md

@@ -68,15 +68,15 @@ The `Missing` data meaning depends on the parameter, for instance:
 
 -   `investment_limit`: There is no investment limit.
 
-#### `assets-base-periods-profiles.csv` and `assets-rep-periods-profiles.csv`
+#### `assets-timeframe-profiles.csv` and `assets-rep-periods-profiles.csv`
 
-These files contain the reference to profiles for each asset at each base period step, or at each representative period.
+These files contain the reference to profiles for each asset at each period, or at each representative period.
 
 #### `flows-rep-periods-profiles.csv`
 
 Similar to their `asset` counterpart.
 
-#### `profiles-base-periods-<type>.csv` and `profiles-rep-periods-<type>.csv`
+#### `profiles-timeframe-<type>.csv` and `profiles-rep-periods-<type>.csv`
 
 For each `type` defined in either `assets-*-periods-profiles` or `flows-rep-periods-profiles`, one of these files must exist.
 They store the profile data as indexed by a profile name.
@@ -106,9 +106,9 @@ The table below shows various results for different formats for a representative
 
 Similar to `assets-rep-periods-partitions.csv`, but for flows.
 
-#### `assets-base-periods-partitions.csv`
+#### `assets-timeframe-partitions.csv`
 
-Similar to their `rep-periods` counterpart, but for the base periods.
+Similar to their `rep-periods` counterpart, but for the timeframe.
 
 #### `rep-periods-data.csv`
 
@@ -147,7 +147,7 @@ It hides the complexity behind the energy problem, making the usage more friendl
 -   `graph`: The [Graph](@ref) object that defines the geometry of the energy problem.
 -   `representative_periods`: A vector of [Representative Periods](@ref representative-periods).
 -   `constraints_partitions`: Dictionaries that connect pairs of asset and representative periods to [time partitions (vectors of time blocks)](@ref Partition).
--   `base_periods`: The number of periods of the `representative_periods`.
+-   `timeframe`: The number of periods of the `representative_periods`.
 -   `dataframes`: The data frames used to linearize the variables and constraints. These are used internally in the model only.
 -   `model`: A JuMP.Model object representing the optimization model.
 -   `solved`: A boolean indicating whether the `model` has been solved or not.
@@ -161,7 +161,7 @@ It hides the complexity behind the energy problem, making the usage more friendl
 
 The `EnergyProblem` can also be constructed using the minimal constructor below.
 
--   `EnergyProblem(graph, representative_periods, base_periods)`: Constructs a new `EnergyProblem` object with the given graph, representative periods, and base periods. The `constraints_partitions` field is computed from the `representative_periods`, and the other fields are initialized with default values.
+-   `EnergyProblem(graph, representative_periods, timeframe)`: Constructs a new `EnergyProblem` object with the given graph, representative periods, and timeframe. The `constraints_partitions` field is computed from the `representative_periods`, and the other fields are initialized with default values.
 
 See the [basic example tutorial](@ref basic-example) to see how these can be used.
 
@@ -227,9 +227,9 @@ The solution object is a NamedTuple with the following fields:
 -   `objective_value`: A Float64 with the objective value at the solution.
 -   `assets_investment[a]`: The investment for each asset, indexed on the investable asset `a`.
 -   `flows_investment[u, v]`: The investment for each flow, indexed on the investable flow `(u, v)`.
--   `flow[(u, v), rp, time_block]`: The flow value for a given flow `(u, v)` at a given representative period `rp`, and time block `time_block`. The list of time blocks is defined by `graph[(u, v)].partitions[rp]`.
--   `storage_level_intra_rp[a, rp, time_block]`: The storage level for the storage asset `a` within (intra) a representative period `rp` and a time block `time_block`. The list of time blocks is defined by `constraints_partitions`, which was used to create the model.
--   `storage_level_inter_rp[a, bp]`: The storage level for the storage asset `a` between (inter) representative periods in the base periods `bp`.
+-   `flow[(u, v), rp, timesteps_block]`: The flow value for a given flow `(u, v)` at a given representative period `rp`, and time block `timesteps_block`. The list of time blocks is defined by `graph[(u, v)].partitions[rp]`.
+-   `storage_level_intra_rp[a, rp, timesteps_block]`: The storage level for the storage asset `a` within (intra) a representative period `rp` and a time block `timesteps_block`. The list of time blocks is defined by `constraints_partitions`, which was used to create the model.
+-   `storage_level_inter_rp[a, pb]`: The storage level for the storage asset `a` between (inter) representative periods in the periods block `pb`.
 
 For tips on manipulating the solution, check the [tutorial](@ref solution-tutorial).
 

docs/src/tutorial.md

@@ -84,7 +84,7 @@ using TulipaEnergyModel
 
 input_dir = "../../test/inputs/Tiny" # hide
 # input_dir should be the path to Tiny
-graph, representative_periods, base_periods = create_graph_and_representative_periods_from_csv_folder(input_dir)
+graph, representative_periods, timeframe = create_graph_and_representative_periods_from_csv_folder(input_dir)
 ```
 
 To create the model we also need a time partition for the constraints.
@@ -99,13 +99,13 @@ The `constraints_partitions` has two dictionaries with the keys `:lowest_resolut
 Finally, we also need dataframes that store the linearized indexes of the variables.
 
 ```@example manual
-dataframes = construct_dataframes(graph, representative_periods, constraints_partitions, base_periods)
+dataframes = construct_dataframes(graph, representative_periods, constraints_partitions, timeframe)
 ```
 
 Now we can compute the model.
 
 ```@example manual
-model = create_model(graph, representative_periods, dataframes, base_periods)
+model = create_model(graph, representative_periods, dataframes, timeframe)
 ```
 
 Finally, we can compute the solution.
@@ -343,7 +343,7 @@ df = filter(
     energy_problem.dataframes[:flows],
     view = true,
 )
-[energy_problem.graph[u, v].flow[(rp, row.time_block)] for row in eachrow(df)]
+[energy_problem.graph[u, v].flow[(rp, row.timesteps_block)] for row in eachrow(df)]
 ```
 
 To create a vector with the all values of `storage_level_intra_rp` for a given `a` and `rp`, one can run
@@ -356,7 +356,7 @@ df = filter(
     energy_problem.dataframes[:lowest_storage_level_intra_rp],
     view = true,
 )
-[energy_problem.graph[a].storage_level_intra_rp[(rp, row.time_block)] for row in eachrow(df)]
+[energy_problem.graph[a].storage_level_intra_rp[(rp, row.timesteps_block)] for row in eachrow(df)]
 ```
 
 To create a vector with the all values of `storage_level_inter_rp` for a given `a`, one can run
@@ -368,7 +368,7 @@ df = filter(
     energy_problem.dataframes[:storage_level_inter_rp],
     view = true,
 )
-[energy_problem.graph[a].storage_level_inter_rp[row.base_period_block] for row in eachrow(df)]
+[energy_problem.graph[a].storage_level_inter_rp[row.periods_block] for row in eachrow(df)]
 ```
 
 ### The solution inside the dataframes object