Add database schemas, convenience functions, and small tutorial

codecov.yml

@@ -0,0 +1,9 @@
+coverage:
+  status:
+    project:
+      default:
+        target: 95%
+    patch:
+      default:
+        target: 95%
+  range: "90...95"

docs/Project.toml

@@ -1,7 +1,9 @@
 [deps]
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
+DuckDB = "d2f5444f-75bc-4fdf-ac35-56f514c445e1"
 LiveServer = "16fef848-5104-11e9-1b77-fb7a48bbb589"
+Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
 TulipaClustering = "314fac8b-c762-4aa3-9d12-851379729163"
 
 [compat]

docs/src/10-tutorial.md

@@ -0,0 +1,177 @@
+# Tutorial
+
+## Explanation
+
+To simplify, let's consider a single profile, for a single year.
+Let's denote it as $p_i$, where $i = 1,\dots,N$.
+The clustering process consists of:
+
+1. Split `N` into (let's assume equal) _periods_ of size `m = period_duration`.
+   We can rename $p_i$ as
+
+   $$p_{j,k}, \qquad \text{where} \qquad j = 1,\dots,m, \quad k = 1,\dots,N/m.$$
+2. Compute `num_rps` representative periods
+
+   $$r_{j,\ell}, \qquad \text{where} \qquad j = 1,\dots,m, \qquad \ell = 1,\dots,\text{num\_rps}.$$
+3. During computation of the representative periods, we obtained weight
+   $w_{k,\ell}$ between the period $k$ and the representative period $\ell$,
+   such that
+
+   $$p_{j,k} = \sum_{\ell = 1}^{\text{num\_rps}} r_{j,\ell} \ w_{k,\ell}, \qquad \forall j = 1,\dots,m, \quad k = 1,\dots,N/m$$
+
+## High level API/DuckDB API
+
+!!! note "High level API"
+    This tutorial focuses on the highest level of the API, which requires the
+    use of a DuckDB connection.
+
+The high-level API of TulipaClustering focuses on using TulipaClustering as part of the [Tulipa workflow](https://tulipaenergy.github.io/TulipaEnergyModel.jl/stable/).
+This API consists of three main functions: [`transform_wide_to_long!`](@ref), [`cluster!`](@ref), and [`dummy_cluster!`](@ref).
+In this tutorial we'll use all three.
+
+Normally, you will have the DuckDB connection from the larger Tulipa workflow,
+so here we will create a temporary connection with fake data to show an example
+of the workflow. You can look into the source code of this documentation to see
+how to create this fake data.
+
+```@setup duckdb_example
+using DuckDB
+
+connection = DBInterface.connect(DuckDB.DB)
+DuckDB.query(
+  connection,
+  "CREATE TABLE profiles_wide AS
+  SELECT
+      2030 AS year,
+          i + 24 * (p - 1) AS timestep,
+      4 + 0.3 * cos(4 * 3.14 * i / 24) + random() * 0.2 AS avail,
+      solar_rand * greatest(0, (5 + random()) * cos(2 * 3.14 * (i - 12.5) / 24)) AS solar,
+      3.6 + 3.6 * sin(3.14 * i / 24) ^ 2 * (1 + 0.3 * random()) AS demand,
+  FROM
+    generate_series(1, 24) AS _timestep(i)
+  CROSS JOIN (
+    SELECT p, RANDOM() AS solar_rand
+    FROM generate_series(1, 7 * 4) AS _period(p)
+  )
+  ORDER BY timestep
+  ",
+)
+```
+
+Here is the content of that connection:
+
+```@example duckdb_example
+using DataFrames, DuckDB
+
+nice_query(str) = DataFrame(DuckDB.query(connection, str))
+nice_query("show tables")
+```
+
+And here is the first rows of `profiles_wide`:
+
+```@example duckdb_example
+nice_query("from profiles_wide limit 10")
+```
+
+And finally, this is the plot of the data:
+
+```@example duckdb_example
+using Plots
+
+table = DuckDB.query(connection, "from profiles_wide")
+plot(size=(800, 400))
+timestep = [row.timestep for row in table]
+for profile_name in (:avail, :solar, :demand)
+    value = [row[profile_name] for row in table]
+    plot!(timestep, value, lab=string(profile_name))
+end
+plot!()
+```
+
+## Transform a wide profiles table into a long table
+
+!!! warning "Required"
+    The long table format is a requirement of TulipaClustering, even for the dummy clustering example.
+
+In this context, a wide table is a table where each new profile occupies a new column. A long table is a table where the profile names are stacked in a column with the corresponding values in a separate column.
+Given the name of the source table (in this case, `profiles_wide`), we can create a long table with the following call:
+
+```@example duckdb_example
+using TulipaClustering
+
+# We will save the output in the 'input' schema
+DuckDB.query(connection, "CREATE SCHEMA IF NOT EXISTS input")
+transform_wide_to_long!(connection, "profiles_wide", "input.profiles")
+
+nice_query("FROM input.profiles LIMIT 10")
+```
+
+Here, we decided to save the long profiles table in the `input` schema to use in the clustering below.
+
+## Dummy Clustering
+
+A dummy cluster will essentially ignore the clustering and create the necessary tables for the next steps in the Tulipa workflow.
+
+```@example duckdb_example
+for table_name in (
+    "cluster.rep_periods_data",
+    "cluster.rep_periods_mapping",
+    "cluster.profiles_rep_periods",
+    "cluster.timeframe_data",
+)
+    DuckDB.query(connection, "DROP TABLE IF EXISTS $table_name")
+end
+
+clusters = dummy_cluster!(connection)
+
+nice_query("FROM cluster.rep_periods_data LIMIT 5")
+```
+
+```@example duckdb_example
+nice_query("FROM cluster.rep_periods_mapping LIMIT 5")
+```
+
+```@example duckdb_example
+nice_query("FROM cluster.profiles_rep_periods LIMIT 5")
+```
+
+```@example duckdb_example
+nice_query("FROM cluster.timeframe_data LIMIT 5")
+```
+
+## Clustering
+
+We can perform a real clustering by using the [`cluster!`](@ref) function with two extra arguments (see [Explanation](@ref) for their deeped meaning):
+
+- `period_duration`: How long are the split periods;
+- `num_rps`: How many representative periods.
+
+```@example duckdb_example
+period_duration = 24
+num_rps = 3
+
+for table_name in (
+    "cluster.rep_periods_data",
+    "cluster.rep_periods_mapping",
+    "cluster.profiles_rep_periods",
+    "cluster.timeframe_data",
+)
+    DuckDB.query(connection, "DROP TABLE IF EXISTS $table_name")
+end
+
+clusters = cluster!(connection, period_duration, num_rps)
+
+nice_query("FROM cluster.rep_periods_data LIMIT 5")
+```
+
+```@example duckdb_example
+nice_query("FROM cluster.rep_periods_mapping LIMIT 5")
+```
+
+```@example duckdb_example
+nice_query("FROM cluster.profiles_rep_periods LIMIT 5")
+```
+
+```@example duckdb_example
+nice_query("FROM cluster.timeframe_data LIMIT 5")
+```

src/TulipaClustering.jl

@@ -12,8 +12,10 @@ using SparseArrays
 using Statistics
 
 include("structures.jl")
+include("data-validation.jl")
 include("io.jl")
 include("weight_fitting.jl")
 include("cluster.jl")
+include("convenience.jl")
 
 end