Add timeseries

.gitignore

@@ -26,4 +26,5 @@ Manifest.toml
 *.out
 *.ok
 debug_psrclasses
+*.gz
 *.sqlite

docs/make.jl

@@ -23,6 +23,7 @@ makedocs(;
         "PSRDatabaseSQLite Overview" => String[
             "psrdatabasesqlite/introduction.md",
             "psrdatabasesqlite/rules.md",
+            "psrdatabasesqlite/time_series.md",
         ],
         "OpenStudy and OpenBinary Examples" => String[
             "examples/reading_parameters.md",

docs/src/psrdatabasesqlite/rules.md

@@ -132,10 +132,10 @@ CREATE TABLE HydroPlant_vector_GaugingStation(
 
 ```
 
-### Time Series
+### Time Series Files
 
-- All Time Series for the elements from a Collection should be stored in a Table
-- The Table name should be the same as the name of the Collection followed by `_timeseriesfiles`, as presented below
+- All Time Series files for the elements from a Collection should be stored in a Table
+- The Table name should be the same as the name of the Collection followed by `_time_series_files`, as presented below
 
 <p style="text-align: center"> COLLECTION_vector_ATTRIBUTE</p>
 
@@ -145,12 +145,36 @@ CREATE TABLE HydroPlant_vector_GaugingStation(
 Example:
 
 ```sql
-CREATE TABLE Plant_timeseriesfiles (
+CREATE TABLE Plant_time_series_files (
     generation TEXT,
     cost TEXT
 ) STRICT;
 ```
 
+### Time Series
+- Time Series stored in the database should be stored in a table with the name of the Collection followed by `_time_series_` and the name of the attribute group, as presented below.
+
+<p style="text-align: center"> COLLECTION_time_series_GROUP_OF_ATTRIBUTES</p>
+
+Notice that it is quite similar to the vector attributes, but without the `vector_index` column.
+Instead, a mandatory column named `date_time` should be created to store the date of the time series data.
+
+Example:
+
+```sql
+CREATE TABLE Resource_time_series_group1 (
+    id INTEGER, 
+    date_time TEXT NOT NULL,
+    some_vector1 REAL,
+    some_vector2 REAL,
+    FOREIGN KEY(id) REFERENCES Resource(id) ON DELETE CASCADE ON UPDATE CASCADE,
+    PRIMARY KEY (id, date_time)
+) STRICT; 
+```
+
+!!! tip
+For more information on how to handle time series data, please refer to the [Time Series](./time_series.md) section.
+
 ## Migrations
 
 Migrations are an important part of the `DatabaseSQLite` framework. They are used to update the database schema to a new version without the need to delete the database and create a new one from scratch. Migrations are defined by two separate `.sql` files that are stored in the `migrations` directory of the model. The first file is the `up` migration and it is used to update the database schema to a new version. The second file is the `down` migration and it is used to revert the changes made by the `up` migration. Migrations are stored in directories in the model and they have a specific naming convention. The name of the migration folder should be the number of the version (e.g. `/migrations/1/`).

docs/src/psrdatabasesqlite/time_series.md

@@ -0,0 +1,254 @@
+# Time Series
+
+It is possible to store time series data in your database. Time series in `PSRDatabaseSQLite` are very flexible. You can have missing values, and you can have sparse data. 
+
+There is a specific table format that must be followed. Consider the following example:
+
+```sql
+CREATE TABLE Resource (
+    id INTEGER PRIMARY KEY AUTOINCREMENT,
+    label TEXT UNIQUE NOT NULL
+) STRICT;
+
+CREATE TABLE Resource_time_series_group1 (
+    id INTEGER, 
+    date_time TEXT NOT NULL,
+    some_vector1 REAL,
+    some_vector2 REAL,
+    FOREIGN KEY(id) REFERENCES Resource(id) ON DELETE CASCADE ON UPDATE CASCADE,
+    PRIMARY KEY (id, date_time)
+) STRICT; 
+```
+
+It is mandatory for a time series to be indexed by a `date_time` column with the following format: `YYYY-MM-DD HH:MM:SS`. You can use the `Dates.jl` package for handling this format.
+
+```julia
+using Dates
+date = DateTime(2024, 3, 1) # 2024-03-01T00:00:00 (March 1st, 2024)
+```
+
+Notice that in this example, there are two value columns `some_vector1` and `some_vector2`. You can have as many value columns as you want. You can also separate the time series data into different tables, by creating a table `Resource_time_series_group2` for example.
+
+It is also possible to add more dimensions to your time series, such as `block` and `scenario`.
+
+```sql	
+CREATE TABLE Resource_time_series_group2 (
+    id INTEGER, 
+    date_time TEXT NOT NULL,
+    block INTEGER NOT NULL,
+    some_vector3 REAL,
+    some_vector4 REAL,
+    FOREIGN KEY(id) REFERENCES Resource(id) ON DELETE CASCADE ON UPDATE CASCADE,
+    PRIMARY KEY (id, date_time, block)
+) STRICT; 
+```
+
+## Rules 
+
+Time series in `PSRDatabaseSQLite` are very flexible. You can have missing values, and you can have sparse data. 
+
+If you are querying for a time series row entry that has a missing value, it first checks if there is a data with a `date_time` earlier than the queried `date_time`. If there is, it returns the value of the previous data. If there is no data earlier than the queried `date_time`, it returns a specified value according to the type of data you are querying.
+
+- For `Float64`, it returns `NaN`.
+- For `Int64`, it returns `typemin(Int)`.
+- For `String`, it returns `""` (empty String).
+- For `DateTime`, it returns `typemin(DateTime)`.
+
+For example, if you have the following data:
+
+| **Date** | **some_vector1(Float64)** | **some_vector2(Float64)** |
+|:--------:|:-----------:|:-----------:|
+|   2020   |      1.0      |   missing   |
+|   2021   |   missing   |      1.0      |
+|   2022   |      3.0      |   missing   |
+
+1. If you query for `some_vector1` at `2020`, it returns `1.0`. 
+2. If you query for `some_vector2` at `2020`, it returns `NaN`. 
+3. If you query for `some_vector1` at `2021`, it returns `1.0`. 
+4. If you query for `some_vector2` at `2021`, it returns `1.0`. 
+5. If you query for `some_vector1` at `2022`, it returns `3.0`. 
+6. If you query for `some_vector2` at `2022`, it returns `1.0`.
+
+
+## Inserting data
+
+When creating a new element that has a time series, you can pass this information via a `DataFrame`. Consider the collection `Resource` with the two time series tables `Resource_time_series_group1` and `Resource_time_series_group2`.
+
+```julia
+using DataFrames
+using Dates
+using PSRClassesInterface
+PSRDatabaseSQLite = PSRClassesInterface.PSRDatabaseSQLite
+
+db = PSRDatabaseSQLite.create_empty_db_from_schema(db_path, path_schema; force = true)
+
+PSRDatabaseSQLite.create_element!(db, "Configuration"; label = "Toy Case", value1 = 1.0)
+
+df_group1 = DataFrame(;
+        date_time = [DateTime(2000), DateTime(2001), DateTime(2002)],
+        some_vector1 = [missing, 1.0, 2.0],
+        some_vector2 = [1.0, missing, 5.0],
+    )
+
+df_group2 = DataFrame(;
+            date_time = [
+                DateTime(2000),
+                DateTime(2000),
+                DateTime(2000),
+                DateTime(2000),
+                DateTime(2001),
+                DateTime(2001),
+                DateTime(2001),
+                DateTime(2009),
+            ],
+            block = [1, 1, 1, 1, 2, 2, 2, 2],
+            some_vector3 = [1.0, 2.0, 3.0, 4.0, 1, 2, 3, 4],
+            some_vector4 = [1.0, 2.0, 3.0, 4.0, 1, 2, 3, 4],
+        )
+
+
+PSRDatabaseSQLite.create_element!(
+    db,
+    "Resource";
+    label = "Resource 1",
+    group1 = df_group1,
+    group2 = df_group2,
+)
+```
+
+It is also possible to insert a single row of a time series. This is useful when you want to insert a specific dimension entry. This way of inserting time series is less efficient than inserting a whole `DataFrame`.
+
+```julia
+using DataFrames
+using Dates
+using PSRClassesInterface
+PSRDatabaseSQLite = PSRClassesInterface.PSRDatabaseSQLite
+
+db = PSRDatabaseSQLite.create_empty_db_from_schema(db_path, path_schema; force = true)
+
+PSRDatabaseSQLite.create_element!(db, "Configuration"; label = "Toy Case", value1 = 1.0)
+
+PSRDatabaseSQLite.create_element!(
+    db,
+    "Resource";
+    label = "Resource 1"
+)
+
+PSRDatabaseSQLite.add_time_series_row!(
+    db,
+    "Resource",
+    "some_vector1",
+    "Resource 1",
+    10.0; # new value
+    date_time = DateTime(2000)
+)
+
+PSRDatabaseSQLite.add_time_series_row!(
+    db,
+    "Resource",
+    "some_vector1",
+    "Resource 1",
+    11.0; # new value
+    date_time = DateTime(2001)
+)
+```
+
+## Reading data
+
+You can read the information from the time series in two different ways.
+
+### Reading as a table
+First, you can read the whole time series table for a given value, as a `DataFrame`.
+
+```julia
+df = PSRDatabaseSQLite.read_time_series_table(
+    db,
+    "Resource",
+    "some_vector1",
+    "Resource 1",
+)
+```
+
+### Reading a single row
+
+It is also possible to read a single row of the time series in the form of an array. This is useful when you want to query a specific dimension entry.
+For this function, there are performance improvements when reading the data via caching the previous and next non-missing values. 
+
+```julia
+values = PSRDatabaseSQLite.read_time_series_row(
+    db,
+    "Resource",
+    "some_vector1",
+    Float64;
+    date_time = DateTime(2020)
+)
+```
+
+When querying a row, all values should non-missing. However, if there is a missing value, the function will return the previous non-missing value. And if even the previous value is missing, it will return a specified value according to the type of data you are querying.
+
+
+- For `Float64`, it returns `NaN`.
+- For `Int64`, it returns `typemin(Int)`.
+- For `String`, it returns `""` (empty String).
+- For `DateTime`, it returns `typemin(DateTime)`.
+
+For example, if you have the following data for the time series `some_vector1`:
+
+| **Date** | **Resource 1** | **Resource 2** |
+|:--------:|:-----------:|:-----------:|
+|   2020   |      1.0      |   missing   |
+|   2021   |   missing   |      1.0      |
+|   2022   |      3.0      |   missing   |
+
+1. If you query at `2020`, it returns `[1.0, NaN]`. 
+3. If you query at `2021`, it returns `[1.0, 1.0]`. 
+5. If you query at `2022`, it returns `[3.0, 1.0]`. 
+
+
+## Updating data
+
+When updating one of the entries of a time series for a given element and attribute, you need to specify the exact dimension values of the row you want to update. 
+
+
+For example, consider a time series that has `block` and `data_time` dimensions.
+
+```julia
+PSRDatabaseSQLite.update_time_series_row!(
+    db,
+    "Resource",
+    "some_vector3",
+    "Resource 1",
+    10.0; # new value
+    date_time = DateTime(2000),
+    block = 1
+)
+```
+
+## Deleting data
+
+You can delete the whole time series of an element for a given time series group.
+Consider the following table:
+
+```sql
+CREATE TABLE Resource_time_series_group1 (
+    id INTEGER, 
+    date_time TEXT NOT NULL,
+    some_vector1 REAL,
+    some_vector2 REAL,
+    FOREIGN KEY(id) REFERENCES Resource(id) ON DELETE CASCADE ON UPDATE CASCADE,
+    PRIMARY KEY (id, date_time)
+) STRICT; 
+```
+
+This table represents a "group" that stores two time series `some_vector1` and `some_vector2`. You can delete all the data from this group by calling the following function:
+
+```julia
+PSRDatabaseSQLite.delete_time_series!(
+    db,
+    "Resource",
+    "group1",
+    "Resource 1",
+)
+```
+
+When trying to read a time series that has been deleted, the function will return an empty `DataFrame`.

profiling/Project.toml

@@ -0,0 +1,3 @@
+[deps]
+PProf = "e4faabce-9ead-11e9-39d9-4379958e3056"
+Profile = "9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"

profiling/create_profile.jl

@@ -0,0 +1,12 @@
+# You should run the script from the profiling directory
+
+using Profile
+using PProf
+import Pkg
+root_path = dirname(@__DIR__)
+Pkg.activate(root_path)
+using PSRClassesInterface
+
+include("../script_time_controller.jl")
+@profile include("../script_time_controller.jl")
+pprof()

profiling/open_profile.jl

@@ -0,0 +1,8 @@
+# You should run the script from the profiling directory
+
+using Profile
+using PProf
+
+file_name = "profile.pb.gz"
+
+PProf.refresh(; file = file_name, webport = 57998)

src/PSRDatabaseSQLite/PSRDatabaseSQLite.jl

@@ -20,6 +20,7 @@ include("exceptions.jl")
 include("utils.jl")
 include("attribute.jl")
 include("collection.jl")
+include("time_controller.jl")
 include("database_sqlite.jl")
 include("create.jl")
 include("read.jl")

src/PSRDatabaseSQLite/attribute.jl

@@ -99,6 +99,18 @@ mutable struct VectorRelation{T} <: VectorAttribute
     end
 end
 
+mutable struct TimeSeries{T} <: VectorAttribute
+    id::String
+    type::Type{T}
+    default_value::Union{Missing, T}
+    not_null::Bool
+    group_id::String
+    parent_collection::String
+    table_where_is_located::String
+    dimension_names::Vector{String}
+    num_dimensions::Int
+end
+
 mutable struct TimeSeriesFile{T} <: ReferenceToFileAttribute
     id::String
     type::Type{T}