add missing grapher step

etl/steps/data/grapher/artificial_intelligence/2024-10-01/epoch.meta.yml

@@ -0,0 +1,18 @@
+definitions:
+  common:
+    unit: ''
+    short_unit: ''
+    display:
+      zeroDay: '1949-01-01'
+      yearIsDay: true
+
+tables:
+  epoch:
+    variables:
+      max_compute:
+        title: Maximum compute
+      max_data:
+        title: Maximum data
+      max_parameters:
+        title: Maximum parameters
+

etl/steps/data/grapher/artificial_intelligence/2024-10-01/epoch.py

@@ -0,0 +1,101 @@
+"""Load a garden dataset and create a grapher dataset."""
+
+import owid.catalog.processing as pr
+from owid.catalog import Table
+
+from etl.helpers import PathFinder, create_dataset, grapher_checks
+
+# Get paths and naming conventions for current step.
+paths = PathFinder(__file__)
+
+
+def run(dest_dir: str) -> None:
+    #
+    # Load inputs.
+    #
+    # Load garden dataset.
+    ds_garden = paths.load_dataset("epoch")
+
+    # Read table from garden dataset.
+    tb = ds_garden["epoch"].reset_index()
+    #
+    # Process data.
+    #
+    # Extract year from 'publication_date' and create a new 'year' column
+    tb["year"] = tb["publication_date"].dt.year
+
+    # For visualization purposes I am adding the rows with the maximum values of compute, data, and parameters in each year to the table as a separate "system". I don't want to do this in garden as it'd affect other datasets that depend on this one.
+    columns = {
+        "training_computation_petaflop": "compute",
+        "training_dataset_size__datapoints": "data",
+        "parameters": "parameters",
+    }
+    # Find maximum values for a given column (compute, data, params) per year, label them, and add summary rows.
+    for column, label in columns.items():
+        tb = find_max_label_and_concat(tb, column, label)
+
+    # Update metadata
+    for col in ["max_compute", "max_parameters", "max_data"]:
+        tb[col].metadata.origins = tb["system"].metadata.origins
+
+    # Drop year as we don't need it anymore
+    tb = tb.drop("year", axis=1)
+
+    # Rename for plotting model name as country in grapher
+    tb = tb.rename(columns={"system": "country", "days_since_1949": "year"})
+    tb = tb.format(["country", "year"])
+
+    #
+    # Save outputs.
+    #
+    # Create a new grapher dataset with the same metadata as the garden dataset.
+    ds_grapher = create_dataset(
+        dest_dir, tables=[tb], default_metadata=ds_garden.metadata, check_variables_metadata=True
+    )
+
+    #
+    # Checks.
+    #
+    grapher_checks(ds_grapher)
+
+    # Save changes in the new grapher dataset.
+    ds_grapher.save()
+
+
+def find_max_label_and_concat(tb, column, label):
+    """
+    Find maximum values for a given column per year, label them, and add summary rows.
+
+    This function:
+    1. Identifies rows with maximum values for the specified column in each year.
+    2. Labels these maximum value rows in a new column using their original system names.
+    3. Creates new summary rows for these maximum values.
+    4. Adds these new summary rows to the original table.
+
+    Note:
+    - Creates a new column named f"max_{label}" to indicate maximum values.
+    - Preserves original data and system names.
+    - Adds new summary rows with "system" set to f"Maximum {label}".
+    """
+    tb = tb.sort_values(by=["year"])  # Ensure the DataFrame is sorted by year
+    max_value = -float("inf")
+    rows_to_keep = []
+
+    for _, row in tb.iterrows():
+        if row[column] > max_value:
+            max_value = row[column]
+            rows_to_keep.append(row)
+
+    tb_filtered = Table(rows_to_keep)
+
+    idx = tb_filtered[[column, "year"]].fillna(0).groupby("year")[column].idxmax()
+
+    tb_filtered[f"max_{label}"] = "Other"
+    tb_filtered.loc[idx, f"max_{label}"] = f"Maximum {label}"
+
+    max_rows = tb_filtered.loc[idx].copy()
+    max_rows["system"] = f"Maximum {label}"
+
+    tb = pr.concat([tb, max_rows], ignore_index=True)
+
+    return tb