Add API demo notebook

.readthedocs.yaml

@@ -5,7 +5,6 @@
 # Required
 version: 2
 
-
 # Set the version of Python and other tools you might need
 build:
   os: ubuntu-24.04
@@ -20,4 +19,4 @@ build:
     - make fetch-test-data
     - uv run ref datasets ingest --source-type cmip6 $READTHEDOCS_REPOSITORY_PATH/tests/test-data/sample-data/CMIP6
     # Run a strict build
-    - NO_COLOR=1 uv run mkdocs build --strict --site-dir $READTHEDOCS_OUTPUT/html
+    - unset NO_COLOR; FORCE_COLOR=1 uv run mkdocs build --strict --site-dir $READTHEDOCS_OUTPUT/html

changelog/466.docs.md

@@ -0,0 +1 @@
+Add a Jupyter notebook showing how to use the CMIP7 Assessment Fast Track website OpenAPI.

docs/how-to-guides/dataset-selection.py

@@ -5,7 +5,7 @@
 #       extension: .py
 #       format_name: percent
 #       format_version: '1.3'
-#       jupytext_version: 1.16.4
+#       jupytext_version: 1.17.1
 #   kernelspec:
 #     display_name: Python 3 (ipykernel)
 #     language: python
@@ -121,7 +121,7 @@ def display_groups(frames):
 
 
 # %% [markdown]
-
+#
 # ### Facet filters
 # The simplest data request is a `FacetFilter`.
 # This filters the data catalog to include only the data required for a given diagnostic run.
@@ -141,7 +141,7 @@ def display_groups(frames):
 display_groups(groups)
 
 # %% [markdown]
-
+#
 # ### Group by
 # The `group_by` field can be used to split the filtered data into multiple groups,
 # each of which has a unique set of values in the specified facets.
@@ -166,15 +166,13 @@ def display_groups(frames):
 
 
 # %% [markdown]
-
+#
 # ### Constraints
 # A data requirement can optionally specify `Constraint`s.
 # These constraints are applied to each group independently to modify a group or ignore it.
-# All constraints must hold for a group to be executed.
+# A group must not be empty after modification for it to be executed.
 #
-# One type of constraint is a `GroupOperation`.
-# This constraint allows for the manipulation of a given group.
-# This can be used to remove datasets or include additional datasets from the catalog,
+# Constraints can be used to remove datasets or include additional datasets from the catalog,
 # which is useful to select common datasets for all groups (e.g. cell areas).
 #
 # Below, an `IncludeTas` GroupOperation is included which adds the corresponding `tas` dataset to each group.
@@ -187,6 +185,8 @@ def apply(self, group: pd.DataFrame, data_catalog: pd.DataFrame) -> pd.DataFrame
         tas = data_catalog[
             (data_catalog["variable_id"] == "tas")
             & data_catalog["source_id"].isin(group["source_id"].unique())
+            & data_catalog["experiment_id"].isin(group["experiment_id"].unique())
+            & data_catalog["member_id"].isin(group["member_id"].unique())
         ]
 
         return pd.concat([group, tas])
@@ -195,7 +195,7 @@ def apply(self, group: pd.DataFrame, data_catalog: pd.DataFrame) -> pd.DataFrame
 data_requirement = DataRequirement(
     source_type=SourceDatasetType.CMIP6,
     filters=(FacetFilter(facets={"frequency": "mon"}),),
-    group_by=("variable_id", "source_id", "member_id"),
+    group_by=("variable_id", "source_id", "member_id", "experiment_id"),
     constraints=(IncludeTas(),),
 )
 
@@ -205,26 +205,26 @@ def apply(self, group: pd.DataFrame, data_catalog: pd.DataFrame) -> pd.DataFrame
 
 
 # %% [markdown]
-# In addition to operations, a `GroupValidator` constraint can be specified.
-# This validator is used to determine if a group is valid or not.
-# If the validator does not return True, then the group is excluded from the list of groups for execution.
+# In addition to operations adding datasets, it is also possible to remove datasets.
 
 
 # %%
 class AtLeast2:
-    def validate(self, group: pd.DataFrame) -> bool:
-        return len(group["instance_id"].drop_duplicates()) >= 2
+    def apply(self, group: pd.DataFrame, data_catalog: pd.DataFrame) -> pd.DataFrame:
+        if len(group["variable_id"].drop_duplicates()) >= 2:
+            return group
+        return group.loc[[]]
 
 
 # %% [markdown]
 # Here we add a simple validator which ensures that at least 2 unique datasets are present.
-# This removes the tas-only group from above.
+# This removes the groups from above where tas was not available.
 
 # %%
 data_requirement = DataRequirement(
     source_type=SourceDatasetType.CMIP6,
     filters=(FacetFilter(facets={"frequency": "mon"}),),
-    group_by=("variable_id", "source_id", "member_id"),
+    group_by=("variable_id", "source_id", "member_id", "experiment_id"),
     constraints=(IncludeTas(), AtLeast2()),
 )
 

docs/how-to-guides/using-pre-computed-results.py

@@ -0,0 +1,285 @@
+# ---
+# jupyter:
+#   jupytext:
+#     text_representation:
+#       extension: .py
+#       format_name: percent
+#       format_version: '1.3'
+#       jupytext_version: 1.17.1
+#   kernelspec:
+#     display_name: Python 3 (ipykernel)
+#     language: python
+#     name: python3
+# ---
+
+# %% [markdown]
+# # Using pre-computed results
+#
+# Results computed by the CMIP7 Assessment Fast Track Rapid Evaluation Framework are available from
+# the website: https://dashboard.climate-ref.org and the associated API: https://api.climate-ref.org.
+# This API provides an [OpenAPI](https://www.openapis.org) schema that documents what queries are available.
+# The API documentation can be viewed at: https://api.climate-ref.org/docs.
+#
+# This Jupyter notebook shows how to use this API to download pre-computed results and use those to do
+# your own analyses.
+
+# %% [markdown]
+# ## Generate and install
+#
+# We start by generating and installing a Python package for interacting with the API
+# from the OpenAPI-compatible [schema](https://api.climate-ref.org/api/v1/openapi.json).
+
+# %%
+# !uvx --quiet --from openapi-python-client openapi-python-client generate --url https://api.climate-ref.org/api/v1/openapi.json --meta setup --output-path climate_ref_client --overwrite
+
+# %%
+# !pip install --quiet ./climate_ref_client
+
+# %% [markdown]
+# ## Set up the notebook
+#
+# Import some libraries and load the [rich](https://rich.readthedocs.io/en/latest/introduction.html)
+# Jupyter notebook extension for conveniently viewing the large data structures produced by the client
+# package.
+
+# %%
+from pathlib import Path
+
+import cartopy.crs
+import matplotlib.pyplot as plt
+import pandas as pd
+import requests
+import seaborn as sns
+import xarray as xr
+from climate_rapid_evaluation_framework_client import Client
+from climate_rapid_evaluation_framework_client.api.diagnostics import (
+    diagnostics_list,
+    diagnostics_list_metric_values,
+)
+from climate_rapid_evaluation_framework_client.api.executions import executions_get
+from climate_rapid_evaluation_framework_client.models.metric_value_type import (
+    MetricValueType,
+)
+from IPython.display import Markdown
+from pandas_indexing import formatlevel
+
+# %%
+# %load_ext rich
+
+# %% [markdown]
+# ## View the available diagnostics
+#
+# We start by setting up a client for interacting with the server:
+
+# %%
+client = Client("https://api.climate-ref.org")
+
+# %% [markdown]
+# Retrieve the available diagnostics from the server, and inspect the first one:
+
+# %%
+diagnostics = diagnostics_list.sync(client=client).data
+diagnostics[0]
+
+# %% [markdown]
+# To get an idea of what is available, we create a list of all diagnostics
+# with short descriptions (a full overview is available in Appendix C of
+# [Hoffman et al., 2025](https://doi.org/10.5194/egusphere-2025-2685)):
+
+# %%
+txt = ""
+for diagnostic in sorted(diagnostics, key=lambda diagnostic: diagnostic.name):
+    title = f"### {diagnostic.name}"
+    description = diagnostic.description.strip()
+    if not description.endswith("."):
+        description += "."
+    if diagnostic.aft_link:
+        description += f" {diagnostic.aft_link.short_description.strip()}"
+        if not description.endswith("."):
+            description += "."
+        if (aft_description := diagnostic.aft_link.description.strip()) != "nan":
+            description += f" {aft_description}"
+        if not description.endswith("."):
+            description += "."
+    txt += f"{title}\n{description}\n\n"
+Markdown(txt)
+
+# %% [markdown]
+# ## Metrics
+#
+# Many of the diagnostics provide "metric" values, single values that describe some property
+# of a model. Here we show how to access these values and create a plot.
+
+# %%
+# Select the "Atlantic Meridional Overturning Circulation (RAPID)"
+# diagnostic as an example
+diagnostic_name = "Atlantic Meridional Overturning Circulation (RAPID)"
+diagnostic = next(d for d in diagnostics if d.name == diagnostic_name)
+# Inspect an example value.
+diagnostics_list_metric_values.sync(
+    diagnostic.provider.slug,
+    diagnostic.slug,
+    value_type=MetricValueType.SCALAR,
+    client=client,
+).data[0]
+
+# %% [markdown]
+# Read the metric values into a Pandas DataFrame:
+
+# %%
+df = (
+    pd.DataFrame(
+        metric.dimensions.additional_properties | {"value": metric.value}
+        for metric in diagnostics_list_metric_values.sync(
+            diagnostic.provider.slug,
+            diagnostic.slug,
+            value_type=MetricValueType.SCALAR,
+            client=client,
+        ).data
+    )
+    .replace("None", pd.NA)
+    .drop_duplicates()
+)
+# Drop a few columns that appear to be the same for all entries of
+# particular diagnostic.
+df.drop(columns=["experiment_id", "metric", "region"], inplace=True)
+# Use the columns that do not contain the metric value for indexing
+df.set_index([c for c in df.columns if c != "value"], inplace=True)
+df
+
+# %% [markdown]
+# and create a portrait diagram:
+
+# %%
+# Use the median metric value for models with multiple ensemble
+# members to keep the figure readable.
+df = df.groupby(level=["source_id", "grid_label", "statistic"]).median()
+# Convert df to a "2D" dataframe for use with the seaborn heatmap plot
+df_2D = (
+    formatlevel(df, model="{source_id}.{grid_label}", drop=True)
+    .reset_index()
+    .pivot(columns="statistic", index="model", values="value")
+)
+figure, ax = plt.subplots(figsize=(5, 8))
+_ = sns.heatmap(
+    df_2D / df_2D.median(),
+    annot=df_2D,
+    cmap="viridis",
+    linewidths=0.5,
+    ax=ax,
+    cbar_kws={"label": "Color indicates value relative to the median"},
+)
+# %% [markdown]
+# ## Series
+#
+# Many of the diagnostics provide "series" values, a range of values along with an index
+# that describe some property of a model. Here we show how to access these values and create a plot.
+
+# %%
+# Select the "Sea Ice Area Basic Metrics" diagnostic as an example
+diagnostic_name = "Sea Ice Area Basic Metrics"
+diagnostic = next(d for d in diagnostics if d.name == diagnostic_name)
+# Inspect an example series value:
+diagnostics_list_metric_values.sync(
+    diagnostic.provider.slug,
+    diagnostic.slug,
+    value_type=MetricValueType.SERIES,
+    client=client,
+).data[0]
+
+# %% [markdown]
+# Read the metric values into a Pandas DataFrame:
+
+# %%
+statistic_name = "20-year average seasonal cycle"
+value_name = "sea ice area (1e6 km2)"
+df = pd.DataFrame(
+    metric.dimensions.additional_properties | {value_name: value, "month": int(month)}
+    for metric in diagnostics_list_metric_values.sync(
+        diagnostic.provider.slug,
+        diagnostic.slug,
+        value_type=MetricValueType.SERIES,
+        client=client,
+    ).data
+    if metric.dimensions.additional_properties["statistic"].startswith(statistic_name)
+    for value, month in zip(metric.values, metric.index)
+    if value < 1e10  # Ignore some invalid values.
+)
+df
+
+# %% [markdown]
+# and create a plot:
+
+# %%
+_ = sns.relplot(
+    data=df.sort_values("source_id"),
+    x="month",
+    y=value_name,
+    col="region",
+    hue="source_id",
+    kind="line",
+)
+# %% [markdown]
+# ## Files
+#
+# Many of the diagnostics produce NetCDF files that can be used for further analysis or custom plotting.
+# We will look at the global warming levels diagnostic and create our own figure using the available data.
+#
+# Each diagnostic can be run (executed) multiple times with different input data. The global warmings
+# levels diagnostic has been executed several times, leading to multiple "execution groups":
+
+# %%
+diagnostic_name = "Climate at Global Warming Levels"
+diagnostic = next(d for d in diagnostics if d.name == diagnostic_name)
+[executions_get.sync(g, client=client).key for g in diagnostic.execution_groups]
+
+# %% [markdown]
+# Let's select the "ssp585" scenario and look at the output files that were produced:
+
+# %%
+for group in diagnostic.execution_groups:
+    execution = executions_get.sync(group, client=client)
+    if execution.key.endswith("ssp585"):
+        ssp585_outputs = execution.latest_execution.outputs
+        break
+else:
+    msg = "Failed to find the ssp585 execution group"
+    raise ValueError(msg)
+[o.filename for o in ssp585_outputs]
+
+# %% [markdown]
+# Select one of the output files and inspect it:
+
+# %%
+filename = "tas/plot_gwl_stats/CMIP6_mm_mean_2.0.nc"
+file = next(f for f in ssp585_outputs if f.filename.endswith(filename))
+file
+
+# %% [markdown]
+# Download the file and open it with `xarray`:
+
+# %%
+local_file = Path(Path(file.filename).name)
+local_file.write_bytes(requests.get(file.url, timeout=120).content)
+ds = xr.open_dataset(local_file).drop_vars("cube_label")
+ds
+
+# %% [markdown]
+# Create our own plot:
+
+# %%
+plot = ds.tas.plot.contourf(
+    cmap="viridis",
+    vmin=-30,
+    vmax=30,
+    levels=11,
+    figsize=(12, 5),
+    transform=cartopy.crs.PlateCarree(),
+    subplot_kws={
+        "projection": cartopy.crs.Orthographic(
+            central_longitude=-100,
+            central_latitude=40,
+        ),
+    },
+)
+_ = plot.axes.coastlines()