dump lower bounds for packages in schedule.json

spec-0000/SPEC0_versions.py

@@ -1,4 +1,5 @@
 import requests
+import json
 import collections
 from datetime import datetime, timedelta
 
@@ -148,8 +149,32 @@ def get_release_dates(package, support_time=plus24):
 df = pd.DataFrame(data, columns=["package", "version", "release", "drop"])
 
 df["quarter"] = df["drop"].dt.to_period("Q")
+df["new_min_version"] = (
+    df[["package", "version", "quarter"]].groupby("package").shift(-1)["version"]
+)
+dq = df.set_index(["quarter", "package"]).sort_index().dropna()
+
+new_min_versions = (
+    dq.groupby(["quarter", "package"]).agg({"new_min_version": "max"}).reset_index()
+)
+
+
+# we want to build a dict with the structure [{start_date: timestamp, packages: {package: lower_bound}}]
+new_min_versions_list = []
+for q, packages in new_min_versions.groupby("quarter"):
+    package_lower_bounds = {
+        p: str(v) for p, v in packages.drop("quarter", axis=1).itertuples(index=False)
+    }
+    # jq is really insistent the Z should be there
+    quarter_start_time_str = str(q.start_time.isoformat()) + "Z"
+    new_min_versions_list.append(
+        {"start_date": quarter_start_time_str, "packages": package_lower_bounds}
+    )
+
+print("Saving drop schedule to schedule.json")
 
-dq = df.set_index(["quarter", "package"]).sort_index()
+with open("schedule.json", "w") as f:
+    f.write(json.dumps(new_min_versions_list, sort_keys=True))
 
 
 print("Saving drop schedule to schedule.md")

spec-0000/index.md

@@ -97,6 +97,89 @@ You may want to delay the removal of support of an older Python version until yo
 
 {{< include-md "schedule.md" >}}
 
+### Automatically updating dependencies
+
+To help projects stay compliant with this spec, we additionally provide a `schedule.json` file that can be used by CI systems to deterime new version boundaries. The structure of the file is as follows:
+
+```json
+[
+  {
+    "start_date": "iso8601_timestamp",
+    "packages": {
+      "package_name": "version"
+    }
+  }
+]
+```
+
+All information in the json file is in a string format that should be easy to use. The date is the first timestamp of the relevant quarter. Thus a workflow for using this file could be:
+
+1. fetch `schedule.json`
+2. determine maximum date that is smaller than current date
+3. update packages listed with new minimum versions
+
+You can obtain the new versions you should set by using this `jq` expression:
+
+```sh
+
+jq 'map(select(.start_date |fromdateiso8601 |tonumber  < now))| sort_by("start_date") | reverse | .[0].packages ' schedule.json
+
+```
+
+If you use a package manager like pixi you could update the dependencies with a bash script like this:
+
+```sh
+curl -Ls -o schedule.json https://raw.githubusercontent.com/scientific-python/specs/main/spec-0000/schedule.json
+for line in $(jq 'map(select(.start_date |fromdateiso8601 |tonumber  < now))| sort_by("start_date") | reverse | .[0].packages | to_entries | map(.key + ":" + .value)[]' --raw-output schedule.json); do
+	package=$(echo "$line" | cut -d ':' -f 1)
+	version=$(echo "$line" | cut -d ':' -f 2)
+  if pixi list -x "^$package" &>/dev/null| grep "No packages" -q; then
+  	pixi add "$package>=$version";
+  fi
+done
+
+```
+
+You can create a GH action that runs every quarter like so
+
+```yaml
+name: Update SPEC 0 dependencies
+
+on:
+  schedule:
+    # At 00:00 on day-of-month 1 in every 3rd month. (i.e. every quarter)
+    - cron: "0 0 1 */3 *"
+  # on demand
+  workflow_dispatch:
+
+jobs:
+  auto-update:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - name: update
+        run: |
+
+          curl -Ls -o schedule.json https://raw.githubusercontent.com/scientific-python/specs/main/spec-0000/schedule.json
+          for line in $(jq 'map(select(.start_date |fromdateiso8601 |tonumber  < now))| sort_by("start_date") | reverse | .[0].packages | to_entries | map(.key + ":" + .value)[]' --raw-output schedule.json); do
+          	package=$(echo "$line" | cut -d ':' -f 1)
+          	version=$(echo "$line" | cut -d ':' -f 2)
+
+            # don't add packages that aren't already added.
+            if pixi list -x "^$package" &>/dev/null| grep "No packages" -q; then
+            	pixi add "$package>=$version";
+            fi
+          done
+      - uses: peter-evans/create-pull-request@v6
+        with:
+          token: ${{ secrets.GITHUB_TOKEN }}
+          branch: update/spec-0
+          title: Drop support for packages according to SPEC 0
+          commit-message: "Update dependencies"
+          body: Increase minimum version of .
+          author: "GitHub <[email protected]>"
+```
+
 ## Notes
 
 - This document builds on [NEP 29](https://numpy.org/neps/nep-0029-deprecation_policy.html), which describes several alternatives including ad hoc version support, all CPython supported versions, default version on Linux distribution, N minor versions of Python, and time window from the X.Y.1 Python release.

spec-0000/schedule.json

@@ -0,0 +1 @@
+[{"packages": {"ipython": "8.5.0", "networkx": "3.0", "numpy": "1.24.0", "scikit-learn": "1.2.0", "zarr": "2.13.0"}, "start_date": "2024-04-01T00:00:00Z"}, {"packages": {"ipython": "8.6.0", "matplotlib": "3.7.0", "pandas": "2.0.0", "scipy": "1.10.0", "xarray": "2022.10.0", "zarr": "2.14.0"}, "start_date": "2024-07-01T00:00:00Z"}, {"packages": {"ipython": "8.8.0", "numpy": "1.25.0", "python": "3.11", "scikit-learn": "1.3.0", "xarray": "2023.1.0"}, "start_date": "2024-10-01T00:00:00Z"}, {"packages": {"ipython": "8.13.0", "matplotlib": "3.8.0", "networkx": "3.1", "scikit-image": "0.21.0", "scipy": "1.11.0", "xarray": "2023.4.0", "zarr": "2.15.0"}, "start_date": "2025-01-01T00:00:00Z"}, {"packages": {"ipython": "8.15.0", "networkx": "3.2", "numpy": "1.26.0", "pandas": "2.1.0", "scikit-image": "0.22.0", "scikit-learn": "1.4.0", "scipy": "1.12.0", "xarray": "2023.7.0", "zarr": "2.16.0"}, "start_date": "2025-04-01T00:00:00Z"}, {"packages": {"ipython": "8.17.0", "matplotlib": "3.9.0", "numpy": "2.0.0", "pandas": "2.2.0", "xarray": "2023.10.0", "zarr": "2.17.0"}, "start_date": "2025-07-01T00:00:00Z"}, {"packages": {"ipython": "8.20.0", "networkx": "3.3", "python": "3.12", "scikit-image": "0.23.0", "xarray": "2024.1.0"}, "start_date": "2025-10-01T00:00:00Z"}, {"packages": {"ipython": "8.24.0", "scikit-learn": "1.5.0", "scipy": "1.13.0", "xarray": "2024.5.0", "zarr": "2.18.0"}, "start_date": "2026-01-01T00:00:00Z"}, {"packages": {"ipython": "8.27.0", "matplotlib": "3.10.0", "networkx": "3.4", "numpy": "2.1.0", "scikit-image": "0.25.0", "scikit-learn": "1.6.0", "scipy": "1.15.0", "xarray": "2024.7.0", "zarr": "3.0.0"}, "start_date": "2026-04-01T00:00:00Z"}, {"packages": {"ipython": "8.28.0", "numpy": "2.2.0", "xarray": "2024.10.0"}, "start_date": "2026-07-01T00:00:00Z"}, {"packages": {"ipython": "8.32.0", "python": "3.13", "xarray": "2025.1.0"}, "start_date": "2026-10-01T00:00:00Z"}]