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import json
from datetime import datetime, timedelta
import pandas as pd
from spec0_action import CORE_PACKAGES, _get_feature_release_dates
PY_RELEASES = {
"3.8": "Oct 14, 2019",
"3.9": "Oct 5, 2020",
"3.10": "Oct 4, 2021",
"3.11": "Oct 24, 2022",
"3.12": "Oct 2, 2023",
"3.13": "Oct 7, 2024",
"3.14": "Oct 7, 2025",
}
PLUS_36_MONTHS = timedelta(days=365 * 3)
PLUS_24_MONTHS = timedelta(days=365 * 2)
# Release data
# We put the cutoff at 3 quarters ago - we do not use "just" -9 months
# to avoid the content of the quarter to change depending on when we
# generate this file during the current quarter.
CURRENT_DATE = pd.Timestamp.now(tz="UTC").tz_localize(None)
CURRENT_QUARTER_START = pd.Timestamp(
CURRENT_DATE.year, (CURRENT_DATE.quarter - 1) * 3 + 1, 1
)
CUTOFF = CURRENT_QUARTER_START - pd.DateOffset(months=9)
def get_release_dates(package, support_time=PLUS_24_MONTHS):
releases = {}
for version, release_date in _get_feature_release_dates(package):
# Pandas quarter calculations below use naive UTC dates.
release_date = release_date.replace(tzinfo=None)
releases[version] = {
"release_date": release_date,
"drop_date": release_date + support_time,
}
if not releases:
raise RuntimeError(f"Could not find feature releases for {package}")
return releases
package_releases = {
"python": {
version: {
"release_date": datetime.strptime(release_date, "%b %d, %Y"),
"drop_date": datetime.strptime(release_date, "%b %d, %Y") + PLUS_36_MONTHS,
}
for version, release_date in PY_RELEASES.items()
}
}
package_releases |= {package: get_release_dates(package) for package in CORE_PACKAGES}
# Filter all items whose drop_date are in the past
package_releases = {
package: {
version: dates
for version, dates in releases.items()
if dates["drop_date"] > CUTOFF
}
for package, releases in package_releases.items()
}
# Save Gantt chart
print("Saving Mermaid chart to chart.md")
with open("chart.md", "w") as fh:
fh.write(
"""gantt
dateFormat YYYY-MM-DD
axisFormat %m / %Y
title Support Window"""
)
for name, releases in package_releases.items():
fh.write(f"\n\nsection {name}")
fh.writelines(
f"\n{version} : {dates['release_date'].strftime('%Y-%m-%d')},{dates['drop_date'].strftime('%Y-%m-%d')}"
for version, dates in releases.items()
)
fh.write("\n")
# Print drop schedule
data = []
for k, versions in package_releases.items():
for v, dates in versions.items():
data.append(
(
k,
v,
pd.to_datetime(dates["release_date"]),
pd.to_datetime(dates["drop_date"]),
)
)
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")
with open("schedule.json", "w") as f:
f.write(json.dumps(new_min_versions_list, sort_keys=True))
def pad_table(table):
rows = [[el.strip() for el in row.split("|")] for row in table]
col_widths = [max(map(len, column)) for column in zip(*rows)]
rows[1] = [
el if el != "----" else "-" * col_widths[i] for i, el in enumerate(rows[1])
]
padded_table = []
for row in rows:
line = ""
for entry, width in zip(row, col_widths):
if not width:
continue
line += f"| {str.ljust(entry, width)} "
line += "|"
padded_table.append(line)
return padded_table
def make_table(sub):
table = []
table.append("| | | |")
table.append("|----|----|----|")
for package in sorted(set(sub.index.get_level_values(0))):
vers = sub.loc[[package]]["version"]
minv, maxv = min(vers), max(vers)
rels = sub.loc[[package]]["release"]
rel_min, rel_max = min(rels), max(rels)
version_range = str(minv) if minv == maxv else f"{minv} to {maxv}"
rel_range = (
str(rel_min.strftime("%b %Y"))
if rel_min == rel_max
else f"{rel_min.strftime('%b %Y')} and {rel_max.strftime('%b %Y')}"
)
table.append(f"|{package:<15}|{version_range:<19}|released {rel_range}|")
return pad_table(table)
def make_quarter(quarter, dq):
table = ["#### " + str(quarter).replace("Q", " - Quarter ") + ":\n"]
table.append("###### Recommend drop support for:\n")
sub = dq.loc[quarter]
table.extend(make_table(sub))
return "\n".join(table)
print("Saving drop schedule to schedule.md")
with open("schedule.md", "w") as fh:
# We collect packages 6 month in the past, and drop the first quarter
# as we might have filtered some of the packages out depending on
# when we ran the script.
tb = []
for quarter in sorted(set(dq.index.get_level_values(0)))[1:]:
tb.append(make_quarter(quarter, dq))
fh.write("\n\n".join(tb))
fh.write("\n")