Add matched training signals, daily recovery and performance analysis

app/stats_analysis.py

@@ -8,6 +8,7 @@ import statistics
 from .stats_dsl import UNITS, QueryError, parse_dsl
 
 FUNCTIONS = {
+    "latest",
     "sum",
     "mean",
     "weighted_mean",
@@ -134,6 +135,13 @@ EXTRA_UNITS = {
 }
 
 
+from .training_metrics import METRICS as TRAINING_METRICS
+from .training_metrics import UNITS as TRAINING_UNITS
+
+EXTRA_METRICS.update(TRAINING_METRICS)
+EXTRA_UNITS.update(TRAINING_UNITS)
+
+
 def valid(value):
     return isinstance(value, (int, float)) and not isinstance(value, bool) and math.isfinite(value)
 
@@ -261,7 +269,7 @@ def parse_analysis(text, metrics, defaults):
         function, field = match.groups()
         if function not in FUNCTIONS or (not field and function != "count"):
             raise QueryError(
-                "Use sum, mean, weighted_mean, median, min, max, stddev, count or p10/p25/p75/p90/p95/p99.",
+                "Use latest, sum, mean, weighted_mean, median, min, max, stddev, count or p10/p25/p75/p90/p95/p99.",
                 line,
             )
         if field and field not in catalog:
@@ -531,6 +539,8 @@ def describe(key, catalog, aggregates):
         )
         + ". Percentiles use linear interpolation; stddev is population standard deviation. Missing values are excluded."
     )
+    if function == "latest":
+        definition = "Value on the latest input by local date/time, including a missing value on that input. Sorting does not change which input is latest."
     return label, unit, definition