import json
from typing import Dict, Any, Tuple, List
from statistics import mean, median, pvariance, pstdev, quantiles
def _require_type(obj: Any, expected: type, name: str) -> Tuple[bool, str]:
if not isinstance(obj, expected):
return False, f"'{name}' debe ser de tipo {expected.__name__}"
return True, ""
DEF = {
"name": "timeseries_stats",
"title": "Estadística descriptiva de series",
"description": "Calcula media, mediana, varianza, desviación estándar, cuartiles (Q1, Q3), IQR y outliers (regla de Tukey).",
"inputSchema": {
"type": "object",
"properties": {
"values": {
"type": "array",
"items": {"type": "number"},
"description": "Serie numérica."
},
"iqrMultiplier": {
"type": "number",
"description": "Multiplicador para detección de outliers (por defecto 1.5)."
}
},
"required": ["values"]
},
"outputSchema": {
"type": "object",
"properties": {
"count": {"type": "integer"},
"min": {"type": "number"},
"max": {"type": "number"},
"mean": {"type": "number"},
"median": {"type": "number"},
"variance": {"type": "number"},
"stdev": {"type": "number"},
"q1": {"type": "number"},
"q3": {"type": "number"},
"iqr": {"type": "number"},
"lowerFence": {"type": "number"},
"upperFence": {"type": "number"},
"outliers": {
"type": "array",
"items": {"type": "object", "properties": {
"index": {"type": "integer"},
"value": {"type": "number"}
}, "required": ["index","value"]}
}
},
"required": ["count","min","max","mean","median","variance","stdev","q1","q3","iqr","lowerFence","upperFence","outliers"]
}
}
def IMPL(args: Dict[str, Any]) -> Dict[str, Any]:
ok, err = _require_type(args, dict, "arguments")
if not ok:
raise ValueError(err)
values = args.get("values")
ok, err = _require_type(values, list, "values")
if not ok:
raise ValueError(err)
if len(values) == 0:
raise ValueError("'values' no debe estar vacío")
xs: List[float] = []
for i, v in enumerate(values):
if not isinstance(v, (int, float)):
raise ValueError(f"'values[{i}]' debe ser number")
xs.append(float(v))
k = args.get("iqrMultiplier", 1.5)
if not isinstance(k, (int, float)):
raise ValueError("'iqrMultiplier' debe ser number")
xs_sorted = sorted(xs)
q = quantiles(xs_sorted, n=4, method='inclusive') if len(xs_sorted) >= 2 else [xs_sorted[0], xs_sorted[0], xs_sorted[0]]
q1, q2, q3 = q[0], q[1], q[2]
q2 = median(xs_sorted)
iqr = q3 - q1
lower = q1 - k * iqr
upper = q3 + k * iqr
outs = [{"index": i, "value": v} for i, v in enumerate(xs) if v < lower or v > upper]
payload = {
"count": len(xs),
"min": min(xs),
"max": max(xs),
"mean": mean(xs),
"median": q2,
"variance": pvariance(xs) if len(xs) > 1 else 0.0,
"stdev": pstdev(xs) if len(xs) > 1 else 0.0,
"q1": q1,
"q3": q3,
"iqr": iqr,
"lowerFence": lower,
"upperFence": upper,
"outliers": outs
}
return {
"content": [{"type": "text", "text": json.dumps(payload, ensure_ascii=False)}],
"structuredContent": payload,
"isError": False
}
