"""Canonical Polars schemas for every published config."""

from __future__ import annotations

from collections.abc import Mapping

import polars as pl

UTC_DATETIME = pl.Datetime(time_unit="us", time_zone="UTC")

BUILD_VERSION = "1.0.0"

# One dividend as a filing stated it: an amount per share for a fiscal period.
#
# Not an event with an ex-date. SEC's structured data carries no dividend dates
# at all -- the date-typed XBRL facts exist in filings but `num.txt` holds only
# numeric ones, and the XBRL API returns 404 for them. What is here is what can
# be had: how much per share, over which period, known from when.
DIVIDEND_SCHEMA: dict[str, pl.DataType] = {
    "cik": pl.String,
    "accession_number": pl.String,
    "kind": pl.String,
    "security_class": pl.String,
    "period_start": pl.Date,
    "period_end": pl.Date,
    "quarters": pl.Int32,
    "amount_per_share": pl.Float64,
    "currency": pl.String,
    "is_subsequent_event": pl.Boolean,
    "form": pl.String,
    "fiscal_year": pl.Int32,
    "fiscal_period": pl.String,
    "filed_date": pl.Date,
    "accepted_at": UTC_DATETIME,
    "revision": pl.Int32,
    "inserted_at": UTC_DATETIME,
}

# One split, and the window it must have happened in.
#
# A split has no date here either, and saying otherwise would be invention. It
# is detected by the trace it leaves: a filing restates an earlier period's per
# share figures by the split ratio, so the split falls between the filing that
# still used the old numbers and the one that used the new.
SPLIT_SCHEMA: dict[str, pl.DataType] = {
    "cik": pl.String,
    "ratio": pl.Float64,
    "ratio_label": pl.String,
    "is_reverse": pl.Boolean,
    "detected_after": UTC_DATETIME,
    "detected_before": UTC_DATETIME,
    "effective_period_end": pl.Date,
    "method": pl.String,
    "confidence": pl.String,
    "corroborated_by": pl.String,
    "evidence_observations": pl.Int32,
    "inserted_at": UTC_DATETIME,
}

# The number a user actually needs: multiply an as-filed per-share figure by
# this to put it on the same footing as a split-adjusted price series.
ADJUSTMENT_SCHEMA: dict[str, pl.DataType] = {
    "cik": pl.String,
    "valid_from": pl.Date,
    "valid_to": pl.Date,
    "cumulative_split_factor": pl.Float64,
    "splits_after": pl.Int32,
    "confidence": pl.String,
    "inserted_at": UTC_DATETIME,
}

PIT_SCHEMA: dict[str, pl.DataType] = {
    "pit_event_id": pl.String,
    "entity_id": pl.String,
    "event_date": pl.Date,
    "knowledge_date": UTC_DATETIME,
    "knowledge_estimated": pl.Boolean,
    "action_type": pl.String,
    "amount_per_share": pl.Float64,
    "currency": pl.String,
    "split_ratio": pl.Float64,
    "security_class": pl.String,
    "quarters": pl.Int32,
    "is_subsequent_event": pl.Boolean,
    "confidence": pl.String,
    "ingested_at": UTC_DATETIME,
}

CONFIG_SCHEMAS: dict[str, dict[str, pl.DataType]] = {
    "dividends": DIVIDEND_SCHEMA,
    "splits": SPLIT_SCHEMA,
    "adjustment_factors": ADJUSTMENT_SCHEMA,
    "pit": PIT_SCHEMA,
}


def empty_frame(schema: Mapping[str, pl.DataType]) -> pl.DataFrame:
    return pl.DataFrame(schema=dict(schema))


def align(frame: pl.DataFrame, schema: Mapping[str, pl.DataType]) -> pl.DataFrame:
    missing = [
        pl.lit(None, dtype=dtype).alias(name)
        for name, dtype in schema.items()
        if name not in frame.columns
    ]
    if missing:
        frame = frame.with_columns(missing)
    return frame.select([pl.col(n).cast(d, strict=False) for n, d in schema.items()])