"""
config.py — Central configuration for the SHEQ Analysis Tool.

Holds file paths, column name mappings, activity type definitions,
severity orders, and brand constants.  Edit this file when source
column names change; do not touch the analysis or report modules.
"""

from __future__ import annotations

import os

# ── Default file paths (resolved relative to this file's directory) ──────────
BASE_DIR = os.path.dirname(os.path.abspath(__file__))

EVENTS_FILE       = os.environ.get("SHEQ_EVENTS_FILE",       os.path.join(BASE_DIR, "Events.xlsx"))
LLC_FILE          = os.environ.get("SHEQ_LLC_FILE",          os.path.join(BASE_DIR, "LLC_Data.xlsx"))
SAFETY_ENERGY_FILE = os.environ.get("SHEQ_SE_FILE",          os.path.join(BASE_DIR, "Safety_Energy.xlsx"))
OUTPUT_DIR        = os.environ.get("SHEQ_OUTPUT_DIR",        os.path.join(BASE_DIR, "output"))


# ── Events.xlsx column mapping ─────────────────────────────────────────────────
#   Maps a normalised internal name → list of candidate column names in order
#   of preference.  data_loader picks the first match it finds.
EVENTS_COL_MAP: dict[str, list[str]] = {
    "date":              ["EventDate", "Event Date", "Date"],
    "event_type":        ["EventType", "Event Type"],
    "consequence":       ["Actual Consequence"],
    "potential":         ["Potential Consequence"],
    "status":            ["Status"],
    "business_unit":     ["Business Unit"],
    "project":           ["Project"],
    "location":          ["Location", "Location.1"],
    "crp":               ["CRP Involved", "CRPInvolved"],
    "root_cause_cat":    ["Root Cause Category"],
    "root_cause_sub":    ["Root Cause Sub-Category"],
    "injury_class":      ["Ventia Injury Classification"],
    "body_part":         ["Bodily Location"],
    "brief_desc":        ["Brief Description"],
    "event_desc":        ["Event Description"],
    "days_to_enter":     ["Days to Enter"],
    "event_lag":         ["Event Lag"],
    "report_lag":        ["Report Lag"],
    "investigation_done":["Investigation Completed"],
    "hipo":              ["HiPo"],
    "critical_event":    ["Critical Event"],
}

# ── Safety_Energy.xlsx column mapping ─────────────────────────────────────────
SE_COL_MAP: dict[str, list[str]] = {
    "date":           ["EventDate", "Date Conducted", "CompletedDate"],
    "module_name":    ["ModuleName"],
    "module_prefix":  ["ModulePrefix"],
    "module_type":    ["ModuleType"],
    "leader":         ["CompletedByName", "Conducted By"],
    "business_unit":  ["Business Unit"],
    "project":        ["Project"],
    "location":       ["Location", "Specific Location"],
    "shift":          ["Shift"],
    "at_risk_aspects":["At Risk Aspects"],
    "total_questions":["Total Questions"],
    "actions":        ["Actions"],
    "atl_actions":    ["ATL Actions"],
    "at_risk_crp":    ["At risk CRP"],
    "llc_topic":      ["LLC Topic"],
    "at_risk_obs":    ["At risk situation/observation"],
    "positive_obs":   ["Positive Observation"],
    "find_fix":       ["Find & Fix", "Find&Fix"],
    "participants":   ["Number of people spoken to", "Participants"],
    "time_spent":     ["Time Spent on LLC"],
}

# ── LLC_Data.xlsx column mapping ───────────────────────────────────────────────
LLC_COL_MAP: dict[str, list[str]] = {
    "date":          ["EventDate", "Date Conducted", "Date"],
    "topic":         ["LLC Topic"],
    "leader":        ["Conducted by"],
    "business_unit": ["Business Unit"],
    "project":       ["Project"],
    "location":      ["Location", "Specific Location"],
    "crp_focus":     ["CRP in Focus"],
    "at_risk_obs":   ["At risk situation/observation"],
    "positive_obs":  ["Positive Observation"],
    "at_risk_flag":  ["At risk work practices observed"],
    "participants":  ["Participants"],
    "find_fix":      ["Find&Fix", "Find & Fix"],
    "review_action": ["Review & Action"],
    "shift":         ["Shift"],
}


# ── Activity type normalisation ────────────────────────────────────────────────
#   Safety_Energy ModuleType values → display label
MODULE_TYPE_LABELS: dict[str, str] = {
    "Leader Learning Conversation": "LLC",
    "Critical Control Check":       "CCC",
    "Operational Control Check":    "OCC",
}

# Canonical leading-activity types used throughout the report
LEADING_ACTIVITY_TYPES = ["LLC", "CCC", "OCC"]

# NOTE on duplicate "OCC" label:
#   In some legacy notes and older exports the label "OCC" appeared for items
#   that are now split into "CCC" (Critical Control Check) and "OCC"
#   (Operational Control Check).  In the current Safety_Energy export both
#   CCC and OCC are already correctly separated via ModuleType.  The LLC_Data
#   export contains only LLC-type records.  No manual deduplication is
#   required; however we collapse all three under "Safety Energy" when
#   computing the combined domain total.


# ── Consequence severity ordering (low → high) ────────────────────────────────
CONSEQUENCE_ORDER = ["Negligible", "Minor", "Moderate", "Major", "Substantial"]
CONSEQUENCE_SERIOUS = {"Moderate", "Major", "Substantial"}

# ── Brand colours (hex) per DESIGN.md ─────────────────────────────────────────
DEEP_BLUE    = "#0b3254"
SKY_BLUE     = "#13b5ea"
DARK_GREEN   = "#006e47"
MID_GREEN    = "#009946"
LIGHT_GREEN  = "#7bc143"
PURPLE       = "#96358d"
AMBER        = "#d97706"
RED          = "#dc2626"
MUTED        = "#64748b"
CARD_BG      = "#f0f5fa"
PAGE_BG      = "#f8fafc"
BORDER       = "#e2e8f0"

CHART_PALETTE = [DEEP_BLUE, SKY_BLUE, DARK_GREEN, MID_GREEN,
                 LIGHT_GREEN, PURPLE, AMBER, RED]

# Activity type → colour mapping for charts
ACTIVITY_COLOURS: dict[str, str] = {
    "LLC": DEEP_BLUE,
    "CCC": SKY_BLUE,
    "OCC": DARK_GREEN,
}

# ── Report defaults ────────────────────────────────────────────────────────────
DEFAULT_START_DATE = "2024-01-01"
DEFAULT_SPLIT_DATE = "2025-04-01"
DEFAULT_PD1_NAME   = "Matthew Arthur"
DEFAULT_PD2_NAME   = "Manga"

# Minimum activity count for a leader to be included in focus tables
LEADER_MIN_ACTIVITIES = 5

# Correlation: minimum month-count required before reporting a correlation
CORR_MIN_MONTHS = 4

# Rolling window used for deeper Safety Energy trend analysis
TWO_YEAR_WINDOW_MONTHS = 24

# Quality scoring bands for leading-activity records
QUALITY_SCORE_BANDS = {
    "high_value": 70,
    "meaningful": 55,
    "shallow": 35,
}

# Keyword groups for at-risk theme extraction from free-text fields
AT_RISK_KEYWORDS: dict[str, list[str]] = {
    "Manual Handling":   ["manual handling", "lifting", "carrying", "musculoskeletal", "msd"],
    "Working at Height": ["height", "ladder", "scaffold", "fall", "elevated"],
    "Traffic/MVA":       ["vehicle", "traffic", "driving", "reversing", "motor", "mva", "collision"],
    "Hazardous Energy":  ["energy", "electrical", "isolation", "loto", "stored energy", "pressure"],
    "Slips/Trips/Falls": ["slip", "trip", "fall", "housekeeping", "wet floor", "uneven"],
    "PPE":               ["ppe", "personal protective", "helmet", "harness", "gloves", "safety glasses"],
    "Fatigue":           ["fatigue", "tired", "hours", "shift length", "rest"],
    "Communication":     ["communication", "briefing", "toolbox", "handover", "instruction"],
    "Supervision":       ["supervision", "supervision", "oversight", "leadership", "monitoring"],
    "CRP Compliance":    ["crp", "critical risk", "permit", "isolation", "confined space", "work at height"],
}