Robert Helewka
a2420ed692
Reorganize Palladium codebase into a modular architecture with `core/` shared logic and `app/` Streamlit UI, separating per-study assets into `studies/YYYYMM_<Vendor>/` folders containing notebooks, seed data, and configuration. Update README to reflect new structure, add `.gitignore` entries for `.env` and study exports, and refresh component documentation.
128 lines
4.2 KiB
Python
128 lines
4.2 KiB
Python
"""
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Pandas dataframe builders for benefit / cost / summary tables.
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Each builder accepts the value-row dicts produced by
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``core.tei_client.TEIClient._normalize_value`` and returns a
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nicely-formatted DataFrame for display in notebooks.
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"""
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from __future__ import annotations
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from collections.abc import Iterable
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from typing import Any
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import pandas as pd
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from core.calculations import risk_adjust_benefit, risk_adjust_cost
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def _years_in_data(items: Iterable[dict]) -> list[int]:
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years: set[int] = set()
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for it in items:
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for k in (it.get("year_values") or {}):
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try:
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years.add(int(k))
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except (TypeError, ValueError):
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continue
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return sorted(years)
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def benefits_table(items: list[dict]) -> pd.DataFrame:
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"""Tidy benefits dataframe with one row per benefit, year columns, totals."""
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if not items:
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return pd.DataFrame(
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columns=["field_key", "label", "category", "risk_adjustment"]
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)
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years = _years_in_data(items)
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rows: list[dict[str, Any]] = []
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for it in items:
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rf = float(it.get("risk_adjustment") or 0.0)
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yv = it.get("year_values") or {}
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row = {
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"field_key": it.get("field_key", ""),
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"label": it.get("label", "") or it.get("field_key", ""),
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"category": it.get("category", ""),
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"risk_adjustment": rf,
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}
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nominal_total = 0.0
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ra_total = 0.0
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for y in years:
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v = float(yv.get(str(y)) or 0.0)
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ra = risk_adjust_benefit(v, rf)
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row[f"Year {y}"] = v
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row[f"Year {y} (RA)"] = ra
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nominal_total += v
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ra_total += ra
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row["Total"] = nominal_total
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row["Total (RA)"] = ra_total
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rows.append(row)
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return pd.DataFrame(rows)
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def costs_table(items: list[dict]) -> pd.DataFrame:
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"""Tidy costs dataframe — adds an Initial column when present."""
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if not items:
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return pd.DataFrame(
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columns=["field_key", "label", "category", "risk_adjustment", "Initial"]
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)
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years = _years_in_data(items)
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rows: list[dict[str, Any]] = []
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for it in items:
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rf = float(it.get("risk_adjustment") or 0.0)
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yv = it.get("year_values") or {}
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initial = float(it.get("initial") or 0.0)
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row = {
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"field_key": it.get("field_key", ""),
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"label": it.get("label", "") or it.get("field_key", ""),
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"category": it.get("category", ""),
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"risk_adjustment": rf,
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"Initial": initial,
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"Initial (RA)": risk_adjust_cost(initial, rf),
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}
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nominal_total = initial
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ra_total = risk_adjust_cost(initial, rf)
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for y in years:
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v = float(yv.get(str(y)) or 0.0)
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ra = risk_adjust_cost(v, rf)
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row[f"Year {y}"] = v
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row[f"Year {y} (RA)"] = ra
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nominal_total += v
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ra_total += ra
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row["Total"] = nominal_total
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row["Total (RA)"] = ra_total
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rows.append(row)
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return pd.DataFrame(rows)
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def summary_table(summary: dict) -> pd.DataFrame:
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"""Single-row summary dataframe of headline KPIs."""
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pb = summary.get("payback_months")
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pb_str = f"{float(pb):.1f} months" if pb not in (None, "") else "N/A"
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data = {
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"NPV": [float(summary.get("npv") or 0)],
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"ROI %": [float(summary.get("roi") or summary.get("roi_pct") or 0)],
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"Payback": [pb_str],
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"Benefits PV": [float(summary.get("total_benefits_pv") or 0)],
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"Costs PV": [float(summary.get("total_costs_pv") or 0)],
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"Discount rate": [float(summary.get("discount_rate") or 0)],
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"Analysis years": [int(summary.get("analysis_years") or 0)],
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}
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return pd.DataFrame(data)
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def cashflow_table(summary: dict) -> pd.DataFrame:
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"""Per-year cashflow dataframe from a summary's ``yearly_breakdown``."""
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yb = summary.get("yearly_breakdown") or []
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if not yb:
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return pd.DataFrame(columns=["Year", "Benefits", "Costs", "Net", "Cumulative"])
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df = pd.DataFrame(yb)
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rename = {
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"year": "Year",
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"benefits": "Benefits",
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"costs": "Costs",
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"net": "Net",
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"cumulative_net": "Cumulative",
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}
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df = df.rename(columns=rename)
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return df
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