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feat: add remix mode for spark table rolling

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Introduces a `remix` option that allows each data column in a spark
table to be rolled independently. This is enabled via a `remix=true`
attribute in the directive's extra attributes.

Also refactors spark table scanning by moving markdown parsing logic
from the frontend to a new CLI-side `spark-scanner.ts` utility,
improving separation of concerns.
This commit is contained in:
hypercross
2026-07-11 11:58:01 +08:00
parent 719bb6ad8a
commit 2068ecad10
7 changed files with 215 additions and 178 deletions
Download Patch File Download Diff File Expand all files Collapse all files
src/components/journal/JournalInput.tsx
+2 -1
View File
@@ -128,7 +128,8 @@ export const JournalInput: Component = () => {
const key = (parsed.payload as { key: string }).key;
const match = comp.data.sparkTables.find((s) => s.slug === key);
const csvPath = match?.csvPath ?? "";
const p = await resolveSparkPayload({ key, csvPath });
const remix = match?.remix ?? false;
const p = await resolveSparkPayload({ key, csvPath, remix });
const result = sendMessage("spark", p);
const r = unwrap(result);
finish(r.ok, r.err);
src/components/journal/completions.ts
+1
View File
@@ -54,6 +54,7 @@ export interface SparkTableCompletion {
filePath: string;
csvPath: string;
headers: string[];
remix: boolean;
}
export interface JournalCompletions {
src/components/journal/types/spark.tsx
+3 -1
View File
@@ -70,10 +70,12 @@ export type SparkPayload = z.infer<typeof schema>;
*
* `key` is the combined slug (pageName-columnSlug).
* `csvPath` is the resolved .csv file path from completions.
* `remix` controls whether each column gets an independent roll.
*/
export async function resolveSparkPayload(raw: {
key: string;
csvPath: string;
remix: boolean;
}): Promise<SparkPayload> {
let csv: string;
try {
@@ -89,7 +91,7 @@ export async function resolveSparkPayload(raw: {
);
}
const sparkResult = rollSparkTable(meta);
const sparkResult = rollSparkTable(meta, { remix: raw.remix });
const firstRoll = rollFormula(meta.notation);
return {
src/components/utils/spark-table.ts
+73 -174
View File
@@ -1,13 +1,19 @@
/**
* Spark Table — markdown table where the first column header is a dice
* formula (d6, d20, d100, etc.). Rolling a spark table means:
* Spark Table — runtime rolling and CSV parsing.
*
* 1. Roll the dice formula once for each data column (non-dice columns)
* 2. Look up the row whose dice-column value matches each roll
* 3. Return the rolled values keyed by column slug
* A spark table is a CSV whose first column header is a dice formula
* (d6, d20, d100, etc.). Rolling a spark table means:
*
* 1. Roll the dice formula once
* 2. Look up the row whose dice-column value matches the roll
* 3. Return all column values from that row
*
* When `remix` is true, each data column gets its own independent roll
* and may come from different rows.
*/
import Slugger from "github-slugger";
import { parse } from "csv-parse/browser/esm/sync";
import { parseCSVString } from "../utils/csv-loader";
import { rollFormula } from "../md-commander/hooks";
@@ -15,11 +21,6 @@ import { rollFormula } from "../md-commander/hooks";
// Types
// ---------------------------------------------------------------------------
export interface MarkdownTable {
headers: string[];
rows: string[][];
}
export interface SparkTableColumn {
header: string;
slug: string;
@@ -40,6 +41,8 @@ export interface SparkTableMeta {
notation: string;
/** Concatenated slug of data columns */
slug: string;
/** The header name of the dice column (first column) */
diceHeader: string;
/** Data column headers (excluding dice column) */
dataHeaders: string[];
/** Full list of rows as objects keyed by header */
@@ -47,185 +50,52 @@ export interface SparkTableMeta {
}
// ---------------------------------------------------------------------------
// Markdown table parser
// ---------------------------------------------------------------------------
/**
* Parse all markdown tables from a markdown string.
* Handles both leading/trailing `|` styles and bare styles.
*/
export function parseMarkdownTables(markdown: string): MarkdownTable[] {
const tables: MarkdownTable[] = [];
const lines = markdown.split(/\r?\n/);
for (let i = 0; i < lines.length; i++) {
const headerCells = splitTableRow(lines[i]);
if (!headerCells || headerCells.length < 2) continue;
// Peek at the next line — must be a separator row
if (i + 1 >= lines.length) continue;
const sepCells = splitTableRow(lines[i + 1]);
if (!sepCells || sepCells.length < headerCells.length) continue;
if (!sepCells.every((c) => /^:?-{3,}:?$/.test(c))) continue;
// Valid table header + separator — collect body rows
const rows: string[][] = [];
let j = i + 2;
while (j < lines.length) {
const rowCells = splitTableRow(lines[j]);
if (!rowCells) break;
// Allow rows with fewer cells (unfilled trailing columns)
rows.push(rowCells);
j++;
}
// Only include tables with at least one data row
if (rows.length > 0) {
tables.push({ headers: headerCells, rows });
}
i = j - 1;
}
return tables;
}
/** Split a pipe-delimited table row, stripping optional leading/trailing `|` */
function splitTableRow(line: string): string[] | null {
const trimmed = line.trim();
if (!trimmed.includes("|")) return null;
// Strip optional leading and trailing `|`
let inner = trimmed;
if (inner.startsWith("|")) inner = inner.slice(1);
if (inner.endsWith("|")) inner = inner.slice(0, -1);
return inner.split("|").map((c) => c.trim());
}
// ---------------------------------------------------------------------------
// Spark table detection & metadata
// CSV parsing
// ---------------------------------------------------------------------------
const DICE_HEADER_RE = /^d\d+$/i;
/** Check whether a table is a spark table (first header is a dice formula) */
export function isSparkTable(table: MarkdownTable): boolean {
if (table.headers.length < 2) return false;
return DICE_HEADER_RE.test(table.headers[0]);
}
/**
* Generate the spark table slug by concatenating slugs of all data column
* headers (excluding the dice column).
*/
export function sparkTableSlug(table: MarkdownTable): string {
const slugger = new Slugger();
return table.headers
.slice(1)
.map((h) => slugger.slug(h.toLowerCase()))
.join("-");
}
/**
* Find a spark table in the given markdown content matching `slug`.
* Returns null if not found.
*/
export function findSparkTable(
markdown: string,
slug: string,
): SparkTableMeta | null {
const tables = parseMarkdownTables(markdown);
for (const table of tables) {
if (!isSparkTable(table)) continue;
if (sparkTableSlug(table) === slug) {
return {
notation: table.headers[0],
slug,
dataHeaders: table.headers.slice(1),
rows: rowsToObjects(table),
};
}
}
return null;
}
/**
* Find a spark table by its combined slug (`pageName-columnSlug`).
* Iterates every spark table in the content, constructs the combined slug,
* and returns the first match. Avoids the need to guess where the page
* name ends and the column slug begins (page names may contain `-`).
*/
export function findSparkTableByCombinedSlug(
markdown: string,
combinedSlug: string,
pageName: string,
): SparkTableMeta | null {
const tables = parseMarkdownTables(markdown);
for (const table of tables) {
if (!isSparkTable(table)) continue;
const colSlug = sparkTableSlug(table);
const candidate = `${pageName}-${colSlug}`;
if (candidate === combinedSlug) {
return {
notation: table.headers[0],
slug: colSlug,
dataHeaders: table.headers.slice(1),
rows: rowsToObjects(table),
};
}
}
return null;
}
/** Convert a MarkdownTable's string[][] rows to Record<string, string>[] */
function rowsToObjects(table: MarkdownTable): Record<string, string>[] {
return table.rows.map((row) => {
const obj: Record<string, string> = {};
for (let i = 0; i < table.headers.length; i++) {
obj[table.headers[i]] = row[i] ?? "";
}
return obj;
});
}
/**
* Parse a CSV string into a SparkTableMeta.
* The CSV must have a dice formula as its first column header.
* Returns null if the CSV is not a valid spark table.
*/
export function parseSparkTableCsv(csv: string): SparkTableMeta | null {
const parsed = parseCSVString(csv);
const headers = Object.keys(parsed[0] ?? {});
// Parse raw headers first — before parseCSVString injects frontmatter keys
// into rows. We use csv-parse directly to get the header order reliably.
const rawParsed = parse(csv, {
columns: false,
comment: "#",
trim: true,
skipEmptyLines: true,
bom: true,
}) as string[][];
if (rawParsed.length < 2) return null;
const headers = rawParsed[0];
if (headers.length < 2) return null;
if (!DICE_HEADER_RE.test(headers[0])) return null;
// Now parse with csv-loader for full frontmatter + quoting support
const parsed = parseCSVString(csv);
const slugger = new Slugger();
const diceHeader = headers[0];
const dataHeaders = headers.slice(1);
const slug = dataHeaders
.map((h) => slugger.slug(h.toLowerCase()))
.join("-");
return {
notation: headers[0],
notation: diceHeader,
slug,
diceHeader,
dataHeaders,
rows: parsed as Record<string, string>[],
};
}
/** Scan all spark tables in a markdown file and return their metadata */
export function scanSparkTables(
markdown: string,
): Omit<SparkTableMeta, "rows">[] {
const tables = parseMarkdownTables(markdown);
return tables.filter(isSparkTable).map((table) => ({
notation: table.headers[0],
slug: sparkTableSlug(table),
dataHeaders: table.headers.slice(1),
}));
}
// ---------------------------------------------------------------------------
// Range parsing
// ---------------------------------------------------------------------------
@@ -267,33 +137,62 @@ function matchesCell(diceCell: string, rolledValue: number): boolean {
// Rolling
// ---------------------------------------------------------------------------
export interface RollSparkTableOptions {
/** When true, each data column gets its own independent roll. Default false. */
remix?: boolean;
}
/**
* Roll a spark table: for each data column, roll the dice formula and look
* up the corresponding row value.
* Roll a spark table.
*
* By default, rolls the dice once and reads all columns from the matched row.
* When `remix` is true, each data column gets its own independent roll and
* may come from different rows.
*/
export function rollSparkTable(meta: SparkTableMeta): SparkTableResult {
export function rollSparkTable(
meta: SparkTableMeta,
options: RollSparkTableOptions = {},
): SparkTableResult {
const slugger = new Slugger();
const columns: SparkTableColumn[] = [];
const diceHeader = Object.keys(meta.rows[0] ?? {})[0] ?? "";
if (options.remix) {
// Independent roll per column
for (const header of meta.dataHeaders) {
const slug = slugger.slug(header.toLowerCase());
const roll = rollFormula(meta.notation);
const rolledValue = roll.result.total;
for (const header of meta.dataHeaders) {
const slug = slugger.slug(header.toLowerCase());
let value = `(no row for ${rolledValue})`;
for (const row of meta.rows) {
if (matchesCell(row[meta.diceHeader] ?? "", rolledValue)) {
value = row[header] ?? "";
break;
}
}
columns.push({ header, slug, value });
}
} else {
// Single roll — all columns from the same row
const roll = rollFormula(meta.notation);
const rolledValue = roll.result.total;
// Find the row matching the rolled value
let value = `(no row for ${rolledValue})`;
let matchedRow: Record<string, string> | null = null;
for (const row of meta.rows) {
const diceCell = row[diceHeader] ?? "";
if (matchesCell(diceCell, rolledValue)) {
value = row[header] ?? "";
if (matchesCell(row[meta.diceHeader] ?? "", rolledValue)) {
matchedRow = row;
break;
}
}
columns.push({ header, slug, value });
for (const header of meta.dataHeaders) {
const slug = slugger.slug(header.toLowerCase());
const value = matchedRow
? (matchedRow[header] ?? "")
: `(no row for ${rolledValue})`;
columns.push({ header, slug, value });
}
}
return {