Data Loading and Processing

/**
 * Load and parse CSV file
 * @param url - URL to CSV file
 * @param row - Optional row transformation function
 * @returns Promise resolving to array of objects
 */
function csv(url: string): Promise<DSVRowString[]>;
function csv<T>(url: string, row: (rawRow: DSVRowString, index: number, columns: string[]) => T): Promise<T[]>;

/**
 * Load and parse TSV file
 * @param url - URL to TSV file
 * @param row - Optional row transformation function
 * @returns Promise resolving to array of objects
 */
function tsv(url: string): Promise<DSVRowString[]>;
function tsv<T>(url: string, row: (rawRow: DSVRowString, index: number, columns: string[]) => T): Promise<T[]>;

/**
 * Load and parse DSV file with custom delimiter
 * @param url - URL to DSV file
 * @param row - Optional row transformation function
 * @returns Promise resolving to array of objects
 */
function dsv(delimiter: string, url: string): Promise<DSVRowString[]>;
function dsv<T>(delimiter: string, url: string, row: (rawRow: DSVRowString, index: number, columns: string[]) => T): Promise<T[]>;

/**
 * Load JSON file
 * @param url - URL to JSON file
 * @returns Promise resolving to parsed JSON
 */
function json<T = any>(url: string): Promise<T>;

/**
 * Load plain text file
 * @param url - URL to text file
 * @returns Promise resolving to text content
 */
function text(url: string): Promise<string>;

/**
 * Load XML file
 * @param url - URL to XML file
 * @returns Promise resolving to XML document
 */
function xml(url: string): Promise<Document>;

/**
 * Load HTML file
 * @param url - URL to HTML file
 * @returns Promise resolving to HTML document
 */
function html(url: string): Promise<Document>;

/**
 * Load image file
 * @param url - URL to image file
 * @returns Promise resolving to image element
 */
function image(url: string): Promise<HTMLImageElement>;

/**
 * Load SVG file
 * @param url - URL to SVG file
 * @returns Promise resolving to SVG document
 */
function svg(url: string): Promise<Document>;

/**
 * Load file as ArrayBuffer
 * @param url - URL to file
 * @returns Promise resolving to ArrayBuffer
 */
function buffer(url: string): Promise<ArrayBuffer>;

/**
 * Load file as Blob
 * @param url - URL to file
 * @returns Promise resolving to Blob
 */
function blob(url: string): Promise<Blob>;

import { csv, json } from "d3";

// Load CSV data
const salesData = await csv("sales.csv", d => ({
  date: new Date(d.date),
  sales: +d.sales,
  region: d.region
}));

// Load JSON data
const geoData = await json("countries.json");

// Error handling
try {
  const data = await csv("data.csv");
} catch (error) {
  console.error("Failed to load data:", error);
}

/**
 * Parse CSV string into array of objects
 * @param csvString - CSV string to parse
 * @param row - Optional row transformation function
 * @returns Array of objects
 */
function csvParse(csvString: string): DSVRowString[];
function csvParse<T>(csvString: string, row: (rawRow: DSVRowString, index: number, columns: string[]) => T): T[];

/**
 * Parse CSV string into array of arrays
 * @param csvString - CSV string to parse
 * @param row - Optional row transformation function
 * @returns Array of arrays
 */
function csvParseRows(csvString: string): string[][];
function csvParseRows<T>(csvString: string, row: (rawRow: string[], index: number) => T): T[];

/**
 * Format array of objects as CSV string
 * @param rows - Array of objects to format
 * @param columns - Optional column names to include
 * @returns CSV string
 */
function csvFormat(rows: object[], columns?: string[]): string;

/**
 * Format array of arrays as CSV string
 * @param rows - Array of arrays to format
 * @returns CSV string
 */
function csvFormatRows(rows: string[][]): string;

/**
 * Create DSV parser/formatter for custom delimiter
 * @param delimiter - Field delimiter character
 * @returns DSV parser/formatter object
 */
function dsvFormat(delimiter: string): DSV;

interface DSV {
  parse(text: string): DSVRowString[];
  parse<T>(text: string, row: (rawRow: DSVRowString, index: number, columns: string[]) => T): T[];
  parseRows(text: string): string[][];
  parseRows<T>(text: string, row: (rawRow: string[], index: number) => T): T[];
  format(rows: object[], columns?: string[]): string;
  formatRows(rows: string[][]): string;
}

/**
 * Automatically infer and convert string values to appropriate types
 * @param object - Object with string values to convert
 * @returns Object with inferred types
 */
function autoType(object: {[key: string]: string}): any;

/**
 * Count valid numeric values in array
 * @param values - Array of values
 * @param accessor - Optional value accessor function
 * @returns Count of valid numbers
 */
function count<T>(values: Iterable<T>, accessor?: (d: T, i: number, data: Iterable<T>) => number | null | undefined): number;

/**
 * Compute minimum value
 * @param values - Array of values
 * @param accessor - Optional value accessor function
 * @returns Minimum value or undefined if empty
 */
function min<T>(values: Iterable<T>): T | undefined;
function min<T, U>(values: Iterable<T>, accessor: (d: T, i: number, data: Iterable<T>) => U): U | undefined;

/**
 * Compute maximum value
 * @param values - Array of values
 * @param accessor - Optional value accessor function
 * @returns Maximum value or undefined if empty
 */
function max<T>(values: Iterable<T>): T | undefined;
function max<T, U>(values: Iterable<T>, accessor: (d: T, i: number, data: Iterable<T>) => U): U | undefined;

/**
 * Compute both minimum and maximum values
 * @param values - Array of values
 * @param accessor - Optional value accessor function
 * @returns [min, max] or [undefined, undefined] if empty
 */
function extent<T>(values: Iterable<T>): [T, T] | [undefined, undefined];
function extent<T, U>(values: Iterable<T>, accessor: (d: T, i: number, data: Iterable<T>) => U): [U, U] | [undefined, undefined];

/**
 * Compute sum of numeric values
 * @param values - Array of values
 * @param accessor - Optional value accessor function
 * @returns Sum of values
 */
function sum<T>(values: Iterable<T>, accessor?: (d: T, i: number, data: Iterable<T>) => number | null | undefined): number;

/**
 * Compute arithmetic mean (average)
 * @param values - Array of values
 * @param accessor - Optional value accessor function
 * @returns Mean value or undefined if empty
 */
function mean<T>(values: Iterable<T>, accessor?: (d: T, i: number, data: Iterable<T>) => number | null | undefined): number | undefined;

/**
 * Compute median (0.5-quantile)
 * @param values - Array of values
 * @param accessor - Optional value accessor function
 * @returns Median value or undefined if empty
 */
function median<T>(values: Iterable<T>, accessor?: (d: T, i: number, data: Iterable<T>) => number | null | undefined): number | undefined;

/**
 * Compute quantile (percentile) of sorted values
 * @param values - Array of values
 * @param p - Quantile value between 0 and 1
 * @param accessor - Optional value accessor function
 * @returns Quantile value or undefined if empty
 */
function quantile<T>(values: Iterable<T>, p: number, accessor?: (d: T, i: number, data: Iterable<T>) => number | null | undefined): number | undefined;

/**
 * Compute variance of values
 * @param values - Array of values
 * @param accessor - Optional value accessor function
 * @returns Variance or undefined if empty
 */
function variance<T>(values: Iterable<T>, accessor?: (d: T, i: number, data: Iterable<T>) => number | null | undefined): number | undefined;

/**
 * Compute standard deviation of values
 * @param values - Array of values
 * @param accessor - Optional value accessor function
 * @returns Standard deviation or undefined if empty
 */
function deviation<T>(values: Iterable<T>, accessor?: (d: T, i: number, data: Iterable<T>) => number | null | undefined): number | undefined;

/**
 * Compute mode (most frequent value)
 * @param values - Array of values
 * @param accessor - Optional value accessor function
 * @returns Most frequent value or undefined if empty
 */
function mode<T>(values: Iterable<T>, accessor?: (d: T, i: number, data: Iterable<T>) => T): T | undefined;

import { mean, extent, sum, quantile } from "d3";

const sales = [120, 150, 180, 90, 200, 140];

console.log(mean(sales)); // 146.67
console.log(extent(sales)); // [90, 200]
console.log(sum(sales)); // 880
console.log(quantile(sales, 0.5)); // 145 (median)

// With accessor function
const data = [
  { name: "Alice", age: 25 },
  { name: "Bob", age: 30 },
  { name: "Charlie", age: 35 }
];

console.log(mean(data, d => d.age)); // 30
console.log(extent(data, d => d.age)); // [25, 35]

/**
 * Generate array of consecutive integers
 * @param start - Start value (or stop if only one argument)
 * @param stop - Stop value (exclusive)
 * @param step - Step size (default 1)
 * @returns Array of consecutive integers
 */
function range(stop: number): number[];
function range(start: number, stop: number, step?: number): number[];

/**
 * Compute Cartesian product of arrays
 * @param arrays - Arrays to compute product of
 * @returns Array of tuples representing Cartesian product
 */
function cross<T>(...arrays: T[][]): T[][];

/**
 * Merge multiple arrays into single array
 * @param arrays - Arrays to merge
 * @returns Flattened array
 */
function merge<T>(arrays: Iterable<T>[]): T[];

/**
 * Create array of adjacent pairs from array
 * @param values - Input array
 * @param reducer - Optional function to transform pairs
 * @returns Array of adjacent pairs
 */
function pairs<T>(values: Iterable<T>): Array<[T, T]>;
function pairs<T, U>(values: Iterable<T>, reducer: (a: T, b: T) => U): U[];

/**
 * Transpose array of arrays (matrix transpose)
 * @param matrix - Array of arrays to transpose
 * @returns Transposed matrix
 */
function transpose<T>(matrix: T[][]): T[][];

/**
 * Zip multiple arrays together
 * @param arrays - Arrays to zip
 * @returns Array of tuples
 */
function zip<T>(...arrays: T[][]): T[][];

/**
 * Randomize order of array elements
 * @param array - Array to shuffle (modified in-place)
 * @param lo - Optional start index
 * @param hi - Optional end index
 * @returns Shuffled array
 */
function shuffle<T>(array: T[], lo?: number, hi?: number): T[];

/**
 * Create shuffler function with specific random source
 * @param random - Random number generator function
 * @returns Shuffler function
 */
function shuffler(random: () => number): <T>(array: T[], lo?: number, hi?: number) => T[];

/**
 * Sort array in ascending order
 * @param values - Values to sort
 * @param comparator - Optional comparison function
 * @returns Sorted array
 */
function sort<T>(values: Iterable<T>, comparator?: (a: T, b: T) => number): T[];

/**
 * Natural ascending comparator function
 * @param a - First value
 * @param b - Second value
 * @returns Comparison result
 */
function ascending<T>(a: T, b: T): number;

/**
 * Natural descending comparator function
 * @param a - First value
 * @param b - Second value
 * @returns Comparison result
 */
function descending<T>(a: T, b: T): number;

/**
 * Group iterable by key functions into nested Map
 * @param values - Values to group
 * @param keys - Key functions for grouping
 * @returns Nested Map of grouped values
 */
function group<T, K>(values: Iterable<T>, key: (value: T) => K): Map<K, T[]>;
function group<T, K1, K2>(values: Iterable<T>, key1: (value: T) => K1, key2: (value: T) => K2): Map<K1, Map<K2, T[]>>;

/**
 * Group iterable by key functions into nested array
 * @param values - Values to group
 * @param keys - Key functions for grouping
 * @returns Nested array of [key, values] pairs
 */
function groups<T, K>(values: Iterable<T>, key: (value: T) => K): Array<[K, T[]]>;

/**
 * Group and reduce iterable into nested Map
 * @param values - Values to group and reduce
 * @param reduce - Reduce function
 * @param keys - Key functions for grouping
 * @returns Nested Map of reduced values
 */
function rollup<T, R, K>(values: Iterable<T>, reduce: (values: T[]) => R, key: (value: T) => K): Map<K, R>;

/**
 * Group and reduce iterable into nested array
 * @param values - Values to group and reduce
 * @param reduce - Reduce function  
 * @param keys - Key functions for grouping
 * @returns Nested array of [key, reduced_value] pairs
 */
function rollups<T, R, K>(values: Iterable<T>, reduce: (values: T[]) => R, key: (value: T) => K): Array<[K, R]>;

/**
 * Index iterable by key functions into nested Map
 * @param values - Values to index
 * @param keys - Key functions for indexing
 * @returns Nested Map of indexed values
 */
function index<T, K>(values: Iterable<T>, key: (value: T) => K): Map<K, T>;

/**
 * Sort keys by grouped values
 * @param values - Values to group
 * @param comparator - Comparison function for groups
 * @param key - Key function for grouping
 * @returns Sorted array of keys
 */
function groupSort<T, K>(values: Iterable<T>, comparator: (a: T[], b: T[]) => number, key: (value: T) => K): K[];

import { group, rollup, sum, mean } from "d3";

const sales = [
  { region: "North", product: "A", amount: 100 },
  { region: "North", product: "B", amount: 150 },
  { region: "South", product: "A", amount: 200 },
  { region: "South", product: "B", amount: 80 }
];

// Group by region
const byRegion = group(sales, d => d.region);
// Map { "North" => [...], "South" => [...] }

// Group by region and product
const byRegionProduct = group(sales, d => d.region, d => d.product);
// Map { "North" => Map { "A" => [...], "B" => [...] }, ... }

// Rollup to compute sums
const totalByRegion = rollup(sales, v => sum(v, d => d.amount), d => d.region);
// Map { "North" => 250, "South" => 280 }

/**
 * Create histogram bin generator with default settings
 * @returns Bin generator function
 */
function bin(): BinGenerator<number, number>;
function bin<Datum>(): BinGenerator<Datum, number>;
function bin<Datum, Value>(): BinGenerator<Datum, Value>;

interface BinGenerator<Datum, Value> {
  /** Generate bins for data */
  (data: Datum[]): Bin<Datum, Value>[];
  /** Set value accessor function */
  value(accessor: (d: Datum, i: number, data: Datum[]) => Value): BinGenerator<Datum, Value>;
  /** Set domain [min, max] for binning */
  domain(domain: [Value, Value] | ((values: Value[]) => [Value, Value])): BinGenerator<Datum, Value>;
  /** Set bin thresholds */
  thresholds(thresholds: number | number[] | ((values: Value[], domain: [Value, Value]) => number[])): BinGenerator<Datum, Value>;
}

interface Bin<Datum, Value> extends Array<Datum> {
  x0: Value;  // Lower bound of bin
  x1: Value;  // Upper bound of bin
}

/**
 * Generate nice threshold values for histograms
 * @param values - Array of values to analyze
 * @param count - Approximate number of bins desired
 * @returns Array of threshold values
 */
function thresholdFreedmanDiaconis<T>(values: ArrayLike<T>, min: number, max: number): number[];
function thresholdScott<T>(values: ArrayLike<T>, min: number, max: number): number[];
function thresholdSturges<T>(values: ArrayLike<T>): number;

import { bin, thresholdFreedmanDiaconis } from "d3";

const data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10];

// Basic histogram
const histogram = bin()(data);
histogram.forEach(bin => {
  console.log(`[${bin.x0}, ${bin.x1}): ${bin.length} values`);
});

// Custom bins for objects
const salesData = [{amount: 100}, {amount: 200}, {amount: 150}];
const salesHistogram = bin()
  .value(d => d.amount)
  .thresholds(10);

const bins = salesHistogram(salesData);

/**
 * Find insertion point for value in sorted array (left-biased)
 * @param array - Sorted array to search
 * @param x - Value to find insertion point for
 * @param lo - Lower bound index (default: 0)
 * @param hi - Upper bound index (default: array.length)
 * @returns Index where value should be inserted
 */
function bisectLeft<T>(array: ArrayLike<T>, x: T, lo?: number, hi?: number): number;

/**
 * Find insertion point for value in sorted array (right-biased)
 * @param array - Sorted array to search
 * @param x - Value to find insertion point for
 * @param lo - Lower bound index (default: 0)
 * @param hi - Upper bound index (default: array.length)
 * @returns Index where value should be inserted
 */
function bisectRight<T>(array: ArrayLike<T>, x: T, lo?: number, hi?: number): number;

/**
 * Default bisector (same as bisectRight)
 */
const bisect: typeof bisectRight;

/**
 * Create custom bisector with accessor function
 * @param accessor - Function to extract comparison value
 * @returns Bisector object with left/right methods
 */
function bisector<T, U>(accessor: (d: T) => U): {
  left(array: ArrayLike<T>, x: U, lo?: number, hi?: number): number;
  right(array: ArrayLike<T>, x: U, lo?: number, hi?: number): number;
  center(array: ArrayLike<T>, x: U, lo?: number, hi?: number): number;
};

/**
 * Create custom bisector with comparator function
 * @param comparator - Comparison function
 * @returns Bisector object with left/right methods
 */
function bisector<T>(comparator: (a: T, b: T) => number): {
  left(array: ArrayLike<T>, x: T, lo?: number, hi?: number): number;
  right(array: ArrayLike<T>, x: T, lo?: number, hi?: number): number;
  center(array: ArrayLike<T>, x: T, lo?: number, hi?: number): number;
};

import { bisectLeft, bisectRight, bisector } from "d3";

const data = [1, 2, 4, 4, 6, 8, 10];

// Find insertion points
bisectLeft(data, 4);  // 2 (before existing 4s)
bisectRight(data, 4); // 4 (after existing 4s)

// Custom bisector for objects
const sales = [{date: new Date(2023, 0, 1), amount: 100}, ...];
const dateBisector = bisector(d => d.date);
const insertIndex = dateBisector.left(sales, new Date(2023, 5, 1));

/**
 * Compute variance of numeric values
 * @param values - Array of values
 * @param accessor - Optional value accessor function
 * @returns Sample variance
 */
function variance<T>(values: Iterable<T>, accessor?: (d: T, i: number, data: Iterable<T>) => number | null | undefined): number | undefined;

/**
 * Compute standard deviation of numeric values
 * @param values - Array of values
 * @param accessor - Optional value accessor function
 * @returns Sample standard deviation
 */
function deviation<T>(values: Iterable<T>, accessor?: (d: T, i: number, data: Iterable<T>) => number | null | undefined): number | undefined;

/**
 * Compute mode (most frequent value) of values
 * @param values - Array of values
 * @param accessor - Optional value accessor function
 * @returns Most frequent value or undefined
 */
function mode<T>(values: Iterable<T>, accessor?: (d: T, i: number, data: Iterable<T>) => any): T | undefined;

/**
 * Compute quantile value at given probability
 * @param values - Sorted array of values
 * @param p - Probability (0-1)
 * @param accessor - Optional value accessor function
 * @returns Quantile value
 */
function quantile<T>(values: ArrayLike<T>, p: number, accessor?: (d: T, i: number, data: ArrayLike<T>) => number | null | undefined): number | undefined;

/**
 * Compute quantiles at multiple probabilities
 * @param values - Sorted array of values
 * @param probabilities - Array of probabilities (0-1)
 * @param accessor - Optional value accessor function
 * @returns Array of quantile values
 */
function quantileSorted<T>(values: ArrayLike<T>, p: number, accessor?: (d: T, i: number, data: ArrayLike<T>) => number | null | undefined): number | undefined;

/**
 * Compute cumulative sum of values
 * @param values - Array of values
 * @param accessor - Optional value accessor function
 * @returns Array of cumulative sums
 */
function cumsum<T>(values: Iterable<T>, accessor?: (d: T, i: number, data: Iterable<T>) => number | null | undefined): number[];

import { variance, deviation, quantile, cumsum } from "d3";

const scores = [85, 90, 78, 92, 88, 79, 95, 87];

// Advanced statistics
const v = variance(scores);     // Sample variance
const sd = deviation(scores);   // Standard deviation
const median = quantile(scores.sort((a,b) => a-b), 0.5); // Median
const q25 = quantile(scores.sort((a,b) => a-b), 0.25);   // First quartile

// Cumulative sum
const running = cumsum(scores); // [85, 175, 253, ...]

/**
 * Compute set union (all unique values from both sets)
 * @param values - Iterables to compute union of
 * @returns Set containing union of values
 */
function union<T>(...values: Iterable<T>[]): Set<T>;

/**
 * Compute set intersection (values present in all sets)
 * @param values - Iterables to compute intersection of
 * @returns Set containing intersection of values
 */
function intersection<T>(...values: Iterable<T>[]): Set<T>;

/**
 * Compute set difference (values in first set but not others)
 * @param values - Iterables to compute difference of
 * @returns Set containing difference of values
 */
function difference<T>(values: Iterable<T>, ...others: Iterable<T>[]): Set<T>;

/**
 * Test whether two sets are disjoint (no common elements)
 * @param a - First set
 * @param b - Second set
 * @returns True if sets are disjoint
 */
function disjoint<T>(a: Iterable<T>, b: Iterable<T>): boolean;

/**
 * Test whether first set is subset of second
 * @param a - Potential subset
 * @param b - Potential superset
 * @returns True if a is subset of b
 */
function subset<T>(a: Iterable<T>, b: Iterable<T>): boolean;

/**
 * Test whether first set is superset of second
 * @param a - Potential superset
 * @param b - Potential subset
 * @returns True if a is superset of b
 */
function superset<T>(a: Iterable<T>, b: Iterable<T>): boolean;

// DSV row types
type DSVRowString = { [key: string]: string };
type DSVRowAny = { [key: string]: any };

// Parsed row arrays  
type DSVParsedArray<T> = T[] & { columns: string[] };

// Accessor function type
type Accessor<T, R> = (datum: T, index: number, array: T[]) => R;

// Numeric accessor
type NumericAccessor<T> = (datum: T, index: number, array: T[]) => number | null | undefined;

// Value types for statistics
type Primitive = string | number | boolean | Date;
type Numeric = number | { valueOf(): number };

// Grouping key types  
type GroupKey = string | number | boolean | Date;

// Comparison function type
type Comparator<T> = (a: T, b: T) => number;