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node_modules/d3-random/LICENSE

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+Copyright 2010-2021 Mike Bostock
+
+Permission to use, copy, modify, and/or distribute this software for any purpose
+with or without fee is hereby granted, provided that the above copyright notice
+and this permission notice appear in all copies.
+
+THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH
+REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND
+FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT,
+INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS
+OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER
+TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF
+THIS SOFTWARE.

node_modules/d3-random/README.md

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+# d3-random
+
+Generate random numbers from various distributions.
+
+See the [d3-random collection on Observable](https://observablehq.com/collection/@d3/d3-random) for examples.
+
+## Installing
+
+If you use npm, `npm install d3-random`. You can also download the [latest release on GitHub](https://github.com/d3/d3-random/releases/latest). For vanilla HTML in modern browsers, import d3-random from Skypack:
+
+```html
+<script type="module">
+
+import {randomUniform} from "https://cdn.skypack.dev/d3-random@3";
+
+const random = randomUniform(1, 10);
+
+</script>
+```
+
+For legacy environments, you can load d3-random’s UMD bundle from an npm-based CDN such as jsDelivr; a `d3` global is exported:
+
+```html
+<script src="https://cdn.jsdelivr.net/npm/d3-random@3"></script>
+<script>
+
+const random = d3.randomUniform(1, 10);
+
+</script>
+```
+
+## API Reference
+
+<a name="randomUniform" href="#randomUniform">#</a> d3.<b>randomUniform</b>([<i>min</i>, ][<i>max</i>]) · [Source](https://github.com/d3/d3-random/blob/master/src/uniform.js), [Examples](https://observablehq.com/@d3/d3-random#uniform)
+
+Returns a function for generating random numbers with a [uniform distribution](https://en.wikipedia.org/wiki/Uniform_distribution_\(continuous\)). The minimum allowed value of a returned number is *min* (inclusive), and the maximum is *max* (exclusive). If *min* is not specified, it defaults to 0; if *max* is not specified, it defaults to 1. For example:
+
+```js
+d3.randomUniform(6)(); // Returns a number greater than or equal to 0 and less than 6.
+d3.randomUniform(1, 5)(); // Returns a number greater than or equal to 1 and less than 5.
+```
+
+<a name="randomInt" href="#randomInt">#</a> d3.<b>randomInt</b>([<i>min</i>, ][<i>max</i>]) · [Source](https://github.com/d3/d3-random/blob/master/src/int.js), [Examples](https://observablehq.com/@d3/d3-random#int)
+
+Returns a function for generating random integers with a [uniform distribution](https://en.wikipedia.org/wiki/Uniform_distribution_\(continuous\)). The minimum allowed value of a returned number is ⌊*min*⌋ (inclusive), and the maximum is ⌊*max* - 1⌋ (inclusive). If *min* is not specified, it defaults to 0. For example:
+
+```js
+d3.randomInt(6)(); // Returns an integer greater than or equal to 0 and less than 6.
+d3.randomInt(1, 5)(); // Returns an integer greater than or equal to 1 and less than 5.
+```
+
+<a name="randomNormal" href="#randomNormal">#</a> d3.<b>randomNormal</b>([<i>mu</i>][, <i>sigma</i>]) · [Source](https://github.com/d3/d3-random/blob/master/src/normal.js), [Examples](https://observablehq.com/@d3/d3-random#normal)
+
+Returns a function for generating random numbers with a [normal (Gaussian) distribution](https://en.wikipedia.org/wiki/Normal_distribution). The expected value of the generated numbers is *mu*, with the given standard deviation *sigma*. If *mu* is not specified, it defaults to 0; if *sigma* is not specified, it defaults to 1.
+
+<a name="randomLogNormal" href="#randomLogNormal">#</a> d3.<b>randomLogNormal</b>([<i>mu</i>][, <i>sigma</i>]) · [Source](https://github.com/d3/d3-random/blob/master/src/logNormal.js), [Examples](https://observablehq.com/@d3/d3-random#logNormal)
+
+Returns a function for generating random numbers with a [log-normal distribution](https://en.wikipedia.org/wiki/Log-normal_distribution). The expected value of the random variable’s natural logarithm is *mu*, with the given standard deviation *sigma*. If *mu* is not specified, it defaults to 0; if *sigma* is not specified, it defaults to 1.
+
+<a name="randomBates" href="#randomBates">#</a> d3.<b>randomBates</b>(<i>n</i>) · [Source](https://github.com/d3/d3-random/blob/master/src/bates.js), [Examples](https://observablehq.com/@d3/d3-random#bates)
+
+Returns a function for generating random numbers with a [Bates distribution](https://en.wikipedia.org/wiki/Bates_distribution) with *n* independent variables. The case of fractional *n* is handled as with d3.randomIrwinHall, and d3.randomBates(0) is equivalent to d3.randomUniform().
+
+<a name="randomIrwinHall" href="#randomIrwinHall">#</a> d3.<b>randomIrwinHall</b>(<i>n</i>) · [Source](https://github.com/d3/d3-random/blob/master/src/irwinHall.js), [Examples](https://observablehq.com/@d3/d3-random#irwinHall)
+
+Returns a function for generating random numbers with an [Irwin–Hall distribution](https://en.wikipedia.org/wiki/Irwin–Hall_distribution) with *n* independent variables. If the fractional part of *n* is non-zero, this is treated as adding d3.randomUniform() times that fractional part to the integral part.
+
+<a name="randomExponential" href="#randomExponential">#</a> d3.<b>randomExponential</b>(<i>lambda</i>) · [Source](https://github.com/d3/d3-random/blob/master/src/exponential.js), [Examples](https://observablehq.com/@d3/d3-random#exponential)
+
+Returns a function for generating random numbers with an [exponential distribution](https://en.wikipedia.org/wiki/Exponential_distribution) with the rate *lambda*; equivalent to time between events in a [Poisson process](https://en.wikipedia.org/wiki/Poisson_point_process) with a mean of 1 / *lambda*. For example, exponential(1/40) generates random times between events where, on average, one event occurs every 40 units of time.
+
+<a name="randomPareto" href="#randomPareto">#</a> d3.<b>randomPareto</b>(<i>alpha</i>) · [Source](https://github.com/d3/d3-random/blob/master/src/pareto.js), [Examples](https://observablehq.com/@d3/d3-random#pareto)
+
+Returns a function for generating random numbers with a [Pareto distribution](https://en.wikipedia.org/wiki/Pareto_distribution) with the shape *alpha*. The value *alpha* must be a positive value.
+
+<a name="randomBernoulli" href="#randomBernoulli">#</a> d3.<b>randomBernoulli</b>(<i>p</i>) · [Source](https://github.com/d3/d3-random/blob/master/src/bernoulli.js), [Examples](https://observablehq.com/@d3/d3-random#bernoulli)
+
+Returns a function for generating either 1 or 0 according to a [Bernoulli distribution](https://en.wikipedia.org/wiki/Binomial_distribution) with 1 being returned with success probability *p* and 0 with failure probability *q* = 1 - *p*. The value *p* is in the range [0, 1].
+
+<a name="randomGeometric" href="#randomGeometric">#</a> d3.<b>randomGeometric</b>(<i>p</i>) · [Source](https://github.com/d3/d3-random/blob/master/src/geometric.js), [Examples](https://observablehq.com/@d3/d3-random#geometric)
+
+Returns a function for generating numbers with a [geometric distribution](https://en.wikipedia.org/wiki/Geometric_distribution) with success probability *p*. The value *p* is in the range [0, 1].
+
+<a name="randomBinomial" href="#randomBinomial">#</a> d3.<b>randomBinomial</b>(<i>n</i>, <i>p</i>) · [Source](https://github.com/d3/d3-random/blob/master/src/binomial.js), [Examples](https://observablehq.com/@d3/d3-random#binomial)
+
+Returns a function for generating random numbers with a [binomial distribution](https://en.wikipedia.org/wiki/Binomial_distribution) with *n* the number of trials and *p* the probability of success in each trial. The value *n* is greater or equal to 0, and the value *p* is in the range [0, 1].
+
+<a name="randomGamma" href="#randomGamma">#</a> d3.<b>randomGamma</b>(<i>k</i>, [<i>theta</i>]) · [Source](https://github.com/d3/d3-random/blob/master/src/gamma.js), [Examples](https://observablehq.com/@parcly-taxel/the-gamma-and-beta-distributions)
+
+Returns a function for generating random numbers with a [gamma distribution](https://en.wikipedia.org/wiki/Gamma_distribution) with *k* the shape parameter and *theta* the scale parameter. The value *k* must be a positive value; if *theta* is not specified, it defaults to 1.
+
+<a name="randomBeta" href="#randomBeta">#</a> d3.<b>randomBeta</b>(<i>alpha</i>, <i>beta</i>) · [Source](https://github.com/d3/d3-random/blob/master/src/beta.js), [Examples](https://observablehq.com/@parcly-taxel/the-gamma-and-beta-distributions)
+
+Returns a function for generating random numbers with a [beta distribution](https://en.wikipedia.org/wiki/Beta_distribution) with *alpha* and *beta* shape parameters, which must both be positive.
+
+<a name="randomWeibull" href="#randomWeibull">#</a> d3.<b>randomWeibull</b>(<i>k</i>, [<i>a</i>], [<i>b</i>]) · [Source](https://github.com/d3/d3-random/blob/master/src/weibull.js), [Examples](https://observablehq.com/@parcly-taxel/frechet-gumbel-weibull)
+
+Returns a function for generating random numbers with one of the [generalized extreme value distributions](https://en.wikipedia.org/wiki/Generalized_extreme_value_distribution), depending on *k*:
+
+* If *k* is positive, the [Weibull distribution](https://en.wikipedia.org/wiki/Weibull_distribution) with shape parameter *k*
+* If *k* is zero, the [Gumbel distribution](https://en.wikipedia.org/wiki/Gumbel_distribution)
+* If *k* is negative, the [Fréchet distribution](https://en.wikipedia.org/wiki/Fréchet_distribution) with shape parameter −*k*
+
+In all three cases, *a* is the location parameter and *b* is the scale parameter. If *a* is not specified, it defaults to 0; if *b* is not specified, it defaults to 1.
+
+<a name="randomCauchy" href="#randomCauchy">#</a> d3.<b>randomCauchy</b>([<i>a</i>], [<i>b</i>]) · [Source](https://github.com/d3/d3-random/blob/master/src/cauchy.js), [Examples](https://observablehq.com/@parcly-taxel/cauchy-and-logistic-distributions)
+
+Returns a function for generating random numbers with a [Cauchy distribution](https://en.wikipedia.org/wiki/Cauchy_distribution). *a* and *b* have the same meanings and default values as in d3.randomWeibull.
+
+<a name="randomLogistic" href="#randomLogistic">#</a> d3.<b>randomLogistic</b>([<i>a</i>], [<i>b</i>]) · [Source](https://github.com/d3/d3-random/blob/master/src/logistic.js), [Examples](https://observablehq.com/@parcly-taxel/cauchy-and-logistic-distributions)
+
+Returns a function for generating random numbers with a [logistic distribution](https://en.wikipedia.org/wiki/Logistic_distribution). *a* and *b* have the same meanings and default values as in d3.randomWeibull.
+
+<a name="randomPoisson" href="#randomPoisson">#</a> d3.<b>randomPoisson</b>(<i>lambda</i>) · [Source](https://github.com/d3/d3-random/blob/master/src/poisson.js), [Examples](https://observablehq.com/@parcly-taxel/the-poisson-distribution)
+
+Returns a function for generating random numbers with a [Poisson distribution](https://en.wikipedia.org/wiki/Poisson_distribution) with mean *lambda*.
+
+<a name="random_source" href="#random_source">#</a> <i>random</i>.<b>source</b>(<i>source</i>) · [Examples](https://observablehq.com/@d3/random-source)
+
+Returns the same type of function for generating random numbers but where the given random number generator *source* is used as the source of randomness instead of Math.random. The given random number generator must implement the same interface as Math.random and only return values in the range [0, 1). This is useful when a seeded random number generator is preferable to Math.random. For example:
+
+```js
+import {randomLcg, randomNumber} from "d3-random";
+
+const seed = 0.44871573888282423; // any number in [0, 1)
+const random = randomNormal.source(randomLcg(seed))(0, 1);
+
+random(); // -0.6253955998897069
+```
+
+<a name="randomLcg" href="#randomLcg">#</a> d3.<b>randomLcg</b>([<i>seed</i>]) · [Source](https://github.com/d3/d3-random/blob/master/src/lcg.js), [Examples](https://observablehq.com/@d3/d3-randomlcg)
+
+Returns a [linear congruential generator](https://en.wikipedia.org/wiki/Linear_congruential_generator); this function can be called repeatedly to obtain pseudorandom values well-distributed on the interval [0,1) and with a long period (up to 1 billion numbers), similar to Math.random. A *seed* can be specified as a real number in the interval [0,1) or as any integer. In the latter case, only the lower 32 bits are considered. Two generators instanced with the same seed generate the same sequence, allowing to create reproducible pseudo-random experiments. If the *seed* is not specified, one is chosen using Math.random.

node_modules/d3-random/dist/d3-random.js

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+// https://d3js.org/d3-random/ v3.0.1 Copyright 2010-2021 Mike Bostock
+(function (global, factory) {
+typeof exports === 'object' && typeof module !== 'undefined' ? factory(exports) :
+typeof define === 'function' && define.amd ? define(['exports'], factory) :
+(global = typeof globalThis !== 'undefined' ? globalThis : global || self, factory(global.d3 = global.d3 || {}));
+}(this, (function (exports) { 'use strict';
+
+var defaultSource = Math.random;
+
+var uniform = (function sourceRandomUniform(source) {
+  function randomUniform(min, max) {
+    min = min == null ? 0 : +min;
+    max = max == null ? 1 : +max;
+    if (arguments.length === 1) max = min, min = 0;
+    else max -= min;
+    return function() {
+      return source() * max + min;
+    };
+  }
+
+  randomUniform.source = sourceRandomUniform;
+
+  return randomUniform;
+})(defaultSource);
+
+var int = (function sourceRandomInt(source) {
+  function randomInt(min, max) {
+    if (arguments.length < 2) max = min, min = 0;
+    min = Math.floor(min);
+    max = Math.floor(max) - min;
+    return function() {
+      return Math.floor(source() * max + min);
+    };
+  }
+
+  randomInt.source = sourceRandomInt;
+
+  return randomInt;
+})(defaultSource);
+
+var normal = (function sourceRandomNormal(source) {
+  function randomNormal(mu, sigma) {
+    var x, r;
+    mu = mu == null ? 0 : +mu;
+    sigma = sigma == null ? 1 : +sigma;
+    return function() {
+      var y;
+
+      // If available, use the second previously-generated uniform random.
+      if (x != null) y = x, x = null;
+
+      // Otherwise, generate a new x and y.
+      else do {
+        x = source() * 2 - 1;
+        y = source() * 2 - 1;
+        r = x * x + y * y;
+      } while (!r || r > 1);
+
+      return mu + sigma * y * Math.sqrt(-2 * Math.log(r) / r);
+    };
+  }
+
+  randomNormal.source = sourceRandomNormal;
+
+  return randomNormal;
+})(defaultSource);
+
+var logNormal = (function sourceRandomLogNormal(source) {
+  var N = normal.source(source);
+
+  function randomLogNormal() {
+    var randomNormal = N.apply(this, arguments);
+    return function() {
+      return Math.exp(randomNormal());
+    };
+  }
+
+  randomLogNormal.source = sourceRandomLogNormal;
+
+  return randomLogNormal;
+})(defaultSource);
+
+var irwinHall = (function sourceRandomIrwinHall(source) {
+  function randomIrwinHall(n) {
+    if ((n = +n) <= 0) return () => 0;
+    return function() {
+      for (var sum = 0, i = n; i > 1; --i) sum += source();
+      return sum + i * source();
+    };
+  }
+
+  randomIrwinHall.source = sourceRandomIrwinHall;
+
+  return randomIrwinHall;
+})(defaultSource);
+
+var bates = (function sourceRandomBates(source) {
+  var I = irwinHall.source(source);
+
+  function randomBates(n) {
+    // use limiting distribution at n === 0
+    if ((n = +n) === 0) return source;
+    var randomIrwinHall = I(n);
+    return function() {
+      return randomIrwinHall() / n;
+    };
+  }
+
+  randomBates.source = sourceRandomBates;
+
+  return randomBates;
+})(defaultSource);
+
+var exponential = (function sourceRandomExponential(source) {
+  function randomExponential(lambda) {
+    return function() {
+      return -Math.log1p(-source()) / lambda;
+    };
+  }
+
+  randomExponential.source = sourceRandomExponential;
+
+  return randomExponential;
+})(defaultSource);
+
+var pareto = (function sourceRandomPareto(source) {
+  function randomPareto(alpha) {
+    if ((alpha = +alpha) < 0) throw new RangeError("invalid alpha");
+    alpha = 1 / -alpha;
+    return function() {
+      return Math.pow(1 - source(), alpha);
+    };
+  }
+
+  randomPareto.source = sourceRandomPareto;
+
+  return randomPareto;
+})(defaultSource);
+
+var bernoulli = (function sourceRandomBernoulli(source) {
+  function randomBernoulli(p) {
+    if ((p = +p) < 0 || p > 1) throw new RangeError("invalid p");
+    return function() {
+      return Math.floor(source() + p);
+    };
+  }
+
+  randomBernoulli.source = sourceRandomBernoulli;
+
+  return randomBernoulli;
+})(defaultSource);
+
+var geometric = (function sourceRandomGeometric(source) {
+  function randomGeometric(p) {
+    if ((p = +p) < 0 || p > 1) throw new RangeError("invalid p");
+    if (p === 0) return () => Infinity;
+    if (p === 1) return () => 1;
+    p = Math.log1p(-p);
+    return function() {
+      return 1 + Math.floor(Math.log1p(-source()) / p);
+    };
+  }
+
+  randomGeometric.source = sourceRandomGeometric;
+
+  return randomGeometric;
+})(defaultSource);
+
+var gamma = (function sourceRandomGamma(source) {
+  var randomNormal = normal.source(source)();
+
+  function randomGamma(k, theta) {
+    if ((k = +k) < 0) throw new RangeError("invalid k");
+    // degenerate distribution if k === 0
+    if (k === 0) return () => 0;
+    theta = theta == null ? 1 : +theta;
+    // exponential distribution if k === 1
+    if (k === 1) return () => -Math.log1p(-source()) * theta;
+
+    var d = (k < 1 ? k + 1 : k) - 1 / 3,
+        c = 1 / (3 * Math.sqrt(d)),
+        multiplier = k < 1 ? () => Math.pow(source(), 1 / k) : () => 1;
+    return function() {
+      do {
+        do {
+          var x = randomNormal(),
+              v = 1 + c * x;
+        } while (v <= 0);
+        v *= v * v;
+        var u = 1 - source();
+      } while (u >= 1 - 0.0331 * x * x * x * x && Math.log(u) >= 0.5 * x * x + d * (1 - v + Math.log(v)));
+      return d * v * multiplier() * theta;
+    };
+  }
+
+  randomGamma.source = sourceRandomGamma;
+
+  return randomGamma;
+})(defaultSource);
+
+var beta = (function sourceRandomBeta(source) {
+  var G = gamma.source(source);
+
+  function randomBeta(alpha, beta) {
+    var X = G(alpha),
+        Y = G(beta);
+    return function() {
+      var x = X();
+      return x === 0 ? 0 : x / (x + Y());
+    };
+  }
+
+  randomBeta.source = sourceRandomBeta;
+
+  return randomBeta;
+})(defaultSource);
+
+var binomial = (function sourceRandomBinomial(source) {
+  var G = geometric.source(source),
+      B = beta.source(source);
+
+  function randomBinomial(n, p) {
+    n = +n;
+    if ((p = +p) >= 1) return () => n;
+    if (p <= 0) return () => 0;
+    return function() {
+      var acc = 0, nn = n, pp = p;
+      while (nn * pp > 16 && nn * (1 - pp) > 16) {
+        var i = Math.floor((nn + 1) * pp),
+            y = B(i, nn - i + 1)();
+        if (y <= pp) {
+          acc += i;
+          nn -= i;
+          pp = (pp - y) / (1 - y);
+        } else {
+          nn = i - 1;
+          pp /= y;
+        }
+      }
+      var sign = pp < 0.5,
+          pFinal = sign ? pp : 1 - pp,
+          g = G(pFinal);
+      for (var s = g(), k = 0; s <= nn; ++k) s += g();
+      return acc + (sign ? k : nn - k);
+    };
+  }
+
+  randomBinomial.source = sourceRandomBinomial;
+
+  return randomBinomial;
+})(defaultSource);
+
+var weibull = (function sourceRandomWeibull(source) {
+  function randomWeibull(k, a, b) {
+    var outerFunc;
+    if ((k = +k) === 0) {
+      outerFunc = x => -Math.log(x);
+    } else {
+      k = 1 / k;
+      outerFunc = x => Math.pow(x, k);
+    }
+    a = a == null ? 0 : +a;
+    b = b == null ? 1 : +b;
+    return function() {
+      return a + b * outerFunc(-Math.log1p(-source()));
+    };
+  }
+
+  randomWeibull.source = sourceRandomWeibull;
+
+  return randomWeibull;
+})(defaultSource);
+
+var cauchy = (function sourceRandomCauchy(source) {
+  function randomCauchy(a, b) {
+    a = a == null ? 0 : +a;
+    b = b == null ? 1 : +b;
+    return function() {
+      return a + b * Math.tan(Math.PI * source());
+    };
+  }
+
+  randomCauchy.source = sourceRandomCauchy;
+
+  return randomCauchy;
+})(defaultSource);
+
+var logistic = (function sourceRandomLogistic(source) {
+  function randomLogistic(a, b) {
+    a = a == null ? 0 : +a;
+    b = b == null ? 1 : +b;
+    return function() {
+      var u = source();
+      return a + b * Math.log(u / (1 - u));
+    };
+  }
+
+  randomLogistic.source = sourceRandomLogistic;
+
+  return randomLogistic;
+})(defaultSource);
+
+var poisson = (function sourceRandomPoisson(source) {
+  var G = gamma.source(source),
+      B = binomial.source(source);
+
+  function randomPoisson(lambda) {
+    return function() {
+      var acc = 0, l = lambda;
+      while (l > 16) {
+        var n = Math.floor(0.875 * l),
+            t = G(n)();
+        if (t > l) return acc + B(n - 1, l / t)();
+        acc += n;
+        l -= t;
+      }
+      for (var s = -Math.log1p(-source()), k = 0; s <= l; ++k) s -= Math.log1p(-source());
+      return acc + k;
+    };
+  }
+
+  randomPoisson.source = sourceRandomPoisson;
+
+  return randomPoisson;
+})(defaultSource);
+
+// https://en.wikipedia.org/wiki/Linear_congruential_generator#Parameters_in_common_use
+const mul = 0x19660D;
+const inc = 0x3C6EF35F;
+const eps = 1 / 0x100000000;
+
+function lcg(seed = Math.random()) {
+  let state = (0 <= seed && seed < 1 ? seed / eps : Math.abs(seed)) | 0;
+  return () => (state = mul * state + inc | 0, eps * (state >>> 0));
+}
+
+exports.randomBates = bates;
+exports.randomBernoulli = bernoulli;
+exports.randomBeta = beta;
+exports.randomBinomial = binomial;
+exports.randomCauchy = cauchy;
+exports.randomExponential = exponential;
+exports.randomGamma = gamma;
+exports.randomGeometric = geometric;
+exports.randomInt = int;
+exports.randomIrwinHall = irwinHall;
+exports.randomLcg = lcg;
+exports.randomLogNormal = logNormal;
+exports.randomLogistic = logistic;
+exports.randomNormal = normal;
+exports.randomPareto = pareto;
+exports.randomPoisson = poisson;
+exports.randomUniform = uniform;
+exports.randomWeibull = weibull;
+
+Object.defineProperty(exports, '__esModule', { value: true });
+
+})));

node_modules/d3-random/dist/d3-random.min.js

@@ -0,0 +1,2 @@
+// https://d3js.org/d3-random/ v3.0.1 Copyright 2010-2021 Mike Bostock
+!function(n,r){"object"==typeof exports&&"undefined"!=typeof module?r(exports):"function"==typeof define&&define.amd?define(["exports"],r):r((n="undefined"!=typeof globalThis?globalThis:n||self).d3=n.d3||{})}(this,(function(n){"use strict";var r=Math.random,t=function n(r){function t(n,t){return n=null==n?0:+n,t=null==t?1:+t,1===arguments.length?(t=n,n=0):t-=n,function(){return r()*t+n}}return t.source=n,t}(r),o=function n(r){function t(n,t){return arguments.length<2&&(t=n,n=0),n=Math.floor(n),t=Math.floor(t)-n,function(){return Math.floor(r()*t+n)}}return t.source=n,t}(r),u=function n(r){function t(n,t){var o,u;return n=null==n?0:+n,t=null==t?1:+t,function(){var e;if(null!=o)e=o,o=null;else do{o=2*r()-1,e=2*r()-1,u=o*o+e*e}while(!u||u>1);return n+t*e*Math.sqrt(-2*Math.log(u)/u)}}return t.source=n,t}(r),e=function n(r){var t=u.source(r);function o(){var n=t.apply(this,arguments);return function(){return Math.exp(n())}}return o.source=n,o}(r),a=function n(r){function t(n){return(n=+n)<=0?()=>0:function(){for(var t=0,o=n;o>1;--o)t+=r();return t+o*r()}}return t.source=n,t}(r),i=function n(r){var t=a.source(r);function o(n){if(0==(n=+n))return r;var o=t(n);return function(){return o()/n}}return o.source=n,o}(r),c=function n(r){function t(n){return function(){return-Math.log1p(-r())/n}}return t.source=n,t}(r),f=function n(r){function t(n){if((n=+n)<0)throw new RangeError("invalid alpha");return n=1/-n,function(){return Math.pow(1-r(),n)}}return t.source=n,t}(r),l=function n(r){function t(n){if((n=+n)<0||n>1)throw new RangeError("invalid p");return function(){return Math.floor(r()+n)}}return t.source=n,t}(r),h=function n(r){function t(n){if((n=+n)<0||n>1)throw new RangeError("invalid p");return 0===n?()=>1/0:1===n?()=>1:(n=Math.log1p(-n),function(){return 1+Math.floor(Math.log1p(-r())/n)})}return t.source=n,t}(r),s=function n(r){var t=u.source(r)();function o(n,o){if((n=+n)<0)throw new RangeError("invalid k");if(0===n)return()=>0;if(o=null==o?1:+o,1===n)return()=>-Math.log1p(-r())*o;var u=(n<1?n+1:n)-1/3,e=1/(3*Math.sqrt(u)),a=n<1?()=>Math.pow(r(),1/n):()=>1;return function(){do{do{var n=t(),i=1+e*n}while(i<=0);i*=i*i;var c=1-r()}while(c>=1-.0331*n*n*n*n&&Math.log(c)>=.5*n*n+u*(1-i+Math.log(i)));return u*i*a()*o}}return o.source=n,o}(r),d=function n(r){var t=s.source(r);function o(n,r){var o=t(n),u=t(r);return function(){var n=o();return 0===n?0:n/(n+u())}}return o.source=n,o}(r),M=function n(r){var t=h.source(r),o=d.source(r);function u(n,r){return n=+n,(r=+r)>=1?()=>n:r<=0?()=>0:function(){for(var u=0,e=n,a=r;e*a>16&&e*(1-a)>16;){var i=Math.floor((e+1)*a),c=o(i,e-i+1)();c<=a?(u+=i,e-=i,a=(a-c)/(1-c)):(e=i-1,a/=c)}for(var f=a<.5,l=t(f?a:1-a),h=l(),s=0;h<=e;++s)h+=l();return u+(f?s:e-s)}}return u.source=n,u}(r),v=function n(r){function t(n,t,o){var u;return 0==(n=+n)?u=n=>-Math.log(n):(n=1/n,u=r=>Math.pow(r,n)),t=null==t?0:+t,o=null==o?1:+o,function(){return t+o*u(-Math.log1p(-r()))}}return t.source=n,t}(r),m=function n(r){function t(n,t){return n=null==n?0:+n,t=null==t?1:+t,function(){return n+t*Math.tan(Math.PI*r())}}return t.source=n,t}(r),p=function n(r){function t(n,t){return n=null==n?0:+n,t=null==t?1:+t,function(){var o=r();return n+t*Math.log(o/(1-o))}}return t.source=n,t}(r),g=function n(r){var t=s.source(r),o=M.source(r);function u(n){return function(){for(var u=0,e=n;e>16;){var a=Math.floor(.875*e),i=t(a)();if(i>e)return u+o(a-1,e/i)();u+=a,e-=i}for(var c=-Math.log1p(-r()),f=0;c<=e;++f)c-=Math.log1p(-r());return u+f}}return u.source=n,u}(r);const w=1/4294967296;n.randomBates=i,n.randomBernoulli=l,n.randomBeta=d,n.randomBinomial=M,n.randomCauchy=m,n.randomExponential=c,n.randomGamma=s,n.randomGeometric=h,n.randomInt=o,n.randomIrwinHall=a,n.randomLcg=function(n=Math.random()){let r=0|(0<=n&&n<1?n/w:Math.abs(n));return()=>(r=1664525*r+1013904223|0,w*(r>>>0))},n.randomLogNormal=e,n.randomLogistic=p,n.randomNormal=u,n.randomPareto=f,n.randomPoisson=g,n.randomUniform=t,n.randomWeibull=v,Object.defineProperty(n,"__esModule",{value:!0})}));

node_modules/d3-random/package.json

@@ -0,0 +1,51 @@
+{
+  "name": "d3-random",
+  "version": "3.0.1",
+  "description": "Generate random numbers from various distributions.",
+  "homepage": "https://d3js.org/d3-random/",
+  "repository": {
+    "type": "git",
+    "url": "https://github.com/d3/d3-random.git"
+  },
+  "keywords": [
+    "d3",
+    "d3-module",
+    "random",
+    "rng"
+  ],
+  "license": "ISC",
+  "author": {
+    "name": "Mike Bostock",
+    "url": "http://bost.ocks.org/mike"
+  },
+  "type": "module",
+  "files": [
+    "dist/**/*.js",
+    "src/**/*.js"
+  ],
+  "module": "src/index.js",
+  "main": "src/index.js",
+  "jsdelivr": "dist/d3-random.min.js",
+  "unpkg": "dist/d3-random.min.js",
+  "exports": {
+    "umd": "./dist/d3-random.min.js",
+    "default": "./src/index.js"
+  },
+  "sideEffects": false,
+  "devDependencies": {
+    "d3-array": "1 - 2",
+    "eslint": "7",
+    "jsdom": "16",
+    "mocha": "8",
+    "rollup": "2",
+    "rollup-plugin-terser": "7"
+  },
+  "scripts": {
+    "test": "mocha 'test/**/*-test.js' && eslint src test",
+    "prepublishOnly": "rm -rf dist && yarn test && rollup -c",
+    "postpublish": "git push && git push --tags && cd ../d3.github.com && git pull && cp ../${npm_package_name}/dist/${npm_package_name}.js ${npm_package_name}.v${npm_package_version%%.*}.js && cp ../${npm_package_name}/dist/${npm_package_name}.min.js ${npm_package_name}.v${npm_package_version%%.*}.min.js && git add ${npm_package_name}.v${npm_package_version%%.*}.js ${npm_package_name}.v${npm_package_version%%.*}.min.js && git commit -m \"${npm_package_name} ${npm_package_version}\" && git push && cd -"
+  },
+  "engines": {
+    "node": ">=12"
+  }
+}

node_modules/d3-random/src/bates.js

@@ -0,0 +1,19 @@
+import defaultSource from "./defaultSource.js";
+import irwinHall from "./irwinHall.js";
+
+export default (function sourceRandomBates(source) {
+  var I = irwinHall.source(source);
+
+  function randomBates(n) {
+    // use limiting distribution at n === 0
+    if ((n = +n) === 0) return source;
+    var randomIrwinHall = I(n);
+    return function() {
+      return randomIrwinHall() / n;
+    };
+  }
+
+  randomBates.source = sourceRandomBates;
+
+  return randomBates;
+})(defaultSource);

node_modules/d3-random/src/bernoulli.js

@@ -0,0 +1,14 @@
+import defaultSource from "./defaultSource.js";
+
+export default (function sourceRandomBernoulli(source) {
+  function randomBernoulli(p) {
+    if ((p = +p) < 0 || p > 1) throw new RangeError("invalid p");
+    return function() {
+      return Math.floor(source() + p);
+    };
+  }
+
+  randomBernoulli.source = sourceRandomBernoulli;
+
+  return randomBernoulli;
+})(defaultSource);

node_modules/d3-random/src/beta.js

@@ -0,0 +1,19 @@
+import defaultSource from "./defaultSource.js";
+import gamma from "./gamma.js";
+
+export default (function sourceRandomBeta(source) {
+  var G = gamma.source(source);
+
+  function randomBeta(alpha, beta) {
+    var X = G(alpha),
+        Y = G(beta);
+    return function() {
+      var x = X();
+      return x === 0 ? 0 : x / (x + Y());
+    };
+  }
+
+  randomBeta.source = sourceRandomBeta;
+
+  return randomBeta;
+})(defaultSource);

node_modules/d3-random/src/binomial.js

@@ -0,0 +1,38 @@
+import defaultSource from "./defaultSource.js";
+import beta from "./beta.js";
+import geometric from "./geometric.js";
+
+export default (function sourceRandomBinomial(source) {
+  var G = geometric.source(source),
+      B = beta.source(source);
+
+  function randomBinomial(n, p) {
+    n = +n;
+    if ((p = +p) >= 1) return () => n;
+    if (p <= 0) return () => 0;
+    return function() {
+      var acc = 0, nn = n, pp = p;
+      while (nn * pp > 16 && nn * (1 - pp) > 16) {
+        var i = Math.floor((nn + 1) * pp),
+            y = B(i, nn - i + 1)();
+        if (y <= pp) {
+          acc += i;
+          nn -= i;
+          pp = (pp - y) / (1 - y);
+        } else {
+          nn = i - 1;
+          pp /= y;
+        }
+      }
+      var sign = pp < 0.5,
+          pFinal = sign ? pp : 1 - pp,
+          g = G(pFinal);
+      for (var s = g(), k = 0; s <= nn; ++k) s += g();
+      return acc + (sign ? k : nn - k);
+    };
+  }
+
+  randomBinomial.source = sourceRandomBinomial;
+
+  return randomBinomial;
+})(defaultSource);

node_modules/d3-random/src/cauchy.js

@@ -0,0 +1,15 @@
+import defaultSource from "./defaultSource.js";
+
+export default (function sourceRandomCauchy(source) {
+  function randomCauchy(a, b) {
+    a = a == null ? 0 : +a;
+    b = b == null ? 1 : +b;
+    return function() {
+      return a + b * Math.tan(Math.PI * source());
+    };
+  }
+
+  randomCauchy.source = sourceRandomCauchy;
+
+  return randomCauchy;
+})(defaultSource);

node_modules/d3-random/src/defaultSource.js

@@ -0,0 +1 @@
+export default Math.random;

node_modules/d3-random/src/exponential.js

@@ -0,0 +1,13 @@
+import defaultSource from "./defaultSource.js";
+
+export default (function sourceRandomExponential(source) {
+  function randomExponential(lambda) {
+    return function() {
+      return -Math.log1p(-source()) / lambda;
+    };
+  }
+
+  randomExponential.source = sourceRandomExponential;
+
+  return randomExponential;
+})(defaultSource);

node_modules/d3-random/src/gamma.js

@@ -0,0 +1,34 @@
+import defaultSource from "./defaultSource.js";
+import normal from "./normal.js";
+
+export default (function sourceRandomGamma(source) {
+  var randomNormal = normal.source(source)();
+
+  function randomGamma(k, theta) {
+    if ((k = +k) < 0) throw new RangeError("invalid k");
+    // degenerate distribution if k === 0
+    if (k === 0) return () => 0;
+    theta = theta == null ? 1 : +theta;
+    // exponential distribution if k === 1
+    if (k === 1) return () => -Math.log1p(-source()) * theta;
+
+    var d = (k < 1 ? k + 1 : k) - 1 / 3,
+        c = 1 / (3 * Math.sqrt(d)),
+        multiplier = k < 1 ? () => Math.pow(source(), 1 / k) : () => 1;
+    return function() {
+      do {
+        do {
+          var x = randomNormal(),
+              v = 1 + c * x;
+        } while (v <= 0);
+        v *= v * v;
+        var u = 1 - source();
+      } while (u >= 1 - 0.0331 * x * x * x * x && Math.log(u) >= 0.5 * x * x + d * (1 - v + Math.log(v)));
+      return d * v * multiplier() * theta;
+    };
+  }
+
+  randomGamma.source = sourceRandomGamma;
+
+  return randomGamma;
+})(defaultSource);

node_modules/d3-random/src/geometric.js

@@ -0,0 +1,17 @@
+import defaultSource from "./defaultSource.js";
+
+export default (function sourceRandomGeometric(source) {
+  function randomGeometric(p) {
+    if ((p = +p) < 0 || p > 1) throw new RangeError("invalid p");
+    if (p === 0) return () => Infinity;
+    if (p === 1) return () => 1;
+    p = Math.log1p(-p);
+    return function() {
+      return 1 + Math.floor(Math.log1p(-source()) / p);
+    };
+  }
+
+  randomGeometric.source = sourceRandomGeometric;
+
+  return randomGeometric;
+})(defaultSource);

node_modules/d3-random/src/index.js

@@ -0,0 +1,18 @@
+export {default as randomUniform} from "./uniform.js";
+export {default as randomInt} from "./int.js";
+export {default as randomNormal} from "./normal.js";
+export {default as randomLogNormal} from "./logNormal.js";
+export {default as randomBates} from "./bates.js";
+export {default as randomIrwinHall} from "./irwinHall.js";
+export {default as randomExponential} from "./exponential.js";
+export {default as randomPareto} from "./pareto.js";
+export {default as randomBernoulli} from "./bernoulli.js";
+export {default as randomGeometric} from "./geometric.js";
+export {default as randomBinomial} from "./binomial.js";
+export {default as randomGamma} from "./gamma.js";
+export {default as randomBeta} from "./beta.js";
+export {default as randomWeibull} from "./weibull.js";
+export {default as randomCauchy} from "./cauchy.js";
+export {default as randomLogistic} from "./logistic.js";
+export {default as randomPoisson} from "./poisson.js";
+export {default as randomLcg} from "./lcg.js";

node_modules/d3-random/src/int.js

@@ -0,0 +1,16 @@
+import defaultSource from "./defaultSource.js";
+
+export default (function sourceRandomInt(source) {
+  function randomInt(min, max) {
+    if (arguments.length < 2) max = min, min = 0;
+    min = Math.floor(min);
+    max = Math.floor(max) - min;
+    return function() {
+      return Math.floor(source() * max + min);
+    };
+  }
+
+  randomInt.source = sourceRandomInt;
+
+  return randomInt;
+})(defaultSource);

node_modules/d3-random/src/irwinHall.js

@@ -0,0 +1,15 @@
+import defaultSource from "./defaultSource.js";
+
+export default (function sourceRandomIrwinHall(source) {
+  function randomIrwinHall(n) {
+    if ((n = +n) <= 0) return () => 0;
+    return function() {
+      for (var sum = 0, i = n; i > 1; --i) sum += source();
+      return sum + i * source();
+    };
+  }
+
+  randomIrwinHall.source = sourceRandomIrwinHall;
+
+  return randomIrwinHall;
+})(defaultSource);

node_modules/d3-random/src/lcg.js

@@ -0,0 +1,9 @@
+// https://en.wikipedia.org/wiki/Linear_congruential_generator#Parameters_in_common_use
+const mul = 0x19660D;
+const inc = 0x3C6EF35F;
+const eps = 1 / 0x100000000;
+
+export default function lcg(seed = Math.random()) {
+  let state = (0 <= seed && seed < 1 ? seed / eps : Math.abs(seed)) | 0;
+  return () => (state = mul * state + inc | 0, eps * (state >>> 0));
+}

node_modules/d3-random/src/logNormal.js

@@ -0,0 +1,17 @@
+import defaultSource from "./defaultSource.js";
+import normal from "./normal.js";
+
+export default (function sourceRandomLogNormal(source) {
+  var N = normal.source(source);
+
+  function randomLogNormal() {
+    var randomNormal = N.apply(this, arguments);
+    return function() {
+      return Math.exp(randomNormal());
+    };
+  }
+
+  randomLogNormal.source = sourceRandomLogNormal;
+
+  return randomLogNormal;
+})(defaultSource);

node_modules/d3-random/src/logistic.js

@@ -0,0 +1,16 @@
+import defaultSource from "./defaultSource.js";
+
+export default (function sourceRandomLogistic(source) {
+  function randomLogistic(a, b) {
+    a = a == null ? 0 : +a;
+    b = b == null ? 1 : +b;
+    return function() {
+      var u = source();
+      return a + b * Math.log(u / (1 - u));
+    };
+  }
+
+  randomLogistic.source = sourceRandomLogistic;
+
+  return randomLogistic;
+})(defaultSource);

node_modules/d3-random/src/normal.js

@@ -0,0 +1,28 @@
+import defaultSource from "./defaultSource.js";
+
+export default (function sourceRandomNormal(source) {
+  function randomNormal(mu, sigma) {
+    var x, r;
+    mu = mu == null ? 0 : +mu;
+    sigma = sigma == null ? 1 : +sigma;
+    return function() {
+      var y;
+
+      // If available, use the second previously-generated uniform random.
+      if (x != null) y = x, x = null;
+
+      // Otherwise, generate a new x and y.
+      else do {
+        x = source() * 2 - 1;
+        y = source() * 2 - 1;
+        r = x * x + y * y;
+      } while (!r || r > 1);
+
+      return mu + sigma * y * Math.sqrt(-2 * Math.log(r) / r);
+    };
+  }
+
+  randomNormal.source = sourceRandomNormal;
+
+  return randomNormal;
+})(defaultSource);

node_modules/d3-random/src/pareto.js

@@ -0,0 +1,15 @@
+import defaultSource from "./defaultSource.js";
+
+export default (function sourceRandomPareto(source) {
+  function randomPareto(alpha) {
+    if ((alpha = +alpha) < 0) throw new RangeError("invalid alpha");
+    alpha = 1 / -alpha;
+    return function() {
+      return Math.pow(1 - source(), alpha);
+    };
+  }
+
+  randomPareto.source = sourceRandomPareto;
+
+  return randomPareto;
+})(defaultSource);

node_modules/d3-random/src/poisson.js

@@ -0,0 +1,27 @@
+import defaultSource from "./defaultSource.js";
+import binomial from "./binomial.js";
+import gamma from "./gamma.js";
+
+export default (function sourceRandomPoisson(source) {
+  var G = gamma.source(source),
+      B = binomial.source(source);
+
+  function randomPoisson(lambda) {
+    return function() {
+      var acc = 0, l = lambda;
+      while (l > 16) {
+        var n = Math.floor(0.875 * l),
+            t = G(n)();
+        if (t > l) return acc + B(n - 1, l / t)();
+        acc += n;
+        l -= t;
+      }
+      for (var s = -Math.log1p(-source()), k = 0; s <= l; ++k) s -= Math.log1p(-source());
+      return acc + k;
+    };
+  }
+
+  randomPoisson.source = sourceRandomPoisson;
+
+  return randomPoisson;
+})(defaultSource);

node_modules/d3-random/src/uniform.js

@@ -0,0 +1,17 @@
+import defaultSource from "./defaultSource.js";
+
+export default (function sourceRandomUniform(source) {
+  function randomUniform(min, max) {
+    min = min == null ? 0 : +min;
+    max = max == null ? 1 : +max;
+    if (arguments.length === 1) max = min, min = 0;
+    else max -= min;
+    return function() {
+      return source() * max + min;
+    };
+  }
+
+  randomUniform.source = sourceRandomUniform;
+
+  return randomUniform;
+})(defaultSource);

node_modules/d3-random/src/weibull.js

@@ -0,0 +1,22 @@
+import defaultSource from "./defaultSource.js";
+
+export default (function sourceRandomWeibull(source) {
+  function randomWeibull(k, a, b) {
+    var outerFunc;
+    if ((k = +k) === 0) {
+      outerFunc = x => -Math.log(x);
+    } else {
+      k = 1 / k;
+      outerFunc = x => Math.pow(x, k);
+    }
+    a = a == null ? 0 : +a;
+    b = b == null ? 1 : +b;
+    return function() {
+      return a + b * outerFunc(-Math.log1p(-source()));
+    };
+  }
+
+  randomWeibull.source = sourceRandomWeibull;
+
+  return randomWeibull;
+})(defaultSource);