removed pull request trigger from codeql analysis

.github/workflows/codeql-analysis.yml

@@ -18,13 +18,6 @@ on:
       - production
       - staging
       - develop
-  pull_request:
-    # The branches below must be a subset of the branches above
-    branches:
-      - master
-      - production
-      - staging
-      - develop
   schedule:
     - cron: "42 19 * * 0"
 

.parcelrc

@@ -1,6 +0,0 @@
-{
-    "extends": "@parcel/config-default",
-    "transformers": {
-        "*.res": ["@parcel/transformer-raw"]
-    }
-}

packages/website/docs/Features/Functions.mdx

@@ -1,69 +1,101 @@
 ---
+title: Functions reference
 sidebar_position: 7
 ---
 
 import { SquiggleEditor } from "../../src/components/SquiggleEditor";
 
-# Squiggle Functions Reference
+_The source of truth for this document is [this file of code](https://github.com/quantified-uncertainty/squiggle/blob/develop/packages/squiggle-lang/src/rescript/ReducerInterface/ReducerInterface_GenericDistribution.res)_
 
-## Distributions
+# Inventory distributions
 
-### Normal distribution
+We provide starter distributions, computed symbolically. 
+
+## Normal distribution
 
 The `normal(mean, sd)` function creates a normal distribution with the given mean
 and standard deviation.
 
 <SquiggleEditor initialSquiggleString="normal(5, 1)" />
 
-### Uniform distribution
+### Validity
+- `sd > 0`
+
+## Uniform distribution
 
 The `uniform(low, high)` function creates a uniform distribution between the
 two given numbers.
 
 <SquiggleEditor initialSquiggleString="uniform(3, 7)" />
 
-### Lognormal distribution
+### Validity
+- `low < high`
+
+## Lognormal distribution
 
 The `lognormal(mu, sigma)` returns the log of a normal distribution with parameters
-mu and sigma. The log of lognormal(mu, sigma) is a normal distribution with parameters
+`mu` and `sigma`. The log of `lognormal(mu, sigma)` is a normal distribution with mean `mu` and standard deviation `sigma`.
-mean mu and standard deviation sigma.
 
 <SquiggleEditor initialSquiggleString="lognormal(0, 0.7)" />
 
-An alternative format is also available. The "to" notation creates a lognormal
+An alternative format is also available. The `to` notation creates a lognormal
 distribution with a 90% confidence interval between the two numbers. We add
 this convinience as lognormal distributions are commonly used in practice.
 
 <SquiggleEditor initialSquiggleString="2 to 10" />
 
+### Future feature: 
 Furthermore, it's also possible to create a lognormal from it's actual mean
 and standard deviation, using `lognormalFromMeanAndStdDev`.
 
 <SquiggleEditor initialSquiggleString="lognormalFromMeanAndStdDev(20, 10)" />
 
-### Beta distribution
+### Validity
+- `sigma > 0`
+- In `x to y` notation, `x < y`
 
-The `beta(a, b)` function creates a beta distribution with parameters a and b:
+## Beta distribution
+
+The `beta(a, b)` function creates a beta distribution with parameters `a` and `b`:
 
 <SquiggleEditor initialSquiggleString="beta(20, 20)" />
 
-### Exponential distribution
+### Validity
+- `a > 0`
+- `b > 0`
+- Empirically, we have noticed that numerical instability arises when `a < 1` or `b < 1`
 
-The `exponential(mean)` function creates an exponential distribution with the given
+## Exponential distribution
-mean.
+
+The `exponential(rate)` function creates an exponential distribution with the given
+rate.
 
 <SquiggleEditor initialSquiggleString="exponential(1)" />
 
-### The Triangular distribution
+### Validity
+- `rate > 0`
+
+## Triangular distribution
 
 The `triangular(a,b,c)` function creates a triangular distribution with lower
-bound a, mode b and upper bound c.
+bound `a`, mode `b` and upper bound `c`.
+
+### Validity
+- `a < b < c`
 
 <SquiggleEditor initialSquiggleString="triangular(1, 2, 4)" />
 
-### Multimodal distriutions
+## Scalar (constant dist)
 
-The multimodal function combines 2 or more other distributions to create a weighted
+Squiggle, when the context is right, automatically casts a float to a constant distribution. 
+
+# Operating on distributions
+
+Here are the ways we combine distributions. 
+
+## Mixture of distributions
+
+The `mx` function combines 2 or more other distributions to create a weighted
 combination of the two. The first positional arguments represent the distributions
 to be combined, and the last argument is how much to weigh every distribution in the
 combination.
@@ -78,37 +110,114 @@ As well as mixed distributions:
 
 <SquiggleEditor initialSquiggleString="mx(3, 8, 1 to 10, [0.2, 0.3, 0.5])" />
 
-## Other Functions
+An alias of `mx` is `mixture` 
 
-### PDF of a distribution
+### Validity
+Using javascript's variable arguments notation, consider `mx(...dists, weights)`: 
+- `dists.length == weights.length`
 
-The `pdf(distribution, x)` function returns the density of a distribution at the
+## Addition (horizontal right shift)
+
+<SquiggleEditor initialSquiggleString="dist1 = 1 to 10; dist2 = triangular(1,2,3); dist1 + dist2">
+
+## Subtraction (horizontal left shift)
+
+<SquiggleEditor initialSquiggleString="dist1 = 1 to 10; dist2 = triangular(1,2,3); dist1 - dist2">
+
+## Multiplication (??) 
+
+<SquiggleEditor initialSquiggleString="dist1 = 1 to 10; dist2 = triangular(1,2,3); dist1 * dist2">
+
+## Division (???)
+
+<SquiggleEditor initialSquiggleString="dist1 = 1 to 10; dist2 = triangular(1,2,3); dist1 / dist2">
+
+## Taking the base `e` exponential
+
+<SquiggleEditor initialSquiggleString="dist = triangular(1,2,3); exp(dist)">
+
+## Taking the base `e` and base `10` logarithm 
+
+<SquiggleEditor initialSquiggleString="dist = triangular(1,2,3); log(dist)">
+
+<SquiggleEditor initialSquiggleString="dist = beta(1,2); log10(dist)">
+
+### Validity
+- See [the current discourse](https://github.com/quantified-uncertainty/squiggle/issues/304)
+
+# Standard functions on distributions 
+
+## Probability density function
+
+The `pdf(dist, x)` function returns the density of a distribution at the
 given point x.
 
 <SquiggleEditor initialSquiggleString="pdf(normal(0,1),0)" />
 
-### Inverse of a distribution
+### Validity
+- `x` must be a scalar
+- `dist` must be a distribution
 
-The `inv(distribution, prob)` gives the value x or which the probability for all values
+## Cumulative density function
-lower than x is equal to prob. It is the inverse of `cdf`.
 
-<SquiggleEditor initialSquiggleString="inv(normal(0,1),0.5)" />
+The `cdf(dist, x)` gives the cumulative probability of the distribution
-
-### CDF of a distribution
-
-The `cdf(distribution,x)` gives the cumulative probability of the distribution
 or all values lower than x. It is the inverse of `inv`.
 
 <SquiggleEditor initialSquiggleString="cdf(normal(0,1),0)" />
 
-### Mean of a distribution
+### Validity
+- `x` must be a scalar
+- `dist` must be a distribution
+
+## Inverse CDF
+
+The `inv(dist, prob)` gives the value x or which the probability for all values
+lower than x is equal to prob. It is the inverse of `cdf`.
+
+<SquiggleEditor initialSquiggleString="inv(normal(0,1),0.5)" />
+
+### Validity
+- `prob` must be a scalar (please only put it in `(0,1)`)
+- `dist` must be a distribution
+
+## Mean
 
 The `mean(distribution)` function gives the mean (expected value) of a distribution.
 
 <SquiggleEditor initialSquiggleString="mean(normal(5, 10))" />
 
-### Sampling a distribution
+## Sampling a distribution
 
 The `sample(distribution)` samples a given distribution.
 
 <SquiggleEditor initialSquiggleString="sample(normal(0, 10))" />
+
+# Normalization
+
+Some distribution operations (like horizontal shift) return an unnormalized distriibution. 
+
+We provide a `normalize` function
+
+<SquiggleEditor initialSquiggleString="normalize((1e-1 to 1e0) + triangular(1e-1, 1e0, 1e1))" />
+### Valdity
+- Input to `normalize` must be a dist
+
+We provide a predicate `isNormalized`, for when we have simple control flow 
+
+<SquiggleEditor initialSquiggleString="isNormalized((1e-1 to 1e0) * triangular(1e-1, 1e0, 1e1))" />
+
+### Validity 
+- Input to `isNormalized` must be a dist
+
+# Convert any distribution to a sample set distribution
+
+`toSampleSet` has two signatures
+
+It is unary when you use an internal hardcoded number of samples
+
+<SquiggleEditor initialSquiggleString="toSampleSet(1e-1 to 1e0)" />
+
+And binary when you provide a number of samples (truncated)
+
+<SquiggleEditor initialSquiggleString="toSampleSet(1e-1 to 1e0, 1e2)" />
+

packages/website/docs/Internal/Invariants.md

@@ -1,5 +1,5 @@
 ---
-title: Statistical properties of algebraic combinations of distributions for property testing.
+title: Invariants
 urlcolor: blue
 author:
   - Nuño Sempere
@@ -7,8 +7,12 @@ author:
 abstract: This document outlines some properties about algebraic combinations of distributions. It is meant to facilitate property tests for [Squiggle](https://squiggle-language.com/), an estimation language for forecasters. So far, we are focusing on the means, the standard deviation and the shape of the pdfs.
 ---
 
+Invariants to check with property tests. 
+
 _This document right now is normative and aspirational, not a description of the testing that's currently done_.
 
+# Algebraic combinations
+
 The academic keyword to search for in relation to this document is "[algebra of random variables](https://wikiless.org/wiki/Algebra_of_random_variables?lang=en)". Squiggle doesn't yet support getting the standard deviation, denoted by $\sigma$, but such support could yet be added.
 
 ## Means and standard deviations
@@ -118,6 +122,20 @@ TODO
 
 TODO
 
+# `pdf`, `cdf`, and `inv` 
+
+With $\forall dist, pdf := x \mapsto \texttt{pdf}(dist, x) \land cdf := x \mapsto \texttt{cdf}(dist, x) \land inv := p \mapsto \texttt{inv}(dist, p)$, 
+
+## `cdf` and `inv` are inverses
+$$
+\forall x \in (0,1), cdf(inv(x)) = x \land \forall x \in \texttt{dom}(cdf), x = inv(cdf(x))
+$$
+
+## The codomain of `cdf` equals the open interval `(0,1)` equals the codomain of `pdf`
+$$
+\texttt{cod}(cdf) = (0,1) = \texttt{cod}(pdf)
+$$
+
 # To do:
 
 - Provide sources or derivations, useful as this document becomes more complicated