[pre-commit.ci] pre-commit autoupdate (#112)

* [pre-commit.ci] pre-commit autoupdate updates: - [github.com/kynan/nbstripout: 0.6.1 → 0.7.1](kynan/nbstripout@0.6.1...0.7.1) * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
exoplanet-dev · Feb 7, 2024 · 2ebe865 · 2ebe865
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diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
@@ -16,7 +16,7 @@ repos:
     hooks:
       - id: black-jupyter
   - repo: https://github.com/kynan/nbstripout
-    rev: "0.6.1"
+    rev: "0.7.1"
     hooks:
       - id: nbstripout
   - repo: https://github.com/hadialqattan/pycln

diff --git a/docs/tutorials/first.ipynb b/docs/tutorials/first.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "ad9d5df3",
+   "id": "0",
    "metadata": {},
    "source": [
     "(first)=\n",
@@ -20,7 +20,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "975491fc",
+   "id": "1",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -56,7 +56,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dc91b632",
+   "id": "2",
    "metadata": {},
    "source": [
     "Now, let's fit this dataset using a mixture of `SHOTerm` terms: one quasi-periodic component and one non-periodic component.\n",
@@ -66,7 +66,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "df8082ee",
+   "id": "3",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -89,7 +89,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4f6a8dfd",
+   "id": "4",
    "metadata": {},
    "source": [
     "Let's look at the underlying power spectral density of this initial model:"
@@ -98,7 +98,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e555464e",
+   "id": "5",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -122,7 +122,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1d88da99",
+   "id": "6",
    "metadata": {},
    "source": [
     "And then we can also plot the prediction that this model makes for the missing data and compare it to the truth:"
@@ -131,7 +131,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "4c549946",
+   "id": "7",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -158,7 +158,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3b249feb",
+   "id": "8",
    "metadata": {},
    "source": [
     "Ok, that looks pretty terrible, but we can get a better fit by numerically maximizing the likelihood as described in the following section.\n",
@@ -172,7 +172,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "3b28a76a",
+   "id": "9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -202,7 +202,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9af1515e",
+   "id": "10",
    "metadata": {},
    "source": [
     "Now let's make the same plots for the maximum likelihood model:"
@@ -211,7 +211,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "63ffb7a6",
+   "id": "11",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -226,7 +226,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "31c1fb90",
+   "id": "12",
    "metadata": {},
    "source": [
     "These predictions are starting to look much better!\n",
@@ -241,7 +241,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "34d4103a",
+   "id": "13",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -270,7 +270,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "45c9aa84",
+   "id": "14",
    "metadata": {},
    "source": [
     "After running our MCMC, we can plot the predictions that the model makes for a handful of samples from the chain.\n",
@@ -280,7 +280,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "86bc6816",
+   "id": "15",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -297,7 +297,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d443d8ed",
+   "id": "16",
    "metadata": {},
    "source": [
     "Similarly, we can plot the posterior expectation for the power spectral density:"
@@ -306,7 +306,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "6336d052",
+   "id": "17",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -325,7 +325,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f4b49974",
+   "id": "18",
    "metadata": {},
    "source": [
     "## Posterior inference using PyMC\n",
@@ -336,7 +336,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "7261591c",
+   "id": "19",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -382,7 +382,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e82ad4fc",
+   "id": "20",
    "metadata": {},
    "source": [
     "Like before, we can plot the posterior estimate of the power spectrum to show that the results are qualitatively similar:"
@@ -391,7 +391,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "85c008c7",
+   "id": "21",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -410,7 +410,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "81e50441",
+   "id": "22",
    "metadata": {},
    "source": [
     "## Posterior inference using numpyro\n",
@@ -421,7 +421,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c7840a39",
+   "id": "23",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -479,7 +479,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6f3abdcf",
+   "id": "24",
    "metadata": {},
    "source": [
     "This runtime was similar to the PyMC result from above, and (as we'll see below) the convergence is also similar.\n",
@@ -491,7 +491,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "0f58f1e2",
+   "id": "25",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -510,7 +510,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2781b23e",
+   "id": "26",
    "metadata": {},
    "source": [
     "## Comparison\n",
@@ -522,7 +522,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e29ff05b",
+   "id": "27",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -574,7 +574,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4e7f5ac4",
+   "id": "28",
    "metadata": {},
    "source": [
     "That looks pretty consistent.\n",
@@ -585,7 +585,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c1b8e18a",
+   "id": "29",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -606,7 +606,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "0969c015",
+   "id": "30",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -627,7 +627,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "337f182b",
+   "id": "31",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -647,7 +647,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "835b4542",
+   "id": "32",
    "metadata": {},
    "source": [
     "Overall these results are consistent, but the $\\hat{R}$ values are a bit high for the emcee run, so I'd probably run that for longer.\n",