Refactor constraints

.pre-commit-config.yaml

@@ -2,28 +2,28 @@ default_language_version:
     python: python3
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.4.0
+    rev: v5.0.0
     hooks:
       - id: check-yaml
       - id: end-of-file-fixer
       - id: trailing-whitespace
   - repo: https://github.com/ambv/black
-    rev: 23.1.0
+    rev: 24.10.0
     hooks:
       - id: black
         language_version: python3
       - id: black-jupyter
         language_version: python3
   - repo: https://github.com/pycqa/flake8
-    rev: 6.0.0
+    rev: 7.1.1
     hooks:
       - id: flake8
   - repo: https://github.com/pycqa/isort
-    rev: 5.12.0
+    rev: 5.13.2
     hooks:
       - id: isort
         args: ["--profile", "black"]
   - repo: https://github.com/kynan/nbstripout
-    rev: 0.6.1
+    rev: 0.8.1
     hooks:
       - id: nbstripout

docs/source/tutorials/hyperparameters.ipynb

@@ -3,7 +3,7 @@
   {
    "attachments": {},
    "cell_type": "markdown",
-   "id": "e7b5e13a-c059-441d-8d4f-fff080d52054",
+   "id": "0",
    "metadata": {},
    "source": [
     "# Hyperparameters\n",
@@ -14,7 +14,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "22438de8",
+   "id": "1",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -37,7 +37,7 @@
   {
    "attachments": {},
    "cell_type": "markdown",
-   "id": "7a88c7bd",
+   "id": "2",
    "metadata": {},
    "source": [
     "The optimization goes faster if our model understands how the function changes as we change the inputs in different ways. The way it picks up on this is by starting from a general model that could describe a lot of functions, and making it specific to this one by choosing the right hyperparameters. Our Bayesian agent is very good at this, and only needs a few samples to figure out what the function looks like:"
@@ -46,7 +46,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "7e9c949e",
+   "id": "3",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -60,7 +60,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "071a829f-a390-40dc-9d5b-ae75702e119e",
+   "id": "4",
    "metadata": {
     "tags": []
    },
@@ -97,7 +97,7 @@
   {
    "attachments": {},
    "cell_type": "markdown",
-   "id": "9ab3be01",
+   "id": "5",
    "metadata": {},
    "source": [
     "In addition to modeling the fitness of the task, the agent models the probability that an input will be feasible:"
@@ -106,7 +106,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "bc53bf67",
+   "id": "6",
    "metadata": {
     "tags": []
    },
@@ -118,7 +118,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ebc65169",
+   "id": "7",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -129,7 +129,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "b70eaf9b",
+   "id": "8",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -153,7 +153,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.9.20"
   },
   "vscode": {
    "interpreter": {

docs/source/tutorials/introduction.ipynb

@@ -3,7 +3,7 @@
   {
    "attachments": {},
    "cell_type": "markdown",
-   "id": "e7b5e13a-c059-441d-8d4f-fff080d52054",
+   "id": "0",
    "metadata": {},
    "source": [
     "# Introduction (Himmelblau's function)\n",
@@ -13,7 +13,7 @@
   {
    "attachments": {},
    "cell_type": "markdown",
-   "id": "c18ef717",
+   "id": "1",
    "metadata": {},
    "source": [
     "Let's use ``blop`` to minimize Himmelblau's function, which has four global minima:"
@@ -22,7 +22,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "cf27fc9e-d11c-40f4-a200-98e7814f506b",
+   "id": "2",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -34,7 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "22438de8",
+   "id": "3",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -56,7 +56,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2500c410",
+   "id": "4",
    "metadata": {},
    "source": [
     "There are several things that our agent will need. The first ingredient is some degrees of freedom (these are always `ophyd` devices) which the agent will move around to different inputs within each DOF's bounds (the second ingredient). We define these here:"
@@ -65,7 +65,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "5d6df7a4",
+   "id": "5",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -79,7 +79,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "54b6f23e",
+   "id": "6",
    "metadata": {},
    "source": [
     "We also need to give the agent something to do. We want our agent to look in the feedback for a variable called 'himmelblau', and try to minimize it."
@@ -88,7 +88,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c8556bc9",
+   "id": "7",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -100,7 +100,7 @@
   {
    "attachments": {},
    "cell_type": "markdown",
-   "id": "7a88c7bd",
+   "id": "8",
    "metadata": {},
    "source": [
     "In our digestion function, we define our objective as a deterministic function of the inputs:"
@@ -109,7 +109,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e6bfcf73",
+   "id": "9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -123,7 +123,7 @@
   {
    "attachments": {},
    "cell_type": "markdown",
-   "id": "0d3d91c3",
+   "id": "10",
    "metadata": {},
    "source": [
     "We then combine these ingredients into an agent, giving it an instance of ``databroker`` so that it can see the output of the plans it runs."
@@ -132,7 +132,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "071a829f-a390-40dc-9d5b-ae75702e119e",
+   "id": "11",
    "metadata": {
     "tags": []
    },
@@ -150,7 +150,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "27685849",
+   "id": "12",
    "metadata": {},
    "source": [
     "Without any data, we can't make any inferences about what the function looks like, and so we can't use any non-trivial acquisition functions. Let's start by quasi-randomly sampling the parameter space, and plotting our model of the function:"
@@ -159,7 +159,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "996da937",
+   "id": "13",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -169,7 +169,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dc264346-10fb-4c88-9925-4bfcf0dd3b07",
+   "id": "14",
    "metadata": {},
    "source": [
     "To decide which points to sample, the agent needs an acquisition function. The available acquisition function are here:"
@@ -178,7 +178,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "fb06739b",
+   "id": "15",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -188,7 +188,7 @@
   {
    "attachments": {},
    "cell_type": "markdown",
-   "id": "ab608930",
+   "id": "16",
    "metadata": {},
    "source": [
     "Now we can start to learn intelligently. Using the shorthand acquisition functions shown above, we can see the output of a few different ones:"
@@ -197,7 +197,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "43b55f4f",
+   "id": "17",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -207,7 +207,7 @@
   {
    "attachments": {},
    "cell_type": "markdown",
-   "id": "18210f81-0e23-42b7-8589-77dc260e3131",
+   "id": "18",
    "metadata": {},
    "source": [
     "To decide where to go, the agent will find the inputs that maximize a given acquisition function:"
@@ -216,7 +216,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "b902172e-e89c-4346-89f3-bf9571cba6b3",
+   "id": "19",
    "metadata": {
     "tags": []
    },
@@ -228,7 +228,7 @@
   {
    "attachments": {},
    "cell_type": "markdown",
-   "id": "9a888385-4e09-4fea-9282-cd6a6fe2c3df",
+   "id": "20",
    "metadata": {},
    "source": [
     "We can also ask the agent for multiple points to sample and it will jointly maximize the acquisition function over all sets of inputs, and find the most efficient route between them:"
@@ -237,7 +237,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "28c5c0df",
+   "id": "21",
    "metadata": {
     "tags": []
    },
@@ -251,7 +251,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "23f3f7ef-c024-4ac1-9144-d0b6fb8a3944",
+   "id": "22",
    "metadata": {},
    "source": [
     "All of this is automated inside the ``learn`` method, which will find a point (or points) to sample, sample them, and retrain the model and its hyperparameters with the new data. To do 4 learning iterations of 8 points each, we can run"
@@ -260,7 +260,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ff1c5f1c",
+   "id": "23",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -269,7 +269,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b52f3352-3b67-431c-b5af-057e02def5ba",
+   "id": "24",
    "metadata": {},
    "source": [
     "Our agent has found all the global minima of Himmelblau's function using Bayesian optimization, and we can ask it for the best point: "
@@ -278,7 +278,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "0d5cc0c8-33cf-4fb1-b91c-81828e249f6a",
+   "id": "25",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -303,7 +303,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.0"
+   "version": "3.9.20"
   },
   "vscode": {
    "interpreter": {