note units of max size

arjunsavel · Nov 8, 2023 · 3c7a263 · 3c7a263
1 parent 8020d64
commit 3c7a263
Showing 1 changed file with 96 additions and 20 deletions.
diff --git a/docs/pages/optimizing.ipynb b/docs/pages/optimizing.ipynb
@@ -3,15 +3,23 @@
   {
    "cell_type": "markdown",
    "id": "c72aa35f-e048-4b21-8dbb-543f2784a7c1",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
    "source": [
     "# Optimizing fits"
    ]
   },
   {
    "cell_type": "markdown",
    "id": "ce5e8765-93d8-469d-b489-ed33402fd4c9",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
    "source": [
     "At this point, we know how to fit functions to opacity dependence as a function of temperature, pressure, and wavelength. The question remains: Is our fit as good as it can be?\n",
     "\n",
@@ -25,7 +33,11 @@
   {
    "cell_type": "markdown",
    "id": "a0eb8ec2-c1e6-431d-a51e-d3de9c363f2c",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
    "source": [
     "## Setting up the fit"
    ]
@@ -34,7 +46,11 @@
    "cell_type": "code",
    "execution_count": 1,
    "id": "465e0289-4f8f-4785-9227-1291fbdd70ca",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -58,7 +74,11 @@
    "cell_type": "code",
    "execution_count": 2,
    "id": "9d382ad1-9356-41ad-95ec-175be6835eff",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [
     {
      "name": "stderr",
@@ -88,7 +108,11 @@
    "cell_type": "code",
    "execution_count": 3,
    "id": "d1d67aca-ae6f-4597-8181-c989d738620c",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -124,7 +148,11 @@
   {
    "cell_type": "markdown",
    "id": "aa5a4c0f-bcaf-4bae-8529-12ddb3ed2ac8",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
    "source": [
     "Things look pretty good — let's check to be sure."
    ]
@@ -133,7 +161,11 @@
    "cell_type": "code",
    "execution_count": 4,
    "id": "287fa918-89a3-4e35-9839-edcee1167d8d",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -184,7 +216,11 @@
   {
    "cell_type": "markdown",
    "id": "ff68ce2e-2524-493a-aa9b-55be0531a0d8",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
    "source": [
     "## Optimization"
    ]
@@ -193,7 +229,11 @@
    "cell_type": "code",
    "execution_count": 5,
    "id": "923edfe8-6c3f-4612-9025-9fbfb9ade1a8",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "optimizer = Optimizer(fitter)"
@@ -202,7 +242,11 @@
   {
    "cell_type": "markdown",
    "id": "b3dadac9-7d03-4530-84e6-e9ac09fbfb44",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
    "source": [
     "Next, we need to restrict how many hyperparameters the `Optimizer` tests. \n",
     "\n",
@@ -213,16 +257,24 @@
    "cell_type": "code",
    "execution_count": 6,
    "id": "e193f1ca-4488-4526-95bd-9cb4c72d35d6",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
-    "max_size = 1.6"
+    "max_size = 1.6  # in KB"
    ]
   },
   {
    "cell_type": "markdown",
    "id": "2be5ac80-23d4-434c-a850-8116f724f703",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
    "source": [
     "In addition to this parameter, we need to set the `max_evaluations` parameter. This parameter sets how many models the `Optimizer` tests, essentially setting how long the optimization takes. For the purposes of this tutorial, we'll set a relatively low number. Note that this is an upper bound — setting `max_evaluations = 8`, for instance, will only run 2 model evaluations."
    ]
@@ -231,7 +283,11 @@
    "cell_type": "code",
    "execution_count": 7,
    "id": "55ceac04-e41e-489f-8e2d-75862552eb39",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "max_evaluations = 16"
@@ -240,7 +296,11 @@
   {
    "cell_type": "markdown",
    "id": "a11cdfa2-db6f-4eff-8967-fd718dd7d5fa",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
    "source": [
     "Now, it's time to run the optimizer!"
    ]
@@ -249,7 +309,11 @@
    "cell_type": "code",
    "execution_count": 8,
    "id": "9850ba7d-1ad9-4db4-a53c-004af1f3995d",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -291,7 +355,11 @@
   {
    "cell_type": "markdown",
    "id": "a0f11df9-2c38-43d7-aad1-f2966d21746e",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
    "source": [
     "This took a couple of minutes on a laptop. Let's look at those best-fit parameters."
    ]
@@ -300,7 +368,11 @@
    "cell_type": "code",
    "execution_count": 9,
    "id": "ff63a90b-a667-4779-838c-0dcaa918c698",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -320,7 +392,11 @@
   {
    "cell_type": "markdown",
    "id": "cb6f7c1e-fa37-4e7e-84c5-1dffcb7b9416",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
    "source": [
     "Note that if you'd like, you can set custom ranges for each of the neural net parameters."
    ]