Some tweaks

Author: gavinmacaulay

docs/WGFAST2025_demo.ipynb

@@ -60,7 +60,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "id": "2aacad82-0a13-4875-9078-a99e74309f05",
    "metadata": {
     "id": "2aacad82-0a13-4875-9078-a99e74309f05"
@@ -72,7 +72,7 @@
        "'0.9.1'"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -98,7 +98,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "id": "5e20146b-4e70-48a2-8d93-4715a59caf6f",
    "metadata": {
     "colab": {
@@ -146,7 +146,7 @@
        " 'Cu64 calibration sphere']"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -158,7 +158,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "id": "0771d377-547a-4fa3-bed1-a0374f74d88e",
    "metadata": {
     "colab": {
@@ -184,7 +184,7 @@
        " 'benchmark_model': 'mss'}"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -202,12 +202,12 @@
    "source": [
     "## Shapes from the literature\n",
     "\n",
-    "EchoSMs also contains other fish model data - with the aim of easing access to historical datasets easier and facilitating testing of new model implementations."
+    "EchoSMs also contains other fish model data - with the aim of easing access to historical datasets easier and facilitating testing of new model implementations. Cod A, B, C, and D are the model shapes used in the Clay & Horne (1992) KRM paper."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "id": "7b077a2d-77a0-4de5-b91a-a2ba9f757994",
    "metadata": {},
    "outputs": [
@@ -252,7 +252,7 @@
        " 'SkipjackTuna_backboneSkull_41.18cm']"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -263,7 +263,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "id": "1d8f70f2-85fb-42cb-a00d-0e23fd28ac8e",
    "metadata": {},
    "outputs": [
@@ -282,6 +282,14 @@
     "echosms.KRMdata().model('Cod').plot()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "4d482e99-a214-439c-8388-233cd7e91589",
+   "metadata": {},
+   "source": [
+    "There are also some DWBA model shapes available from echoSMs:"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 7,
@@ -408,7 +416,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 12,
    "id": "51addec9-dcb2-435e-9d55-dc22464fbbbb",
    "metadata": {
     "colab": {
@@ -422,7 +430,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "mss: 100%|███████████████████████████████████████████████████████████████████████████████████ [798/798; 89.50 models/s]\n"
+      "mss: 100%|███████████████████████████████████████████████████████████████████████████████████ [798/798; 92.86 models/s]\n"
      ]
     },
     {
@@ -452,7 +460,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 13,
    "id": "d1dc1ee4-accc-4b22-b0bd-5aa72c3db6ea",
    "metadata": {
     "colab": {
@@ -480,9 +488,17 @@
     "plt.legend(['echoSMs', 'benchmark']);"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "ac109413-9e40-4206-a613-b4b32e7ac60b",
+   "metadata": {},
+   "source": [
+    "Can also have multiple parameter arrays, for example radius and frequency:"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 14,
    "id": "56d33db0-ae04-400f-bb2d-1ad7f53e21b2",
    "metadata": {
     "colab": {
@@ -550,7 +566,7 @@
        "        194242.42424242, 196161.61616162, 198080.80808081, 200000.        ])}"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -561,9 +577,17 @@
     "parameters"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "0f096584-b6ce-4721-90cc-dba918e1deae",
+   "metadata": {},
+   "source": [
+    "These parameters define 10,000 model runs, so use the multiprocessing option to spread the calculations across CPU's"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 15,
    "id": "cb88a047-56b4-42a7-af83-427320346ef4",
    "metadata": {
     "colab": {
@@ -590,7 +614,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "de4e187694bb455094601a31baf20b3b",
+       "model_id": "16fff000d8004b62b6a223fbc46e9081",
        "version_major": 2,
        "version_minor": 0
       },
@@ -609,7 +633,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 16,
    "id": "fd686bd8-847c-4b6f-b7ef-c308f0261e39",
    "metadata": {
     "id": "fd686bd8-847c-4b6f-b7ef-c308f0261e39"
@@ -641,14 +665,6 @@
     "- 9+ scattering models\n",
     "- Simple and consistent interface"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "782973d9-56af-448f-9eda-35518d823769",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {