deploy: f4fb40d

_sources/notebook/02-data.ipynb

@@ -451,10 +451,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from eddymotion.data.splitting import lovo_split as logo_split\n",
     "from eddymotion.viz import plot_dwi\n",
     "\n",
-    "data_train, data_test = dmri_dataset.logo_split(10)\n",
-    "plot_dwi(data_test[0], dmri_dataset.affine, gradient=data_test[1]);"
+    "data_train, data_test = logo_split(dmri_dataset, 10)\n",
+    "plot_dwi(np.squeeze(data_test[0]), dmri_dataset.affine, gradient=data_test[1]);"
    ]
   },
   {

_sources/notebook/03-models.ipynb

@@ -2,6 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
+   "id": "73bc033f",
    "metadata": {},
    "source": [
     "# Diffusion modeling"
@@ -10,6 +11,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "86034e8b",
    "metadata": {
     "tags": [
      "hide-cell"
@@ -18,12 +20,14 @@
    "outputs": [],
    "source": [
     "import warnings\n",
+    "import numpy as np\n",
     "\n",
     "warnings.filterwarnings(\"ignore\")"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "16e127b6",
    "metadata": {},
    "source": [
     "The proposed method requires inferring a motion-less, reference DW map for a given diffusion orientation for which we want to estimate the misalignment.\n",
@@ -46,16 +50,19 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "3225815e",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from eddymotion.dmri import DWI\n",
+    "from eddymotion.data.dmri import DWI\n",
+    "from eddymotion.data.splitting import lovo_split as logo_split\n",
     "from eddymotion.viz import plot_dwi\n",
     "dmri_dataset = DWI.from_filename(\"../../data/dwi.h5\")"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "1a677d9e",
    "metadata": {},
    "source": [
     "## Implementing a trivial model\n",
@@ -73,6 +80,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "2875c19b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -106,6 +114,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "4ddb8436",
    "metadata": {},
    "source": [
     "The model can easily be initialized as follows (assuming we still have our dataset loaded):"
@@ -114,6 +123,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "19da1464",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -125,6 +135,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "52034adb",
    "metadata": {},
    "source": [
     "Then, at each iteration of our estimation strategy, we will fit this model to the data, after holding one particular direction (`data_test`) out, using the `logo_split` method of the dataset. In every iteration, this finds the b=0 volumes in the data and averages their values in every voxel:"
@@ -133,15 +144,17 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "6a65d321",
    "metadata": {},
    "outputs": [],
    "source": [
-    "data_train, data_test = dmri_dataset.logo_split(10)\n",
+    "data_train, data_test = logo_split(dmri_dataset, 10)\n",
     "model.fit(data_train[0])"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "dc3f7299",
    "metadata": {},
    "source": [
     "Finally, we can generate our registration reference with the `predict()` method:"
@@ -150,6 +163,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "5229302c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -159,6 +173,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "dce0260f",
    "metadata": {},
    "source": [
     "As expected, the *b=0* doesn't look very much like the particular left-out direction, but it is a start!"
@@ -167,14 +182,16 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "9b6dd61a",
    "metadata": {},
    "outputs": [],
    "source": [
-    "plot_dwi(data_test[0], dmri_dataset.affine, gradient=data_test[1]);"
+    "plot_dwi(np.squeeze(data_test[0]), dmri_dataset.affine, gradient=data_test[1]);"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "231a0729",
    "metadata": {},
    "source": [
     "## Implementing a *regression to the mean* model\n",
@@ -187,6 +204,11 @@
    ]
   },
   {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "95130912",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "class AverageDWModel:\n",
     "    \"\"\"A trivial model that returns an average map.\"\"\"\n",
@@ -204,14 +226,11 @@
     "    def predict(self, gradient, **kwargs):\n",
     "        \"\"\"Return the average map.\"\"\"\n",
     "        return self._data"
-   ],
-   "cell_type": "code",
-   "metadata": {},
-   "execution_count": null,
-   "outputs": []
+   ]
   },
   {
    "cell_type": "markdown",
+   "id": "893ae6de",
    "metadata": {},
    "source": [
     "**Exercise**\n",
@@ -224,6 +243,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "77c74c0e",
    "metadata": {
     "tags": [
      "hide-cell"
@@ -234,6 +254,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "63a5bc6b",
    "metadata": {},
    "source": [
     "## Investigating the tensor model\n",
@@ -245,6 +266,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "8dda846f",
    "metadata": {
     "tags": [
      "remove-cell"
@@ -267,11 +289,12 @@
     "\n",
     "dmri_dataset = DWI.from_filename(datapath)\n",
     "datapath.unlink()\n",
-    "data_train, data_test = dmri_dataset.logo_split(88, with_b0=True)"
+    "data_train, data_test = logo_split(dmri_dataset, 88, with_b0=True)"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "f9b5a9b0",
    "metadata": {},
    "source": [
     "### The model factory\n",
@@ -283,6 +306,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "3a957d5e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -299,6 +323,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "3157aa22",
    "metadata": {},
    "source": [
     "### Leveraging the `fit()` / `predict()` API\n",
@@ -309,6 +334,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "615b8a23",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -318,6 +344,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "1428316d",
    "metadata": {},
    "source": [
     "Now, the predicted map for the particular ***b*** gradient looks much closer to the original:"
@@ -326,6 +353,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "2d1c921a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -334,6 +362,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "17ac6e64",
    "metadata": {},
    "source": [
     "Here's the original DW map, for reference:"
@@ -342,14 +371,16 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "41078b66",
    "metadata": {},
    "outputs": [],
    "source": [
-    "plot_dwi(data_test[0], dmri_dataset.affine, gradient=data_test[1]);"
+    "plot_dwi(np.squeeze(data_test[0]), dmri_dataset.affine, gradient=data_test[1]);"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "d1603ab8",
    "metadata": {},
    "source": [
     "**Exercise**\n",
@@ -361,6 +392,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "25b9ff4d",
    "metadata": {
     "tags": [
      "hide-cell"
@@ -371,6 +403,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "40c6b7ec",
    "metadata": {},
    "source": [
     "**Exercise**\n",
@@ -383,12 +416,14 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "267008fe",
    "metadata": {},
    "outputs": [],
    "source": []
   },
   {
    "cell_type": "markdown",
+   "id": "9b947d74",
    "metadata": {},
    "source": [
     "## Next steps: image registration\n",
@@ -399,11 +434,23 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}