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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]);" ] }, { diff --git a/_sources/notebook/03-models.ipynb b/_sources/notebook/03-models.ipynb index afe79c3..4e6850a 100644 --- a/_sources/notebook/03-models.ipynb +++ b/_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 -} \ No newline at end of file +} diff --git a/_sources/notebook/04-registration.ipynb b/_sources/notebook/04-registration.ipynb index 047183c..9325b43 100644 --- a/_sources/notebook/04-registration.ipynb +++ b/_sources/notebook/04-registration.ipynb @@ -2,6 +2,7 @@ "cells": [ { "cell_type": "markdown", + "id": "d5cde43a", "metadata": {}, "source": [ "# Image registration (spatial alignment)" @@ -10,6 +11,7 @@ { "cell_type": "code", "execution_count": null, + "id": "795729f6", "metadata": { "tags": [ "remove-cell" @@ -24,7 +26,8 @@ "import numpy as np\n", "import nibabel as nb\n", "\n", - "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", "from eddymotion.estimator import _advanced_clip\n", "\n", @@ -56,6 +59,7 @@ }, { "cell_type": "markdown", + "id": "e94a2727", "metadata": {}, "source": [ "At this point of the tutorial we have covered two of the three initial requirements:\n", @@ -147,12 +151,13 @@ { "cell_type": "code", "execution_count": null, + "id": "1af4766c", "metadata": {}, "outputs": [], "source": [ "from eddymotion.model import ModelFactory\n", "\n", - "data_train, data_test = dmri_dataset.logo_split(7, with_b0=True)\n", + "data_train, data_test = logo_split(dmri_dataset, 7, with_b0=True)\n", "\n", "model = ModelFactory.init(\n", " gtab=data_train[1],\n", @@ -165,6 +170,7 @@ }, { "cell_type": "markdown", + "id": "098e67d5", "metadata": {}, "source": [ "Since we are using the command-line interface of ANTs, the software must be installed in the computer and the input data is provided via files in the filesystem.\n", @@ -174,6 +180,7 @@ { "cell_type": "code", "execution_count": null, + "id": "18c4df34", "metadata": {}, "outputs": [], "source": [ @@ -193,6 +200,7 @@ }, { "cell_type": "markdown", + "id": "0e1f3e8e", "metadata": {}, "source": [ "We can now visualize our reference (the prediction) and the actual DW map.\n", @@ -202,10 +210,11 @@ { "cell_type": "code", "execution_count": null, + "id": "a8768046", "metadata": {}, "outputs": [], "source": [ - "from niworkflows.viz.notebook import display\n", + "from nireports.reportlets.notebook import display\n", "\n", "display(\n", " fixed_path,\n", @@ -217,6 +226,7 @@ }, { "cell_type": "markdown", + "id": "0adb200c", "metadata": {}, "source": [ "Let's configure ANTs via NiPype:" @@ -225,6 +235,7 @@ { "cell_type": "code", "execution_count": null, + "id": "17d86a04", "metadata": {}, "outputs": [], "source": [ @@ -238,6 +249,7 @@ { "cell_type": "code", "execution_count": null, + "id": "34c6537e", "metadata": {}, "outputs": [], "source": [ @@ -260,6 +272,7 @@ }, { "cell_type": "markdown", + "id": "a7f05826", "metadata": {}, "source": [ "which will run the following command-line:" @@ -268,6 +281,7 @@ { "cell_type": "code", "execution_count": null, + "id": "1f326a92", "metadata": {}, "outputs": [], "source": [ @@ -276,6 +290,7 @@ }, { "cell_type": "markdown", + "id": "4c9247b3", "metadata": {}, "source": [ "Nipype interfaces can be submitted for execution with the `run()` method:" @@ -284,6 +299,7 @@ { "cell_type": "code", "execution_count": null, + "id": "1758610e", "metadata": {}, "outputs": [], "source": [ @@ -292,6 +308,7 @@ }, { "cell_type": "markdown", + "id": "3b297ba2", "metadata": {}, "source": [ "If everything worked out, we can now retrieve the aligned file with the output `result.outputs.warped_image`.\n", @@ -301,6 +318,7 @@ { "cell_type": "code", "execution_count": null, + "id": "666b4795", "metadata": {}, "outputs": [], "source": [ @@ -314,6 +332,7 @@ }, { "cell_type": "markdown", + "id": "45c27711", "metadata": {}, "source": [ "## Resampling an image\n", @@ -333,6 +352,7 @@ { "cell_type": "code", "execution_count": null, + "id": "8bad9b5a", "metadata": {}, "outputs": [], "source": [ @@ -345,6 +365,7 @@ }, { "cell_type": "markdown", + "id": "b57b6a98", "metadata": {}, "source": [ "Resampling an image requires two pieces of information: the *reference* image (which provides the new grid where we want to have the data) and the *moving* image which contains the actual data we are interested in:" @@ -353,6 +374,7 @@ { "cell_type": "code", "execution_count": null, + "id": "19796849", "metadata": {}, "outputs": [], "source": [ @@ -370,6 +392,7 @@ }, { "cell_type": "markdown", + "id": "b1a1d770", "metadata": {}, "source": [ "**Exercise**\n", @@ -382,6 +405,7 @@ { "cell_type": "code", "execution_count": null, + "id": "9827cd66", "metadata": { "tags": [ "hide-cell" @@ -393,11 +417,23 @@ ], "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 -} \ No newline at end of file +} diff --git a/_sources/tutorial/data.md b/_sources/tutorial/data.md index 16d0c02..ce4d005 100644 --- a/_sources/tutorial/data.md +++ b/_sources/tutorial/data.md @@ -120,7 +120,7 @@ Let's get some insights from it: ```{code-cell} python # import the class from the library -from eddymotion.dmri import DWI +from eddymotion.data.dmri import DWI # load the sample file dmri_dataset = DWI.from_filename("../../data/dwi.h5") @@ -301,10 +301,11 @@ def logo_split(self, index, with_b0=False): This function is contained in the `DWI` class shown earlier and will allow us to easily partition the dataset as follows: ```{code-cell} python +from eddymotion.data.splitting import lovo_split as logo_split from eddymotion.viz import plot_dwi -data_train, data_test = dmri_dataset.logo_split(10) -plot_dwi(data_test[0], dmri_dataset.affine, gradient=data_test[1]); +data_train, data_test = logo_split(dmri_dataset, 10) +plot_dwi(np.squeeze(data_test[0]), dmri_dataset.affine, gradient=data_test[1]); ``` `data_train` is a tuple containing all diffusion-weighted volumes and the corresponding gradient table, excluding the left-out, which is stored in `data_test` (the 11th gradient indexed by `10`, in this example). diff --git a/_sources/tutorial/intro.md b/_sources/tutorial/intro.md index 29f65c4..190c1cb 100644 --- a/_sources/tutorial/intro.md +++ b/_sources/tutorial/intro.md @@ -94,6 +94,8 @@ Briefly anticipate further requirements that are important, but do not alter the - Parallelism: simulation and registration are CPU-intensive processes - for the runtime to be in a manageable scale, we'll need to leverage parallelism. ``` +![eddymotion](https://raw.githubusercontent.com/nipreps/eddymotion/507fc9bab86696d5330fd6a86c3870968243aea8/docs/_static/eddymotion-flowchart.svg) + ### Step 2: Sketch out an API (Application Programming Interface) Plan how the new software will expose the implementation downstream. Assuming our DWI data is encapsulated in an object (holding not just the data array, but also metadata such as the gradient table) diff --git a/_sources/tutorial/models.md b/_sources/tutorial/models.md index 20393bf..81debf1 100644 --- a/_sources/tutorial/models.md +++ b/_sources/tutorial/models.md @@ -16,6 +16,7 @@ kernelspec: :tags: [remove-cell] import warnings +import numpy as np warnings.filterwarnings("ignore") ``` @@ -37,7 +38,8 @@ We must reload the dataset again to use it in this notebook. ``` ```{code-cell} python -from eddymotion.dmri import DWI +from eddymotion.data.dmri import DWI +from eddymotion.data.splitting import lovo_split as logo_split from eddymotion.viz import plot_dwi dmri_dataset = DWI.from_filename("../../data/dwi.h5") ``` @@ -94,8 +96,8 @@ model = TrivialB0Model( 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: ```{code-cell} python -data_train, data_test = dmri_dataset.logo_split(10) -model.fit(data_train[0]) +data_train, data_test = logo_split(dmri_dataset, 10) +model.fit(np.squeeze(data_train[0])) ``` Finally, we can generate our registration reference with the `predict()` method: @@ -108,7 +110,7 @@ plot_dwi(predicted, dmri_dataset.affine, gradient=data_test[1]); As expected, the *b=0* doesn't look very much like the particular left-out direction, but it is a start! ```{code-cell} python -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]); ``` ## Implementing a *regression to the mean* model @@ -174,7 +176,7 @@ model = AverageDWModel( model.fit(data_train[0]) predicted = model.predict(data_test[1]) plot_dwi(predicted, dmri_dataset.affine, gradient=data_test[1]); -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]); ``` ## Investigating the tensor model @@ -200,7 +202,7 @@ if datapath.stat().st_size == 0: dmri_dataset = DWI.from_filename(datapath) datapath.unlink() -data_train, data_test = dmri_dataset.logo_split(88, with_b0=True) +data_train, data_test = logo_split(dmri_dataset, 88, with_b0=True) ``` ### The model factory @@ -238,7 +240,7 @@ plot_dwi(predicted, dmri_dataset.affine, gradient=data_test[1], black_bg=True); Here's the original DW map, for reference: ```{code-cell} python -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]); ``` ```{admonition} Exercise @@ -263,7 +265,7 @@ Once the model has been initialized, we can easily generate a new prediction. model.fit(data_train[0]) predicted = model.predict(data_test[1]) plot_dwi(predicted, dmri_dataset.affine, gradient=data_test[1], black_bg=True); -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]); ``` ## Next steps: image registration diff --git a/_sources/tutorial/registration.md b/_sources/tutorial/registration.md index aa5d183..b39ee9e 100644 --- a/_sources/tutorial/registration.md +++ b/_sources/tutorial/registration.md @@ -23,7 +23,8 @@ from pathlib import Path import numpy as np import nibabel as nb -from eddymotion.dmri import DWI +from eddymotion.data.dmri import DWI +from eddymotion.data.splitting import lovo_split as logo_split from eddymotion.viz import plot_dwi from eddymotion.estimator import _advanced_clip @@ -143,7 +144,7 @@ For this example, we have selected the 8th DW map (`index=7`) because ```{code-cell} python from eddymotion.model import ModelFactory -data_train, data_test = dmri_dataset.logo_split(7, with_b0=True) +data_train, data_test = logo_split(dmri_dataset, 7, with_b0=True) model = ModelFactory.init( gtab=data_train[1], @@ -178,7 +179,7 @@ Please notice the subtle *nodding* of the head, perhaps more apparent when looki ```{code-cell} python :tags: [remove-output] -from niworkflows.viz.notebook import display +from nireports.reportlets.notebook import display display( fixed_path, diff --git a/notebook/02-data.html b/notebook/02-data.html index 1282821..8be4d3a 100644 --- a/notebook/02-data.html +++ b/notebook/02-data.html @@ -657,10 +657,11 @@

The LOGO (leave-one-gradient-out) splitterThis function is contained in the DWI class shown earlier and will allow us to easily partition the dataset as follows:

-
from eddymotion.viz import plot_dwi
+
from eddymotion.data.splitting import lovo_split as logo_split
+from eddymotion.viz import plot_dwi
 
-data_train, data_test = dmri_dataset.logo_split(10)
-plot_dwi(data_test[0], dmri_dataset.affine, gradient=data_test[1]);
+data_train, data_test = logo_split(dmri_dataset, 10)
+plot_dwi(np.squeeze(data_test[0]), dmri_dataset.affine, gradient=data_test[1]);
diff --git a/notebook/03-models.html b/notebook/03-models.html index 529c1d3..faf3214 100644 --- a/notebook/03-models.html +++ b/notebook/03-models.html @@ -414,7 +414,8 @@

Diffusion modelingHide code cell content
-
import warnings
+
import warnings
+import numpy as np
 
 warnings.filterwarnings("ignore")
 

@@ -437,7 +438,8 @@ 
Diffusion modeling
 

-
from eddymotion.dmri import DWI
+
from eddymotion.data.dmri import DWI
+from eddymotion.data.splitting import lovo_split as logo_split
 from eddymotion.viz import plot_dwi
 dmri_dataset = DWI.from_filename("../../data/dwi.h5")
 

@@ -455,7 +457,7 @@ 
Implementing a trivial model
 

-
class TrivialB0Model:
+
class TrivialB0Model:
     """
     A trivial model that returns a *b=0* map always.
 
@@ -488,7 +490,7 @@ 
Implementing a trivial model
 

-
model = TrivialB0Model(
+
model = TrivialB0Model(
     dmri_dataset.gradients,
     S0=dmri_dataset.bzero,
 )
@@ -499,7 +501,7 @@ 
Implementing a trivial modeldata_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:

 

 

-
data_train, data_test = dmri_dataset.logo_split(10)
+
data_train, data_test = logo_split(dmri_dataset, 10)
 model.fit(data_train[0])
 

 

@@ -508,7 +510,7 @@ 
Implementing a trivial modelpredict() method:

 

 

-
predicted = model.predict(data_test[1])
+
predicted = model.predict(data_test[1])
 plot_dwi(predicted, dmri_dataset.affine, gradient=data_test[1]);
 

 

@@ -517,7 +519,7 @@ 
Implementing a trivial model
 

-
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]);
 

 

 

@@ -530,7 +532,7 @@ 
Implementing a regression to the mean modelSolution

 

 

-
class AverageDWModel:
+
class AverageDWModel:
     """A trivial model that returns an average map."""
 
     __slots__ = ("_data",)
@@ -564,7 +566,7 @@ 
The model factoryModelFactory makes it easier for the user to switch between models:

 

 

-
from eddymotion.model import ModelFactory
+
from eddymotion.model import ModelFactory
 
 # We are using now a full dataset, we need to split the data again
 
@@ -583,7 +585,7 @@ 
Leveraging the 
 
The ModelFactory returns a model object that is compliant with the interface sketched above:

 

 

-
model.fit(data_train[0])
+
model.fit(data_train[0])
 predicted = model.predict(data_test[1])
 

 

@@ -592,7 +594,7 @@ 
Leveraging the 
 
Now, the predicted map for the particular b gradient looks much closer to the original:

 

 

-
plot_dwi(predicted, dmri_dataset.affine, gradient=data_test[1], black_bg=True);
+
plot_dwi(predicted, dmri_dataset.affine, gradient=data_test[1], black_bg=True);
 

 

 

@@ -600,7 +602,7 @@ 
Leveraging the 
 
Here’s the original DW map, for reference:

 

 

-
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]);
 

 

 

diff --git a/notebook/04-registration.html b/notebook/04-registration.html
index 0fe126c..af436c9 100644
--- a/notebook/04-registration.html
+++ b/notebook/04-registration.html
@@ -483,9 +483,9 @@ 
Example registrationth DW map (index=7) because it contains a sudden motion spike, resembling a nodding movement.

 

 

-
from eddymotion.model import ModelFactory
+
from eddymotion.model import ModelFactory
 
-data_train, data_test = dmri_dataset.logo_split(7, with_b0=True)
+data_train, data_test = logo_split(dmri_dataset, 7, with_b0=True)
 
 model = ModelFactory.init(
     gtab=data_train[1],
@@ -502,7 +502,7 @@ 
Example registration
 

 

-
from pathlib import Path
+
from pathlib import Path
 from tempfile import mkdtemp
 
 tempdir = Path(mkdtemp())
@@ -522,7 +522,7 @@ 
Example registrationnodding of the head, perhaps more apparent when looking at the corpus callosum in the sagittal views:

 

 

-
from niworkflows.viz.notebook import display
+
from nireports.reportlets.notebook import display
 
 display(
     fixed_path,
@@ -537,7 +537,7 @@ 
Example registrationLet’s configure ANTs via NiPype:

 

 

-
# ensure that NiPype is able to find and execute our ANTs command
+
# ensure that NiPype is able to find and execute our ANTs command
 
 import os
 
@@ -548,7 +548,7 @@ 
Example registration
 

 

-
from os import cpu_count
+
from os import cpu_count
 from pkg_resources import resource_filename as pkg_fn
 from nipype.interfaces.ants.registration import Registration
 
@@ -570,7 +570,7 @@ 
Example registrationwhich will run the following command-line:

 

 

-
registration.cmdline
+
registration.cmdline
 

 

 

@@ -578,7 +578,7 @@ 
Example registrationNipype interfaces can be submitted for execution with the run() method:

 

 

-
result = registration.run(cwd=str(tempdir.absolute()))
+
result = registration.run(cwd=str(tempdir.absolute()))
 

 

 

@@ -587,7 +587,7 @@ 
Example registration
 

 

-
display(
+
display(
     fixed_path,
     result.outputs.warped_image,
     fixed_label="Predicted",
@@ -608,7 +608,7 @@ 
Resampling an image
 

-
import nitransforms as nt
+
import nitransforms as nt
 
 itk_xform = nt.io.itk.ITKLinearTransform.from_filename(result.outputs.forward_transforms[0])
 matrix = itk_xform.to_ras(reference=fixed_path, moving=moving_path)
@@ -620,7 +620,7 @@ 
Resampling an image
 

-
xfm = nt.linear.Affine(matrix)
+
xfm = nt.linear.Affine(matrix)
 xfm.reference = fixed_path
 resampled = xfm.apply(moving_path)
 resampled.to_filename(tempdir / "resampled-nitransforms.nii.gz")
diff --git a/reports/tutorial/data.err.log b/reports/tutorial/data.err.log
deleted file mode 100644
index 8e372e8..0000000
--- a/reports/tutorial/data.err.log
+++ /dev/null
@@ -1,39 +0,0 @@
-Traceback (most recent call last):
-  File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/jupyter_cache/executors/utils.py", line 58, in single_nb_execution
-    executenb(
-  File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/nbclient/client.py", line 1314, in execute
-    return NotebookClient(nb=nb, resources=resources, km=km, **kwargs).execute()
-           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-  File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/jupyter_core/utils/__init__.py", line 165, in wrapped
-    return loop.run_until_complete(inner)
-           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-  File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/asyncio/base_events.py", line 654, in run_until_complete
-    return future.result()
-           ^^^^^^^^^^^^^^^
-  File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/nbclient/client.py", line 709, in async_execute
-    await self.async_execute_cell(
-  File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/nbclient/client.py", line 1062, in async_execute_cell
-    await self._check_raise_for_error(cell, cell_index, exec_reply)
-  File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/nbclient/client.py", line 918, in _check_raise_for_error
-    raise CellExecutionError.from_cell_and_msg(cell, exec_reply_content)
-nbclient.exceptions.CellExecutionError: An error occurred while executing the following cell:
-------------------
-# import the class from the library
-from eddymotion.dmri import DWI
-
-# load the sample file
-dmri_dataset = DWI.from_filename("../../data/dwi.h5")
-print(len(dmri_dataset))
-------------------
-
-
----------------------------------------------------------------------------
-ModuleNotFoundError                       Traceback (most recent call last)
-Cell In[5], line 2
-      1 # import the class from the library
-----> 2 from eddymotion.dmri import DWI
-      4 # load the sample file
-      5 dmri_dataset = DWI.from_filename("../../data/dwi.h5")
-
-ModuleNotFoundError: No module named 'eddymotion.dmri'
-
diff --git a/reports/tutorial/models.err.log b/reports/tutorial/models.err.log
deleted file mode 100644
index ddea3b7..0000000
--- a/reports/tutorial/models.err.log
+++ /dev/null
@@ -1,35 +0,0 @@
-Traceback (most recent call last):
-  File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/jupyter_cache/executors/utils.py", line 58, in single_nb_execution
-    executenb(
-  File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/nbclient/client.py", line 1314, in execute
-    return NotebookClient(nb=nb, resources=resources, km=km, **kwargs).execute()
-           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-  File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/jupyter_core/utils/__init__.py", line 165, in wrapped
-    return loop.run_until_complete(inner)
-           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-  File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/asyncio/base_events.py", line 654, in run_until_complete
-    return future.result()
-           ^^^^^^^^^^^^^^^
-  File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/nbclient/client.py", line 709, in async_execute
-    await self.async_execute_cell(
-  File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/nbclient/client.py", line 1062, in async_execute_cell
-    await self._check_raise_for_error(cell, cell_index, exec_reply)
-  File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/nbclient/client.py", line 918, in _check_raise_for_error
-    raise CellExecutionError.from_cell_and_msg(cell, exec_reply_content)
-nbclient.exceptions.CellExecutionError: An error occurred while executing the following cell:
-------------------
-from eddymotion.dmri import DWI
-from eddymotion.viz import plot_dwi
-dmri_dataset = DWI.from_filename("../../data/dwi.h5")
-------------------
-
-
----------------------------------------------------------------------------
-ModuleNotFoundError                       Traceback (most recent call last)
-Cell In[2], line 1
-----> 1 from eddymotion.dmri import DWI
-      2 from eddymotion.viz import plot_dwi
-      3 dmri_dataset = DWI.from_filename("../../data/dwi.h5")
-
-ModuleNotFoundError: No module named 'eddymotion.dmri'
-
diff --git a/reports/tutorial/registration.err.log b/reports/tutorial/registration.err.log
deleted file mode 100644
index 468dda4..0000000
--- a/reports/tutorial/registration.err.log
+++ /dev/null
@@ -1,70 +0,0 @@
-Traceback (most recent call last):
-  File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/jupyter_cache/executors/utils.py", line 58, in single_nb_execution
-    executenb(
-  File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/nbclient/client.py", line 1314, in execute
-    return NotebookClient(nb=nb, resources=resources, km=km, **kwargs).execute()
-           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-  File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/jupyter_core/utils/__init__.py", line 165, in wrapped
-    return loop.run_until_complete(inner)
-           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-  File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/asyncio/base_events.py", line 654, in run_until_complete
-    return future.result()
-           ^^^^^^^^^^^^^^^
-  File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/nbclient/client.py", line 709, in async_execute
-    await self.async_execute_cell(
-  File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/nbclient/client.py", line 1062, in async_execute_cell
-    await self._check_raise_for_error(cell, cell_index, exec_reply)
-  File "/opt/hostedtoolcache/Python/3.11.9/x64/lib/python3.11/site-packages/nbclient/client.py", line 918, in _check_raise_for_error
-    raise CellExecutionError.from_cell_and_msg(cell, exec_reply_content)
-nbclient.exceptions.CellExecutionError: An error occurred while executing the following cell:
-------------------
-import warnings
-from IPython.display import HTML
-import requests
-from tempfile import mkstemp
-from pathlib import Path
-import numpy as np
-import nibabel as nb
-
-from eddymotion.dmri import DWI
-from eddymotion.viz import plot_dwi
-from eddymotion.estimator import _advanced_clip
-
-warnings.filterwarnings("ignore")
-
-url = "https://files.osf.io/v1/resources/8k95s/providers/osfstorage/6070b4c2f6585f03fb6123a2"
-datapath = Path(mkstemp(suffix=".h5")[1])
-if datapath.stat().st_size == 0:
-    datapath.write_bytes(
-        requests.get(url, allow_redirects=True).content
-    )
-
-dmri_dataset = DWI.from_filename(datapath)
-datapath.unlink()
-
-
-def _to_nifti(
-    data, affine, filename, clip=True
-):
-    data = np.squeeze(data)
-    if clip:
-        data = _advanced_clip(data)
-    nb.Nifti1Image(
-        data,
-        affine,
-        None,
-    ).to_filename(filename)
-------------------
-
-
----------------------------------------------------------------------------
-ModuleNotFoundError                       Traceback (most recent call last)
-Cell In[1], line 9
-      6 import numpy as np
-      7 import nibabel as nb
-----> 9 from eddymotion.dmri import DWI
-     10 from eddymotion.viz import plot_dwi
-     11 from eddymotion.estimator import _advanced_clip
-
-ModuleNotFoundError: No module named 'eddymotion.dmri'
-
diff --git a/searchindex.js b/searchindex.js
index ba263dc..4a987c5 100644
--- a/searchindex.js
+++ b/searchindex.js
@@ -1 +1 @@
-Search.setIndex({"alltitles": {"1. Header: contains metadata about the image, such as image dimensions, data type, etc.": [[1, "header-contains-metadata-about-the-image-such-as-image-dimensions-data-type-etc"]], "1. Robust with very diverse data": [[3, "robust-with-very-diverse-data"]], "2. Data": [[1, "data"]], "2. Easy to use": [[3, "easy-to-use"]], "3. Affine: tells the position of the image array data in a reference space": [[1, "affine-tells-the-position-of-the-image-array-data-in-a-reference-space"]], "3. \u201cGlass box\u201d philosophy": [[3, "glass-box-philosophy"]], " Using Binder": [[9, "using-binder"]], " Local installation (\u201cdocker containers\u201d)": [[9, "local-installation-docker-containers"]], " Local installation (\u201cbare-metal\u201d)": [[9, "local-installation-bare-metal"]], "ANTs - Advanced Normalization ToolS": [[7, "ants-advanced-normalization-tools"], [13, "ants-advanced-normalization-tools"]], "About dMRIPrep": [[2, "about-dmriprep"]], "Algorithm": [[11, null]], "And after testing? - Validation!": [[8, "and-after-testing-validation"], [14, "and-after-testing-validation"]], "Augmenting the scanner to produce \u201canalysis grade\u201d data": [[3, "augmenting-the-scanner-to-produce-analysis-grade-data"]], "Before we start: How to follow this tutorial": [[9, "before-we-start-how-to-follow-this-tutorial"]], "Checking the contributing guidelines": [[0, "checking-the-contributing-guidelines"]], "Community development": [[0, "community-development"]], "Data structures": [[10, null]], "Development": [[2, "development"]], "Diffusion modeling": [[6, "diffusion-modeling"], [12, "diffusion-modeling"]], "Dimensions of the head-motion problem": [[4, "dimensions-of-the-head-motion-problem"], [11, "dimensions-of-the-head-motion-problem"]], "Example registration": [[7, "example-registration"], [13, "example-registration"]], "Exercise": [[10, null], [10, null], [10, null], [10, null], [10, null], [10, null], [12, null], [12, null], [12, null], [14, null], [14, null]], "Getting started with GitHub": [[0, "getting-started-with-github"]], "Image registration (spatial alignment)": [[7, "image-registration-spatial-alignment"], [13, "image-registration-spatial-alignment"]], "Implementing a head-motion correction algorithm for diffusion MRI in Python, using DIPY and NiTransforms": [[15, "implementing-a-head-motion-correction-algorithm-for-diffusion-mri-in-python-using-dipy-and-nitransforms"]], "Implementing a regression to the mean model": [[6, "implementing-a-regression-to-the-mean-model"], [12, "implementing-a-regression-to-the-mean-model"]], "Implementing a trivial model": [[6, "implementing-a-trivial-model"], [12, "implementing-a-trivial-model"]], "Inputs": [[11, null]], "Introduction to dMRI data": [[5, "introduction-to-dmri-data"], [10, "introduction-to-dmri-data"]], "Investigating NIfTI images with NiBabel": [[1, "investigating-nifti-images-with-nibabel"]], "Investigating the tensor model": [[6, "investigating-the-tensor-model"], [12, "investigating-the-tensor-model"]], "Leveraging the fit() / predict() API": [[6, "leveraging-the-fit-predict-api"], [12, "leveraging-the-fit-predict-api"]], "Main elements of a dMRI dataset": [[10, null]], "Making a change": [[0, "making-a-change"]], "NeuroImaging PREProcessing toolS (NiPreps)": [[3, "neuroimaging-preprocessing-tools-nipreps"]], "Next steps: diffusion modeling": [[5, "next-steps-diffusion-modeling"], [10, "next-steps-diffusion-modeling"]], "Next steps: image registration": [[6, "next-steps-image-registration"], [12, "next-steps-image-registration"]], "NiPreps driving principles": [[3, "nipreps-driving-principles"]], "Nonfunctional requirements": [[11, null]], "Objective: Implement a head-motion estimation code": [[4, "objective-implement-a-head-motion-estimation-code"], [11, "objective-implement-a-head-motion-estimation-code"]], "Objectives": [[0, null], [15, null]], "Outputs": [[11, null]], "Plan ahead": [[0, "plan-ahead"]], "Preparing a proposal for a new feature, documentation, or a bug fix": [[0, "preparing-a-proposal-for-a-new-feature-documentation-or-a-bug-fix"]], "Putting everything together": [[8, "putting-everything-together"], [14, "putting-everything-together"]], "Python and object oriented programming": [[5, "python-and-object-oriented-programming"], [10, "python-and-object-oriented-programming"]], "References": [[2, "references"], [3, "references"]], "Resampling an image": [[7, "resampling-an-image"], [13, "resampling-an-image"]], "Step 1: Identify an I/O (inputs/outputs) specification": [[11, "step-1-identify-an-i-o-inputs-outputs-specification"]], "Step 2: Sketch out an API (Application Programming Interface)": [[11, "step-2-sketch-out-an-api-application-programming-interface"]], "Step 3: Software architecture": [[11, "step-3-software-architecture"]], "The LOGO (leave-one-gradient-out) splitter": [[5, "the-logo-leave-one-gradient-out-splitter"], [10, "the-logo-leave-one-gradient-out-splitter"]], "The extra mile": [[1, "the-extra-mile"]], "The model factory": [[6, "the-model-factory"], [12, "the-model-factory"]], "The problem of head-motion in dMRI": [[4, "the-problem-of-head-motion-in-dmri"], [11, "the-problem-of-head-motion-in-dmri"]], "The problem of methodological variability": [[3, "the-problem-of-methodological-variability"]], "Using the new data representation object": [[5, "using-the-new-data-representation-object"], [10, "using-the-new-data-representation-object"]], "Visualizing the data": [[5, "visualizing-the-data"], [10, "visualizing-the-data"]], "Visualizing the gradient information": [[5, "visualizing-the-gradient-information"], [10, "visualizing-the-gradient-information"]], "Welcome!": [[15, "welcome"]], "What\u2019s next? - Testing!": [[8, "what-s-next-testing"], [14, "what-s-next-testing"]]}, "docnames": ["extra/community_development", "extra/nifti", "nipreps/dmriprep", "nipreps/nipreps", "notebook/01-intro", "notebook/02-data", "notebook/03-models", "notebook/04-registration", "notebook/05-solution", "preparation/step0", "tutorial/data", "tutorial/intro", "tutorial/models", "tutorial/registration", "tutorial/solution", "welcome"], "envversion": {"sphinx": 61, "sphinx.domains.c": 3, "sphinx.domains.changeset": 1, "sphinx.domains.citation": 1, 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\ No newline at end of file
diff --git a/tutorial/data.html b/tutorial/data.html
index e6aba13..c279c4b 100644
--- a/tutorial/data.html
+++ b/tutorial/data.html
@@ -507,7 +507,7 @@ 
Using the new data representation object
 

 
# import the class from the library
-from eddymotion.dmri import DWI
+from eddymotion.data.dmri import DWI
 
 # load the sample file
 dmri_dataset = DWI.from_filename("../../data/dwi.h5")
@@ -516,15 +516,7 @@ 
Using the new data representation object
-
---------------------------------------------------------------------------
-ModuleNotFoundError                       Traceback (most recent call last)
-Cell In[5], line 2
-      1 # import the class from the library
-----> 2 from eddymotion.dmri import DWI
-      4 # load the sample file
-      5 dmri_dataset = DWI.from_filename("../../data/dwi.h5")
-
-ModuleNotFoundError: No module named 'eddymotion.dmri'
+
102
 

 

 

@@ -538,6 +530,11 @@ 
Using the new data representation object
+
DWI(dataobj=<118x118x78x102 (int16)>, affine=<4x4 (float64)>, brainmask=None, bzero=<118x118x78 (int16)>, gradients=<4x102 (float32)>, em_affines=None, fieldmap=None)
+

+

+

 

 
We’ll go over some of the components of dmri_dataset through this lesson.

 
@@ -564,6 +561,9 @@ 
Visualizing the data

 

 

+

+../_images/399c5ec60f7735d3b8113b93a1685264a382990c0567f5293eb05934c28a1663.png
+

 
 

 
When calling plot_mosaic() without any arguments, the b=0 reference is plotted.
@@ -586,6 +586,9 @@ 
Visualizing the data

 

 

+

+../_images/e0a96d6f52575f61a54293618c4e5d2e021210d1b3ecb4e43d19d429af2c09fd.png
+

 
 

 

@@ -599,6 +602,9 @@ 
Visualizing the data

 

 

+

+../_images/998b77b9a08ee47c07fdca877148d71b21c097fe428b9d273a5df1f6699512d0.png
+

 
 

 
Diffusion that exhibits directionality in the same direction as the gradient results in a loss of signal.
@@ -626,6 +632,11 @@ 
Visualizing the gradient information
+
(4, 102)
+

+

+

 
 

 
We get a \(4\times102\) – three spatial coordinates (\(b_x\), \(b_y\), \(b_z\)) of the unit-norm “b-vector”, plus the gradient sensitization magnitude (the “b-value”), with a total of 102 different orientations for the case at hand.

@@ -646,6 +657,112 @@ 
Visualizing the gradient information
+
[[-6.04914188e-01 -6.79875135e-01 -4.14546251e-01  3.00000000e+02]
+ [ 7.51762651e-03  9.17056799e-01 -3.98685753e-01  3.00000000e+02]
+ [-8.86579394e-01  3.21218759e-01 -3.32859576e-01  3.00000000e+02]
+ [ 8.16364467e-01  2.68300444e-01 -5.11433184e-01  3.00000000e+02]
+ [ 4.34362024e-01 -7.33946085e-01 -5.22161603e-01  3.00000000e+02]
+ [ 9.60559174e-02 -7.31587410e-02  9.92683768e-01  3.05000000e+02]
+ [ 5.85605621e-01  7.62755051e-02  8.06999445e-01  1.00500000e+03]
+ [ 1.32749856e-01  3.81094962e-01 -9.14955795e-01  9.95000000e+02]
+ [-9.10080016e-01  3.49084914e-01  2.23369867e-01  1.00000000e+03]
+ [-1.42672375e-01 -9.87846315e-01 -6.16788231e-02  1.00000000e+03]
+ [-3.14908028e-01  4.70339447e-01  8.24386895e-01  1.00500000e+03]
+ [ 9.26234543e-01  3.46899480e-01 -1.47479758e-01  1.00000000e+03]
+ [ 6.95218086e-01  6.17206156e-01 -3.68413389e-01  9.95000000e+02]
+ [ 1.79834515e-01 -9.48279917e-01  2.61581242e-01  1.00000000e+03]
+ [-5.72267652e-01  8.19448650e-01 -3.18384580e-02  1.00000000e+03]
+ [ 6.62053525e-01 -3.44300926e-01  6.65689111e-01  1.00500000e+03]
+ [-1.81866080e-01  7.04759300e-01 -6.85739756e-01  9.95000000e+02]
+ [-6.36256397e-01 -7.67920136e-01 -7.40036741e-02  1.00000000e+03]
+ [-2.50486493e-01 -7.25317061e-01  6.41226709e-01  1.00500000e+03]
+ [-1.81372240e-01 -5.50868332e-01 -8.14646065e-01  9.95000000e+02]
+ [ 2.51025885e-01 -9.41051543e-01 -2.26733208e-01  1.00000000e+03]
+ [ 8.59194577e-01  4.16565776e-01  2.97081828e-01  1.00000000e+03]
+ [-5.87923348e-01 -4.98144925e-01 -6.37336493e-01  9.95000000e+02]
+ [-2.08524555e-01  6.58749491e-02  9.75796103e-01  1.00500000e+03]
+ [-8.81649256e-01 -4.38893167e-03 -4.71884906e-01  9.95000000e+02]
+ [-6.17551386e-01  6.92788363e-01  3.72390330e-01  1.00000000e+03]
+ [-2.11339086e-01 -1.57031611e-01 -9.64715958e-01  9.95000000e+02]
+ [ 5.63584268e-01  3.84537101e-01 -7.31097817e-01  9.95000000e+02]
+ [-1.04902856e-01  7.80609608e-01  6.16152585e-01  1.00500000e+03]
+ [-3.27980161e-01 -8.34259689e-01 -4.43215251e-01  1.00000000e+03]
+ [ 9.97081280e-01 -3.60786468e-02  6.72850981e-02  1.00000000e+03]
+ [-3.64686340e-01 -8.92125309e-01  2.66676456e-01  1.00000000e+03]
+ [ 2.97879040e-01 -3.06824297e-01  9.03950751e-01  1.00500000e+03]
+ [ 7.01884270e-01 -3.75511825e-01 -6.05267942e-01  9.95000000e+02]
+ [ 5.85624397e-01 -2.57630497e-02 -8.10173035e-01  9.95000000e+02]
+ [ 8.74492407e-01  2.43720673e-02 -4.84426469e-01  9.95000000e+02]
+ [ 5.51209927e-01 -7.06570625e-01  4.43762958e-01  1.00500000e+03]
+ [ 8.55460048e-01 -4.86520320e-01  1.77443221e-01  1.00000000e+03]
+ [-6.43364310e-01 -2.89421439e-01 -7.08743691e-01  1.99500000e+03]
+ [ 1.21839322e-01  3.67268413e-01  9.22100365e-01  2.01000000e+03]
+ [ 8.67053568e-01 -2.20949143e-01 -4.46541786e-01  1.99500000e+03]
+ [-7.46472776e-01  8.38321110e-04 -6.65415406e-01  1.99500000e+03]
+ [ 3.49373311e-01  4.06477451e-01 -8.44224155e-01  1.99000000e+03]
+ [-5.60097814e-01  1.76819220e-01 -8.09336364e-01  1.99500000e+03]
+ [-4.30994742e-02  9.74978864e-01  2.18079522e-01  2.00000000e+03]
+ [-2.40879446e-01  8.55759144e-01  4.57879245e-01  2.00500000e+03]
+ [-7.79660225e-01 -6.15284383e-01  1.16426118e-01  2.00000000e+03]
+ [ 4.48004901e-01 -8.49978268e-01  2.77179599e-01  2.00500000e+03]
+ [ 3.74817163e-01 -9.27097678e-01 -1.41696935e-03  2.00000000e+03]
+ [ 6.10155761e-01 -5.36033213e-01  5.83419502e-01  2.00500000e+03]
+ [ 9.50321257e-01 -1.07752405e-01  2.92025506e-01  2.00000000e+03]
+ [-8.09516788e-01  2.45182857e-01 -5.33449113e-01  1.99500000e+03]
+ [ 9.56617057e-01 -2.84275055e-01  6.38084337e-02  2.00000000e+03]
+ [ 4.44206297e-01  6.86384022e-01  5.75810552e-01  2.00500000e+03]
+ [ 9.54805911e-01  1.77383393e-01  2.38497064e-01  2.00000000e+03]
+ [ 6.78448856e-01  4.65783864e-01 -5.68113148e-01  1.99500000e+03]
+ [-1.22661926e-01 -5.87784886e-01  7.99664319e-01  2.00500000e+03]
+ [ 4.10763621e-01 -1.32623598e-01 -9.02044475e-01  1.99000000e+03]
+ [-9.28761899e-01 -1.47203013e-01  3.40195030e-01  2.00500000e+03]
+ [-4.32663143e-01  4.41911817e-01 -7.85822213e-01  1.99500000e+03]
+ [-1.73938900e-01 -7.22244203e-01 -6.69409096e-01  1.99500000e+03]
+ [ 3.93927962e-01  4.76803184e-01  7.85798609e-01  2.00500000e+03]
+ [ 2.16246262e-01 -8.66339266e-01  4.50215280e-01  2.00500000e+03]
+ [ 5.89937627e-01 -5.72140634e-01 -5.69761992e-01  1.99500000e+03]
+ [ 3.72014821e-01  9.28178847e-01 -9.43302456e-03  2.00000000e+03]
+ [ 8.53485584e-01 -5.17996848e-01 -5.69352657e-02  2.00000000e+03]
+ [ 2.51451343e-01  9.32365298e-01  2.59744376e-01  2.00000000e+03]
+ [ 6.35405779e-02 -5.95680475e-01 -8.00704300e-01  1.99500000e+03]
+ [-7.51151621e-01 -6.28546119e-01 -2.01744899e-01  2.00000000e+03]
+ [-1.09398387e-01  9.92227495e-01 -5.93008772e-02  2.00000000e+03]
+ [-5.18000782e-01  3.63550693e-01  7.74277806e-01  2.00500000e+03]
+ [-8.37094843e-01 -4.31998879e-01  3.35632533e-01  2.00500000e+03]
+ [ 7.27129698e-01  1.49743423e-01 -6.69969618e-01  1.99500000e+03]
+ [-5.17419696e-01 -7.40754366e-01  4.28438783e-01  2.00500000e+03]
+ [ 7.27875590e-01  4.97373462e-01  4.72034663e-01  2.00500000e+03]
+ [-3.26542199e-01  7.36291781e-02 -9.42310452e-01  1.99000000e+03]
+ [-6.14776909e-02 -8.78977180e-01 -4.72884387e-01  1.99500000e+03]
+ [-7.73990631e-01  5.15239596e-01 -3.68057936e-01  1.99500000e+03]
+ [-3.58383171e-03  1.07271522e-01  9.94223297e-01  2.01000000e+03]
+ [-1.60011083e-01  5.34122169e-01 -8.30126464e-01  1.99000000e+03]
+ [ 9.88182724e-01  1.43299058e-01 -5.44080175e-02  2.00000000e+03]
+ [-9.05943990e-01 -4.18793827e-01 -6.22674413e-02  2.00000000e+03]
+ [ 2.03286484e-01  1.53597519e-01 -9.66996610e-01  1.99000000e+03]
+ [ 4.36532289e-01  6.08863235e-01 -6.62363291e-01  1.99500000e+03]
+ [ 6.82894647e-01 -7.17134774e-01 -1.39185578e-01  2.00000000e+03]
+ [ 7.86479354e-01 -4.98139858e-01 -3.65112156e-01  1.99500000e+03]
+ [-4.65987802e-01  8.39519799e-01  2.79395491e-01  2.00000000e+03]
+ [ 7.21606970e-01 -1.46813065e-01 -6.76556945e-01  1.99500000e+03]
+ [ 5.12317955e-01  8.17944586e-01  2.61719227e-01  2.00000000e+03]
+ [ 3.80590975e-01 -6.98121727e-01  6.06445849e-01  2.00500000e+03]
+ [ 3.18718821e-01 -6.50580406e-01 -6.89320982e-01  1.99500000e+03]
+ [ 4.12478864e-01  2.02624902e-01  8.88146579e-01  2.00500000e+03]
+ [ 1.32435739e-01  9.71343756e-01 -1.97362855e-01  1.99500000e+03]
+ [-9.72415447e-01  1.24751076e-01  1.97092354e-01  2.00000000e+03]
+ [-2.45995522e-01 -8.56899619e-01  4.53000307e-01  2.00500000e+03]
+ [ 1.21794799e-02  7.88615406e-01 -6.14766181e-01  1.99500000e+03]
+ [-8.40300739e-01 -2.33427882e-01 -4.89291459e-01  1.99500000e+03]
+ [ 4.91548866e-01  1.66130796e-01 -8.54856849e-01  1.99000000e+03]
+ [ 6.91248178e-01 -6.99574709e-01  1.81028187e-01  2.00000000e+03]
+ [-5.76671958e-01 -8.02036345e-01  1.55522212e-01  2.00000000e+03]
+ [-7.08378106e-02  2.88473666e-01 -9.54863846e-01  1.99000000e+03]
+ [-1.85595229e-01  3.32799673e-01  9.24553275e-01  2.01000000e+03]]
+

+

+

 
 

 
Later, we’ll refer to this array as the gradient table.

@@ -661,6 +778,9 @@ 
Visualizing the gradient information
+../_images/8d6b247e26b2bfebc80d5b824813532419103e3785c59f168b7768873b2ee18e.png
+

 

 
We’ve projected all of the gradient directions onto the surface of a sphere, with each unique gradient strength colour-coded.
 Darkest hues correspond to the lowest b-values and brighter to the highest.

@@ -727,13 +847,17 @@ 
The LOGO (leave-one-gradient-out) splitterThis function is contained in the DWI class shown earlier and will allow us to easily partition the dataset as follows:

 

 

-
from eddymotion.viz import plot_dwi
+
from eddymotion.data.splitting import lovo_split as logo_split
+from eddymotion.viz import plot_dwi
 
-data_train, data_test = dmri_dataset.logo_split(10)
-plot_dwi(data_test[0], dmri_dataset.affine, gradient=data_test[1]);
+data_train, data_test = logo_split(dmri_dataset, 10)
+plot_dwi(np.squeeze(data_test[0]), dmri_dataset.affine, gradient=data_test[1]);
 

 

 

+

+../_images/9ab3f12b0129d7fc6ffbaada7ea931a1b0bb41fc89a09e65519a04e1548397aa.png
+

 

 
data_train is a tuple containing all diffusion-weighted volumes and the corresponding gradient table, excluding the left-out, which is stored in data_test (the 11th gradient indexed by 10, in this example).
 data_test[0] contains the held-out diffusion-weighted volume and data_test[1], the corresponding gradient table.

@@ -754,6 +878,12 @@ 
The LOGO (leave-one-gradient-out) splitter

 

 

+

+
data_train[0] is the DW maps dataset and has (118, 118, 78, 101) dimensions
+data_train[1] is a gradient table and has (4, 101) dimensions
+

+

+

 
 

 

@@ -773,6 +903,12 @@ 
The LOGO (leave-one-gradient-out) splitter

 

 

+

+
data_test[0] is left-out DW map and has (118, 118, 78, 1) dimensions
+data_test[1] is the corresponding DW gradient and has (4, 1) dimensions
+

+

+

 
 

 
diff --git a/tutorial/intro.html b/tutorial/intro.html
index 8371a5d..8989d74 100644
--- a/tutorial/intro.html
+++ b/tutorial/intro.html
@@ -481,6 +481,7 @@ 
Step 1: Identify an I/O (inputs/outputs) specification
Parallelism: simulation and registration are CPU-intensive processes - for the runtime to be in a manageable scale, we’ll need to leverage parallelism.

 
 

+
eddymotion

 
 

 
Step 2: Sketch out an API (Application Programming Interface)#

diff --git a/tutorial/models.html b/tutorial/models.html
index 7bb007b..47a5e13 100644
--- a/tutorial/models.html
+++ b/tutorial/models.html
@@ -424,24 +424,13 @@ 
Diffusion modeling
 

-
from eddymotion.dmri import DWI
+
from eddymotion.data.dmri import DWI
+from eddymotion.data.splitting import lovo_split as logo_split
 from eddymotion.viz import plot_dwi
 dmri_dataset = DWI.from_filename("../../data/dwi.h5")
 

 

 

-

-
---------------------------------------------------------------------------
-ModuleNotFoundError                       Traceback (most recent call last)
-Cell In[2], line 1
-----> 1 from eddymotion.dmri import DWI
-      2 from eddymotion.viz import plot_dwi
-      3 dmri_dataset = DWI.from_filename("../../data/dwi.h5")
-
-ModuleNotFoundError: No module named 'eddymotion.dmri'
-

-

-

 

 

 
Implementing a trivial model#

@@ -498,8 +487,8 @@ 
Implementing a trivial modeldata_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:

 

 

-
data_train, data_test = dmri_dataset.logo_split(10)
-model.fit(data_train[0])
+
data_train, data_test = logo_split(dmri_dataset, 10)
+model.fit(np.squeeze(data_train[0]))
 

 

 

@@ -512,14 +501,20 @@ 
Implementing a trivial model
+../_images/9908b8e6382fea9cb89ff415d47e615df4e5e472d215f102f6048d4f3d08a521.png
+

 

 
As expected, the b=0 doesn’t look very much like the particular left-out direction, but it is a start!

 

 

-
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]);
 

 

 

+

+../_images/9ab3f12b0129d7fc6ffbaada7ea931a1b0bb41fc89a09e65519a04e1548397aa.png
+

 

 

 

@@ -597,10 +592,14 @@ 
Implementing a regression to the mean modelmodel.fit(data_train[0])
 predicted = model.predict(data_test[1])
 plot_dwi(predicted, dmri_dataset.affine, gradient=data_test[1]);
-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]);
 

 

 

+

+../_images/0fcb0be4db4b0507047aff099c5299c52372eb311563a87bed319c809772242f.png
+../_images/9ab3f12b0129d7fc6ffbaada7ea931a1b0bb41fc89a09e65519a04e1548397aa.png
+

 
 

 
@@ -646,14 +645,20 @@ 
Leveraging the 
 

 

 

+

+../_images/ca6816f3048b9e54370bde884de48c587bcaaf596cabee101d64d07a7d17e927.png
+

 

 
Here’s the original DW map, for reference:

 

 

-
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]);
 

 

 

+

+../_images/aedecb9b7970c400029435d0d1b973567aec29f2465465c67a94e29a02137d9a.png
+

 

 

 
Exercise

@@ -683,9 +688,3301 @@ 
Leveraging the 
 
model.fit(data_train[0])
 predicted = model.predict(data_test[1])
 plot_dwi(predicted, dmri_dataset.affine, gradient=data_test[1], black_bg=True);
-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]);
+

+

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+

 

 

+../_images/0d366bb088aecab2d0e67194dbf720704f45279468c752c48592534e49248795.png
+../_images/aedecb9b7970c400029435d0d1b973567aec29f2465465c67a94e29a02137d9a.png
 

 

 
diff --git a/tutorial/registration.html b/tutorial/registration.html
index 93c75ee..5306872 100644
--- a/tutorial/registration.html
+++ b/tutorial/registration.html
@@ -485,7 +485,7 @@ 
Example registration
 
from eddymotion.model import ModelFactory
 
-data_train, data_test = dmri_dataset.logo_split(7, with_b0=True)
+data_train, data_test = logo_split(dmri_dataset, 7, with_b0=True)
 
 model = ModelFactory.init(
     gtab=data_train[1],
@@ -522,7 +522,7 @@ 
Example registrationnodding of the head, perhaps more apparent when looking at the corpus callosum in the sagittal views:

 

 

-
from niworkflows.viz.notebook import display
+
from nireports.reportlets.notebook import display
 
 display(
     fixed_path,
@@ -535,6 +535,8 @@ 
Example registration
 

 

+

+

 

 
Let’s configure ANTs via NiPype:

 

@@ -565,6 +567,11 @@ 
Example registration

 

 

+

+
'antsRegistration --collapse-output-transforms 1 --dimensionality 3 --initialize-transforms-per-stage 0 --interpolation Linear --output [ transform, transform_Warped.nii.gz ] --transform Affine[ 0.01 ] --metric Mattes[ /tmp/tmprxbygtcb/fixed.nii.gz, /tmp/tmprxbygtcb/moving.nii.gz, 1, 32, Regular, 0.2 ] --convergence [ 100, 1e-06, 15 ] --smoothing-sigmas 2.0vox --shrink-factors 1 --use-histogram-matching 1 --transform Affine[ 0.001 ] --metric GC[ /tmp/tmprxbygtcb/fixed.nii.gz, /tmp/tmprxbygtcb/moving.nii.gz, 1, 5, Random, 0.1 ] --convergence [ 50, 1e-07, 5 ] --smoothing-sigmas 0.0vox --shrink-factors 1 --use-histogram-matching 1 --winsorize-image-intensities [ 0.0001, 0.9998 ]  --write-composite-transform 0'
+

+

+

 

 
Nipype interfaces can be submitted for execution with the run() method:

 

@@ -589,6 +596,8 @@ 
Example registration
 

 

+

+

 

 
 

@@ -609,6 +618,18 @@ 
Resampling an image
+
array([[ 9.95961328e-01, -4.11835057e-03, -6.30936087e-04,
+         8.03217513e-02],
+       [ 3.71423770e-03,  9.96977865e-01, -1.66952058e-02,
+        -2.31156911e-01],
+       [-1.89529771e-03,  2.73982307e-02,  9.91098497e-01,
+        -9.24907250e-01],
+       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00,
+         1.00000000e+00]])
+

+

+

 

 
Resampling an image requires two pieces of information: the reference image (which provides the new grid where we want to have the data) and the moving image which contains the actual data we are interested in:

 

@@ -629,6 +650,8 @@ 
Resampling an image
+

+

 

 
Exercise

 
Use the display() function to visualize the image aligned as generated by ANTs vs. that generated by NiTransforms – they should be aligned!.

@@ -652,6 +675,14 @@ 
Resampling an image
+

+
+
+Hide code cell outputs
+
+

+

+