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+<article id="content">
+<header>
+<h1 class="title">Module <code>pymskt.statistics.pca</code></h1>
+</header>
+<section id="section-intro">
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">from tracemalloc import start
+import numpy as np
+from scipy.linalg import svd
+import vtk
+from vtk.util.numpy_support import numpy_to_vtk
+from pymskt.mesh.utils import GIF
+def pca_svd(data):
+    &#34;&#34;&#34;
+    Calculate eigenvalues &amp; eigenvectors of `data` using Singular Value Decomposition (SVD)
+    Parameters
+    ----------
+    data : numpy.ndarray
+        MxN matrix
+        M = # of features / dimensions of data
+        N = # of trials / participants in dataset
+    Returns
+    -------
+    tuple (PC = numpy.ndarray, V = numpy.ndarray)
+        PC - each volumn is a principal component (eigenvector)
+        V - Mx1 matrix of variances (coinciding with each PC)
+    Notes
+    -----
+    Adapted from:
+    &#34;A Tutorial on Principal Component Analysis by Jonathon Shlens&#34;
+    https://arxiv.org/abs/1404.1100
+    Inputs
+    data = MxN matrix (M dimensions, N trials)
+    Returns
+    PC - each column is a PC
+    V - Mx1 matrix of variances
+    &#34;&#34;&#34;
+    M, N = data.shape
+    mn = np.mean(data, axis=1)
+    data = data - mn[:, None]  # produce centered data. If already centered this shouldnt be harmful.
+    Y = data.T / np.sqrt(N - 1)
+    U, S, V = svd(Y, full_matrices=False)
+    PC = V.T  # V are the principle components (PC)
+    V = S ** 2  # The squared singular values are the variances (V)
+    return PC, V
+def get_ssm_deformation(PCs, Vs, mean_coords, pc=0, n_sds=3):
+    &#34;&#34;&#34;
+    Function to Statistical Shape Model (SSM) deformed along given Principal Component.
+    Parameters
+    ----------
+    PCs : numpy.ndarray
+        NxM ndarray; N = number of points on surface, M = number of principal components in model
+        Each column is a principal component.
+    Vs : numpy.ndarray
+        M ndarray; M = number of principal components in model
+        Each entry is the variance for the coinciding principal component in PCs
+    mean_coords : numpy.ndarray
+xN ndarray; N = number of points on surface.
+    pc : int, optional
+        The principal component of the SSM to deform, by default 0
+    n_sds : int, optional
+        The number of standard deviations (sd) to deform the SSM.
+        This can be positive or negative to scale the model in either direction. , by default 3
+    Returns
+    -------
+    numpy.ndarray
+xN ndarray; N=number of points on mesh surface.
+        This includes the x/y/z position of each surface node after deformation using the SSM and
+        the specified characteristics (pc, n_sds)
+    &#34;&#34;&#34;
+    pc_vector = PCs[:, pc]
+    pc_vector_scale = np.sqrt(Vs[pc]) * n_sds # convert Variances to SDs &amp; multiply by n_sds (negative/positive important)
+    coords_deformation = pc_vector * pc_vector_scale
+    deformed_coords = (mean_coords.flatten() + coords_deformation).reshape(mean_coords.shape)
+    return deformed_coords
+def get_rand_bone_shape(PCs, Vs, mean_coords, n_pcs=100, n_samples=1, mean_=0., sd_=1.0):
+    &#34;&#34;&#34;
+    Function to get random bones using a Statistical Shape Model (SSM).
+    Parameters
+    ----------
+    PCs : numpy.ndarray
+        NxM ndarray; N = number of points on surface, M = number of principal components in model
+        Each column is a principal component.
+    Vs : numpy.ndarray
+        M ndarray; M = number of principal components in model
+        Each entry is the variance for the coinciding principal component in PCs
+    mean_coords : numpy.ndarray
+xN ndarray; N = number of points on surface.
+    n_pcs : int, optional
+        Number of PCs to randomly sample from (sequentially), by default 100
+    n_samples : int, optional
+        number of bones to create, by default 1
+    mean_ : float, optional
+        Mean of the normal distribution to sample PCs from, by default 0.
+    sd_ : float, optional
+        Standard deviation of the normal distribution to sample PCs from, by default 1.0
+    Returns
+    -------
+    numpy.ndarray
+        nx(3xN) ndarray; N=number of points on mesh surface, n=number of new meshes
+        This includes the x/y/z position of each surface node(N) for the random bones(n).
+    &#34;&#34;&#34;
+    rand_pc_scores = np.random.normal(mean_, sd_, size=[n_samples, n_pcs])
+    rand_pc_weights = rand_pc_scores * np.sqrt(Vs[:n_pcs])
+    rand_data = rand_pc_weights @ PCs[:, :n_pcs].T
+    rand_data = mean_coords.flatten() + rand_data
+    return rand_data
+def create_vtk_mesh_from_deformed_points(mean_mesh, new_points):
+    &#34;&#34;&#34;
+    Create new vtk mesh (polydata) from a set of points (ndarray) deformed using the SSM.
+    Parameters
+    ----------
+    mean_mesh : vtk.PolyData
+        vtk polydata of the mean mesh
+    new_points : numpy.ndarray
+xN ndarray; N=number of points on mesh surface (same as number of points on mean_mesh).
+        This includes the x/y/z position of each surface node should be deformed to.
+    Returns
+    -------
+    vtk.PolyData
+        vtk polydata of the deformed mesh
+    &#34;&#34;&#34;
+    new_mesh = vtk.vtkPolyData()
+    new_mesh.DeepCopy(mean_mesh)
+    new_mesh.GetPoints().SetData(numpy_to_vtk(new_points))
+    return new_mesh
+def save_gif(
+    path_save,
+    PCs,
+    Vs,
+    mean_coords,  # mean_coords could be extracted from mean mesh...?
+    mean_mesh,
+    pc=0,
+    min_sd=-3.,
+    max_sd=3.,
+    step=0.25,
+    color=&#39;orange&#39;,
+    show_edges=True,
+    edge_color=&#39;black&#39;,
+    camera_position=&#39;xz&#39;,
+    window_size=[3000, 4000],
+    background_color=&#39;white&#39;,
+    verbose=False,
+):
+    &#34;&#34;&#34;
+    Function to save a gif of the SSM deformation.
+    Parameters
+    ----------
+    path_save : str
+        Path to save the gif to.
+    PCs : numpy.ndarray
+        SSM Principal Components.
+    Vs : numpy.ndarray
+        SSM Variances.
+    mean_coords : numpy.ndarray
+        NxM ndarray; N = number of meshes, M = number of points x n_dimensions
+    mean_mesh : vtk.PolyData
+        vtk polydata of the mean mesh
+    pc : int, optional
+        The principal component of the SSM to deform, by default 0
+    min_sd : float, optional
+        The lower bound (minimum) standard deviations (sd) to deform the SSM from
+        This can be positive or negative to scale the model in either direction. , by default -3.
+    max_sd : float, optional
+        The upper bound (maximum) standard deviations (sd) to deform the SSM from
+        This can be positive or negative to scale the model in either direction. , by default 3.
+    step : float, optional
+        The step size (sd) to deform the SSM by, by default 0.25
+    color : str, optional
+        The color of the SSM surface during rendering, by default &#39;orange&#39;
+    show_edges : bool, optional
+        Whether to show the edges of the SSM surface during rendering, by default True
+    edge_color : str, optional
+        The color of the edges of the SSM surface during rendering, by default &#39;black&#39;
+    camera_position : str, optional
+        The camera position to use during rendering, by default &#39;xz&#39;
+    window_size : list, optional
+        The window size to use during rendering, by default [3000, 4000]
+    background_color : str, optional
+        The background color to use during rendering, by default &#39;white&#39;
+    verbose : bool, optional
+        Whether to print progress to console, by default False
+    &#34;&#34;&#34;
+    # ALTERNATIVELY... could pass a bunch of the above parameters as kwargs..?? but thats less clear
+    gif = GIF(
+        path_save=path_save,
+        color=color,
+        show_edges=show_edges,
+        edge_color=edge_color,
+        camera_position=camera_position,
+        window_size=window_size,
+        background_color=background_color,
+    )
+    for idx, sd in enumerate(np.arange(min_sd, max_sd + step, step)):
+        if verbose is True:
+            print(f&#39;Deforming SSM with idx={idx} sd={sd}&#39;)
+        pts = get_ssm_deformation(PCs, Vs, mean_coords, pc=pc, n_sds=sd)
+        if type(mean_mesh) == dict:
+            mesh = []
+            start_idx = 0
+            for mesh_name, mesh_params in mean_mesh.items():
+                mesh.append(
+                    create_vtk_mesh_from_deformed_points(
+                        mesh_params[&#39;mesh&#39;],
+                        pts[start_idx:start_idx+mesh_params[&#39;n_points&#39;], :],
+                    )
+                )
+                start_idx += mesh_params[&#39;n_points&#39;]
+        if type(mean_mesh) in (list, tuple):
+            mesh = []
+            start_idx = 0
+            for mesh_ in mean_mesh:
+                n_pts = mesh_.GetNumberOfPoints()
+                mesh.append(
+                    create_vtk_mesh_from_deformed_points(
+                        mesh_,
+                        pts[start_idx:start_idx+n_pts, :],
+                    )
+                )
+                start_idx += n_pts
+        else:
+            mesh = create_vtk_mesh_from_deformed_points(mean_mesh, pts)
+        gif.add_mesh_frame(mesh)
+    gif.done()</code></pre>
+</details>
+</section>
+<section>
+</section>
+<section>
+</section>
+<section>
+<h2 class="section-title" id="header-functions">Functions</h2>
+<dl>
+<dt id="pymskt.statistics.pca.create_vtk_mesh_from_deformed_points"><code class="name flex">
+<span>def <span class="ident">create_vtk_mesh_from_deformed_points</span></span>(<span>mean_mesh, new_points)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>Create new vtk mesh (polydata) from a set of points (ndarray) deformed using the SSM. </p>
+<h2 id="parameters">Parameters</h2>
+<dl>
+<dt><strong><code>mean_mesh</code></strong> :&ensp;<code>vtk.PolyData</code></dt>
+<dd>vtk polydata of the mean mesh</dd>
+<dt><strong><code>new_points</code></strong> :&ensp;<code>numpy.ndarray</code></dt>
+<dd>3xN ndarray; N=number of points on mesh surface (same as number of points on mean_mesh).
+This includes the x/y/z position of each surface node should be deformed to.</dd>
+</dl>
+<h2 id="returns">Returns</h2>
+<dl>
+<dt><code>vtk.PolyData</code></dt>
+<dd>vtk polydata of the deformed mesh</dd>
+</dl></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def create_vtk_mesh_from_deformed_points(mean_mesh, new_points):
+    &#34;&#34;&#34;
+    Create new vtk mesh (polydata) from a set of points (ndarray) deformed using the SSM.
+    Parameters
+    ----------
+    mean_mesh : vtk.PolyData
+        vtk polydata of the mean mesh
+    new_points : numpy.ndarray
+xN ndarray; N=number of points on mesh surface (same as number of points on mean_mesh).
+        This includes the x/y/z position of each surface node should be deformed to.
+    Returns
+    -------
+    vtk.PolyData
+        vtk polydata of the deformed mesh
+    &#34;&#34;&#34;
+    new_mesh = vtk.vtkPolyData()
+    new_mesh.DeepCopy(mean_mesh)
+    new_mesh.GetPoints().SetData(numpy_to_vtk(new_points))
+    return new_mesh</code></pre>
+</details>
+</dd>
+<dt id="pymskt.statistics.pca.get_rand_bone_shape"><code class="name flex">
+<span>def <span class="ident">get_rand_bone_shape</span></span>(<span>PCs, Vs, mean_coords, n_pcs=100, n_samples=1, mean_=0.0, sd_=1.0)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>Function to get random bones using a Statistical Shape Model (SSM).</p>
+<h2 id="parameters">Parameters</h2>
+<dl>
+<dt><strong><code>PCs</code></strong> :&ensp;<code>numpy.ndarray</code></dt>
+<dd>NxM ndarray; N = number of points on surface, M = number of principal components in model
+Each column is a principal component.</dd>
+<dt><strong><code>Vs</code></strong> :&ensp;<code>numpy.ndarray</code></dt>
+<dd>M ndarray; M = number of principal components in model
+Each entry is the variance for the coinciding principal component in PCs</dd>
+<dt><strong><code>mean_coords</code></strong> :&ensp;<code>numpy.ndarray</code></dt>
+<dd>3xN ndarray; N = number of points on surface.</dd>
+<dt><strong><code>n_pcs</code></strong> :&ensp;<code>int</code>, optional</dt>
+<dd>Number of PCs to randomly sample from (sequentially), by default 100</dd>
+<dt><strong><code>n_samples</code></strong> :&ensp;<code>int</code>, optional</dt>
+<dd>number of bones to create, by default 1</dd>
+<dt><strong><code>mean_</code></strong> :&ensp;<code>float</code>, optional</dt>
+<dd>Mean of the normal distribution to sample PCs from, by default 0.</dd>
+<dt><strong><code>sd_</code></strong> :&ensp;<code>float</code>, optional</dt>
+<dd>Standard deviation of the normal distribution to sample PCs from, by default 1.0</dd>
+</dl>
+<h2 id="returns">Returns</h2>
+<dl>
+<dt><code>numpy.ndarray</code></dt>
+<dd>nx(3xN) ndarray; N=number of points on mesh surface, n=number of new meshes
+This includes the x/y/z position of each surface node(N) for the random bones(n).</dd>
+</dl></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def get_rand_bone_shape(PCs, Vs, mean_coords, n_pcs=100, n_samples=1, mean_=0., sd_=1.0):
+    &#34;&#34;&#34;
+    Function to get random bones using a Statistical Shape Model (SSM).
+    Parameters
+    ----------
+    PCs : numpy.ndarray
+        NxM ndarray; N = number of points on surface, M = number of principal components in model
+        Each column is a principal component.
+    Vs : numpy.ndarray
+        M ndarray; M = number of principal components in model
+        Each entry is the variance for the coinciding principal component in PCs
+    mean_coords : numpy.ndarray
+xN ndarray; N = number of points on surface.
+    n_pcs : int, optional
+        Number of PCs to randomly sample from (sequentially), by default 100
+    n_samples : int, optional
+        number of bones to create, by default 1
+    mean_ : float, optional
+        Mean of the normal distribution to sample PCs from, by default 0.
+    sd_ : float, optional
+        Standard deviation of the normal distribution to sample PCs from, by default 1.0
+    Returns
+    -------
+    numpy.ndarray
+        nx(3xN) ndarray; N=number of points on mesh surface, n=number of new meshes
+        This includes the x/y/z position of each surface node(N) for the random bones(n).
+    &#34;&#34;&#34;
+    rand_pc_scores = np.random.normal(mean_, sd_, size=[n_samples, n_pcs])
+    rand_pc_weights = rand_pc_scores * np.sqrt(Vs[:n_pcs])
+    rand_data = rand_pc_weights @ PCs[:, :n_pcs].T
+    rand_data = mean_coords.flatten() + rand_data
+    return rand_data   </code></pre>
+</details>
+</dd>
+<dt id="pymskt.statistics.pca.get_ssm_deformation"><code class="name flex">
+<span>def <span class="ident">get_ssm_deformation</span></span>(<span>PCs, Vs, mean_coords, pc=0, n_sds=3)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>Function to Statistical Shape Model (SSM) deformed along given Principal Component.</p>
+<h2 id="parameters">Parameters</h2>
+<dl>
+<dt><strong><code>PCs</code></strong> :&ensp;<code>numpy.ndarray</code></dt>
+<dd>NxM ndarray; N = number of points on surface, M = number of principal components in model
+Each column is a principal component.</dd>
+<dt><strong><code>Vs</code></strong> :&ensp;<code>numpy.ndarray</code></dt>
+<dd>M ndarray; M = number of principal components in model
+Each entry is the variance for the coinciding principal component in PCs</dd>
+<dt><strong><code>mean_coords</code></strong> :&ensp;<code>numpy.ndarray</code></dt>
+<dd>3xN ndarray; N = number of points on surface.</dd>
+<dt><strong><code>pc</code></strong> :&ensp;<code>int</code>, optional</dt>
+<dd>The principal component of the SSM to deform, by default 0</dd>
+<dt><strong><code>n_sds</code></strong> :&ensp;<code>int</code>, optional</dt>
+<dd>The number of standard deviations (sd) to deform the SSM.
+This can be positive or negative to scale the model in either direction. , by default 3</dd>
+</dl>
+<h2 id="returns">Returns</h2>
+<dl>
+<dt><code>numpy.ndarray</code></dt>
+<dd>3xN ndarray; N=number of points on mesh surface.
+This includes the x/y/z position of each surface node after deformation using the SSM and
+the specified characteristics (pc, n_sds)</dd>
+</dl></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def get_ssm_deformation(PCs, Vs, mean_coords, pc=0, n_sds=3):
+    &#34;&#34;&#34;
+    Function to Statistical Shape Model (SSM) deformed along given Principal Component.
+    Parameters
+    ----------
+    PCs : numpy.ndarray
+        NxM ndarray; N = number of points on surface, M = number of principal components in model
+        Each column is a principal component.
+    Vs : numpy.ndarray
+        M ndarray; M = number of principal components in model
+        Each entry is the variance for the coinciding principal component in PCs
+    mean_coords : numpy.ndarray
+xN ndarray; N = number of points on surface.
+    pc : int, optional
+        The principal component of the SSM to deform, by default 0
+    n_sds : int, optional
+        The number of standard deviations (sd) to deform the SSM.
+        This can be positive or negative to scale the model in either direction. , by default 3
+    Returns
+    -------
+    numpy.ndarray
+xN ndarray; N=number of points on mesh surface.
+        This includes the x/y/z position of each surface node after deformation using the SSM and
+        the specified characteristics (pc, n_sds)
+    &#34;&#34;&#34;
+    pc_vector = PCs[:, pc]
+    pc_vector_scale = np.sqrt(Vs[pc]) * n_sds # convert Variances to SDs &amp; multiply by n_sds (negative/positive important)
+    coords_deformation = pc_vector * pc_vector_scale
+    deformed_coords = (mean_coords.flatten() + coords_deformation).reshape(mean_coords.shape)
+    return deformed_coords</code></pre>
+</details>
+</dd>
+<dt id="pymskt.statistics.pca.pca_svd"><code class="name flex">
+<span>def <span class="ident">pca_svd</span></span>(<span>data)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>Calculate eigenvalues &amp; eigenvectors of <code>data</code> using Singular Value Decomposition (SVD)</p>
+<h2 id="parameters">Parameters</h2>
+<dl>
+<dt><strong><code>data</code></strong> :&ensp;<code>numpy.ndarray</code></dt>
+<dd>MxN matrix
+M = # of features / dimensions of data
+N = # of trials / participants in dataset</dd>
+</dl>
+<h2 id="returns">Returns</h2>
+<dl>
+<dt><code>tuple (PC = numpy.ndarray, V = numpy.ndarray)</code></dt>
+<dd>PC - each volumn is a principal component (eigenvector)
+V - Mx1 matrix of variances (coinciding with each PC)</dd>
+</dl>
+<h2 id="notes">Notes</h2>
+<p>Adapted from:
+"A Tutorial on Principal Component Analysis by Jonathon Shlens"
+<a href="https://arxiv.org/abs/1404.1100">https://arxiv.org/abs/1404.1100</a>
+Inputs
+data = MxN matrix (M dimensions, N trials)
+Returns
+PC - each column is a PC
+V - Mx1 matrix of variances</p></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def pca_svd(data):
+    &#34;&#34;&#34;
+    Calculate eigenvalues &amp; eigenvectors of `data` using Singular Value Decomposition (SVD)
+    Parameters
+    ----------
+    data : numpy.ndarray
+        MxN matrix
+        M = # of features / dimensions of data
+        N = # of trials / participants in dataset
+    Returns
+    -------
+    tuple (PC = numpy.ndarray, V = numpy.ndarray)
+        PC - each volumn is a principal component (eigenvector)
+        V - Mx1 matrix of variances (coinciding with each PC)
+    Notes
+    -----
+    Adapted from:
+    &#34;A Tutorial on Principal Component Analysis by Jonathon Shlens&#34;
+    https://arxiv.org/abs/1404.1100
+    Inputs
+    data = MxN matrix (M dimensions, N trials)
+    Returns
+    PC - each column is a PC
+    V - Mx1 matrix of variances
+    &#34;&#34;&#34;
+    M, N = data.shape
+    mn = np.mean(data, axis=1)
+    data = data - mn[:, None]  # produce centered data. If already centered this shouldnt be harmful.
+    Y = data.T / np.sqrt(N - 1)
+    U, S, V = svd(Y, full_matrices=False)
+    PC = V.T  # V are the principle components (PC)
+    V = S ** 2  # The squared singular values are the variances (V)
+    return PC, V</code></pre>
+</details>
+</dd>
+<dt id="pymskt.statistics.pca.save_gif"><code class="name flex">
+<span>def <span class="ident">save_gif</span></span>(<span>path_save, PCs, Vs, mean_coords, mean_mesh, pc=0, min_sd=-3.0, max_sd=3.0, step=0.25, color='orange', show_edges=True, edge_color='black', camera_position='xz', window_size=[3000, 4000], background_color='white', verbose=False)</span>
+</code></dt>
+<dd>
+<div class="desc"><p>Function to save a gif of the SSM deformation.</p>
+<h2 id="parameters">Parameters</h2>
+<dl>
+<dt><strong><code>path_save</code></strong> :&ensp;<code>str</code></dt>
+<dd>Path to save the gif to.</dd>
+<dt><strong><code>PCs</code></strong> :&ensp;<code>numpy.ndarray</code></dt>
+<dd>SSM Principal Components.</dd>
+<dt><strong><code>Vs</code></strong> :&ensp;<code>numpy.ndarray</code></dt>
+<dd>SSM Variances.</dd>
+<dt><strong><code>mean_coords</code></strong> :&ensp;<code>numpy.ndarray</code></dt>
+<dd>NxM ndarray; N = number of meshes, M = number of points x n_dimensions</dd>
+<dt><strong><code>mean_mesh</code></strong> :&ensp;<code>vtk.PolyData</code></dt>
+<dd>vtk polydata of the mean mesh</dd>
+<dt><strong><code>pc</code></strong> :&ensp;<code>int</code>, optional</dt>
+<dd>The principal component of the SSM to deform, by default 0</dd>
+<dt><strong><code>min_sd</code></strong> :&ensp;<code>float</code>, optional</dt>
+<dd>The lower bound (minimum) standard deviations (sd) to deform the SSM from
+This can be positive or negative to scale the model in either direction. , by default -3.</dd>
+<dt><strong><code>max_sd</code></strong> :&ensp;<code>float</code>, optional</dt>
+<dd>The upper bound (maximum) standard deviations (sd) to deform the SSM from
+This can be positive or negative to scale the model in either direction. , by default 3.</dd>
+<dt><strong><code>step</code></strong> :&ensp;<code>float</code>, optional</dt>
+<dd>The step size (sd) to deform the SSM by, by default 0.25</dd>
+<dt><strong><code>color</code></strong> :&ensp;<code>str</code>, optional</dt>
+<dd>The color of the SSM surface during rendering, by default 'orange'</dd>
+<dt><strong><code>show_edges</code></strong> :&ensp;<code>bool</code>, optional</dt>
+<dd>Whether to show the edges of the SSM surface during rendering, by default True</dd>
+<dt><strong><code>edge_color</code></strong> :&ensp;<code>str</code>, optional</dt>
+<dd>The color of the edges of the SSM surface during rendering, by default 'black'</dd>
+<dt><strong><code>camera_position</code></strong> :&ensp;<code>str</code>, optional</dt>
+<dd>The camera position to use during rendering, by default 'xz'</dd>
+<dt><strong><code>window_size</code></strong> :&ensp;<code>list</code>, optional</dt>
+<dd>The window size to use during rendering, by default [3000, 4000]</dd>
+<dt><strong><code>background_color</code></strong> :&ensp;<code>str</code>, optional</dt>
+<dd>The background color to use during rendering, by default 'white'</dd>
+<dt><strong><code>verbose</code></strong> :&ensp;<code>bool</code>, optional</dt>
+<dd>Whether to print progress to console, by default False</dd>
+</dl></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def save_gif(
+    path_save,
+    PCs,
+    Vs,
+    mean_coords,  # mean_coords could be extracted from mean mesh...?
+    mean_mesh,
+    pc=0,
+    min_sd=-3.,
+    max_sd=3.,
+    step=0.25,
+    color=&#39;orange&#39;,
+    show_edges=True,
+    edge_color=&#39;black&#39;,
+    camera_position=&#39;xz&#39;,
+    window_size=[3000, 4000],
+    background_color=&#39;white&#39;,
+    verbose=False,
+):
+    &#34;&#34;&#34;
+    Function to save a gif of the SSM deformation.
+    Parameters
+    ----------
+    path_save : str
+        Path to save the gif to.
+    PCs : numpy.ndarray
+        SSM Principal Components.
+    Vs : numpy.ndarray
+        SSM Variances.
+    mean_coords : numpy.ndarray
+        NxM ndarray; N = number of meshes, M = number of points x n_dimensions
+    mean_mesh : vtk.PolyData
+        vtk polydata of the mean mesh
+    pc : int, optional
+        The principal component of the SSM to deform, by default 0
+    min_sd : float, optional
+        The lower bound (minimum) standard deviations (sd) to deform the SSM from
+        This can be positive or negative to scale the model in either direction. , by default -3.
+    max_sd : float, optional
+        The upper bound (maximum) standard deviations (sd) to deform the SSM from
+        This can be positive or negative to scale the model in either direction. , by default 3.
+    step : float, optional
+        The step size (sd) to deform the SSM by, by default 0.25
+    color : str, optional
+        The color of the SSM surface during rendering, by default &#39;orange&#39;
+    show_edges : bool, optional
+        Whether to show the edges of the SSM surface during rendering, by default True
+    edge_color : str, optional
+        The color of the edges of the SSM surface during rendering, by default &#39;black&#39;
+    camera_position : str, optional
+        The camera position to use during rendering, by default &#39;xz&#39;
+    window_size : list, optional
+        The window size to use during rendering, by default [3000, 4000]
+    background_color : str, optional
+        The background color to use during rendering, by default &#39;white&#39;
+    verbose : bool, optional
+        Whether to print progress to console, by default False
+    &#34;&#34;&#34;
+    # ALTERNATIVELY... could pass a bunch of the above parameters as kwargs..?? but thats less clear
+    gif = GIF(
+        path_save=path_save,
+        color=color,
+        show_edges=show_edges,
+        edge_color=edge_color,
+        camera_position=camera_position,
+        window_size=window_size,
+        background_color=background_color,
+    )
+    for idx, sd in enumerate(np.arange(min_sd, max_sd + step, step)):
+        if verbose is True:
+            print(f&#39;Deforming SSM with idx={idx} sd={sd}&#39;)
+        pts = get_ssm_deformation(PCs, Vs, mean_coords, pc=pc, n_sds=sd)
+        if type(mean_mesh) == dict:
+            mesh = []
+            start_idx = 0
+            for mesh_name, mesh_params in mean_mesh.items():
+                mesh.append(
+                    create_vtk_mesh_from_deformed_points(
+                        mesh_params[&#39;mesh&#39;],
+                        pts[start_idx:start_idx+mesh_params[&#39;n_points&#39;], :],
+                    )
+                )
+                start_idx += mesh_params[&#39;n_points&#39;]
+        if type(mean_mesh) in (list, tuple):
+            mesh = []
+            start_idx = 0
+            for mesh_ in mean_mesh:
+                n_pts = mesh_.GetNumberOfPoints()
+                mesh.append(
+                    create_vtk_mesh_from_deformed_points(
+                        mesh_,
+                        pts[start_idx:start_idx+n_pts, :],
+                    )
+                )
+                start_idx += n_pts
+        else:
+            mesh = create_vtk_mesh_from_deformed_points(mean_mesh, pts)
+        gif.add_mesh_frame(mesh)
+    gif.done()</code></pre>
+</details>
+</dd>
+</dl>
+</section>
+<section>
+</section>
+</article>
+<nav id="sidebar">
+<h1>Index</h1>
+<div class="toc">
+<ul></ul>
+</div>
+<ul id="index">
+<li><h3>Super-module</h3>
+<ul>
+<li><code><a title="pymskt.statistics" href="index.html">pymskt.statistics</a></code></li>
+</ul>
+</li>
+<li><h3><a href="#header-functions">Functions</a></h3>
+<ul class="">
+<li><code><a title="pymskt.statistics.pca.create_vtk_mesh_from_deformed_points" href="#pymskt.statistics.pca.create_vtk_mesh_from_deformed_points">create_vtk_mesh_from_deformed_points</a></code></li>
+<li><code><a title="pymskt.statistics.pca.get_rand_bone_shape" href="#pymskt.statistics.pca.get_rand_bone_shape">get_rand_bone_shape</a></code></li>
+<li><code><a title="pymskt.statistics.pca.get_ssm_deformation" href="#pymskt.statistics.pca.get_ssm_deformation">get_ssm_deformation</a></code></li>
+<li><code><a title="pymskt.statistics.pca.pca_svd" href="#pymskt.statistics.pca.pca_svd">pca_svd</a></code></li>
+<li><code><a title="pymskt.statistics.pca.save_gif" href="#pymskt.statistics.pca.save_gif">save_gif</a></code></li>
+</ul>
+</li>
+</ul>
+</nav>
+</main>
+<footer id="footer">
+<p>Generated by <a href="https://pdoc3.github.io/pdoc" title="pdoc: Python API documentation generator"><cite>pdoc</cite> 0.10.0</a>.</p>
+</footer>
+</body>
+</html>