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!important;box-shadow:none !important;text-shadow:none !important}a[href]:after{content:" (" attr(href) ")";font-size:90%}a[href][title]:after{content:none}abbr[title]:after{content:" (" attr(title) ")"}.ir a:after,a[href^="javascript:"]:after,a[href^="#"]:after{content:""}pre,blockquote{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}tr,img{page-break-inside:avoid}img{max-width:100% !important}@page{margin:0.5cm}p,h2,h3{orphans:3;widows:3}h1,h2,h3,h4,h5,h6{page-break-after:avoid}}</style> +<script defer src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/10.1.1/highlight.min.js" integrity="sha256-Uv3H6lx7dJmRfRvH8TH6kJD1TSK1aFcwgx+mdg3epi8=" crossorigin></script> +<script>window.addEventListener('DOMContentLoaded', () => hljs.initHighlighting())</script> +</head> +<body> +<main> +<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): + """ + Calculate eigenvalues & 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: + "A Tutorial on Principal Component Analysis by Jonathon Shlens" + 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 + """ + 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): + """ + 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 + 3xN 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 + 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) + """ + + pc_vector = PCs[:, pc] + pc_vector_scale = np.sqrt(Vs[pc]) * n_sds # convert Variances to SDs & 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): + """ + 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 + 3xN 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). + """ + + 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): + """ + 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 + 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. + + Returns + ------- + vtk.PolyData + vtk polydata of the deformed mesh + """ + + 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='orange', + show_edges=True, + edge_color='black', + camera_position='xz', + window_size=[3000, 4000], + background_color='white', + verbose=False, +): + """ + 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 'orange' + 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 'black' + camera_position : str, optional + The camera position to use during rendering, by default 'xz' + 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 'white' + verbose : bool, optional + Whether to print progress to console, by default False + + + """ + # 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'Deforming SSM with idx={idx} sd={sd}') + 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['mesh'], + pts[start_idx:start_idx+mesh_params['n_points'], :], + ) + ) + start_idx += mesh_params['n_points'] + 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> : <code>vtk.PolyData</code></dt> +<dd>vtk polydata of the mean mesh</dd> +<dt><strong><code>new_points</code></strong> : <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): + """ + 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 + 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. + + Returns + ------- + vtk.PolyData + vtk polydata of the deformed mesh + """ + + 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> : <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> : <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> : <code>numpy.ndarray</code></dt> +<dd>3xN ndarray; N = number of points on surface.</dd> +<dt><strong><code>n_pcs</code></strong> : <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> : <code>int</code>, optional</dt> +<dd>number of bones to create, by default 1</dd> +<dt><strong><code>mean_</code></strong> : <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> : <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): + """ + 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 + 3xN 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). + """ + + 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> : <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> : <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> : <code>numpy.ndarray</code></dt> +<dd>3xN ndarray; N = number of points on surface.</dd> +<dt><strong><code>pc</code></strong> : <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> : <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): + """ + 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 + 3xN 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 + 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) + """ + + pc_vector = PCs[:, pc] + pc_vector_scale = np.sqrt(Vs[pc]) * n_sds # convert Variances to SDs & 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 & eigenvectors of <code>data</code> using Singular Value Decomposition (SVD)</p> +<h2 id="parameters">Parameters</h2> +<dl> +<dt><strong><code>data</code></strong> : <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): + """ + Calculate eigenvalues & 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: + "A Tutorial on Principal Component Analysis by Jonathon Shlens" + 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 + """ + 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> : <code>str</code></dt> +<dd>Path to save the gif to.</dd> +<dt><strong><code>PCs</code></strong> : <code>numpy.ndarray</code></dt> +<dd>SSM Principal Components.</dd> +<dt><strong><code>Vs</code></strong> : <code>numpy.ndarray</code></dt> +<dd>SSM Variances.</dd> +<dt><strong><code>mean_coords</code></strong> : <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> : <code>vtk.PolyData</code></dt> +<dd>vtk polydata of the mean mesh</dd> +<dt><strong><code>pc</code></strong> : <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> : <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> : <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> : <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> : <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> : <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> : <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> : <code>str</code>, optional</dt> +<dd>The camera position to use during rendering, by default 'xz'</dd> +<dt><strong><code>window_size</code></strong> : <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> : <code>str</code>, optional</dt> +<dd>The background color to use during rendering, by default 'white'</dd> +<dt><strong><code>verbose</code></strong> : <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='orange', + show_edges=True, + edge_color='black', + camera_position='xz', + window_size=[3000, 4000], + background_color='white', + verbose=False, +): + """ + 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 'orange' + 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 'black' + camera_position : str, optional + The camera position to use during rendering, by default 'xz' + 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 'white' + verbose : bool, optional + Whether to print progress to console, by default False + + + """ + # 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'Deforming SSM with idx={idx} sd={sd}') + 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['mesh'], + pts[start_idx:start_idx+mesh_params['n_points'], :], + ) + ) + start_idx += mesh_params['n_points'] + 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> \ No newline at end of file