Renamed pca to ssm

AmpScan/ssm.py

+# -*- coding: utf-8 -*-
+"""
+Package for using AmpScan to run statistical shape modeling using mean-centered principal component analysis 
+on a group of scans 
+Copyright: Joshua Steer 2018, Joshua.Steer@soton.ac.uk
+"""
+
+import os
+import numpy as np
+from .core import AmpObject
+from .registration import registration
+
+
+class pca(object):
+    r"""
+    Principal Component Analysis methods for a training data set of AmpObjects
+    Used to investigate modes of variation across the population 
+    
+    Examples
+    --------
+    >>> p = pca()
+    >>> p.importFolder('/path/')
+    >>> p.baseline('dir/baselinefh.stl')
+    >>> p.register(save = '/regpath/')
+    >>> p.pca()
+    >>> sfs = [0, 0.1, -0.5 ... 0]
+    >>> newS = p.newShape(sfs)
+    
+    """
+    
+    def __init__(self):
+        self.shapes = []
+        
+        
+    def setBaseline(self, baseline):
+        r"""
+        Function to set the baseline mesh used for registration of the 
+        pca training data meshes
+        
+        Parameters
+        ----------
+        baseline: str
+            The file handle of the stl file used as the baseline
+            
+        """
+        self.baseline = AmpObject(baseline, 'limb')
+        
+        
+    def importFolder(self, path, unify=True):
+        r"""
+        Function to import multiple stl files from folder into the pca object 
+        
+        Parameters
+        ----------
+        path: str
+            The path to the folder containing the stl files to be used as the 
+            training data for the PCA model
+        unify: bool, default True
+            Designmate whether to unify the vertices on stl import 
+
+        """
+        self.fnames = [f for f in os.listdir(path) if f.endswith('.stl')]
+        self.shapes = [AmpObject(path + f, 'limb', unify=unify) for f in self.fnames]
+        for s in self.shapes:
+            s.lp_smooth(3, brim=True)
+        
+    def sliceFiles(self, height):
+        r"""
+        Function to run a planar trim on all the training data for the PCA 
+        model
+        
+        Parameters
+        ----------
+        height: float
+            The hight of the z slice plane 
+
+        """
+        for s in self.shapes:
+            s.planarTrim(height)
+        
+    def register(self, scale=None, save=None, baseline=True):
+        r"""
+        Register all the AmpObject training data to the baseline AmpObject 
+        
+        Parameters
+        ----------
+        scale: float, default None
+            scale parameter used for the registration
+        save: str, default None
+            If not None, this will save the registered
+        baseline: bool, default True
+            If True, the baseline AmpObject will also be included in the PCA 
+            model
+        
+        """
+        self.registered = []
+        for t in self.shapes:
+            r = registration(self.baseline, t, fixBrim=True, steps=5, scale=scale, smooth=1, neigh=50).reg
+            r.lp_smooth()
+            self.registered.append(r)
+        if save is not None:
+            for f, r in zip(self.fnames, self.registered):
+                r.save(save + f)
+        self.X = np.array([r.vert.flatten() for r in self.registered]).T
+        if baseline is True:
+            self.X = np.c_[self.X, self.baseline.vert.flatten()]
+        
+        
+    def pca(self):
+        r"""
+        Function to run mean centered pca using a singular value decomposition 
+        method
+        
+        """
+        self.pca_mean = self.X.mean(axis=1)
+        X_meanC = self.X - self.pca_mean[:, None]
+        (self.pca_U, self.pca_S, self.pca_V) = np.linalg.svd(X_meanC, full_matrices=False)
+        self.pc_weights = np.dot(np.diag(self.pca_S), self.pca_V)
+        self.pc_stdevs = np.std(self.pc_weights, axis=1)
+    
+    def newShape(self, sfs, scale = 'eigs'):
+        r"""
+        Function to calculate a new shape based upon the eigenvalues 
+        or stdevs
+        
+        Parameters
+        ----------
+        sfs: array_like
+            An array of scaling factors to generate the new shape. This 
+            must be of the same length as the number of principal components
+        scale: str, default 'eigs'
+            A string to indicate whether the sfs correspond to mode energy or
+            to standard deviations about the mean
+
+        """
+        try: len(sfs) == len(self.pc_stdevs)
+        except: ValueError('sfs must be of the same length as the number of '
+                           'principal components')
+        if scale == 'eigs':
+            sf = (self.pca_U * sfs).sum(axis=1)
+        elif scale == 'std':
+            sf = (self.pca_U * self.pc_stdevs * sfs).sum(axis=1)
+        return self.pca_mean + sf

docs/source/ssm.rst

+pca module
+==========
+
+.. automodule:: AmpScan.ssm
+    :members:
+    :undoc-members:
+    :show-inheritance:
+    :noindex:
\ No newline at end of file