| Analyzer Name | Parameters | Description |
| cir_avg | int maxr: Maximum radius. int step: Thickness of the ring. int verbose: Display progress if set, more detail with larger numbers |
Calculate the circular average around the center in real space |
| inertiamatrix | int verbose: Display progress if set, more detail with larger numbers |
Compute Inertia matrix for a volume |
| kmeans | int calcsigmamean: Computes standard deviation of the mean image for each class-average (center), and returns them at the end of the list of centers int maxiter: maximum number of iterations (default=100) int minchange: Terminate if fewer than minchange members move in an iteration int mininclass: Minumum number of particles to keep a class as good (not enforced at termination int ncls: number of desired classes int outlierclass: The last class will be reserved for outliers. Any class containing fewer than n particles will be permanently moved to the outlier group. default = disabled int seedmode: How to generate initial seeds. 0 - random element (default), 1 - max sum, min sum, linear int slowseed: Instead of seeding all classes at once, it will gradually increase the number of classes by adding new seeds in groups with large standard deviations int verbose: Display progress if set, more detail with larger numbers (9 max) |
k-means classification |
| pca | emdata mask: mask image int nvec: number of desired principal components |
Principal component analysis |
| pca_large | emdata mask: mask image int nvec: number of desired principal components string tmpfile: Name of temporary file during processing |
Principal component analysis - Warning, have detected anomalous results from this algorithm with specific inputs. Python/NumPy routine now used in most EMAN2 code. |
| shape | int verbose: Display progress if set, more detail with larger numbers |
Experimental. Computes a set of values characterizing a 3-D volume. Returns a 3x2x1 image containing X, Y and Z axial distributions using axis squared and axis linear weighting. |
| svd_gsl | emdata mask: mask image int nimg: total number of input images, required even with insert_image() int nvec: number of desired basis vectors |
Singular Value Decomposition from GSL. Comparable to pca |
| varimax | emdata mask: mask image |
varimax rotation of PCA results |