usage: prog [options] <input stack> <output basis> [reprojections] This too provides a variety of dimensionality reduction methods. This new version uses scikit.learn, which provides a greater variety of algorithms, but must load all data into memory. If working with a large file, you may want to consider using --step to operate on a limited subset of the data. If specified, [reprojections] will contain projections of the full input stack (ignoring --step) into the basis subspace represented as a single image. This obviates the need for e2basis.py, and permits use of nonlinear decompositions. --- Performs multivariate statistical analysis on a stack of images. Writes a set of Eigenimages which can be uses as a basis set for reducing the dimensionality of a data set (noise reduction). Typically this basis set is then used to reproject the data (e2basis.py) and classify the data based on the projected vectors. If the output file supports arbitrary metadata (like HDF), Eigenvalues are stored in the 'eigval' parameter in each image. Note: The mean value is subtracted from each image prior to MSA calculation. The mean image is stored as the first image in the output file, though it is not part of the orthonormal basis when handled this way.

Option | Type | Description |
---|---|---|

--version | None | show program's version number and exit |

--mode | str | Mode should be one of: pca, sparsepca, fastica, factan, lda, nmf |

--nomean | None | Suppress writing the average image as the first output image |

--nomeansub | None | Suppress subtracting the mean from each input image, also implies --nomean |

--nbasis, -n | int | Number of basis images to generate. |

--maskfile, -M | str | File containing a mask defining the pixels to include in the Eigenimages |

--projin | str | When generating subspace projections, use this file instead of the input used for the MSA |

--normproj | None | When generating subspace projections, normalize each projection vector to unit length |

--mask | int | Mask radius, negative values imply ny/2+1+mask, --mask=0 disables, --maskfile overrides |

--simmx | str | Will use transformations from simmx on each particle prior to analysis |

--normalize | None | Perform a careful normalization of input images before MSA. Otherwise normalization is not modified until after mean subtraction. |

--step | str | Specify <init>,<step>[,last]. Processes only a subset of the input data. For example, 0,2 would process only the even numbered particles |

--ppid | int | Set the PID of the parent process, used for cross platform PPID |

--verbose, -v | int | verbose level [0-9], higher number means higher level of verboseness |