Source code for pyklip.fmlib.nofm

import multiprocessing as mp
import ctypes

import numpy as np

[docs] class NoFM(object): """ Super class for all forward modelling classes. Has fall-back functions for all fm dependent calls so that each FM class does not need to implement functions it doesn't want to. Should do no forward modelling and just do regular KLIP by itself """ def __init__(self, inputs_shape, numbasis): """ Initializes the NoFM class Args: inputs_shape: shape of the inputs numpy array. Typically (N, y, x) numbasis: 1d numpy array consisting of the number of basis vectors to use Returns: None """ self.inputs_shape = inputs_shape self.numbasis = numbasis self.outputs_shape = inputs_shape + numbasis.shape self.need_aux = False # Use float64 # self.data_type = ctypes.c_double # Use float32 self.data_type = ctypes.c_float self.supports_rdi = False # while techncially true, this is a default for all other RDI classes that don't support RDI yet
[docs] def alloc_output(self): """ Allocates shared memory array for final output Only use multiprocessing data structors as we are using the multiprocessing class Args: Returns: outputs: mp.array to store final outputs in (shape of (N*wv, y, x, numbasis)) outputs_shape: shape of outputs array to convert to numpy arrays """ outputs_size = * np.size(self.numbasis) outputs = mp.Array(self.data_type, outputs_size) outputs_shape = self.outputs_shape return outputs, outputs_shape
[docs] def alloc_interm(self, max_sector_size, numsciframes): """ Allocates shared memory array for intermediate step Intermediate step is allocated for a sector by sector basis Args: max_sector_size: number of pixels in this sector. Max because this can be variable. Stupid rotating sectors Returns: interm: mp.array to store intermediate products from one sector in interm_shape:shape of interm array (used to convert to numpy arrays) """ return None, None
[docs] def alloc_fmout(self, output_img_shape): """ Allocates shared memory for the output of the forward modelling Args: output_img_shape: shape of output image (usually N,y,x,b) Returns: fmout: mp.array to store FM data in fmout_shape: shape of FM data array """ return None, None
[docs] def alloc_perturbmag(self, output_img_shape, numbasis): """ Allocates shared memory to store the fractional magnitude of the linear KLIP perturbation Args: output_img_shape: shape of output image (usually N,y,x,b) numbasis: array/list of number of KL basis cutoffs requested Returns: perturbmag: mp.array to store linaer perturbation magnitude perturbmag_shape: shape of linear perturbation magnitude """ return None, None
[docs] def fm_from_eigen(self, **kwargs): """ Generate forward models using the KL modes, eigenvectors, and eigenvectors from KLIP This is called immediately after regular KLIP """ return
[docs] def fm_end_sector(self, **kwargs): """ Does some forward modelling at the end of a sector after all images have been klipped for that sector. """ return
[docs] def cleanup_fmout(self, fmout): """ After running KLIP-FM, if there's anything to do to the fmout array (such as reshaping it), now's the time to do that before outputting it Args: fmout: numpy array of ouput of FM Returns: fmout: same but cleaned up if necessary """ return fmout
[docs] def skip_section(self, radstart, radend, phistart, phiend,flipx=None): """ Returns a boolean indicating if the section defined by (radstart, radend, phistart, phiend) should be skipped. When True is returned the current section in the loop in klip_parallelized() is skipped. Args: radstart: minimum radial distance of sector [pixels] radend: maximum radial distance of sector [pixels] phistart: minimum azimuthal coordinate of sector [radians] phiend: maximum azimuthal coordinate of sector [radians] flipx: if True, flip x coordinate in final image Returns: Boolean: False so by default it never skips. """ return False
[docs] def save_fmout(self, dataset, fmout, outputdir, fileprefix, numbasis, klipparams=None, calibrate_flux=False, spectrum=None, pixel_weights=1): """ Saves the fmout data to disk following the instrument's savedata function Args: dataset: Instruments.Data instance. Will use its dataset.savedata() function to save data fmout: the fmout data passed from fm.klip_parallelized which is passed as the output of cleanup_fmout outputdir: output directory fileprefix: the fileprefix to prepend the file name numbasis: KL mode cutoffs used klipparams: string with KLIP-FM parameters calibrate_flux: if True, flux calibrate the data (if applicable) spectrum: if not None, the spectrum to weight the data by. Length same as dataset.wvs pixel_weights: weights for each pixel for weighted mean. Leave this as a single number for simple mean """ return