def pipeline_dli(filename, output_file, keywords, verbose=False): import numpy as np import linear_operators as lo import tamasis as tm # verbosity tm.var.verbose = verbose # define observation obs = tm.PacsObservation(filename, **keywords["PacsObservation"]) # extra masking tm.step_scanline_masking(obs, **keywords["scanline_masking"]) # get data tod = obs.get_tod(**keywords["get_tod"]) # deglitching tm.step_deglitching(obs, tod, **keywords["deglitching"]) # median filtering tod = tm.filter_median(tod, **keywords["filter_median"]) # define projector projection = obs.get_projection_operator(**keywords["Projection"]) # build instrument model response = tm.ResponseTruncatedExponentialOperator( obs.pack(obs.instrument.detector.time_constant) / obs.instrument.SAMPLING_PERIOD) compression = tm.CompressionAverageOperator(obs.slice.compression_factor) masking = tm.MaskOperator(tod.mask) model = masking * compression * response * projection # set tod masked values to zero tod = masking(tod) # N^-1 operator invntt = tm.InvNttOperator(obs) # for the dli algorithm M = map_mask(tod, model, **keywords["map_mask"]) # recast model as a new-style LinearOperator from linear_operators package H = lo.aslinearoperator(model) * M.T N = lo.aslinearoperator(invntt) # vectorize data so it is accepted by LinearOperators y = tod.ravel() Ds = [tm.DiscreteDifferenceOperator(axis=i, shapein=projection.shapein) \ for i in (0, 1)] Ds = [lo.aslinearoperator(D) for D in Ds] Ds = [D * M.T for D in Ds] D = lo.concatenate(Ds) # handle tau which needs to be an ndarray keywords["dli"]["tau"] *= np.ones(D.shape[0]) algo = lo.DoubleLoopAlgorithm(H, y, D, noise_covariance=N, fmin_args=keywords["fmin_args"], lanczos=keywords["lanczos"], **keywords["dli"]) # optimize xe = algo() # reshape xe = (M.T * xe).reshape(projection.shapein) # recast as tamasis map xe = tm.Map(xe) # save xe.save(output_file)
def pipeline_photproject(filenames, output_file, keywords): """ Perform regularized least-square inversion of state of the art PACS model. The PACS model includes tod mask, compression, response, projection and noise covariance (invntt). Processing steps are as follows : - define PacsObservation instance - mask first scanlines to avoid drift - get Time Ordered Data (get_tod) - 2nd level deglitching (with standard projector and tod median filtering with a narrow window) - median filtering - define projection - perform inversion on model - save file Arguments --------- filenames: list of strings List of data filenames. output_file : string Name of the output fits file. keywords: dict Dictionary containing options for all steps as dictionary. Returns ------- Returns nothing. Save result as a fits file. """ from scipy.sparse.linalg import cgs import tamasis as tm # define observation obs = tm.PacsObservation(filenames, **keywords["PacsObservation"]) # extra masking tm.step_scanline_masking(obs, **keywords["scanline_masking"]) # get data tod = obs.get_tod(**keywords["get_tod"]) # deglitching tm.step_deglitching(obs, tod, **keywords["deglitching"]) # median filtering tod = tm.filter_median(tod, **keywords["filter_median"]) # define projector projection = obs.get_projection_operator(downsampling=True, **keywords["Projection"]) # build instrument model masking = tm.MaskOperator(tod.mask) model = masking * projection # set tod masked values to zero tod = masking(tod) # perform map-making map_naive = tm.mapper_naive(tod, model, **keywords["mapper_naive"]) # save map_naive.save(output_file)
def pipeline_rls(filenames, output_file, keywords, verbose=False): """ Perform regularized least-square inversion of a simple model (tod mask and projection). Processing steps are as follows : - define PacsObservation instance - get Time Ordered Data (get_tod) - define projection - perform inversion on model - save file Arguments --------- filenames: list of strings List of data filenames. output_file : string Name of the output fits file. keywords: dict Dictionary containing options for all steps as dictionary. verbose: boolean (default False) Set verbosity. Returns ------- Returns nothing. Save result as a fits file. """ from scipy.sparse.linalg import cgs import tamasis as tm # verbosity keywords["mapper_rls"]["verbose"] = verbose # define observation obs_keys = keywords.get("PacsObservation", {}) obs = tm.PacsObservation(filenames, **obs_keys) # get data tod_keys = keywords.get("get_tod", {}) tod = obs.get_tod(**tod_keys) # define projector proj_keys = keywords.get("Projection", {}) projection = tm.Projection(obs, **proj_keys) # define mask masking_tod = tm.Masking(tod.mask) # define full model model = masking_tod * projection # perform map-making inversion mapper_keys = keywords.get("mapper_rls", {}) map_rls = tm.mapper_rls(tod, model, solver=cgs, **mapper_keys) # save map_rls.save(output_file)
def generate_compressed_data(filenames, **keywords): """ Compress data to a given factor using a custom compression matrix or operator. """ obs = tm.PacsObservation(filenames, **keywords["PacsObservation"]) data = obs.get_tod(**keywords["get_tod"]) # model A = tm.PacsConversionAdu(obs, **keywords["PacsConversionAdu"]) mode = compressions[keywords["compression"].pop("mode")] factor = keywords["compression"].pop("factor") C = mode(data, factor, **keywords["compression"]) # convert digital_data = A(data) # apply compression y = C * digital_data.ravel() # reshape and recast cshape = list(digital_data.shape) cshape[1] = y.size / cshape[0] compressed_data = fa.FitsArray(data=y.reshape(cshape)) return compressed_data
def load_data(filename, header=None, resolution=3.): #mask = np.zeros((32, 64), dtype=np.int8) obs = tm.PacsObservation(filename=filename, fine_sampling_factor=1, #detector_mask=mask ) tod = obs.get_tod() if header is None: header = obs.get_map_header() header.update('CDELT1', resolution / 3600) header.update('CDELT2', resolution / 3600) npix = 5 good_npix = False while good_npix is False: try: projection = tm.Projection(obs, header=header, resolution=resolution, oversampling=False, npixels_per_sample=npix) good_npix = True except(RuntimeError): npix +=1 return tod, projection, header, obs
def pipeline_huber(filenames, output_file, keywords, verbose=False): """ Perform huber regularized inversion of state of the art PACS model. The PACS model includes tod mask, compression, response, projection and noise covariance (invntt). Processing steps are as follows : - define PacsObservation instance - mask first scanlines to avoid drift - get Time Ordered Data (get_tod) - 2nd level deglitching (with standard projector and tod median filtering with a narrow window) - median filtering - define projection - perform inversion on model - save file Arguments --------- filenames: list of strings List of data filenames. output_file : string Name of the output fits file. keywords: dict Dictionary containing options for all steps as dictionary. verbose: boolean (default False) Set verbosity. Returns ------- Returns nothing. Save result as a fits file. """ from scipy.sparse.linalg import cgs import tamasis as tm import linear_operators as lo # verbosity # define observation obs = tm.PacsObservation(filenames, **keywords["PacsObservation"]) # extra masking tm.step_scanline_masking(obs, **keywords["scanline_masking"]) # get data tod = obs.get_tod(**keywords["get_tod"]) # deglitching # need to adapt degltiching to any compression model tm.step_deglitching(obs, tod, **keywords["deglitching"]) # median filtering tod = tm.filter_median(tod, **keywords["filter_median"]) # define projector projection = tm.Projection(obs, **keywords["Projection"]) P = lo.aslinearoperator(projection) # build instrument model masking = tm.Masking(tod.mask) Mt = lo.aslinearoperator(masking) # compression mode = compressions[keywords["compression"].pop("mode")] factor = keywords["compression"].pop("factor") C = mode(data, factor, **keywords["compression"]) # define map mask M = map_mask(tod, model, **keywords["map_mask"]) # recast model as a new-style LinearOperator from linear_operators package H = Mt * C * P * M # vectorize data so it is accepted by LinearOperators y = tod.ravel() Ds = [ tm.DiscreteDifference(axis=i, shapein=projection.shapein) for i in (0, 1) ] Ds = [lo.aslinearoperator(D) for D in Ds] Ds = [D * M.T for D in Ds] # set tod masked values to zero tod = masking(tod) # perform map-making inversion hypers = (keywords["hacg"].pop("hyper"), ) * len(Ds) deltas = (keywords["hacg"].pop("delta"), ) * (len(Ds) + 1) map_huber = lo.hacg(H, tod.ravel(), priors=Ds, hypers=hypers, deltas=deltas, **keywords["hacg"]) # save map_huber = (M.T * map_huber).reshape(projection.shapein) map_huber = tm.Map(map_huber) map_huber.save(output_file)
#!/usr/bin/env python import numpy as np import tamasis as tm import lo import csh.filter as filt from time import time import scipy.sparse.linalg as spl # data pacs = tm.PacsObservation(filename=tm.tamasis_dir+'tests/frames_blue.fits') tod = pacs.get_tod() # projector model = tm.Projection(pacs, resolution=3.2, oversampling=False, npixels_per_sample=6) # naive map backmap = model.transpose(tod) # transform to lo P = lo.aslinearoperator(model.aslinearoperator()) # derive filter kernel = filt.kernel_from_tod(tod, length=10) #kern = np.mean(kernel, axis=0) N = filt.kernels_convolve(tod.shape, 1 / np.sqrt(kernel)) # apply to data yn = N * tod.flatten() # apply to model M = N * P # priors Ds = [lo.diff(backmap.shape, axis=axis) for axis in xrange(backmap.ndim)] #Ds.append(lo.pywt_lo.wavelet2(backmap.shape, "haar")) # inversion #y = tod.flatten() x, conv = lo.rls(M, Ds, (1e1, 1e1, 1e-1), yn, spl.bicgstab)
#!/usr/bin/env python import numpy as np import os import tamasis as tm import lo import csh # define data set datadir = os.getenv('CSH_DATA') filenames = [ datadir + '/1342185454_blue_PreparedFrames.fits[5954:67617]', datadir + '/1342185455_blue_PreparedFrames.fits[5954:67617]' ] pacs = tm.PacsObservation(filename=filenames, fine_sampling_factor=1, keep_bad_detectors=False) # reset pacs header to have a shape multiple of 4 resolution = 3. header = pacs.get_map_header() #header['NAXIS1'] = 192 #header['NAXIS2'] = 192 #header['CRPIX1'] = 96 #header['CRPIX2'] = 96 header.update('CDELT1', resolution / 3600) header.update('CDELT2', resolution / 3600) # data tod = pacs.get_tod() y = tod.flatten() # remove bad pixels (by updating mask !) #tod = remove_bad_pixels(tod) # compress data factor = 8
def pipeline_wrls(filenames, output_file, keywords, verbose=False): """ Perform regularized least-square inversion of state of the art PACS model. The PACS model includes tod mask, compression, response, projection and noise covariance (invntt). Processing steps are as follows : - define PacsObservation instance - mask first scanlines to avoid drift - get Time Ordered Data (get_tod) - 2nd level deglitching (with standard projector and tod median filtering with a narrow window) - median filtering - define projection - perform inversion on model - save file Arguments --------- filenames: list of strings List of data filenames. output_file : string Name of the output fits file. keywords: dict Dictionary containing options for all steps as dictionary. verbose: boolean (default False) Set verbosity. Returns ------- Returns nothing. Save result as a fits file. """ from scipy.sparse.linalg import cgs import tamasis as tm # verbosity keywords["mapper_rls"]["verbose"] = verbose # define observation obs = tm.PacsObservation(filenames, **keywords["PacsObservation"]) # extra masking tm.step_scanline_masking(obs, **keywords["scanline_masking"]) # get data tod = obs.get_tod(**keywords["get_tod"]) # deglitching tm.step_deglitching(obs, tod, **keywords["deglitching"]) # median filtering tod = tm.filter_median(tod, **keywords["filter_median"]) # define projector projection = obs.get_projection_operator(**keywords["Projection"]) # build instrument model response = tm.ResponseTruncatedExponentialOperator( obs.pack(obs.instrument.detector.time_constant) / obs.instrument.SAMPLING_PERIOD) compression = tm.CompressionAverageOperator(obs.slice.compression_factor) masking = tm.MaskOperator(tod.mask) model = masking * compression * response * projection # set tod masked values to zero tod = masking(tod) # N^-1 operator invntt = tm.InvNttOperator(obs) # perform map-making inversion map_rls = tm.mapper_rls(tod, model, invntt=invntt, solver=cgs, **keywords["mapper_rls"]) # save map_rls.save(output_file)
'csh', 'output', ) # compression modes #compressions = ["", "ca", "cs"] compressions = ["ca"] # median filter length filter_length = 10000 hypers = (1e10, 1e10) resolution = 3. factor = 4 ext = ".fits" pre = "ngc6946_madmap1_" # find a map for each compression and save it obs = tm.PacsObservation(filename=filename, fine_sampling_factor=1, detector_policy='remove') tod = obs.get_tod() header = obs.get_map_header() header.update('CDELT1', resolution / 3600) header.update('CDELT2', resolution / 3600) npix = 5 good_npix = False projection = tm.Projection(obs, header=header, resolution=resolution, oversampling=False, npixels_per_sample=npix) model = projection #C = csh.averaging(tod.shape, factor=factor) compression_shape = [
#!/usr/bin/env python import tamasis as tm import csh import csh.score import numpy as np import lo import scipy.sparse.linalg as spl # data pacs = tm.PacsObservation(filename=tm.tamasis_dir+'tests/frames_blue.fits', fine_sampling_factor=1, keep_bad_detectors=True) tod = pacs.get_tod() # compression model #C = lo.binning(tod.shape, factor=8, axis=1, dtype=np.float64) shape = (64, 32) + (tod.shape[1], ) C = csh.binning3d( shape, factors=(2, 2, 2)) # compress data ctod = C * tod.flatten() # projector projection = tm.Projection(pacs, resolution=3.2, oversampling=False, npixels_per_sample=6) model = projection # naive map backmap = model.transpose(tod) # transform to lo #P = lo.ndsubclass(backmap, tod, matvec=model.direct, rmatvec=model.transpose) P = lo.aslinearoperator(model.aslinearoperator()) # full model A = C * P # priors Dx = lo.diff(backmap.shape, axis=0, dtype=np.float64)