diff --git a/python/pysettings.cc b/python/pysettings.cc index 3fef95aef5838c4bad4b09b32f5a54024848366d..d68708323de75290666588d69607387da0faab8c 100644 --- a/python/pysettings.cc +++ b/python/pysettings.cc @@ -38,17 +38,23 @@ void init_settings(py::module& m) { .value("python", radler::AlgorithmType::kPython, DOC(radler_AlgorithmType_kPython)); - py::enum_<radler::LocalRmsMethod>( - m, "LocalRmsMethod", DOC(radler_LocalRmsMethod)) - .value("none", radler::LocalRmsMethod::kNone, DOC(radler_LocalRmsMethod_kNone)) - .value("rms_window", radler::LocalRmsMethod::kRmsWindow, DOC(radler_LocalRmsMethod_kRmsWindow)) - .value("rms_and_minimum_window", radler::LocalRmsMethod::kRmsAndMinimumWindow, DOC(radler_LocalRmsMethod_kRmsAndMinimumWindow)); + py::enum_<radler::LocalRmsMethod>(m, "LocalRmsMethod", + DOC(radler_LocalRmsMethod)) + .value("none", radler::LocalRmsMethod::kNone, + DOC(radler_LocalRmsMethod_kNone)) + .value("rms_window", radler::LocalRmsMethod::kRmsWindow, + DOC(radler_LocalRmsMethod_kRmsWindow)) + .value("rms_and_minimum_window", + radler::LocalRmsMethod::kRmsAndMinimumWindow, + DOC(radler_LocalRmsMethod_kRmsAndMinimumWindow)); - py::enum_<radler::MultiscaleShape>( - m, "MultiscaleShape", DOC(radler_MultiscaleShape)) + py::enum_<radler::MultiscaleShape>(m, "MultiscaleShape", + DOC(radler_MultiscaleShape)) .value("tapered_quadratic", - radler::MultiscaleShape::kTaperedQuadraticShape, DOC(radler_MultiscaleShape_kTaperedQuadraticShape)) - .value("gaussian", radler::MultiscaleShape::kGaussianShape, DOC(radler_MultiscaleShape_kGaussianShape)); + radler::MultiscaleShape::kTaperedQuadraticShape, + DOC(radler_MultiscaleShape_kTaperedQuadraticShape)) + .value("gaussian", radler::MultiscaleShape::kGaussianShape, + DOC(radler_MultiscaleShape_kGaussianShape)); py::class_<radler::Settings> settings( m, "Settings", diff --git a/python/pysettings_docstrings.h b/python/pysettings_docstrings.h index dfa6c092834b2937392c27e42192c42ea55aa7f5..c85d5566041b26fc62976688201544e281887440 100644 --- a/python/pysettings_docstrings.h +++ b/python/pysettings_docstrings.h @@ -3,215 +3,226 @@ Do not edit! They were automatically extracted by pybind11_mkdoc. */ -#define __EXPAND(x) x -#define __COUNT(_1, _2, _3, _4, _5, _6, _7, COUNT, ...) COUNT -#define __VA_SIZE(...) __EXPAND(__COUNT(__VA_ARGS__, 7, 6, 5, 4, 3, 2, 1)) -#define __CAT1(a, b) a ## b -#define __CAT2(a, b) __CAT1(a, b) -#define __DOC1(n1) __doc_##n1 -#define __DOC2(n1, n2) __doc_##n1##_##n2 -#define __DOC3(n1, n2, n3) __doc_##n1##_##n2##_##n3 -#define __DOC4(n1, n2, n3, n4) __doc_##n1##_##n2##_##n3##_##n4 -#define __DOC5(n1, n2, n3, n4, n5) __doc_##n1##_##n2##_##n3##_##n4##_##n5 -#define __DOC6(n1, n2, n3, n4, n5, n6) __doc_##n1##_##n2##_##n3##_##n4##_##n5##_##n6 -#define __DOC7(n1, n2, n3, n4, n5, n6, n7) __doc_##n1##_##n2##_##n3##_##n4##_##n5##_##n6##_##n7 -#define DOC(...) __EXPAND(__EXPAND(__CAT2(__DOC, __VA_SIZE(__VA_ARGS__)))(__VA_ARGS__)) +#define __EXPAND(x) x +#define __COUNT(_1, _2, _3, _4, _5, _6, _7, COUNT, ...) COUNT +#define __VA_SIZE(...) __EXPAND(__COUNT(__VA_ARGS__, 7, 6, 5, 4, 3, 2, 1)) +#define __CAT1(a, b) a##b +#define __CAT2(a, b) __CAT1(a, b) +#define __DOC1(n1) __doc_##n1 +#define __DOC2(n1, n2) __doc_##n1##_##n2 +#define __DOC3(n1, n2, n3) __doc_##n1##_##n2##_##n3 +#define __DOC4(n1, n2, n3, n4) __doc_##n1##_##n2##_##n3##_##n4 +#define __DOC5(n1, n2, n3, n4, n5) __doc_##n1##_##n2##_##n3##_##n4##_##n5 +#define __DOC6(n1, n2, n3, n4, n5, n6) \ + __doc_##n1##_##n2##_##n3##_##n4##_##n5##_##n6 +#define __DOC7(n1, n2, n3, n4, n5, n6, n7) \ + __doc_##n1##_##n2##_##n3##_##n4##_##n5##_##n6##_##n7 +#define DOC(...) \ + __EXPAND(__EXPAND(__CAT2(__DOC, __VA_SIZE(__VA_ARGS__)))(__VA_ARGS__)) #if defined(__GNUG__) #pragma GCC diagnostic push #pragma GCC diagnostic ignored "-Wunused-variable" #endif +static const char* __doc_radler_AlgorithmType = + R"doc(The deconvolution algorithm type.)doc"; -static const char *__doc_radler_AlgorithmType = R"doc(The deconvolution algorithm type.)doc"; - -static const char *__doc_radler_AlgorithmType_kGenericClean = -R"doc(A "Högbom" CLEAN algorithm, extended with multi frequency/polarization +static const char* __doc_radler_AlgorithmType_kGenericClean = + R"doc(A "Högbom" CLEAN algorithm, extended with multi frequency/polarization clean. It also extends the basic CLEAN algorithm with features such as auto-masking and spectral fitting (both are described in Offringa & Smirnov, 2017).)doc"; -static const char *__doc_radler_AlgorithmType_kIuwt = -R"doc(An algorithm similar to the MORESANE algorithm (A Dabbech et al., +static const char* __doc_radler_AlgorithmType_kIuwt = + R"doc(An algorithm similar to the MORESANE algorithm (A Dabbech et al., 2014), but reimplemented in C++ and extended for multi frequency/polarization clean.)doc"; -static const char *__doc_radler_AlgorithmType_kMoreSane = -R"doc(Makes use of the external MORESANE package that implements the +static const char* __doc_radler_AlgorithmType_kMoreSane = + R"doc(Makes use of the external MORESANE package that implements the algorithm described by A. Dabbech et al. (2014). Requires specification of the location of MORESANE (with Settings::MoreSane::location). This method does not support multi- frequency/polarization cleaning.)doc"; -static const char *__doc_radler_AlgorithmType_kMultiscale = -R"doc(Implements the algorithm described by Offringa & Smirnov (2017). This +static const char* __doc_radler_AlgorithmType_kMultiscale = + R"doc(Implements the algorithm described by Offringa & Smirnov (2017). This algorithms allows deconvolving resolved and/or diffuse emission. It allows cleaning of multiple polarizations or frequencies and integrates auto-masking. This method results in accurate deconvolution and is at present fast enough to deconvolve very large (60K^2 pixels) images. For almost all cases, this should be the preferred algorithm.)doc"; -static const char *__doc_radler_AlgorithmType_kPython = -R"doc(This option allows implementing a custom algorithm in Python. A +static const char* __doc_radler_AlgorithmType_kPython = + R"doc(This option allows implementing a custom algorithm in Python. A location to the Python code should be provided (Settings::Python::filename), and WSClean will call this for performing a major deconvolution iteration. The Python algorithm should then provide its best new estimate for the model image.)doc"; -static const char *__doc_radler_LocalRmsMethod = -R"doc(The value of LocalRmsMethod describes if and how an RMS map should be +static const char* __doc_radler_LocalRmsMethod = + R"doc(The value of LocalRmsMethod describes if and how an RMS map should be used.)doc"; -static const char *__doc_radler_LocalRmsMethod_kNone = R"doc(No local RMS)doc"; +static const char* __doc_radler_LocalRmsMethod_kNone = R"doc(No local RMS)doc"; -static const char *__doc_radler_LocalRmsMethod_kRmsAndMinimumWindow = -R"doc(Spatially varying RMS image with min. Computed as max(window RMS, 0.3 +static const char* __doc_radler_LocalRmsMethod_kRmsAndMinimumWindow = + R"doc(Spatially varying RMS image with min. Computed as max(window RMS, 0.3 x window min))doc"; -static const char *__doc_radler_LocalRmsMethod_kRmsWindow = R"doc(Spatially varying RMS image)doc"; +static const char* __doc_radler_LocalRmsMethod_kRmsWindow = + R"doc(Spatially varying RMS image)doc"; -static const char *__doc_radler_MultiscaleShape = R"doc(Shape used in multi-scale deconvolution.)doc"; +static const char* __doc_radler_MultiscaleShape = + R"doc(Shape used in multi-scale deconvolution.)doc"; -static const char *__doc_radler_MultiscaleShape_kGaussianShape = -R"doc(A simple Gaussian shape. The Gaussian is by default cut off at 12 +static const char* __doc_radler_MultiscaleShape_kGaussianShape = + R"doc(A simple Gaussian shape. The Gaussian is by default cut off at 12 sigma. This function is very similar to kTaperedQuadraticShape, and additionally allows saving component lists, because Gaussians are standard "sky model" shapes. Gaussians and tapered quadratic shapes result in equal accuracy.)doc"; -static const char *__doc_radler_MultiscaleShape_kTaperedQuadraticShape = -R"doc(Quadratic function f(x) = 1 - (x / alpha)^2, tapered with a Hann +static const char* __doc_radler_MultiscaleShape_kTaperedQuadraticShape = + R"doc(Quadratic function f(x) = 1 - (x / alpha)^2, tapered with a Hann function that scales with alpha and normalized. This is the function used by Cornwell (2008). It can't be used when saving source lists, because it is not a fundamental shape allowed in sky models.)doc"; -static const char *__doc_radler_Settings = R"doc()doc"; +static const char* __doc_radler_Settings = R"doc()doc"; -static const char *__doc_radler_Settings_Generic = R"doc()doc"; +static const char* __doc_radler_Settings_Generic = R"doc()doc"; -static const char *__doc_radler_Settings_Generic_use_sub_minor_optimization = R"doc(Corresponds to Multiscale::fast_sub_minor_loop.)doc"; +static const char* __doc_radler_Settings_Generic_use_sub_minor_optimization = + R"doc(Corresponds to Multiscale::fast_sub_minor_loop.)doc"; -static const char *__doc_radler_Settings_LocalRms = R"doc(Settings related to cleaning relative to a local RMS value.)doc"; +static const char* __doc_radler_Settings_LocalRms = + R"doc(Settings related to cleaning relative to a local RMS value.)doc"; -static const char *__doc_radler_Settings_LocalRms_image = -R"doc(If specified, use a manual FITS image instead of a dynamically +static const char* __doc_radler_Settings_LocalRms_image = + R"doc(If specified, use a manual FITS image instead of a dynamically calculated RMS image.)doc"; -static const char *__doc_radler_Settings_LocalRms_method = -R"doc(The method, or LocalRmsMethod::kNone to disable local RMS +static const char* __doc_radler_Settings_LocalRms_method = + R"doc(The method, or LocalRmsMethod::kNone to disable local RMS thresholding.)doc"; -static const char *__doc_radler_Settings_LocalRms_window = R"doc(Size of the sliding window to calculate the "local" RMS over.)doc"; +static const char* __doc_radler_Settings_LocalRms_window = + R"doc(Size of the sliding window to calculate the "local" RMS over.)doc"; -static const char *__doc_radler_Settings_MoreSane = R"doc()doc"; +static const char* __doc_radler_Settings_MoreSane = R"doc()doc"; -static const char *__doc_radler_Settings_MoreSane_arguments = R"doc(Extra command-line arguments provided to MORESANE. */)doc"; +static const char* __doc_radler_Settings_MoreSane_arguments = + R"doc(Extra command-line arguments provided to MORESANE. */)doc"; -static const char *__doc_radler_Settings_MoreSane_location = R"doc(Path of the MORESANE executable. */)doc"; +static const char* __doc_radler_Settings_MoreSane_location = + R"doc(Path of the MORESANE executable. */)doc"; -static const char *__doc_radler_Settings_MoreSane_sigma_levels = -R"doc(Set of threshold levels provided to MORESANE. The first value is used +static const char* __doc_radler_Settings_MoreSane_sigma_levels = + R"doc(Set of threshold levels provided to MORESANE. The first value is used in the first major iteration, the second value in the second major iteration, etc.)doc"; -static const char *__doc_radler_Settings_Multiscale = R"doc()doc"; +static const char* __doc_radler_Settings_Multiscale = R"doc()doc"; -static const char *__doc_radler_Settings_Multiscale_convolution_padding = -R"doc(Controls the padding size of the deconvolution. Higher values should +static const char* __doc_radler_Settings_Multiscale_convolution_padding = + R"doc(Controls the padding size of the deconvolution. Higher values should be more accurate, but it is rarely necessary to change this value. The padding is relative to the sum of the size of the scale and the image size. Problems with multiscale diverging or looping forever can be caused by insufficient padding. However, padding is expensive, so large values should be prevented.)doc"; -static const char *__doc_radler_Settings_Multiscale_fast_sub_minor_loop = -R"doc(Use the fast variant of this algorithm. When ``True``, the minor loops +static const char* __doc_radler_Settings_Multiscale_fast_sub_minor_loop = + R"doc(Use the fast variant of this algorithm. When ``True``, the minor loops are decomposed in subminor loops that keep the scale fixed, which allows a (very) significant speed up. There is no downside of this method, so it is generally recommended to be set to ``True``.)doc"; -static const char *__doc_radler_Settings_Multiscale_max_scales = -R"doc(Limits the number of scales used, to prevent extremely large scales in +static const char* __doc_radler_Settings_Multiscale_max_scales = + R"doc(Limits the number of scales used, to prevent extremely large scales in large imaging runs. When set to zero, scales are used up to the size of the image. The scale sizes increase exponentially and start from a value derived from the size of the PSF. When scale_list is set, this value has no effect. Note that this value represents the number of scales to be used, not the size of the maximum scale.)doc"; -static const char *__doc_radler_Settings_Multiscale_scale_bias = -R"doc(Balances between deconvolving smaller and larger scales. A lower bias +static const char* __doc_radler_Settings_Multiscale_scale_bias = + R"doc(Balances between deconvolving smaller and larger scales. A lower bias value will give more focus to larger scales. The value should be between 0 and 1, and typically be close to 0.6.)doc"; -static const char *__doc_radler_Settings_Multiscale_scale_list = -R"doc(Specify a manual list of scales. If left empty, Radler determines a +static const char* __doc_radler_Settings_Multiscale_scale_list = + R"doc(Specify a manual list of scales. If left empty, Radler determines a good set of scales to use, ranging from the PSF size to the full image size. It is rarely ever necessary to set this parameter. Also consider using max_scales instead of a manual ``scale_list`` when the default just contains scales that are too large.)doc"; -static const char *__doc_radler_Settings_Multiscale_shape = -R"doc(Shape of kernel to be used for deconvolution. +static const char* __doc_radler_Settings_Multiscale_shape = + R"doc(Shape of kernel to be used for deconvolution. See also: MultiscaleShape.)doc"; -static const char *__doc_radler_Settings_Multiscale_sub_minor_loop_gain = -R"doc(Controls how long to keep the scale fixed. The default value of 0.2 +static const char* __doc_radler_Settings_Multiscale_sub_minor_loop_gain = + R"doc(Controls how long to keep the scale fixed. The default value of 0.2 implies that the subminor loop ends when the strongest source and all sources in between have been decreased to 80% of the bright source. This parameter only has effect when fast_sub_minor_loop is set to ``True``.)doc"; -static const char *__doc_radler_Settings_Parallel = -R"doc(Settings for parallel deconvolution that uses multi-threading over +static const char* __doc_radler_Settings_Parallel = + R"doc(Settings for parallel deconvolution that uses multi-threading over sub-images.)doc"; -static const char *__doc_radler_Settings_Parallel_max_size = -R"doc(Maximum size of a sub-image. Will define how many sub-images to make. +static const char* __doc_radler_Settings_Parallel_max_size = + R"doc(Maximum size of a sub-image. Will define how many sub-images to make. */)doc"; -static const char *__doc_radler_Settings_Parallel_max_threads = -R"doc(Number of sub-images to run in parallel. Uses the default when set to +static const char* __doc_radler_Settings_Parallel_max_threads = + R"doc(Number of sub-images to run in parallel. Uses the default when set to zero.)doc"; -static const char *__doc_radler_Settings_PixelScale = R"doc()doc"; +static const char* __doc_radler_Settings_PixelScale = R"doc()doc"; -static const char *__doc_radler_Settings_PixelScale_x = R"doc()doc"; +static const char* __doc_radler_Settings_PixelScale_x = R"doc()doc"; -static const char *__doc_radler_Settings_PixelScale_y = R"doc()doc"; +static const char* __doc_radler_Settings_PixelScale_y = R"doc()doc"; -static const char *__doc_radler_Settings_Python = R"doc()doc"; +static const char* __doc_radler_Settings_Python = R"doc()doc"; -static const char *__doc_radler_Settings_Python_filename = -R"doc(Path to a python file containing the deconvolution algorithm to be +static const char* __doc_radler_Settings_Python_filename = + R"doc(Path to a python file containing the deconvolution algorithm to be used.)doc"; -static const char *__doc_radler_Settings_SpectralFitting = R"doc(Settings related to how components are fitted over frequency channels.)doc"; +static const char* __doc_radler_Settings_SpectralFitting = + R"doc(Settings related to how components are fitted over frequency channels.)doc"; -static const char *__doc_radler_Settings_SpectralFitting_forced_filename = -R"doc(File path to a FITS file that contains spectral index values to force +static const char* __doc_radler_Settings_SpectralFitting_forced_filename = + R"doc(File path to a FITS file that contains spectral index values to force the channels onto. See Ceccoti et al (2022) for details.)doc"; -static const char *__doc_radler_Settings_SpectralFitting_mode = -R"doc(Fitting mode, or schaapcommon::fitters::SpectralFittingMode::NoFitting +static const char* __doc_radler_Settings_SpectralFitting_mode = + R"doc(Fitting mode, or schaapcommon::fitters::SpectralFittingMode::NoFitting to allow frequency channels to vary fully independently.)doc"; -static const char *__doc_radler_Settings_SpectralFitting_terms = -R"doc(Number of spectral terms to constrain the channels to, or zero to +static const char* __doc_radler_Settings_SpectralFitting_terms = + R"doc(Number of spectral terms to constrain the channels to, or zero to disable.)doc"; -static const char *__doc_radler_Settings_algorithm_type = -R"doc(@{ These deconvolution settings are algorithm-specific. For each +static const char* __doc_radler_Settings_algorithm_type = + R"doc(@{ These deconvolution settings are algorithm-specific. For each algorithm type, a single struct holds all algorithm-specific settings for that type.)doc"; -static const char *__doc_radler_Settings_allow_negative_components = -R"doc(When set to ``False``, only positive components are cleaned. This is +static const char* __doc_radler_Settings_allow_negative_components = + R"doc(When set to ``False``, only positive components are cleaned. This is generally not advisable for final scientific results.)doc"; -static const char *__doc_radler_Settings_auto_mask_sigma = -R"doc(Sigma value for automatically creating and applying mask images. +static const char* __doc_radler_Settings_auto_mask_sigma = + R"doc(Sigma value for automatically creating and applying mask images. If set, Radler performs these steps: # Radler starts cleaning towards a threshold of the given sigma value. # Once the sigma level is @@ -223,8 +234,8 @@ the generated mask constrains the cleaning. If unset, automatic masking is not used.)doc"; -static const char *__doc_radler_Settings_auto_threshold_sigma = -R"doc(Sigma value for setting a cleaning threshold relative to the measured +static const char* __doc_radler_Settings_auto_threshold_sigma = + R"doc(Sigma value for setting a cleaning threshold relative to the measured (1-sigma) noise level. If set, Radler will calculate the standard deviation of the residual @@ -237,32 +248,32 @@ in the image. If unset, automatic thresholding is not used.)doc"; -static const char *__doc_radler_Settings_border_ratio = -R"doc(Size of border to avoid in the deconvolution, as a fraction of the +static const char* __doc_radler_Settings_border_ratio = + R"doc(Size of border to avoid in the deconvolution, as a fraction of the image size. Example: a value of 0.1 means that the border is 10% on each side of the image. Therefore, this value should be smaller than 0.5.)doc"; -static const char *__doc_radler_Settings_casa_mask = -R"doc(Filename path of a Casa mask file to be used during deconvolution. If +static const char* __doc_radler_Settings_casa_mask = + R"doc(Filename path of a Casa mask file to be used during deconvolution. If empty, no Casa mask is used. Do not use together with fits_mask.)doc"; -static const char *__doc_radler_Settings_channels_out = R"doc()doc"; +static const char* __doc_radler_Settings_channels_out = R"doc()doc"; -static const char *__doc_radler_Settings_deconvolution_channel_count = -R"doc(The number of channels used during deconvolution. This can be used to +static const char* __doc_radler_Settings_deconvolution_channel_count = + R"doc(The number of channels used during deconvolution. This can be used to image with more channels than used during deconvolution. Before deconvolution, channels are averaged, and after deconvolution they are interpolated. If it is 0, all channels should be used.)doc"; -static const char *__doc_radler_Settings_fits_mask = -R"doc(Filename path of a FITS file containing a mask to be used during +static const char* __doc_radler_Settings_fits_mask = + R"doc(Filename path of a FITS file containing a mask to be used during deconvolution. If empty, no FITS mask is used.)doc"; -static const char *__doc_radler_Settings_generic = R"doc()doc"; +static const char* __doc_radler_Settings_generic = R"doc()doc"; -static const char *__doc_radler_Settings_horizon_mask_distance = -R"doc(The horizon mask distance allows masking out emission beyond the +static const char* __doc_radler_Settings_horizon_mask_distance = + R"doc(The horizon mask distance allows masking out emission beyond the horizon. The value is a floating point value in radians. All emission that is within the given distance of the horizon or @@ -272,25 +283,25 @@ deconvolution further. Leaving the optional value unset disables horizon masking.)doc"; -static const char *__doc_radler_Settings_horizon_mask_filename = -R"doc(The filename for storing the horizon mask FITS image. If unset/empty, +static const char* __doc_radler_Settings_horizon_mask_filename = + R"doc(The filename for storing the horizon mask FITS image. If unset/empty, Radler uses: prefix_name + "-horizon-mask.fits")doc"; -static const char *__doc_radler_Settings_linked_polarizations = -R"doc(List of polarizations that is integrated over when performing peak +static const char* __doc_radler_Settings_linked_polarizations = + R"doc(List of polarizations that is integrated over when performing peak finding. For "joining polarizations", this function should list all the polarizations that are being deconvolved. However, the list can also list a subset of the full list of imaged polarizations.)doc"; -static const char *__doc_radler_Settings_local_rms = R"doc()doc"; +static const char* __doc_radler_Settings_local_rms = R"doc()doc"; -static const char *__doc_radler_Settings_major_iteration_count = -R"doc(Stopping criterion on the total number of major iterations. Radler +static const char* __doc_radler_Settings_major_iteration_count = + R"doc(Stopping criterion on the total number of major iterations. Radler will take this into account to determine the ``reached_major_threshold`` value returned by Radler::Perform().)doc"; -static const char *__doc_radler_Settings_major_loop_gain = -R"doc(Gain value for major loop iterations. +static const char* __doc_radler_Settings_major_loop_gain = + R"doc(Gain value for major loop iterations. This setting specifies when Radler pauses performing minor iterations, so that a major prediction-imaging round can be performed by the @@ -299,36 +310,37 @@ factor. A value of 1.0 implies that minor iterations will continue until the final stopping criteria have been reached. The value should be larger than 0.0.)doc"; -static const char *__doc_radler_Settings_minor_iteration_count = -R"doc(Stopping criterion on the total number of minor iterations. +static const char* __doc_radler_Settings_minor_iteration_count = + R"doc(Stopping criterion on the total number of minor iterations. Radler::Perform() will stop its major iteration and set ``reached_major_threshold``=false when the number of total iterations has passed the requested iteration count. It is generally not advisable to stop deconvolution based on iteration count, except to prevent deconvolution going out of hand.)doc"; -static const char *__doc_radler_Settings_minor_loop_gain = R"doc(Gain value for minor loop iterations.)doc"; +static const char* __doc_radler_Settings_minor_loop_gain = + R"doc(Gain value for minor loop iterations.)doc"; -static const char *__doc_radler_Settings_more_sane = R"doc()doc"; +static const char* __doc_radler_Settings_more_sane = R"doc()doc"; -static const char *__doc_radler_Settings_multiscale = R"doc()doc"; +static const char* __doc_radler_Settings_multiscale = R"doc()doc"; -static const char *__doc_radler_Settings_parallel = R"doc()doc"; +static const char* __doc_radler_Settings_parallel = R"doc()doc"; -static const char *__doc_radler_Settings_pixel_scale = R"doc()doc"; +static const char* __doc_radler_Settings_pixel_scale = R"doc()doc"; -static const char *__doc_radler_Settings_prefix_name = R"doc()doc"; +static const char* __doc_radler_Settings_prefix_name = R"doc()doc"; -static const char *__doc_radler_Settings_python = R"doc()doc"; +static const char* __doc_radler_Settings_python = R"doc()doc"; -static const char *__doc_radler_Settings_save_source_list = -R"doc(If ``True``, maintain a list of components while performing +static const char* __doc_radler_Settings_save_source_list = + R"doc(If ``True``, maintain a list of components while performing deconvolution. This works with the AlgorithmType::kGenericClean and AlgorithmType::kMultiscale algorithms. This is off by default, to prevent extra memory usage and computations when not needed.)doc"; -static const char *__doc_radler_Settings_spectral_correction = -R"doc(List of spectral terms to correct for during deconvolution. Together +static const char* __doc_radler_Settings_spectral_correction = + R"doc(List of spectral terms to correct for during deconvolution. Together with spectral_correction_frequency, this defines a logarithmic polynomial, such that the first term is the spectral index, next is the curvature, etc. This correction might be useful for imaging with a @@ -337,42 +349,41 @@ spectral index (e.g. -0.7), without such a correction, the lowest frequencies will undesirably dominate the peak finding in multi- frequency deconvolution.)doc"; -static const char *__doc_radler_Settings_spectral_correction_frequency = -R"doc(When using a spectral correction with spectral_correction, this value +static const char* __doc_radler_Settings_spectral_correction_frequency = + R"doc(When using a spectral correction with spectral_correction, this value defines the base frequency (in Hz) of the terms specified with spectral_correction.)doc"; -static const char *__doc_radler_Settings_spectral_fitting = R"doc()doc"; +static const char* __doc_radler_Settings_spectral_fitting = R"doc()doc"; -static const char *__doc_radler_Settings_squared_joins = -R"doc(When set to ``True``, all values are squared when integrating over +static const char* __doc_radler_Settings_squared_joins = + R"doc(When set to ``True``, all values are squared when integrating over multiple channels during peak finding. This can cause instability in the multiscale algorithm. This is off by default. It can particularly be useful for RM synthesis, where otherwise polarized flux might decorrelate over the bandwidth. Note that the polarization direction is always squared over, independently of this option setting.)doc"; -static const char *__doc_radler_Settings_stop_on_negative_components = -R"doc(When set to ``True``, finding a negative component as the maximum +static const char* __doc_radler_Settings_stop_on_negative_components = + R"doc(When set to ``True``, finding a negative component as the maximum (absolute) peak will be a criterion to stop and Radler::Perform() will set ``reached_major_threshold``=false.)doc"; -static const char *__doc_radler_Settings_thread_count = R"doc()doc"; +static const char* __doc_radler_Settings_thread_count = R"doc()doc"; -static const char *__doc_radler_Settings_threshold = -R"doc(Value in Jy that defines when to stop cleaning. Radler::Perform() will +static const char* __doc_radler_Settings_threshold = + R"doc(Value in Jy that defines when to stop cleaning. Radler::Perform() will stop its major iteration and set ``reached_major_threshold``=false when the peak residual flux is below the given threshold. The default value is 0.0, which means that Radler will keep continuing until another criterion (e.g. nr. of iterations) is reached.)doc"; -static const char *__doc_radler_Settings_trimmed_image_height = R"doc()doc"; +static const char* __doc_radler_Settings_trimmed_image_height = R"doc()doc"; -static const char *__doc_radler_Settings_trimmed_image_width = -R"doc(@{ Settings that are duplicates from top level settings, and also used +static const char* __doc_radler_Settings_trimmed_image_width = + R"doc(@{ Settings that are duplicates from top level settings, and also used outside deconvolution.)doc"; #if defined(__GNUG__) #pragma GCC diagnostic pop #endif -