enb.plugins.plugin_iraf_photometry package

Submodules

enb.plugins.plugin_iraf_photometry.iraf_photometry module

Plugin to extract photometry information from a file using IRAF.

class enb.plugins.plugin_iraf_photometry.iraf_photometry.LossyPhotometryExperiment(codecs, threshold, dataset_paths=None, csv_experiment_path=None, csv_dataset_path=None, dataset_info_table=None, overwrite_file_properties=False, task_families=None)

Bases: LossyCompressionExperiment

Lossy compression experiment that extracts photometry-based distortion metrics.

__init__(codecs, threshold, dataset_paths=None, csv_experiment_path=None, csv_dataset_path=None, dataset_info_table=None, overwrite_file_properties=False, task_families=None)
Parameters:

  • codecs – list of AbstractCodec instances. Note that codecs are compatible with the interface of ExperimentTask.

  • dataset_paths – list of paths to the files to be used as input for compression. If it is None, this list is obtained automatically from the configured base dataset dir.

  • csv_experiment_path – if not None, path to the CSV file giving persistence support to this experiment. If None, it is automatically determined within options.persistence_dir.

  • csv_dataset_path – if not None, path to the CSV file given persistence support to the dataset file properties. If None, it is automatically determined within options.persistence_dir.

  • dataset_info_table – if not None, it must be a ImagePropertiesTable instance or subclass instance that can be used to obtain dataset file metainformation, and/or gather it from csv_dataset_path. If None, a new ImagePropertiesTable instance is created and used for this purpose.

  • overwrite_file_properties – if True, file properties are recomputed before starting the experiment. Useful for temporary and/or random datasets. Note that overwrite control for the experiment results themselves is controlled in the call to get_df

  • reconstructed_dir_path – if not None, a directory where reconstructed images are to be stored.

  • compressed_copy_dir_path – if not None, it gives the directory where a copy of the compressed images. is to be stored. If may not be generated for images for which all columns are known

  • task_families – if not None, it must be a list of TaskFamily instances. It is used to set the “family_label” column for each row. If the codec is not found within the families, a default label is set indicating so.

column_to_properties = {'F1_score': ColumnProperties('name'='F1_score', 'fun'=<function LossyPhotometryExperiment.set_photometry_columns>, 'label'='F1 score', 'semilog_x'=False, 'semilog_y'=False, 'semilog_x_base'=10, 'semilog_y_base'=10, 'has_dict_values'=False, 'has_iterable_values'=False, 'has_object_values'=False, 'plot_mitrue_positive'=0), 'bpppc': ColumnProperties('name'='bpppc', 'fun'=<function CompressionExperiment.set_bpppc>, 'label'='Compressed data rate (bpppc)', 'plot_min'=0, 'semilog_x'=False, 'semilog_y'=False, 'semilog_x_base'=10, 'semilog_y_base'=10, 'has_dict_values'=False, 'has_iterable_values'=False, 'has_object_values'=False), 'compressed_file_sha256': ColumnProperties('name'='compressed_file_sha256', 'fun'=<function CompressionExperiment.set_comparison_results>, 'label'="Compressed file's SHA256", 'semilog_x'=False, 'semilog_y'=False, 'semilog_x_base'=10, 'semilog_y_base'=10, 'has_dict_values'=False, 'has_iterable_values'=False, 'has_object_values'=False), 'compressed_size_bytes': ColumnProperties('name'='compressed_size_bytes', 'fun'=<function CompressionExperiment.set_compressed_data_size>, 'label'='Compressed data size (Bytes)', 'plot_min'=0, 'semilog_x'=False, 'semilog_y'=False, 'semilog_x_base'=10, 'semilog_y_base'=10, 'has_dict_values'=False, 'has_iterable_values'=False, 'has_object_values'=False), 'compression_efficiency_1byte_entropy': ColumnProperties('name'='compression_efficiency_1byte_entropy', 'fun'=<function CompressionExperiment.set_efficiency>, 'label'='Compression efficiency (1B entropy)', 'plot_min'=0, 'semilog_x'=False, 'semilog_y'=False, 'semilog_x_base'=10, 'semilog_y_base'=10, 'has_dict_values'=False, 'has_iterable_values'=False, 'has_object_values'=False), 'compression_efficiency_2byte_entropy': ColumnProperties('name'='compression_efficiency_2byte_entropy', 'fun'=<function CompressionExperiment.set_efficiency>, 'label'='Compression efficiency (2B entropy)', 'plot_min'=0, 'semilog_x'=False, 'semilog_y'=False, 'semilog_x_base'=10, 'semilog_y_base'=10, 'has_dict_values'=False, 'has_iterable_values'=False, 'has_object_values'=False), 'compression_memory_kb': ColumnProperties('name'='compression_memory_kb', 'fun'=<function CompressionExperiment.set_comparison_results>, 'label'='Compression memory usage (KB)', 'plot_min'=0, 'semilog_x'=False, 'semilog_y'=False, 'semilog_x_base'=10, 'semilog_y_base'=10, 'has_dict_values'=False, 'has_iterable_values'=False, 'has_object_values'=False), 'compression_ratio': ColumnProperties('name'='compression_ratio', 'fun'=<function CompressionExperiment.set_comparison_results>, 'label'='Compression ratio', 'plot_min'=0, 'semilog_x'=False, 'semilog_y'=False, 'semilog_x_base'=10, 'semilog_y_base'=10, 'has_dict_values'=False, 'has_iterable_values'=False, 'has_object_values'=False), 'compression_ratio_dr': ColumnProperties('name'='compression_ratio_dr', 'fun'=<function CompressionExperiment.set_compression_ratio_dr>, 'label'='Compression ratio', 'plot_min'=0, 'semilog_x'=False, 'semilog_y'=False, 'semilog_x_base'=10, 'semilog_y_base'=10, 'has_dict_values'=False, 'has_iterable_values'=False, 'has_object_values'=False), 'compression_time_seconds': ColumnProperties('name'='compression_time_seconds', 'fun'=<function CompressionExperiment.set_comparison_results>, 'label'='Compression time (s)', 'plot_min'=0, 'semilog_x'=False, 'semilog_y'=False, 'semilog_x_base'=10, 'semilog_y_base'=10, 'has_dict_values'=False, 'has_iterable_values'=False, 'has_object_values'=False), 'decompression_memory_kb': ColumnProperties('name'='decompression_memory_kb', 'fun'=<function CompressionExperiment.set_comparison_results>, 'label'='Decompression memory usage (KB)', 'plot_min'=0, 'semilog_x'=False, 'semilog_y'=False, 'semilog_x_base'=10, 'semilog_y_base'=10, 'has_dict_values'=False, 'has_iterable_values'=False, 'has_object_values'=False), 'decompression_time_seconds': ColumnProperties('name'='decompression_time_seconds', 'fun'=<function CompressionExperiment.set_comparison_results>, 'label'='Decompression time (s)', 'plot_min'=0, 'semilog_x'=False, 'semilog_y'=False, 'semilog_x_base'=10, 'semilog_y_base'=10, 'has_dict_values'=False, 'has_iterable_values'=False, 'has_object_values'=False), 'family_label': ColumnProperties('name'='family_label', 'fun'=<function Experiment.set_family_label>, 'label'='Family label', 'semilog_x'=False, 'semilog_y'=False, 'semilog_x_base'=10, 'semilog_y_base'=10, 'has_dict_values'=False, 'has_iterable_values'=False, 'has_object_values'=False), 'lossless_reconstruction': ColumnProperties('name'='lossless_reconstruction', 'fun'=<function CompressionExperiment.set_comparison_results>, 'label'='Lossless?', 'semilog_x'=False, 'semilog_y'=False, 'semilog_x_base'=10, 'semilog_y_base'=10, 'has_dict_values'=False, 'has_iterable_values'=False, 'has_object_values'=False), 'maximum_magnitude_difference': ColumnProperties('name'='maximum_magnitude_difference', 'fun'=<function LossyPhotometryExperiment.set_photometry_columns>, 'label'='Maximum magnitude difference', 'plot_min'=0, 'semilog_x'=False, 'semilog_y'=False, 'semilog_x_base'=10, 'semilog_y_base'=10, 'has_dict_values'=False, 'has_iterable_values'=False, 'has_object_values'=False), 'mean_magnitude_difference': ColumnProperties('name'='mean_magnitude_difference', 'fun'=<function LossyPhotometryExperiment.set_photometry_columns>, 'label'='Mean magnitude difference', 'plot_min'=0, 'semilog_x'=False, 'semilog_y'=False, 'semilog_x_base'=10, 'semilog_y_base'=10, 'has_dict_values'=False, 'has_iterable_values'=False, 'has_object_values'=False), 'mse': ColumnProperties('name'='mse', 'fun'=<function LossyCompressionExperiment.set_MSE>, 'label'='MSE', 'plot_min'=0, 'semilog_x'=False, 'semilog_y'=False, 'semilog_x_base'=10, 'semilog_y_base'=10, 'has_dict_values'=False, 'has_iterable_values'=False, 'has_object_values'=False), 'original_photometry_object_count': ColumnProperties('name'='original_photometry_object_count', 'fun'=<function LossyPhotometryExperiment.set_photometry_columns>, 'label'='Original photometry object count', 'plot_min'=0, 'semilog_x'=False, 'semilog_y'=False, 'semilog_x_base'=10, 'semilog_y_base'=10, 'has_dict_values'=False, 'has_iterable_values'=False, 'has_object_values'=False), 'pae': ColumnProperties('name'='pae', 'fun'=<function LossyCompressionExperiment.set_PAE>, 'label'='PAE', 'plot_min'=0, 'semilog_x'=False, 'semilog_y'=False, 'semilog_x_base'=10, 'semilog_y_base'=10, 'has_dict_values'=False, 'has_iterable_values'=False, 'has_object_values'=False), 'param_dict': ColumnProperties('name'='param_dict', 'fun'=<function Experiment.set_param_dict>, 'label'='Param dict', 'semilog_x'=False, 'semilog_y'=False, 'semilog_x_base'=10, 'semilog_y_base'=10, 'has_dict_values'=True, 'has_iterable_values'=False, 'has_object_values'=False), 'psnr_bps': ColumnProperties('name'='psnr_bps', 'fun'=<function LossyCompressionExperiment.set_PSNR_nominal>, 'label'='PSNR (dB)', 'plot_min'=0, 'semilog_x'=False, 'semilog_y'=False, 'semilog_x_base'=10, 'semilog_y_base'=10, 'has_dict_values'=False, 'has_iterable_values'=False, 'has_object_values'=False), 'psnr_dr': ColumnProperties('name'='psnr_dr', 'fun'=<function LossyCompressionExperiment.set_PSNR_dynamic_range>, 'label'='PSNR (dB)', 'plot_min'=0, 'semilog_x'=False, 'semilog_y'=False, 'semilog_x_base'=10, 'semilog_y_base'=10, 'has_dict_values'=False, 'has_iterable_values'=False, 'has_object_values'=False), 'reconstructed_photometry_object_count': ColumnProperties('name'='reconstructed_photometry_object_count', 'fun'=<function LossyPhotometryExperiment.set_photometry_columns>, 'label'='Reconstructed photometry object count', 'plot_min'=0, 'semilog_x'=False, 'semilog_y'=False, 'semilog_x_base'=10, 'semilog_y_base'=10, 'has_dict_values'=False, 'has_iterable_values'=False, 'has_object_values'=False), 'recovered_objects': ColumnProperties('name'='recovered_objects', 'fun'=<function LossyPhotometryExperiment.set_photometry_columns>, 'label'='Recovered objects', 'plot_min'=0, 'semilog_x'=False, 'semilog_y'=False, 'semilog_x_base'=10, 'semilog_y_base'=10, 'has_dict_values'=False, 'has_iterable_values'=False, 'has_object_values'=False), 'repetitions': ColumnProperties('name'='repetitions', 'fun'=<function CompressionExperiment.set_comparison_results>, 'label'='Number of compression/decompression repetitions', 'plot_min'=0, 'semilog_x'=False, 'semilog_y'=False, 'semilog_x_base'=10, 'semilog_y_base'=10, 'has_dict_values'=False, 'has_iterable_values'=False, 'has_object_values'=False), 'task_apply_time': ColumnProperties('name'='task_apply_time', 'fun'=<function Experiment.set_task_apply_time>, 'label'='Task apply time', 'semilog_x'=False, 'semilog_y'=False, 'semilog_x_base'=10, 'semilog_y_base'=10, 'has_dict_values'=False, 'has_iterable_values'=False, 'has_object_values'=False), 'task_label': ColumnProperties('name'='task_label', 'fun'=<function Experiment.set_task_label>, 'label'='Task label', 'semilog_x'=False, 'semilog_y'=False, 'semilog_x_base'=10, 'semilog_y_base'=10, 'has_dict_values'=False, 'has_iterable_values'=False, 'has_object_values'=False), 'task_name': ColumnProperties('name'='task_name', 'fun'=<function Experiment.set_task_name>, 'label'='Task name', 'semilog_x'=False, 'semilog_y'=False, 'semilog_x_base'=10, 'semilog_y_base'=10, 'has_dict_values'=False, 'has_iterable_values'=False, 'has_object_values'=False)}

The column_properties attribute keeps track of what columns have been defined, and the methods that need to be called to computed them. The keys of this attribute can be used to determine the columns defined in a given class or instance. The values are |ColumnProperties| instances, which can be set manually after definition and before calling |Analyzer| subclasses’ get_df.

set_photometry_columns(index, row)
enb.plugins.plugin_iraf_photometry.iraf_photometry.raw_to_fits(raw_path, fits_path)
enb.plugins.plugin_iraf_photometry.iraf_photometry.raw_to_photometry_df(raw_path, extension=0, fwhm=3.5, sigma=6, threshold=8.0, min_value=3000000, max_value=50000000, annulus=10.0, dannulus=10.0, aperture=4.0, sigma_phot=0.0)

enb.plugins.plugin_iraf_photometry.iraf_photometry_slave module

Slave script to run iraf as a separate process, so that it is automatically cleaned up and memory is not hogged.

enb.plugins.plugin_iraf_photometry.iraf_photometry_slave.fits_to_csv(fits_path, csv_path, extension=0, fwhm=3.5, sigma=6, threshold=8.0, min_value=3000000, max_value=50000000, annulus=10.0, dannulus=10.0, aperture=4.0, sigma_phot=0.0)

Apply IRAF to the raw file in raw_path and store a CSV file into csv_path.

Module contents