idx int64 0 63k | question stringlengths 61 4.03k | target stringlengths 6 1.23k |
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22,600 | def get_ylabel ( self ) : units = self . units if len ( units ) == 1 and str ( units [ 0 ] ) == '' : return '' if len ( units ) == 1 and self . usetex : return units [ 0 ] . to_string ( 'latex' ) elif len ( units ) == 1 : return units [ 0 ] . to_string ( ) elif len ( units ) > 1 : return 'Multiple units' return super (... | Text for y - axis label check if channel defines it |
22,601 | def generate_fft_plan ( length , level = None , dtype = 'float64' , forward = True ) : from . . . utils . lal import ( find_typed_function , to_lal_type_str ) laltype = to_lal_type_str ( dtype ) key = ( length , bool ( forward ) , laltype ) try : return LAL_FFTPLANS [ key ] except KeyError : create = find_typed_functio... | Build a REAL8FFTPlan for a fast Fourier transform . |
22,602 | def generate_window ( length , window = None , dtype = 'float64' ) : from . . . utils . lal import ( find_typed_function , to_lal_type_str ) if window is None : window = ( 'kaiser' , 24 ) laltype = to_lal_type_str ( dtype ) key = ( length , str ( window ) , laltype ) try : return LAL_WINDOWS [ key ] except KeyError : i... | Generate a time - domain window for use in a LAL FFT |
22,603 | def window_from_array ( array ) : from . . . utils . lal import ( find_typed_function ) dtype = array . dtype seq = find_typed_function ( dtype , 'Create' , 'Sequence' ) ( array . size ) seq . data = array return find_typed_function ( dtype , 'Create' , 'WindowFromSequence' ) ( seq ) | Convert a numpy . ndarray into a LAL Window object |
22,604 | def _lal_spectrum ( timeseries , segmentlength , noverlap = None , method = 'welch' , window = None , plan = None ) : import lal from . . . utils . lal import find_typed_function if noverlap is None : noverlap = int ( segmentlength // 2 ) stride = segmentlength - noverlap if window is None : window = generate_window ( ... | Generate a PSD FrequencySeries using |lal|_ |
22,605 | def welch ( timeseries , segmentlength , noverlap = None , window = None , plan = None ) : return _lal_spectrum ( timeseries , segmentlength , noverlap = noverlap , method = 'welch' , window = window , plan = plan ) | Calculate an PSD of this TimeSeries using Welch s method |
22,606 | def bartlett ( timeseries , segmentlength , noverlap = None , window = None , plan = None ) : return _lal_spectrum ( timeseries , segmentlength , noverlap = 0 , method = 'welch' , window = window , plan = plan ) | Calculate an PSD of this TimeSeries using Bartlett s method |
22,607 | def median ( timeseries , segmentlength , noverlap = None , window = None , plan = None ) : return _lal_spectrum ( timeseries , segmentlength , noverlap = noverlap , method = 'median' , window = window , plan = plan ) | Calculate a PSD of this TimeSeries using a median average method |
22,608 | def median_mean ( timeseries , segmentlength , noverlap = None , window = None , plan = None ) : return _lal_spectrum ( timeseries , segmentlength , noverlap = noverlap , method = 'median-mean' , window = window , plan = plan ) | Calculate a PSD of this TimeSeries using a median - mean average method |
22,609 | def open_data_source ( source ) : if isinstance ( source , FILE_LIKE ) : source = source . name if isinstance ( source , CacheEntry ) : source = source . path if ( isinstance ( source , string_types ) and source . endswith ( ( '.lcf' , '.cache' ) ) ) : return lalframe . FrStreamCacheOpen ( lal . CacheImport ( source ) ... | Open a GWF file source into a lalframe . XLALFrStream object |
22,610 | def get_stream_duration ( stream ) : epoch = lal . LIGOTimeGPS ( stream . epoch . gpsSeconds , stream . epoch . gpsNanoSeconds ) nfile = stream . cache . length duration = 0 for dummy_i in range ( nfile ) : for dummy_j in range ( lalframe . FrFileQueryNFrame ( stream . file ) ) : duration += lalframe . FrFileQueryDt ( ... | Find the duration of time stored in a frame stream |
22,611 | def read ( source , channels , start = None , end = None , series_class = TimeSeries , scaled = None ) : if scaled is not None : warnings . warn ( "the `scaled` keyword argument is not supported by lalframe, " "if you require ADC scaling, please install " "python-ldas-tools-framecpp" , ) stream = open_data_source ( sou... | Read data from one or more GWF files using the LALFrame API |
22,612 | def write ( tsdict , outfile , start = None , end = None , name = 'gwpy' , run = 0 ) : if not start : start = list ( tsdict . values ( ) ) [ 0 ] . xspan [ 0 ] if not end : end = list ( tsdict . values ( ) ) [ 0 ] . xspan [ 1 ] duration = end - start detectors = 0 for series in tsdict . values ( ) : try : idx = list ( l... | Write data to a GWF file using the LALFrame API |
22,613 | def segment_content_handler ( ) : from ligo . lw . lsctables import ( SegmentTable , SegmentDefTable , SegmentSumTable ) from ligo . lw . ligolw import PartialLIGOLWContentHandler def _filter ( name , attrs ) : return reduce ( operator . or_ , [ table_ . CheckProperties ( name , attrs ) for table_ in ( SegmentTable , S... | Build a ~xml . sax . handlers . ContentHandler to read segment XML tables |
22,614 | def read_ligolw_dict ( source , names = None , coalesce = False , ** kwargs ) : xmldoc = read_ligolw ( source , contenthandler = segment_content_handler ( ) ) with patch_ligotimegps ( type ( xmldoc . childNodes [ 0 ] ) . __module__ ) : out = DataQualityDict . from_ligolw_tables ( * xmldoc . childNodes , names = names ,... | Read segments for the given flag from the LIGO_LW XML file . |
22,615 | def read_ligolw_flag ( source , name = None , ** kwargs ) : name = [ name ] if name is not None else None return list ( read_ligolw_dict ( source , names = name , ** kwargs ) . values ( ) ) [ 0 ] | Read a single DataQualityFlag from a LIGO_LW XML file |
22,616 | def write_ligolw ( flags , target , attrs = None , ilwdchar_compat = None , ** kwargs ) : if isinstance ( flags , DataQualityFlag ) : flags = DataQualityDict ( { flags . name : flags } ) return write_tables ( target , flags . to_ligolw_tables ( ilwdchar_compat = ilwdchar_compat , ** attrs or dict ( ) ) , ** kwargs ) | Write this DataQualityFlag to the given LIGO_LW Document |
22,617 | def scale_timeseries_unit ( tsunit , scaling = 'density' ) : if scaling == 'density' : baseunit = units . Hertz elif scaling == 'spectrum' : baseunit = units . dimensionless_unscaled else : raise ValueError ( "Unknown scaling: %r" % scaling ) if tsunit : specunit = tsunit ** 2 / baseunit else : specunit = baseunit ** -... | Scale the unit of a TimeSeries to match that of a FrequencySeries |
22,618 | def _iter_cache ( cachefile , gpstype = LIGOTimeGPS ) : try : path = os . path . abspath ( cachefile . name ) except AttributeError : path = None for line in cachefile : try : yield _CacheEntry . parse ( line , gpstype = LIGOTimeGPS ) except ValueError : parts = line . split ( ) if len ( parts ) == 3 and os . path . ab... | Internal method that yields a _CacheEntry for each line in the file |
22,619 | def read_cache ( cachefile , coltype = LIGOTimeGPS , sort = None , segment = None ) : if not isinstance ( cachefile , FILE_LIKE ) : with open ( file_path ( cachefile ) , 'r' ) as fobj : return read_cache ( fobj , coltype = coltype , sort = sort , segment = segment ) cache = [ x . path for x in _iter_cache ( cachefile ,... | Read a LAL - or FFL - format cache file as a list of file paths |
22,620 | def write_cache ( cache , fobj , format = None ) : if isinstance ( fobj , string_types ) : with open ( fobj , 'w' ) as fobj2 : return write_cache ( cache , fobj2 , format = format ) if format is None : formatter = str elif format . lower ( ) == "lal" : formatter = _format_entry_lal elif format . lower ( ) == "ffl" : fo... | Write a list of cache entries to a file |
22,621 | def is_cache ( cache ) : if isinstance ( cache , string_types + FILE_LIKE ) : try : return bool ( len ( read_cache ( cache ) ) ) except ( TypeError , ValueError , UnicodeDecodeError , ImportError ) : return False if HAS_CACHE and isinstance ( cache , Cache ) : return True if ( isinstance ( cache , ( list , tuple ) ) an... | Returns True if cache is a readable cache file or object |
22,622 | def is_cache_entry ( path ) : if HAS_CACHEENTRY and isinstance ( path , CacheEntry ) : return True try : file_segment ( path ) except ( ValueError , TypeError , AttributeError ) : return False return True | Returns True if path can be represented as a cache entry |
22,623 | def filename_metadata ( filename ) : from . . segments import Segment name = Path ( filename ) . name try : obs , desc , start , dur = name . split ( '-' ) except ValueError as exc : exc . args = ( 'Failed to parse {!r} as LIGO-T050017-compatible ' 'filename' . format ( name ) , ) raise start = float ( start ) dur = du... | Return metadata parsed from a filename following LIGO - T050017 |
22,624 | def file_segment ( filename ) : from . . segments import Segment try : return Segment ( filename . segment ) except AttributeError : return filename_metadata ( filename ) [ 2 ] | Return the data segment for a filename following T050017 |
22,625 | def flatten ( * caches ) : return list ( OrderedDict . fromkeys ( e for c in caches for e in c ) ) | Flatten a nested list of cache entries |
22,626 | def find_contiguous ( * caches ) : flat = flatten ( * caches ) for segment in cache_segments ( flat ) : yield sieve ( flat , segment = segment ) | Separate one or more cache entry lists into time - contiguous sub - lists |
22,627 | def sieve ( cache , segment = None ) : return type ( cache ) ( e for e in cache if segment . intersects ( file_segment ( e ) ) ) | Filter the cache to find those entries that overlap segment |
22,628 | def arg_qxform ( cls , parser ) : group = parser . add_argument_group ( 'Q-transform options' ) group . add_argument ( '--plot' , nargs = '+' , type = float , default = [ .5 ] , help = 'One or more times to plot' ) group . add_argument ( '--frange' , nargs = 2 , type = float , help = 'Frequency range to plot' ) group .... | Add an ~argparse . ArgumentGroup for Q - transform options |
22,629 | def get_title ( self ) : def fformat ( x ) : if isinstance ( x , ( list , tuple ) ) : return '[{0}]' . format ( ', ' . join ( map ( fformat , x ) ) ) if isinstance ( x , Quantity ) : x = x . value elif isinstance ( x , str ) : warnings . warn ( 'WARNING: fformat called with a' + ' string. This has ' + 'been depricated ... | Default title for plot |
22,630 | def get_spectrogram ( self ) : args = self . args asd = self . timeseries [ 0 ] . asd ( ) . value if ( asd . min ( ) == 0 ) : self . log ( 0 , 'Input data has a zero in ASD. ' 'Q-transform not possible.' ) self . got_error = True qtrans = None else : gps = self . qxfrm_args [ 'gps' ] outseg = Segment ( gps , gps ) . pr... | Worked on a single timesharing and generates a single Q - transform spectrogram |
22,631 | def _ligotimegps ( s , ns = 0 ) : from lal import LIGOTimeGPS try : return LIGOTimeGPS ( s , ns ) except TypeError : return LIGOTimeGPS ( int ( s ) , int ( ns ) ) | Catch TypeError and cast s and ns to int |
22,632 | def patch_ligotimegps ( module = "ligo.lw.lsctables" ) : module = import_module ( module ) orig = module . LIGOTimeGPS module . LIGOTimeGPS = _ligotimegps try : yield finally : module . LIGOTimeGPS = orig | Context manager to on - the - fly patch LIGOTimeGPS to accept all int types |
22,633 | def get_partial_contenthandler ( element ) : from ligo . lw . ligolw import PartialLIGOLWContentHandler from ligo . lw . table import Table if issubclass ( element , Table ) : def _element_filter ( name , attrs ) : return element . CheckProperties ( name , attrs ) else : def _element_filter ( name , _ ) : return name =... | Build a PartialLIGOLWContentHandler to read only this element |
22,634 | def get_filtering_contenthandler ( element ) : from ligo . lw . ligolw import FilteringLIGOLWContentHandler from ligo . lw . table import Table if issubclass ( element , Table ) : def _element_filter ( name , attrs ) : return ~ element . CheckProperties ( name , attrs ) else : def _element_filter ( name , _ ) : return ... | Build a FilteringLIGOLWContentHandler to exclude this element |
22,635 | def build_content_handler ( parent , filter_func ) : from ligo . lw . lsctables import use_in class _ContentHandler ( parent ) : def __init__ ( self , document ) : super ( _ContentHandler , self ) . __init__ ( document , filter_func ) return use_in ( _ContentHandler ) | Build a ~xml . sax . handler . ContentHandler with a given filter |
22,636 | def read_ligolw ( source , contenthandler = LIGOLWContentHandler , ** kwargs ) : from ligo . lw . ligolw import Document from ligo . lw import types from ligo . lw . lsctables import use_in from ligo . lw . utils import ( load_url , ligolw_add ) topytype = types . ToPyType . copy ( ) for key in types . ToPyType : if ke... | Read one or more LIGO_LW format files |
22,637 | def with_read_ligolw ( func = None , contenthandler = None ) : def decorator ( func_ ) : @ wraps ( func_ ) def decorated_func ( source , * args , ** kwargs ) : from ligo . lw . ligolw import Document from glue . ligolw . ligolw import Document as GlueDocument if not isinstance ( source , ( Document , GlueDocument ) ) :... | Decorate a LIGO_LW - reading function to open a filepath if needed |
22,638 | def open_xmldoc ( fobj , ** kwargs ) : from ligo . lw . ligolw import ( Document , LIGOLWContentHandler ) from ligo . lw . lsctables import use_in from ligo . lw . utils import ( load_filename , load_fileobj ) use_in ( kwargs . setdefault ( 'contenthandler' , LIGOLWContentHandler ) ) try : if isinstance ( fobj , string... | Try and open an existing LIGO_LW - format file or create a new Document |
22,639 | def write_tables ( target , tables , append = False , overwrite = False , ** kwargs ) : from ligo . lw . ligolw import ( Document , LIGO_LW , LIGOLWContentHandler ) from ligo . lw import utils as ligolw_utils if isinstance ( target , ( Document , LIGO_LW ) ) : xmldoc = target elif append : xmldoc = open_xmldoc ( target... | Write an LIGO_LW table to file |
22,640 | def to_table_type ( val , cls , colname ) : from ligo . lw . types import ( ToNumPyType as numpytypes , ToPyType as pytypes , ) if val is None or colname not in cls . validcolumns : return val llwtype = cls . validcolumns [ colname ] if llwtype == 'ilwd:char' : return _to_ilwd ( val , cls . tableName , colname , ilwdch... | Cast a value to the correct type for inclusion in a LIGO_LW table |
22,641 | def is_ligolw ( origin , filepath , fileobj , * args , ** kwargs ) : if fileobj is not None : loc = fileobj . tell ( ) fileobj . seek ( 0 ) try : line1 = fileobj . readline ( ) . lower ( ) line2 = fileobj . readline ( ) . lower ( ) try : return ( line1 . startswith ( XML_SIGNATURE ) and line2 . startswith ( ( LIGOLW_SI... | Identify a file object as LIGO_LW - format XML |
22,642 | def read_hdf5_timeseries ( h5f , path = None , start = None , end = None , ** kwargs ) : kwargs . setdefault ( 'array_type' , TimeSeries ) series = read_hdf5_array ( h5f , path = path , ** kwargs ) if start is not None or end is not None : return series . crop ( start , end ) return series | Read a TimeSeries from HDF5 |
22,643 | def read_hdf5_dict ( h5f , names = None , group = None , ** kwargs ) : if group : h5g = h5f [ group ] else : h5g = h5f if names is None : names = [ key for key in h5g if _is_timeseries_dataset ( h5g [ key ] ) ] out = kwargs . pop ( 'dict_type' , TimeSeriesDict ) ( ) kwargs . setdefault ( 'array_type' , out . EntryClass... | Read a TimeSeriesDict from HDF5 |
22,644 | def write_hdf5_dict ( tsdict , h5f , group = None , ** kwargs ) : if group and group not in h5f : h5g = h5f . create_group ( group ) elif group : h5g = h5f [ group ] else : h5g = h5f kwargs . setdefault ( 'format' , 'hdf5' ) for key , series in tsdict . items ( ) : series . write ( h5g , path = str ( key ) , ** kwargs ... | Write a TimeSeriesBaseDict to HDF5 |
22,645 | def welch ( timeseries , segmentlength , noverlap = None , scheme = None , ** kwargs ) : from pycbc . psd import welch as pycbc_welch kwargs . setdefault ( 'avg_method' , 'mean' ) if scheme is None : scheme = null_context ( ) with scheme : pycbc_fseries = pycbc_welch ( timeseries . to_pycbc ( copy = False ) , seg_len =... | Calculate a PSD using Welch s method with a mean average |
22,646 | def _ordinal ( n ) : idx = int ( ( n // 10 % 10 != 1 ) * ( n % 10 < 4 ) * n % 10 ) return '{}{}' . format ( n , "tsnrhtdd" [ idx : : 4 ] ) | Returns the ordinal string for a given integer |
22,647 | def ratio ( self , operand ) : if isinstance ( operand , string_types ) : if operand == 'mean' : operand = self . mean ( axis = 0 ) elif operand == 'median' : operand = self . median ( axis = 0 ) else : raise ValueError ( "operand %r unrecognised, please give a " "Quantity or one of: 'mean', 'median'" % operand ) out =... | Calculate the ratio of this Spectrogram against a reference |
22,648 | def plot ( self , figsize = ( 12 , 6 ) , xscale = 'auto-gps' , ** kwargs ) : if 'imshow' in kwargs : warnings . warn ( 'the imshow keyword for Spectrogram.plot was ' 'removed, please pass method=\'imshow\' instead' , DeprecationWarning ) kwargs . setdefault ( 'method' , 'imshow' if kwargs . pop ( 'imshow' ) else 'pcolo... | Plot the data for this Spectrogram |
22,649 | def from_spectra ( cls , * spectra , ** kwargs ) : data = numpy . vstack ( [ s . value for s in spectra ] ) spec1 = list ( spectra ) [ 0 ] if not all ( s . f0 == spec1 . f0 for s in spectra ) : raise ValueError ( "Cannot stack spectra with different f0" ) if not all ( s . df == spec1 . df for s in spectra ) : raise Val... | Build a new Spectrogram from a list of spectra . |
22,650 | def percentile ( self , percentile ) : out = scipy . percentile ( self . value , percentile , axis = 0 ) if self . name is not None : name = '{}: {} percentile' . format ( self . name , _ordinal ( percentile ) ) else : name = None return FrequencySeries ( out , epoch = self . epoch , channel = self . channel , name = n... | Calculate a given spectral percentile for this Spectrogram . |
22,651 | def variance ( self , bins = None , low = None , high = None , nbins = 500 , log = False , norm = False , density = False ) : from . . frequencyseries import SpectralVariance return SpectralVariance . from_spectrogram ( self , bins = bins , low = low , high = high , nbins = nbins , log = log , norm = norm , density = d... | Calculate the SpectralVariance of this Spectrogram . |
22,652 | def crop_frequencies ( self , low = None , high = None , copy = False ) : if low is not None : low = units . Quantity ( low , self . _default_yunit ) if high is not None : high = units . Quantity ( high , self . _default_yunit ) if low is not None and low == self . f0 : low = None elif low is not None and low < self . ... | Crop this Spectrogram to the specified frequencies |
22,653 | def find_mappable ( * axes ) : for ax in axes : for aset in ( 'collections' , 'images' ) : try : return getattr ( ax , aset ) [ - 1 ] except ( AttributeError , IndexError ) : continue raise ValueError ( "Cannot determine mappable layer on any axes " "for this colorbar" ) | Find the most recently added mappable layer in the given axes |
22,654 | def get_read_format ( cls , source , args , kwargs ) : ctx = None if isinstance ( source , FILE_LIKE ) : fileobj = source filepath = source . name if hasattr ( source , 'name' ) else None else : filepath = source try : ctx = get_readable_fileobj ( filepath , encoding = 'binary' ) fileobj = ctx . __enter__ ( ) except IO... | Determine the read format for a given input source |
22,655 | def read ( fobj , ** kwargs ) : fsamp , arr = wavfile . read ( fobj , ** kwargs ) return TimeSeries ( arr , sample_rate = fsamp ) | Read a WAV file into a TimeSeries |
22,656 | def write ( series , output , scale = None ) : fsamp = int ( series . sample_rate . decompose ( ) . value ) if scale is None : scale = 1 / numpy . abs ( series . value ) . max ( ) data = ( series . value * scale ) . astype ( 'float32' ) return wavfile . write ( output , fsamp , data ) | Write a TimeSeries to a WAV file |
22,657 | def is_wav ( origin , filepath , fileobj , * args , ** kwargs ) : if origin == 'read' and fileobj is not None : loc = fileobj . tell ( ) fileobj . seek ( 0 ) try : riff , _ , fmt = struct . unpack ( '<4sI4s' , fileobj . read ( 12 ) ) if isinstance ( riff , bytes ) : riff = riff . decode ( 'utf-8' ) fmt = fmt . decode (... | Identify a file as WAV |
22,658 | def parse_column_filter ( definition ) : parts = list ( generate_tokens ( StringIO ( definition . strip ( ) ) . readline ) ) while parts [ - 1 ] [ 0 ] in ( token . ENDMARKER , token . NEWLINE ) : parts = parts [ : - 1 ] if len ( parts ) == 3 : a , b , c = parts if a [ 0 ] in [ token . NAME , token . STRING ] : name = Q... | Parse a str of the form column > 50 |
22,659 | def parse_column_filters ( * definitions ) : fltrs = [ ] for def_ in _flatten ( definitions ) : if is_filter_tuple ( def_ ) : fltrs . append ( def_ ) else : for splitdef in DELIM_REGEX . split ( def_ ) [ : : 2 ] : fltrs . extend ( parse_column_filter ( splitdef ) ) return fltrs | Parse multiple compound column filter definitions |
22,660 | def _flatten ( container ) : if isinstance ( container , string_types ) : container = [ container ] for elem in container : if isinstance ( elem , string_types ) or is_filter_tuple ( elem ) : yield elem else : for elem2 in _flatten ( elem ) : yield elem2 | Flatten arbitrary nested list of filters into a 1 - D list |
22,661 | def is_filter_tuple ( tup ) : return isinstance ( tup , ( tuple , list ) ) and ( len ( tup ) == 3 and isinstance ( tup [ 0 ] , string_types ) and callable ( tup [ 1 ] ) ) | Return whether a tuple matches the format for a column filter |
22,662 | def filter_table ( table , * column_filters ) : keep = numpy . ones ( len ( table ) , dtype = bool ) for name , op_func , operand in parse_column_filters ( * column_filters ) : col = table [ name ] . view ( numpy . ndarray ) keep &= op_func ( col , operand ) return table [ keep ] | Apply one or more column slice filters to a Table |
22,663 | def read_hdf5_array ( source , path = None , array_type = Array ) : dataset = io_hdf5 . find_dataset ( source , path = path ) attrs = dict ( dataset . attrs ) try : attrs [ 'channel' ] = _unpickle_channel ( attrs [ 'channel' ] ) except KeyError : pass for key in attrs : if isinstance ( attrs [ key ] , bytes ) : attrs [... | Read an Array from the given HDF5 object |
22,664 | def _unpickle_channel ( raw ) : try : return pickle . loads ( raw ) except ( ValueError , pickle . UnpicklingError , EOFError , TypeError , IndexError ) as exc : if isinstance ( raw , bytes ) : raw = raw . decode ( 'utf-8' ) try : Channel . MATCH . match ( raw ) except ValueError : raise exc return raw | Try and unpickle a channel with sensible error handling |
22,665 | def _format_metadata_attribute ( value ) : if ( value is None or ( isinstance ( value , Index ) and value . regular ) ) : raise IgnoredAttribute for typekey , func in ATTR_TYPE_MAP . items ( ) : if issubclass ( type ( value ) , typekey ) : return func ( value ) return value | Format a value for writing to HDF5 as a h5py . Dataset attribute |
22,666 | def write_array_metadata ( dataset , array ) : for attr in ( 'unit' , ) + array . _metadata_slots : try : value = _format_metadata_attribute ( getattr ( array , '_%s' % attr , None ) ) except IgnoredAttribute : continue try : dataset . attrs [ attr ] = value except ( TypeError , ValueError , RuntimeError ) as exc : exc... | Write metadata for array into the h5py . Dataset |
22,667 | def write_hdf5_array ( array , h5g , path = None , attrs = None , append = False , overwrite = False , compression = 'gzip' , ** kwargs ) : if path is None : path = array . name if path is None : raise ValueError ( "Cannot determine HDF5 path for %s, " "please set ``name`` attribute, or pass ``path=`` " "keyword when w... | Write the array to an h5py . Dataset |
22,668 | def format_index_array_attrs ( series ) : attrs = { } for i , axis in zip ( range ( series . ndim ) , ( 'x' , 'y' ) ) : unit = '{}unit' . format ( axis ) origin = '{}0' . format ( axis ) delta = 'd{}' . format ( axis ) aunit = getattr ( series , unit ) attrs . update ( { unit : str ( aunit ) , origin : getattr ( series... | Format metadata attributes for and indexed array |
22,669 | def write_hdf5_series ( series , output , path = None , attrs = None , ** kwargs ) : if attrs is None : attrs = format_index_array_attrs ( series ) return write_hdf5_array ( series , output , path = path , attrs = attrs , ** kwargs ) | Write a Series to HDF5 . |
22,670 | def read_multi ( flatten , cls , source , * args , ** kwargs ) : verbose = kwargs . pop ( 'verbose' , False ) try : files = file_list ( source ) except ValueError : files = [ source ] path = None else : path = files [ 0 ] if files else None if kwargs . get ( 'format' , None ) is None : kwargs [ 'format' ] = get_read_fo... | Read sources into a cls with multiprocessing |
22,671 | def read_json_flag ( fobj ) : if isinstance ( fobj , string_types ) : with open ( fobj , 'r' ) as fobj2 : return read_json_flag ( fobj2 ) txt = fobj . read ( ) if isinstance ( txt , bytes ) : txt = txt . decode ( 'utf-8' ) data = json . loads ( txt ) name = '{ifo}:{name}:{version}' . format ( ** data ) out = DataQualit... | Read a DataQualityFlag from a segments - web . ligo . org JSON file |
22,672 | def write_json_flag ( flag , fobj , ** kwargs ) : if isinstance ( fobj , string_types ) : with open ( fobj , 'w' ) as fobj2 : return write_json_flag ( flag , fobj2 , ** kwargs ) data = { } data [ 'ifo' ] = flag . ifo data [ 'name' ] = flag . tag data [ 'version' ] = flag . version data [ 'active' ] = flag . active data... | Write a DataQualityFlag to a JSON file |
22,673 | def in_segmentlist ( column , segmentlist ) : segmentlist = type ( segmentlist ) ( segmentlist ) . coalesce ( ) idx = column . argsort ( ) contains = numpy . zeros ( column . shape [ 0 ] , dtype = bool ) j = 0 try : segstart , segend = segmentlist [ j ] except IndexError : return contains i = 0 while i < contains . sha... | Return the index of values lying inside the given segmentlist |
22,674 | def fft ( self , nfft = None ) : from . . frequencyseries import FrequencySeries if nfft is None : nfft = self . size dft = npfft . rfft ( self . value , n = nfft ) / nfft dft [ 1 : ] *= 2.0 new = FrequencySeries ( dft , epoch = self . epoch , unit = self . unit , name = self . name , channel = self . channel ) try : n... | Compute the one - dimensional discrete Fourier transform of this TimeSeries . |
22,675 | def average_fft ( self , fftlength = None , overlap = 0 , window = None ) : from gwpy . spectrogram import Spectrogram if fftlength is None : fftlength = self . duration if isinstance ( fftlength , units . Quantity ) : fftlength = fftlength . value nfft = int ( ( fftlength * self . sample_rate ) . decompose ( ) . value... | Compute the averaged one - dimensional DFT of this TimeSeries . |
22,676 | def psd ( self , fftlength = None , overlap = None , window = 'hann' , method = DEFAULT_FFT_METHOD , ** kwargs ) : method_func = spectral . get_method ( method ) return spectral . psd ( self , method_func , fftlength = fftlength , overlap = overlap , window = window , ** kwargs ) | Calculate the PSD FrequencySeries for this TimeSeries |
22,677 | def asd ( self , fftlength = None , overlap = None , window = 'hann' , method = DEFAULT_FFT_METHOD , ** kwargs ) : return self . psd ( method = method , fftlength = fftlength , overlap = overlap , window = window , ** kwargs ) ** ( 1 / 2. ) | Calculate the ASD FrequencySeries of this TimeSeries |
22,678 | def csd ( self , other , fftlength = None , overlap = None , window = 'hann' , ** kwargs ) : return spectral . psd ( ( self , other ) , spectral . csd , fftlength = fftlength , overlap = overlap , window = window , ** kwargs ) | Calculate the CSD FrequencySeries for two TimeSeries |
22,679 | def spectrogram ( self , stride , fftlength = None , overlap = None , window = 'hann' , method = DEFAULT_FFT_METHOD , nproc = 1 , ** kwargs ) : method_func = spectral . get_method ( method ) return spectral . average_spectrogram ( self , method_func , stride , fftlength = fftlength , overlap = overlap , window = window... | Calculate the average power spectrogram of this TimeSeries using the specified average spectrum method . |
22,680 | def spectrogram2 ( self , fftlength , overlap = None , window = 'hann' , ** kwargs ) : kwargs . setdefault ( 'fs' , self . sample_rate . to ( 'Hz' ) . value ) return spectral . spectrogram ( self , signal . periodogram , fftlength = fftlength , overlap = overlap , window = window , ** kwargs ) | Calculate the non - averaged power Spectrogram of this TimeSeries |
22,681 | def fftgram ( self , fftlength , overlap = None , window = 'hann' , ** kwargs ) : from . . spectrogram import Spectrogram try : from scipy . signal import spectrogram except ImportError : raise ImportError ( "Must have scipy>=0.16 to utilize " "this method." ) if isinstance ( fftlength , units . Quantity ) : fftlength ... | Calculate the Fourier - gram of this TimeSeries . |
22,682 | def spectral_variance ( self , stride , fftlength = None , overlap = None , method = DEFAULT_FFT_METHOD , window = 'hann' , nproc = 1 , filter = None , bins = None , low = None , high = None , nbins = 500 , log = False , norm = False , density = False ) : specgram = self . spectrogram ( stride , fftlength = fftlength ,... | Calculate the SpectralVariance of this TimeSeries . |
22,683 | def rayleigh_spectrum ( self , fftlength = None , overlap = None ) : return spectral . psd ( self , spectral . rayleigh , fftlength = fftlength , overlap = overlap , ) | Calculate the Rayleigh FrequencySeries for this TimeSeries . |
22,684 | def rayleigh_spectrogram ( self , stride , fftlength = None , overlap = 0 , nproc = 1 , ** kwargs ) : specgram = spectral . average_spectrogram ( self , spectral . rayleigh , stride , fftlength = fftlength , overlap = overlap , nproc = nproc , ** kwargs ) specgram . override_unit ( '' ) return specgram | Calculate the Rayleigh statistic spectrogram of this TimeSeries |
22,685 | def csd_spectrogram ( self , other , stride , fftlength = None , overlap = 0 , window = 'hann' , nproc = 1 , ** kwargs ) : return spectral . average_spectrogram ( ( self , other ) , spectral . csd , stride , fftlength = fftlength , overlap = overlap , window = window , nproc = nproc , ** kwargs ) | Calculate the cross spectral density spectrogram of this TimeSeries with other . |
22,686 | def highpass ( self , frequency , gpass = 2 , gstop = 30 , fstop = None , type = 'iir' , filtfilt = True , ** kwargs ) : filt = filter_design . highpass ( frequency , self . sample_rate , fstop = fstop , gpass = gpass , gstop = gstop , analog = False , type = type , ** kwargs ) return self . filter ( * filt , filtfilt ... | Filter this TimeSeries with a high - pass filter . |
22,687 | def bandpass ( self , flow , fhigh , gpass = 2 , gstop = 30 , fstop = None , type = 'iir' , filtfilt = True , ** kwargs ) : filt = filter_design . bandpass ( flow , fhigh , self . sample_rate , fstop = fstop , gpass = gpass , gstop = gstop , analog = False , type = type , ** kwargs ) return self . filter ( * filt , fil... | Filter this TimeSeries with a band - pass filter . |
22,688 | def resample ( self , rate , window = 'hamming' , ftype = 'fir' , n = None ) : if n is None and ftype == 'iir' : n = 8 elif n is None : n = 60 if isinstance ( rate , units . Quantity ) : rate = rate . value factor = ( self . sample_rate . value / rate ) if numpy . isclose ( factor , 1. , rtol = 1e-09 , atol = 0. ) : wa... | Resample this Series to a new rate |
22,689 | def zpk ( self , zeros , poles , gain , analog = True , ** kwargs ) : return self . filter ( zeros , poles , gain , analog = analog , ** kwargs ) | Filter this TimeSeries by applying a zero - pole - gain filter |
22,690 | def filter ( self , * filt , ** kwargs ) : filtfilt = kwargs . pop ( 'filtfilt' , False ) form , filt = filter_design . parse_filter ( filt , analog = kwargs . pop ( 'analog' , False ) , sample_rate = self . sample_rate . to ( 'Hz' ) . value , ) if form == 'zpk' : try : sos = signal . zpk2sos ( * filt ) except Attribut... | Filter this TimeSeries with an IIR or FIR filter |
22,691 | def coherence ( self , other , fftlength = None , overlap = None , window = 'hann' , ** kwargs ) : from matplotlib import mlab from . . frequencyseries import FrequencySeries if self . sample_rate . to ( 'Hertz' ) != other . sample_rate . to ( 'Hertz' ) : sampling = min ( self . sample_rate . value , other . sample_rat... | Calculate the frequency - coherence between this TimeSeries and another . |
22,692 | def auto_coherence ( self , dt , fftlength = None , overlap = None , window = 'hann' , ** kwargs ) : dt = abs ( dt ) self_ = self . crop ( self . span [ 0 ] , self . span [ 1 ] - dt ) other = self . crop ( self . span [ 0 ] + dt , self . span [ 1 ] ) return self_ . coherence ( other , fftlength = fftlength , overlap = ... | Calculate the frequency - coherence between this TimeSeries and a time - shifted copy of itself . |
22,693 | def coherence_spectrogram ( self , other , stride , fftlength = None , overlap = None , window = 'hann' , nproc = 1 ) : from . . spectrogram . coherence import from_timeseries return from_timeseries ( self , other , stride , fftlength = fftlength , overlap = overlap , window = window , nproc = nproc ) | Calculate the coherence spectrogram between this TimeSeries and other . |
22,694 | def rms ( self , stride = 1 ) : stridesamp = int ( stride * self . sample_rate . value ) nsteps = int ( self . size // stridesamp ) data = numpy . zeros ( nsteps ) for step in range ( nsteps ) : idx = int ( stridesamp * step ) idx_end = idx + stridesamp stepseries = self [ idx : idx_end ] rms_ = numpy . sqrt ( numpy . ... | Calculate the root - mean - square value of this TimeSeries once per stride . |
22,695 | def demodulate ( self , f , stride = 1 , exp = False , deg = True ) : stridesamp = int ( stride * self . sample_rate . value ) nsteps = int ( self . size // stridesamp ) out = type ( self ) ( numpy . zeros ( nsteps , dtype = complex ) ) out . __array_finalize__ ( self ) out . sample_rate = 1 / float ( stride ) w = 2 * ... | Compute the average magnitude and phase of this TimeSeries once per stride at a given frequency . |
22,696 | def taper ( self , side = 'leftright' ) : if side not in ( 'left' , 'right' , 'leftright' ) : raise ValueError ( "side must be one of 'left', 'right', " "or 'leftright'" ) out = self . copy ( ) nleft , nright = 0 , 0 mini , = signal . argrelmin ( out . value ) maxi , = signal . argrelmax ( out . value ) if 'left' in si... | Taper the ends of this TimeSeries smoothly to zero . |
22,697 | def whiten ( self , fftlength = None , overlap = 0 , method = DEFAULT_FFT_METHOD , window = 'hanning' , detrend = 'constant' , asd = None , fduration = 2 , highpass = None , ** kwargs ) : fftlength = fftlength if fftlength else _fft_length_default ( self . dt ) if asd is None : asd = self . asd ( fftlength , overlap = ... | Whiten this TimeSeries using inverse spectrum truncation |
22,698 | def gate ( self , tzero = 1.0 , tpad = 0.5 , whiten = True , threshold = 50. , cluster_window = 0.5 , ** whiten_kwargs ) : try : from scipy . signal import find_peaks except ImportError as exc : exc . args = ( "Must have scipy>=1.1.0 to utilize this method." , ) raise data = self . whiten ( ** whiten_kwargs ) if whiten... | Removes high amplitude peaks from data using inverse Planck window . Points will be discovered automatically using a provided threshold and clustered within a provided time window . |
22,699 | def convolve ( self , fir , window = 'hanning' ) : pad = int ( numpy . ceil ( fir . size / 2 ) ) nfft = min ( 8 * fir . size , self . size ) in_ = self . copy ( ) window = signal . get_window ( window , fir . size ) in_ . value [ : pad ] *= window [ : pad ] in_ . value [ - pad : ] *= window [ - pad : ] if nfft >= self ... | Convolve this TimeSeries with an FIR filter using the overlap - save method |
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