diff --git a/docs/source/_static/icon/UMD_Globe_Icon_Large.png b/docs/source/_static/icon/UMD_Globe_Icon_Large.png new file mode 100644 index 00000000..972364cb Binary files /dev/null and b/docs/source/_static/icon/UMD_Globe_Icon_Large.png differ diff --git a/docs/source/reference/modules/index.rst b/docs/source/reference/modules/index.rst index a2c05f05..36346350 100644 --- a/docs/source/reference/modules/index.rst +++ b/docs/source/reference/modules/index.rst @@ -41,7 +41,7 @@ Modules and APIs :outline: :click-parent: - Go to dysh.spectra + Go to dysh.plot .. grid-item-card:: :shadow: md diff --git a/notebooks/examples/align_spectra.ipynb b/notebooks/examples/align_spectra.ipynb index 29daee33..2ae61aec 100644 --- a/notebooks/examples/align_spectra.ipynb +++ b/notebooks/examples/align_spectra.ipynb @@ -219,7 +219,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -272,7 +272,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -304,7 +304,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -314,11 +314,11 @@ } ], "source": [ - "ps_ref.align_to(ps_sig)\n", + "ps_ref_aligned = ps_ref.align_to(ps_sig)\n", "\n", "plt.figure()\n", "plt.plot(ps_sig.flux)\n", - "plt.plot(ps_ref.flux)\n", + "plt.plot(ps_ref_aligned.flux)\n", "plt.ylabel(f\"Antenna temperature ({ps_sig.flux.unit})\")\n", "plt.xlabel(\"Channel\");" ] @@ -338,7 +338,7 @@ "metadata": {}, "outputs": [], "source": [ - "ps_avg = average_spectra((ps_sig, ps_ref))" + "ps_avg = average_spectra((ps_sig, ps_ref_aligned))" ] }, { @@ -349,7 +349,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -413,7 +413,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.0" + "version": "3.12.0" } }, "nbformat": 4, diff --git a/src/dysh/fits/gbtfitsload.py b/src/dysh/fits/gbtfitsload.py index c10e5019..6ec88553 100644 --- a/src/dysh/fits/gbtfitsload.py +++ b/src/dysh/fits/gbtfitsload.py @@ -12,9 +12,16 @@ from dysh.log import logger from ..coordinates import Observatory, decode_veldef -from ..log import HistoricalBase, dysh_date, log_call_to_history, log_call_to_result +from ..log import HistoricalBase, log_call_to_history, log_call_to_result from ..spectra.scan import FSScan, NodScan, PSScan, ScanBlock, SubBeamNodScan, TPScan -from ..util import consecutive, indices_where_value_changes, keycase, select_from, uniq +from ..util import ( + consecutive, + convert_array_to_mask, + indices_where_value_changes, + keycase, + select_from, + uniq, +) from ..util.selection import Flag, Selection from .sdfitsload import SDFITSLoad @@ -211,6 +218,7 @@ def flags(self): @property def final_flags(self): + # this method is not particularly useful. consider removing it """ The merged flag rules in the Flag object. See :meth:`~dysh.util.SelectionBase.final` @@ -221,13 +229,9 @@ def final_flags(self): The final merged flags """ - all_channels_flagged = np.where(self._table["CHAN"] == "") - + # all_channels_flagged = np.where(self._table["CHAN"] == "")j return self._flag.final - def _set_flags(self): - self.final_flags - def filenames(self): """ The list of SDFITS filenames(s) that make up this GBTFITSLoad object @@ -274,7 +278,7 @@ def index(self, hdu=None, bintable=None, fitsindex=None): return df # override sdfits version - def rawspectra(self, bintable, fitsindex): + def rawspectra(self, bintable, fitsindex, setmask=False): """ Get the raw (unprocessed) spectra from the input bintable. @@ -284,6 +288,8 @@ def rawspectra(self, bintable, fitsindex): The index of the `bintable` attribute fitsindex: int the index of the FITS file contained in this GBTFITSLoad. Default:0 + setmask : boolean + If True, set the mask according to the current flags. Defaultf:false Returns ------- @@ -598,7 +604,7 @@ def select_within(self, tag=None, **kwargs): self._selection.select_within(tag=tag, **kwargs) @log_call_to_history - def select_channel(self, chan, tag=None): + def select_channel(self, channel, tag=None): """ Select channels and/or channel ranges. These are NOT used in :meth:`final` but rather will be used to create a mask for calibration or @@ -620,21 +626,28 @@ def select_channel(self, chan, tag=None): Parameters ---------- - chan : number, or array-like + channel : number, or array-like The channels to select Returns ------- None. """ - self._selection.select_channel(tag=tag, chan=chan) + self._selection.select_channel(tag=tag, channel=channel) + + @log_call_to_history + def clear_selection(self): + """Clear all selections for these data""" + self._selection.clear() @log_call_to_history def flag(self, tag=None, **kwargs): """Add one or more exact flag rules, e.g., `key1 = value1, key2 = value2, ...` - If `value` is array-like then a match to any of the array members will be selected. - For instance `flag(object=['3C273', 'NGC1234'])` will flag data for either of those - objects and `flag(ifnum=[0,2])` will flag IF number 0 or IF number 2. + If `value` is array-like then a match to any of the array members will be flagged. + For instance `flag(object=['3C273', 'NGC1234'])` will select data for either of those + objects and `flag(ifnum=[0,2])` will flag IF number 0 or IF number 2. Channels for selected data + can be flagged using keyword `channel`, e.g., `flag(object='MBM12',channel=[0,23])` + will flag channels 0 through 23 *inclusive* for object MBM12. See `~dysh.util.selection.Flag`. Parameters @@ -708,7 +721,7 @@ def flag_within(self, tag=None, **kwargs): self._flag.flag_within(tag=tag, **kwargs) @log_call_to_history - def flag_channel(self, chan, tag=None): + def flag_channel(self, channel, tag=None): """ Select channels and/or channel ranges. These are NOT used in :meth:`final` but rather will be used to create a mask for @@ -716,6 +729,8 @@ def flag_channel(self, chan, tag=None): nested arrays will be treated as ranges, for instance `` + # flag channel 128 + flag_channel(128) # flags channels 1 and 10 flag_channel([1,10]) # flags channels 1 thru 10 inclusive @@ -730,14 +745,48 @@ def flag_channel(self, chan, tag=None): Parameters ---------- - chan : number, or array-like + channel : number, or array-like The channels to flag Returns ------- None. """ - self._flag.flag_channel(tag=tag, chan=chan) + self._flag.flag_channel(tag=tag, channel=channel) + + @log_call_to_history + def apply_flags(self): + """ + Set the channel flags according to the rules specified in the `flags` attribute. + This sets numpy masks in the underlying `SDFITSLoad` objects. + + Returns + ------- + None. + + """ + # Loop over the dict of flagged channels, which + # have the same key as the flag rules. + # For all SDFs in each flag rule, set the flag mask(s) + # for their rows. The index of the sdf._flagmask array is the bintable index + for key, chan in self._flag._flag_channel_selection.items(): + selection = self._flag.get(key) + # chan will be a list or a list of lists + # If it is a single list, it is just a list of channels + # if it is list of lists, then it is upper lower inclusive + dfs = selection.groupby(["FITSINDEX", "BINTABLE"]) + # the dict key for the groups is a tuple (fitsindex,bintable) + for i, ((fi, bi), g) in enumerate(dfs): + chan_mask = convert_array_to_mask(chan, self._sdf[fi].nchan(bi)) + rows = g["ROW"].to_numpy() + self._sdf[fi]._flagmask[bi][rows] = chan_mask + + @log_call_to_history + def clear_flags(self): + """Clear all flags for these data""" + for sdf in self._sdf: + sdf._init_flags() + self._flag.clear() def _create_index_if_needed(self): if self._selection is not None: @@ -760,9 +809,6 @@ def _create_index_if_needed(self): self._construct_procedure() self._construct_integration_number() - def _create_flagmask(self): - """Creates the mask which is NFILESxNINTxNCHAN which will be used for setting channel flags""" - def _construct_procedure(self): """ Construct the procedure string (PROC) from OBSMODE and add it to the index (i.e., a new SDFITS column). @@ -818,23 +864,23 @@ def _construct_integration_number(self): idx = g.index intnumarray[idx] = intnums[i] self._index["INTNUM"] = intnumarray - # Wait until after INTNUM PR: - # self._flag["INTNUM"] = intnumarray - - # Here need to add it as a new column in the BinTableHDU, - # but we have to sort out FITSINDEX. - # s.add_col("INTNUM",intnumarray) - fits_index_changes = indices_where_value_changes("FITSINDEX", self._index) - lf = len(fits_index_changes) - for i in range(lf): - fic = fits_index_changes[i] - if i + 1 < lf: - fici = fits_index_changes[i + 1] - else: - fici = -1 - fi = self["FITSINDEX"][fic] - # @todo fix this MWP - # self._sdf[fi].add_col("INTNUM", intnumarray[fic:fici]) # bintable index??? + self._flag["INTNUM"] = intnumarray + + if False: + # Here need to add it as a new column in the BinTableHDU, + # but we have to sort out FITSINDEX. + # s.add_col("INTNUM",intnumarray) + fits_index_changes = indices_where_value_changes("FITSINDEX", self._index) + lf = len(fits_index_changes) + for i in range(lf): + fic = fits_index_changes[i] + if i + 1 < lf: + fici = fits_index_changes[i + 1] + else: + fici = -1 + fi = self["FITSINDEX"][fic] + # @todo fix this MWP + # self._sdf[fi].add_col("INTNUM", intnumarray[fic:fici]) # bintable index??? def info(self): """Return information on the HDUs contained in this object. See :meth:`~astropy.HDUList/info()`""" @@ -852,6 +898,7 @@ def gettp( weights="tsys", bintable=None, smoothref=1, + apply_flags=True, **kwargs, ): """ @@ -874,6 +921,8 @@ def gettp( None or 'tsys' to indicate equal weighting or tsys weighting to use in time averaging. Default: 'tsys' bintable : int, optional Limit to the input binary table index. The default is None which means use all binary tables. + smooth_ref: int, optional + the number of channels in the reference to boxcar smooth prior to calibration **kwargs : dict Optional additional selection keyword arguments, typically given as key=value, though a dictionary works too. @@ -886,13 +935,14 @@ def gettp( """ TF = {True: "T", False: "F"} - + if apply_flags: + self.apply_flags() if len(self._selection._selection_rules) > 0: _final = self._selection.final else: _final = self._index scans = kwargs.get("scan", None) - debug = kwargs.pop("debug", False) + # debug = kwargs.pop("debug", False) kwargs = keycase(kwargs) if type(scans) is int: scans = [scans] @@ -953,6 +1003,7 @@ def gettp( bintable, calibrate, smoothref=smoothref, + apply_flags=apply_flags, ) g.merge_commentary(self) scanblock.append(g) @@ -964,7 +1015,15 @@ def gettp( @log_call_to_result def getps( - self, calibrate=True, timeaverage=True, polaverage=False, weights="tsys", bintable=None, smoothref=1, **kwargs + self, + calibrate=True, + timeaverage=True, + polaverage=False, + weights="tsys", + bintable=None, + smoothref=1, + apply_flags=True, + **kwargs, ): """ Retrieve and calibrate position-switched data. @@ -984,6 +1043,10 @@ def getps( bintable : int, optional Limit to the input binary table index. The default is None which means use all binary tables. (This keyword should eventually go away) + smooth_ref: int, optional + the number of channels in the reference to boxcar smooth prior to calibration + apply_flags : boolean, optional. If True, apply flags before calibration. + See :meth:`apply_flags`. Default: True **kwargs : dict Optional additional selection keyword arguments, typically given as key=value, though a dictionary works too. @@ -1000,6 +1063,9 @@ def getps( ScanBlock containing the individual `~spectra.scan.PSScan`s """ + + if apply_flags: + self.apply_flags() # either the user gave scans on the command line (scans !=None) or pre-selected them # with select_fromion.selectXX(). In either case make sure the matching ON or OFF # is in the starting selection. @@ -1092,6 +1158,7 @@ def getps( bintable=bintable, calibrate=calibrate, smoothref=smoothref, + apply_flags=apply_flags, ) g.merge_commentary(self) scanblock.append(g) @@ -1104,7 +1171,15 @@ def getps( @log_call_to_result def getnod( - self, calibrate=True, timeaverage=True, polaverage=False, weights="tsys", bintable=None, smoothref=1, **kwargs + self, + calibrate=True, + timeaverage=True, + polaverage=False, + weights="tsys", + bintable=None, + smoothref=1, + apply_flags=True, + **kwargs, ): """ Retrieve and calibrate nodding data. @@ -1128,6 +1203,10 @@ def getnod( bintable : int, optional Limit to the input binary table index. The default is None which means use all binary tables. (This keyword should eventually go away) + smooth_ref: int, optional + the number of channels in the reference to boxcar smooth prior to calibration + apply_flags : boolean, optional. If True, apply flags before calibration. + See :meth:`apply_flags`. Default: True **kwargs : dict Optional additional selection keyword arguments, typically given as key=value, though a dictionary works too. @@ -1159,8 +1238,8 @@ def get_nod_beams(sdf): if len(d1["FDNUM"].unique()) == 1 and len(d2["FDNUM"].unique()) == 1: beam1 = d1["FDNUM"].unique()[0] beam2 = d2["FDNUM"].unique()[0] - fdnum1 = d1["FEED"].unique()[0] - fdnum2 = d2["FEED"].unique()[0] + # fdnum1 = d1["FEED"].unique()[0] + # fdnum2 = d2["FEED"].unique()[0] return [beam1, beam2] else: # one more attempt (this can happen if PROCSCAN contains "Unknown") @@ -1171,6 +1250,8 @@ def get_nod_beams(sdf): return list(b) return [] + if apply_flags: + self.apply_flags() nod_beams = get_nod_beams(self) feeds = kwargs.pop("fdnum", None) if feeds is None: @@ -1293,6 +1374,7 @@ def get_nod_beams(sdf): bintable=bintable, calibrate=calibrate, smoothref=smoothref, + apply_flags=apply_flags, ) g.merge_commentary(self) scanblock.append(g) @@ -1318,6 +1400,7 @@ def getfs( weights="tsys", bintable=None, smoothref=1, + apply_flags=True, observer_location=Observatory["GBT"], **kwargs, ): @@ -1350,6 +1433,10 @@ def getfs( The default is 'tsys'. bintable : int, optional Limit to the input binary table index. The default is None which means use all binary tables. + smooth_ref: int, optional + the number of channels in the reference to boxcar smooth prior to calibration + apply_flags : boolean, optional. If True, apply flags before calibration. + See :meth:`apply_flags`. Default: True observer_location : `~astropy.coordinates.EarthLocation` Location of the observatory. See `~dysh.coordinates.Observatory`. This will be transformed to `~astropy.coordinates.ITRS` using the time of @@ -1373,6 +1460,9 @@ def getfs( """ debug = kwargs.pop("debug", False) logger.debug(kwargs) + + if apply_flags: + self.apply_flags() # either the user gave scans on the command line (scans !=None) or pre-selected them # with self.selection.selectXX() if len(self._selection._selection_rules) > 0: @@ -1443,6 +1533,7 @@ def getfs( use_sig=use_sig, observer_location=observer_location, smoothref=1, + apply_flags=apply_flags, debug=debug, ) g.merge_commentary(self) @@ -1466,6 +1557,7 @@ def subbeamnod( weights="tsys", bintable=None, smoothref=1, + apply_flags=True, **kwargs, ): """Get a subbeam nod power scan, optionally calibrating it. @@ -1488,6 +1580,10 @@ def subbeamnod( None to indicate equal weighting or 'tsys' to indicate tsys weighting to use in time averaging. Default: 'tsys' bintable : int, optional Limit to the input binary table index. The default is None which means use all binary tables. + smooth_ref: int, optional + the number of channels in the reference to boxcar smooth prior to calibration + apply_flags : boolean, optional. If True, apply flags before calibration. + See :meth:`apply_flags`. Default: True **kwargs : dict Optional additional selection keyword arguments, typically given as key=value, though a dictionary works too. @@ -1498,12 +1594,15 @@ def subbeamnod( data : `~spectra.scan.ScanBlock` A ScanBlock containing one or more `~spectra.scan.SubBeamNodScan` """ + + if apply_flags: + self.apply_flags() if len(self._selection._selection_rules) > 0: _final = self._selection.final else: _final = self._index scans = kwargs.get("scan", None) - debug = kwargs.pop("debug", False) + # debug = kwargs.pop("debug", False) kwargs = keycase(kwargs) logger.debug(kwargs) @@ -1614,6 +1713,7 @@ def subbeamnod( bintable, calibrate=calibrate, smoothref=smoothref, + apply_flags=apply_flags, ) ) calrows = {"ON": sgon, "OFF": sgoff} @@ -1629,9 +1729,17 @@ def subbeamnod( bintable, calibrate=calibrate, smoothref=smoothref, + apply_flags=apply_flags, ) ) - sb = SubBeamNodScan(sigtp, reftp, calibrate=calibrate, weights=weights, smoothref=smoothref) + sb = SubBeamNodScan( + sigtp, + reftp, + calibrate=calibrate, + weights=weights, + smoothref=smoothref, + apply_flags=apply_flags, + ) scanblock.append(sb) elif method == "scan": for sdfi in range(len(self._sdf)): @@ -1657,6 +1765,7 @@ def subbeamnod( weights=weights, calibrate=calibrate, smoothref=smoothref, + apply_flags=apply_flags, ) sigtp.append(tpon[0]) tpoff = self.gettp( @@ -1671,6 +1780,7 @@ def subbeamnod( weights=weights, calibrate=calibrate, smoothref=smoothref, + apply_flags=apply_flags, ) reftp.append(tpoff[0]) # in order to reproduce gbtidl tsys, we need to do a normal @@ -1686,7 +1796,8 @@ def subbeamnod( weights=weights, calibrate=calibrate, smoothref=smoothref, - ) # .timeaverage(weights=w) + apply_flags=apply_flags, + ) fulltp.append(ftp[0]) sb = SubBeamNodScan( sigtp, @@ -1694,6 +1805,7 @@ def subbeamnod( calibrate=calibrate, weights=weights, smoothref=smoothref, + apply_flags=apply_flags, ) sb.merge_commentary(self) scanblock.append(sb) @@ -2208,7 +2320,7 @@ def write( given as key=value, though a dictionary works too. e.g., `ifnum=1, plnum=[2,3]` etc. """ - debug = kwargs.pop("debug", False) + # debug = kwargs.pop("debug", False) logger.debug(kwargs) selection = Selection(self._index) if len(kwargs) > 0: diff --git a/src/dysh/fits/sdfitsload.py b/src/dysh/fits/sdfitsload.py index a36c386b..3a10c60e 100644 --- a/src/dysh/fits/sdfitsload.py +++ b/src/dysh/fits/sdfitsload.py @@ -57,11 +57,11 @@ def __init__(self, filename, source=None, hdu=None, **kwargs): if doindex: self.create_index() # add default channel masks - self._flagmask = [] - # if doflag: - # for i in range(len(self._bintable)): - # nc = self.nchan(i) - # self._flagmask.append(np.full(nc, False)) + # These are numpy masks where False is not flagged, True is flagged. + # There is one 2-D flag mask arraywith shape NROWSxNCHANNELS per bintable + self._flagmask = None + if doflag: + self._init_flags() def __del__(self): # We need to ensure that any open HDUs are properly @@ -72,6 +72,15 @@ def __del__(self): except Exception: pass + def _init_flags(self): + """initialize the channel masks to False""" + self._flagmask = np.empty(len(self._bintable), dtype=object) + for i in range(len(self._flagmask)): + nc = self.nchan(i) + nr = self.nrows(i) + logger.debug(f"{nr=} {nc=}") + self._flagmask[i] = np.full((nr, nc), fill_value=False) + def info(self): """Return the `~astropy.HDUList` info()""" return self._hdu.info() @@ -358,7 +367,7 @@ def _find_bintable_and_row(self, row): """ return (self._index.iloc[row]["BINTABLE"], self._index.iloc[row]["ROW"]) - def rawspectra(self, bintable): + def rawspectra(self, bintable, setmask=False): """ Get the raw (unprocessed) spectra from the input bintable. @@ -366,16 +375,23 @@ def rawspectra(self, bintable): ---------- bintable : int The index of the `bintable` attribute + setmask : bool + If True, set the data mask according to the current flags in the `_flagmask` attribute. If False, set the data mask to False. Returns ------- - rawspectra : ~numpy.ndarray - The DATA column of the input bintable + rawspectra : ~numpy.ma.MaskedArray + The DATA column of the input bintable, masked according to `setmask` """ - return self._bintable[bintable].data[:]["DATA"] + data = self._bintable[bintable].data[:]["DATA"] + if setmask: + rawspec = np.ma.MaskedArray(data, mask=self._flagmask[bintable]) + else: + rawspec = np.ma.MaskedArray(data, mask=False) + return rawspec - def rawspectrum(self, i, bintable=0): + def rawspectrum(self, i, bintable=0, setmask=False): """ Get a single raw (unprocessed) spectrum from the input bintable. @@ -385,18 +401,25 @@ def rawspectrum(self, i, bintable=0): The row index to retrieve. bintable : int or None The index of the `bintable` attribute. If None, the underlying bintable is computed from i - + setmask : bool + If True, set the data mask according to the current flags in the `_flagmask` attribute. Returns ------- - rawspectrum : ~numpy.ndarray - The i-th row of DATA column of the input bintable + rawspectrum : ~numpy.ma.MaskedArray + The i-th row of DATA column of the input bintable, masked according to `setmask` """ if bintable is None: (bt, row) = self._find_bintable_and_row(i) - return self._bintable[bt].data[:]["DATA"][row] + data = self._bintable[bt].data[:]["DATA"][row] + else: + data = self._bintable[bintable].data[:]["DATA"][i] + row = i + if setmask: + rawspec = np.ma.MaskedArray(data, mask=self._flagmask[bintable][row]) else: - return self._bintable[bintable].data[:]["DATA"][i] + rawspec = np.ma.MaskedArray(data, False) + return rawspec def getrow(self, i, bintable=0): """ @@ -444,7 +467,7 @@ def getspec(self, i, bintable=0, observer_location=None): meta["NAXIS1"] = len(data) if "CUNIT1" not in meta: meta["CUNIT1"] = "Hz" # @todo this is in gbtfits.hdu[0].header['TUNIT11'] but is it always TUNIT11? - logger.debug(f"Fixing CUNIT1 to Hz") + logger.debug("Fixing CUNIT1 to Hz") meta["CUNIT2"] = "deg" # is this always true? meta["CUNIT3"] = "deg" # is this always true? restfrq = meta["RESTFREQ"] @@ -472,7 +495,7 @@ def getspec(self, i, bintable=0, observer_location=None): for k, v, c in h.cards: if k == ukey: if bunit != v: - logger.info(f"Found BUNIT={bunit}, now finding {uKey}={v}, using the latter") + logger.info(f"Found BUNIT={bunit}, now finding {ukey}={v}, using the latter") bunit = v break if bunit is not None: @@ -519,7 +542,7 @@ def nchan(self, bintable): Number channels in the first spectrum of the input bintable """ - return np.shape(self.rawspectrum(1, bintable))[0] + return np.shape(self.rawspectrum(0, bintable))[0] def npol(self, bintable): """ @@ -865,7 +888,6 @@ def _update_binary_table_column(self, column_dict): self._bintable[0].data[k] = v # otherwise we need to add rather than replace/update else: - # print("ADDING {k}={v}") self._add_binary_table_column(k, v, 0) else: start = 0 @@ -904,7 +926,6 @@ def _update_binary_table_column(self, column_dict): def __getitem__(self, items): # items can be a single string or a list of strings. # Want case insensitivity - # @todo deal with "DATA" if isinstance(items, str): items = items.upper() elif isinstance(items, (Sequence, np.ndarray)): @@ -923,7 +944,6 @@ def __getitem__(self, items): return self._index[items] def __setitem__(self, items, values): - # @todo deal with "DATA" if isinstance(items, str): items = items.upper() d = {items: values} @@ -943,7 +963,6 @@ def __setitem__(self, items, values): else: iset = set(items) col_exists = len(set(self.columns).intersection(iset)) > 0 - # col_in_selection = if col_exists and "DATA" not in items: warnings.warn("Changing an existing SDFITS column") try: diff --git a/src/dysh/fits/tests/test_gbtfitsload.py b/src/dysh/fits/tests/test_gbtfitsload.py index 7726d6a7..f47d8bd7 100644 --- a/src/dysh/fits/tests/test_gbtfitsload.py +++ b/src/dysh/fits/tests/test_gbtfitsload.py @@ -253,6 +253,7 @@ def test_gettp(self): 8: {"SCAN": 6, "IFNUM": 2, "PLNUM": 0, "CAL": False, "SIG": True}, } for k, v in tests.items(): + print(f"{k}, {v}") if v["SIG"] == False: with pytest.raises(Exception): tps = sdf.gettp(scan=v["SCAN"], ifnum=v["IFNUM"], plnum=v["PLNUM"], cal=v["CAL"], sig=v["SIG"]) @@ -269,8 +270,8 @@ def test_gettp(self): else: # CAL=True cal = tps[0]._refcalon.astype(np.float64) - assert np.all(tp.flux.value == np.nanmean(cal, axis=0)) - + # diff = tp.flux.value - np.nanmean(cal, axis=0) + assert np.all(tp.flux.value - np.nanmean(cal, axis=0) == 0) # Check that selection is being applied properly. tp_scans = sdf.gettp(scan=[6, 7], plnum=0) # Weird that the results are different for a bunch of channels. @@ -433,6 +434,17 @@ def test_getps_smoothref(self): except KeyError: continue + def test_getps_flagging(self): + path = util.get_project_testdata() / "TGBT21A_501_11" + data_file = path / "TGBT21A_501_11.raw.vegas.fits" + sdf = gbtfitsload.GBTFITSLoad(data_file) + sdf.flag_channel([[10, 20], [30, 41]]) + sb = sdf.getps(scan=152, ifnum=0, plnum=0, apply_flags=True) + ta = sb.timeaverage() + # average_spectra masks out the NaN in channel 3072 + expected_mask = np.hstack([np.arange(10, 21), np.arange(30, 42), np.array([3072])]) + assert np.all(np.where(ta.mask) == expected_mask) + def test_write_single_file(self, tmp_path): "Test that writing an SDFITS file works when subselecting data" p = util.get_project_testdata() / "AGBT20B_014_03.raw.vegas" diff --git a/src/dysh/plot/specplot.py b/src/dysh/plot/specplot.py index 43bbb1f3..3cd0c0a1 100644 --- a/src/dysh/plot/specplot.py +++ b/src/dysh/plot/specplot.py @@ -7,6 +7,7 @@ import astropy.units as u import matplotlib.pyplot as plt import numpy as np +from astropy.utils.masked import Masked from ..coordinates import frame_to_label @@ -150,6 +151,7 @@ def plot(self, **kwargs): sf = s.flux if yunit is not None: sf = s.flux.to(yunit) + sf = Masked(sf, s.mask) self._axis.plot(sa, sf, color=this_plot_kwargs["color"], lw=lw) self._axis.set_xlim(this_plot_kwargs["xmin"], this_plot_kwargs["xmax"]) self._axis.set_ylim(this_plot_kwargs["ymin"], this_plot_kwargs["ymax"]) diff --git a/src/dysh/spectra/core.py b/src/dysh/spectra/core.py index cfb35bec..bd6ed2d5 100644 --- a/src/dysh/spectra/core.py +++ b/src/dysh/spectra/core.py @@ -680,7 +680,11 @@ def smooth(data, method="hanning", width=1, kernel=None, show=False): if show: return kernel # the boundary='extend' matches GBTIDL's /edge_truncate CONVOL() method - new_data = convolve(data, kernel, boundary="extend") + if hasattr(data, "mask"): + mask = data.mask + else: + mask = None + new_data = convolve(data, kernel, boundary="extend") # , nan_treatment="fill", fill_value=np.nan, mask=mask) return new_data diff --git a/src/dysh/spectra/scan.py b/src/dysh/spectra/scan.py index 49891404..7827bd23 100644 --- a/src/dysh/spectra/scan.py +++ b/src/dysh/spectra/scan.py @@ -11,21 +11,21 @@ from astropy import constants as ac from astropy.io.fits import BinTableHDU, Column from astropy.table import Table, vstack +from astropy.utils.masked import Masked from dysh.spectra import core from ..coordinates import Observatory from ..log import HistoricalBase, log_call_to_history, logger from ..util import uniq -from .core import ( +from .core import ( # fft_shift, average, - fft_shift, find_non_blanks, mean_tsys, sq_weighted_avg, tsys_weight, ) -from .spectrum import Spectrum +from .spectrum import Spectrum, average_spectra class SpectralAverageMixin: @@ -45,6 +45,9 @@ def timeaverage(self, weights=None): ------- spectrum : :class:`~spectra.spectrum.Spectrum` The time-averaged spectrum + + .. note:: + Data that are masked will have values set to zero. This is a feature of `numpy.ma.average`. Data mask fill value is NaN (np.nan) """ pass @@ -132,12 +135,6 @@ def _validate_defaults(self): if type(self._scan) != int: raise (f"{self.__class__.__name__}._scan is not an int: {type(self._scan)}") - # class ScanMixin: - # """This class describes the common interface to all Scan classes. - ## A Scan represents one IF, one feed, and one or more polarizations. - # Derived classes *must* implement :meth:`calibrate`. - # """ - @property def scan(self): """ @@ -408,7 +405,7 @@ def calibrate(self, **kwargs): scan.calibrate(**kwargs) @log_call_to_history - def timeaverage(self, weights="tsys", mode="old"): + def timeaverage(self, weights="tsys"): r"""Compute the time-averaged spectrum for all scans in this ScanBlock. Parameters @@ -419,60 +416,23 @@ def timeaverage(self, weights="tsys", mode="old"): :math:`w = t_{exp} \times \delta\nu/T_{sys}^2` Default: 'tsys' + Returns ------- timeaverage: list of `~spectra.spectrum.Spectrum` List of all the time-averaged spectra + + .. note:: + Data that are masked will have values set to zero. This is a feature of `numpy.ma.average`. Data mask fill value is NaN (np.nan) """ # warnings.simplefilter("ignore", NoVelocityWarning) - if mode == "old": - # average of the averages - self._timeaveraged = [] - for scan in self.data: - self._timeaveraged.append(scan.timeaverage(weights)) - if weights == "tsys": - # There may be multiple integrations, so need to - # average the Tsys weights - w = np.array([np.nanmean(k.tsys_weight) for k in self.data]) - if len(np.shape(w)) > 1: # remove empty axes - w = w.squeeze() - else: - w = weights - timeavg = np.array([k.data for k in self._timeaveraged]) - # Weight the average of the timeaverages by the weights. - avgdata = average(timeavg, axis=0, weights=w) - avgspec = np.mean(self._timeaveraged) - avgspec.meta = self._timeaveraged[0].meta - avgspec.meta["TSYS"] = np.average(a=[k.meta["TSYS"] for k in self._timeaveraged], axis=0, weights=w) - avgspec.meta["EXPOSURE"] = np.sum([k.meta["EXPOSURE"] for k in self._timeaveraged]) - # observer = self._timeaveraged[0].observer # nope this has to be a location ugh. see @todo in Spectrum constructor - # hardcode to GBT for now - s = Spectrum.make_spectrum( - avgdata * avgspec.flux.unit, meta=avgspec.meta, observer_location=Observatory["GBT"] - ) - s.merge_commentary(self) - elif mode == "new": - # average of the integrations - allcal = np.all([d._calibrate for d in self.data]) - if not allcal: - raise Exception("Data must be calibrated before time averaging.") - c = np.concatenate([d._calibrated for d in self.data]) - if weights == "tsys": - w = np.concatenate([d.tsys_weight for d in self.data]) - # if len(np.shape(w)) > 1: # remove empty axes - # w = w.squeeze() - else: - w = None - timeavg = average(c, weights=w) - avgspec = self.data[0].calibrated(0) - avgspec.meta["TSYS"] = np.nanmean([d.tsys for d in self.data]) - avgspec.meta["EXPOSURE"] = np.sum([d.exposure for d in self.data]) - s = Spectrum.make_spectrum( - timeavg * avgspec.flux.unit, meta=avgspec.meta, observer_location=Observatory["GBT"] - ) - s.merge_commentary(self) - else: - raise Exception(f"unrecognized mode {mode}") + # average of the averages + self._timeaveraged = [] + i = 0 + for scan in self.data: + self._timeaveraged.append(scan.timeaverage(weights)) + s = average_spectra(self._timeaveraged, weights=weights) + s.merge_commentary(self) return s @log_call_to_history @@ -630,6 +590,7 @@ class TPScan(ScanBase): whether or not to calibrate the data. If `True`, the data will be (calon - caloff)*0.5, otherwise it will be SDFITS row data. Default:True smoothref: int the number of channels in the reference to boxcar smooth prior to calibration + apply_flags : boolean, optional. If True, apply flags before calibration. Notes ----- @@ -665,6 +626,7 @@ def __init__( bintable, calibrate=True, smoothref=1, + apply_flags=False, observer_location=Observatory["GBT"], ): ScanBase.__init__(self, gbtfits) @@ -674,6 +636,7 @@ def __init__( self._calstate = calstate self._scanrows = scanrows self._smoothref = smoothref + self._apply_flags = apply_flags if self._smoothref > 1: warnings.warn(f"TP smoothref={self._smoothref} not implemented yet") @@ -702,8 +665,8 @@ def __init__( self._refonrows = sorted(list(set(self._calrows["ON"]).intersection(set(self._scanrows)))) # all cal=F states where sig=sigstate self._refoffrows = sorted(list(set(self._calrows["OFF"]).intersection(set(self._scanrows)))) - self._refcalon = gbtfits.rawspectra(self._bintable_index)[self._refonrows] - self._refcaloff = gbtfits.rawspectra(self._bintable_index)[self._refoffrows] + self._refcalon = gbtfits.rawspectra(self._bintable_index, setmask=apply_flags)[self._refonrows] + self._refcaloff = gbtfits.rawspectra(self._bintable_index, setmask=apply_flags)[self._refoffrows] # now remove blanked integrations # seems like this should be done for all Scan classes! # PS: yes. @@ -921,6 +884,9 @@ def timeaverage(self, weights="tsys"): ------- spectrum : :class:`~spectra.spectrum.Spectrum` The time-averaged spectrum + + .. note:: + Data that are masked will have values set to zero. This is a feature of `numpy.ma.average`. Data mask fill value is NaN (np.nan) """ if self._npol > 1: raise Exception("Can't yet time average multiple polarizations") @@ -930,7 +896,8 @@ def timeaverage(self, weights="tsys"): else: w = np.ones_like(self.tsys_weight) non_blanks = find_non_blanks(self._data)[0] - self._timeaveraged._data = average(self._data, axis=0, weights=w) + self._timeaveraged._data = np.ma.average(self._data, axis=0, weights=w) + self._timeaveraged._data.set_fill_value(np.nan) self._timeaveraged.meta["MEANTSYS"] = np.mean(self._tsys[non_blanks]) self._timeaveraged.meta["WTTSYS"] = sq_weighted_avg(self._tsys[non_blanks], axis=0, weights=w[non_blanks]) self._timeaveraged.meta["TSYS"] = self._timeaveraged.meta["WTTSYS"] @@ -958,6 +925,7 @@ class PSScan(ScanBase): whether or not to calibrate the data. If true, data will be calibrated as TSYS*(ON-OFF)/OFF. Default: True smoothref: int the number of channels in the reference to boxcar smooth prior to calibration + apply_flags : boolean, optional. If True, apply flags before calibration. observer_location : `~astropy.coordinates.EarthLocation` Location of the observatory. See `~dysh.coordinates.Observatory`. This will be transformed to `~astropy.coordinates.ITRS` using the time of @@ -974,6 +942,7 @@ def __init__( bintable, calibrate=True, smoothref=1, + apply_flags=False, observer_location=Observatory["GBT"], ): ScanBase.__init__(self, gbtfits) @@ -984,13 +953,7 @@ def __init__( self._scanrows = scanrows self._nrows = len(self._scanrows["ON"]) self._smoothref = smoothref - # print(f"PJT len(scanrows ON) {len(self._scanrows['ON'])}") - # print(f"PJT len(scanrows OFF) {len(self._scanrows['OFF'])}") - # print("PJT scans", scans) - # print("PJT scanrows", scanrows) - # print("PJT calrows", calrows) - # print(f"len(scanrows ON) {len(self._scanrows['ON'])}") - # print(f"len(scanrows OFF) {len(self._scanrows['OFF'])}") + self._apply_flags = apply_flags # calrows perhaps not needed as input since we can get it from gbtfits object? # calrows['ON'] are rows with noise diode was on, regardless of sig or ref @@ -1016,9 +979,9 @@ def __init__( self._refoffrows = sorted(list(set(self._calrows["OFF"]).intersection(set(self._scanrows["OFF"])))) self._sigcalon = gbtfits.rawspectra(self._bintable_index)[self._sigonrows] self._nchan = len(self._sigcalon[0]) - self._sigcaloff = gbtfits.rawspectra(self._bintable_index)[self._sigoffrows] - self._refcalon = gbtfits.rawspectra(self._bintable_index)[self._refonrows] - self._refcaloff = gbtfits.rawspectra(self._bintable_index)[self._refoffrows] + self._sigcaloff = gbtfits.rawspectra(self._bintable_index, setmask=apply_flags)[self._sigoffrows] + self._refcalon = gbtfits.rawspectra(self._bintable_index, setmask=apply_flags)[self._refonrows] + self._refcaloff = gbtfits.rawspectra(self._bintable_index, setmask=apply_flags)[self._refoffrows] self._tsys = None self._exposure = None self._calibrated = None @@ -1054,8 +1017,11 @@ def calibrated(self, i): ------- spectrum : `~spectra.spectrum.Spectrum` """ + # @todo suppress astropy INFO message "overwriting Masked Quantity's current mask with specified mask." s = Spectrum.make_spectrum( - self._calibrated[i] * u.K, meta=self.meta[i], observer_location=self._observer_location + Masked(self._calibrated[i] * u.K, self._calibrated[i].mask), + meta=self.meta[i], + observer_location=self._observer_location, ) s.merge_commentary(self) return s @@ -1071,10 +1037,10 @@ def calibrate(self, **kwargs): self._status = 1 nspect = self.nrows // 2 - self._calibrated = np.empty((nspect, self._nchan), dtype="d") + self._calibrated = np.ma.empty((nspect, self._nchan), dtype="d") self._tsys = np.empty(nspect, dtype="d") self._exposure = np.empty(nspect, dtype="d") - tcal = list(self._sdfits.index(bintable=self._bintable_index).iloc[self._refonrows]["TCAL"]) + tcal = self._sdfits.index(bintable=self._bintable_index).iloc[self._refonrows]["TCAL"].to_numpy() # @todo this loop could be replaced with clever numpy if len(tcal) != nspect: raise Exception(f"TCAL length {len(tcal)} and number of spectra {nspect} don't match") @@ -1147,28 +1113,34 @@ def timeaverage(self, weights="tsys"): :math:`w = t_{exp} \times \delta\nu/T_{sys}^2` Default: 'tsys' + Returns ------- spectrum : :class:`~spectra.spectrum.Spectrum` The time-averaged spectrum + + .. note:: + Data that are masked will have values set to zero. This is a feature of `numpy.ma.average`. Data mask fill value is NaN (np.nan) """ if self._calibrated is None or len(self._calibrated) == 0: raise Exception("You can't time average before calibration.") if self._npol > 1: raise Exception("Can't yet time average multiple polarizations") - self._timeaveraged = deepcopy(self.calibrated(0)) + self._timeaveraged = deepcopy(self.calibrated(0)) # ._copy() data = self._calibrated if weights == "tsys": w = self.tsys_weight else: w = np.ones_like(self.tsys_weight) - self._timeaveraged._data = average(data, axis=0, weights=w) + self._timeaveraged._data = np.ma.average(data, axis=0, weights=w) + self._timeaveraged._data.set_fill_value(np.nan) non_blanks = find_non_blanks(data) self._timeaveraged.meta["MEANTSYS"] = np.mean(self._tsys[non_blanks]) self._timeaveraged.meta["WTTSYS"] = sq_weighted_avg(self._tsys[non_blanks], axis=0, weights=w[non_blanks]) self._timeaveraged.meta["EXPOSURE"] = np.sum(self._exposure[non_blanks]) self._timeaveraged.meta["TSYS"] = self._timeaveraged.meta["WTTSYS"] self._timeaveraged._history = self._history + self._timeaveraged._observer_location = self._observer_location return self._timeaveraged @@ -1197,6 +1169,7 @@ class NodScan(ScanBase): Default: True smoothref: int the number of channels in the reference to boxcar smooth prior to calibration (if applicable) + apply_flags : boolean, optional. If True, apply flags before calibration. observer_location : `~astropy.coordinates.EarthLocation` Location of the observatory. See `~dysh.coordinates.Observatory`. This will be transformed to `~astropy.coordinates.ITRS` using the time of @@ -1214,6 +1187,7 @@ def __init__( bintable, calibrate=True, smoothref=1, + apply_flags=False, observer_location=Observatory["GBT"], ): ScanBase.__init__(self, gbtfits) @@ -1221,6 +1195,7 @@ def __init__( self._scanrows = scanrows self._nrows = len(self._scanrows["ON"]) self._smoothref = smoothref + self._apply_flags = apply_flags self._beam1 = beam1 # @todo allow having no calrow where noise diode was not fired @@ -1248,15 +1223,15 @@ def __init__( self._refonrows = sorted(list(set(self._calrows["ON"]).intersection(set(self._scanrows["OFF"])))) self._refoffrows = sorted(list(set(self._calrows["OFF"]).intersection(set(self._scanrows["OFF"])))) if beam1: - self._sigcalon = gbtfits.rawspectra(self._bintable_index)[self._sigonrows] - self._sigcaloff = gbtfits.rawspectra(self._bintable_index)[self._sigoffrows] - self._refcalon = gbtfits.rawspectra(self._bintable_index)[self._refonrows] - self._refcaloff = gbtfits.rawspectra(self._bintable_index)[self._refoffrows] + self._sigcalon = gbtfits.rawspectra(self._bintable_index, setmask=apply_flags)[self._sigonrows] + self._sigcaloff = gbtfits.rawspectra(self._bintable_index, setmask=apply_flags)[self._sigoffrows] + self._refcalon = gbtfits.rawspectra(self._bintable_index, setmask=apply_flags)[self._refonrows] + self._refcaloff = gbtfits.rawspectra(self._bintable_index, setmask=apply_flags)[self._refoffrows] else: - self._sigcalon = gbtfits.rawspectra(self._bintable_index)[self._refonrows] - self._sigcaloff = gbtfits.rawspectra(self._bintable_index)[self._refoffrows] - self._refcalon = gbtfits.rawspectra(self._bintable_index)[self._sigonrows] - self._refcaloff = gbtfits.rawspectra(self._bintable_index)[self._sigoffrows] + self._sigcalon = gbtfits.rawspectra(self._bintable_index, setmask=apply_flags)[self._refonrows] + self._sigcaloff = gbtfits.rawspectra(self._bintable_index, setmask=apply_flags)[self._refoffrows] + self._refcalon = gbtfits.rawspectra(self._bintable_index, setmask=apply_flags)[self._sigonrows] + self._refcaloff = gbtfits.rawspectra(self._bintable_index, setmask=apply_flags)[self._sigoffrows] self._nchan = len(self._sigcalon[0]) self._tsys = None self._exposure = None @@ -1294,7 +1269,9 @@ def calibrated(self, i): spectrum : `~spectra.spectrum.Spectrum` """ s = Spectrum.make_spectrum( - self._calibrated[i] * u.K, meta=self.meta[i], observer_location=self._observer_location + Masked(self._calibrated[i] * u.K, self._calibrated[i].mask), + meta=self.meta[i], + observer_location=self._observer_location, ) s.merge_commentary(self) return s @@ -1310,10 +1287,10 @@ def calibrate(self, **kwargs): self._status = 1 nspect = self.nrows // 2 - self._calibrated = np.empty((nspect, self._nchan), dtype="d") + self._calibrated = np.ma.empty((nspect, self._nchan), dtype="d") self._tsys = np.empty(nspect, dtype="d") self._exposure = np.empty(nspect, dtype="d") - tcal = list(self._sdfits.index(bintable=self._bintable_index).iloc[self._refonrows]["TCAL"]) + tcal = self._sdfits.index(bintable=self._bintable_index).iloc[self._refonrows]["TCAL"].to_numpy() # @todo this loop could be replaced with clever numpy if len(tcal) != nspect: raise Exception(f"TCAL length {len(tcal)} and number of spectra {nspect} don't match") @@ -1390,6 +1367,9 @@ def timeaverage(self, weights="tsys"): ------- spectrum : :class:`~spectra.spectrum.Spectrum` The time-averaged spectrum + + .. note:: + Data that are masked will have values set to zero. This is a feature of `numpy.ma.average`. Data mask fill value is NaN (np.nan) """ if self._calibrated is None or len(self._calibrated) == 0: raise Exception("You can't time average before calibration.") @@ -1401,7 +1381,8 @@ def timeaverage(self, weights="tsys"): w = self.tsys_weight else: w = np.ones_like(self.tsys_weight) - self._timeaveraged._data = average(data, axis=0, weights=w) + self._timeaveraged._data = np.ma.average(data, axis=0, weights=w) + self._timeaveraged._data.set_fill_value(np.nan) non_blanks = find_non_blanks(data) self._timeaveraged.meta["MEANTSYS"] = np.mean(self._tsys[non_blanks]) self._timeaveraged.meta["WTTSYS"] = sq_weighted_avg(self._tsys[non_blanks], axis=0, weights=w[non_blanks]) @@ -1441,6 +1422,7 @@ class FSScan(ScanBase): Whether to use the sig as the sig, or the ref as the sig. Default: True smoothref: int The number of channels in the reference to boxcar smooth prior to calibration. + apply_flags : boolean, optional. If True, apply flags before calibration. observer_location : `~astropy.coordinates.EarthLocation` Location of the observatory. See `~dysh.coordinates.Observatory`. This will be transformed to `~astropy.coordinates.ITRS` using the time of @@ -1459,6 +1441,7 @@ def __init__( shift_method="fft", use_sig=True, smoothref=1, + apply_flags=False, observer_location=Observatory["GBT"], debug=False, ): @@ -1472,7 +1455,7 @@ def __init__( self._smoothref = smoothref if self._smoothref > 1: print(f"FS smoothref={self._smoothref} not implemented yet") - + self._apply_flags = apply_flags self._sigonrows = sorted(list(set(self._calrows["ON"]).intersection(set(self._sigrows["ON"])))) self._sigoffrows = sorted(list(set(self._calrows["OFF"]).intersection(set(self._sigrows["ON"])))) self._refonrows = sorted(list(set(self._calrows["ON"]).intersection(set(self._sigrows["OFF"])))) @@ -1481,19 +1464,19 @@ def __init__( self._debug = debug if self._debug: - print("---------------------------------------------------") - print("FSSCAN: ") - print("SigOff", self._sigoffrows) - print("SigOn", self._sigonrows) - print("RefOff", self._refoffrows) - print("RegOn", self._refonrows) + logger.debug("---------------------------------------------------") + logger.debug("FSSCAN: ") + logger.debug(f"SigOff {self._sigoffrows}") + logger.debug(f"SigOn {self._sigonrows}") + logger.debug(f"RefOff {self._refoffrows}") + logger.debug(f"RefOn {self._refonrows}") nsigrows = len(self._sigonrows) + len(self._sigoffrows) nrefrows = len(self._refonrows) + len(self._refoffrows) if nsigrows != nrefrows: raise Exception("Number of sig rows does not match ref rows. Dangerous to proceed") if self._debug: - print("sigonrows", nsigrows, self._sigonrows) + logger.dbeug(f"sigonrows {nsigrows}, {self._sigonrows}") self._nrows = nsigrows a_scanrow = self._sigonrows[0] @@ -1504,27 +1487,27 @@ def __init__( else: self._bintable_index = bintable if self._debug: - print(f"bintable index is {self._bintable_index}") + logger.debug(f"bintable index is {self._bintable_index}") self._observer_location = observer_location self._scanrows = list(set(self._calrows["ON"])) + list(set(self._calrows["OFF"])) df = self._sdfits._index.iloc[self._scanrows] if self._debug: - print("len(df) = ", len(df)) + logger.debug(f"{len(df) = }") self._set_if_fd(df) self._pols = uniq(df["PLNUM"]) if self._debug: - print(f"FSSCAN #pol = {self._pols}") + logger.debug(f"FSSCAN #pol = {self._pols}") self._npol = len(self._pols) if False: self._nint = gbtfits.nintegrations(self._bintable_index) # @todo use gbtfits.velocity_convention(veldef,velframe) # so quick with slicing! - self._sigcalon = gbtfits.rawspectra(self._bintable_index)[self._sigonrows] - self._sigcaloff = gbtfits.rawspectra(self._bintable_index)[self._sigoffrows] - self._refcalon = gbtfits.rawspectra(self._bintable_index)[self._refonrows] - self._refcaloff = gbtfits.rawspectra(self._bintable_index)[self._refoffrows] + self._sigcalon = gbtfits.rawspectra(self._bintable_index, setmask=apply_flags)[self._sigonrows] + self._sigcaloff = gbtfits.rawspectra(self._bintable_index, setmask=apply_flags)[self._sigoffrows] + self._refcalon = gbtfits.rawspectra(self._bintable_index, setmask=apply_flags)[self._refonrows] + self._refcaloff = gbtfits.rawspectra(self._bintable_index, setmask=apply_flags)[self._refoffrows] self._nchan = len(self._sigcalon[0]) self._tsys = None self._exposure = None @@ -1534,7 +1517,7 @@ def __init__( if self._calibrate: self.calibrate(fold=fold, shift_method=shift_method) if self._debug: - print("---------------------------------------------------") + logger.debug("---------------------------------------------------") self._validate_defaults() @property @@ -1562,7 +1545,9 @@ def calibrated(self, i): spectrum : `~spectra.spectrum.Spectrum` """ s = Spectrum.make_spectrum( - self._calibrated[i] * u.K, meta=self.meta[i], observer_location=self._observer_location + Masked(self._calibrated[i] * u.K, self._calibrated[i].mask), + meta=self.meta[i], + observer_location=self._observer_location, ) s.merge_commentary(self) return s @@ -1573,8 +1558,8 @@ def calibrate(self, **kwargs): fold=True or fold=False is required """ if self._debug: - print(f'FOLD={kwargs["fold"]}') - print(f'METHOD={kwargs["shift_method"]}') + logger.debug(f'FOLD={kwargs["fold"]}') + logger.debug(f'METHOD={kwargs["shift_method"]}') # some helper functions, courtesy proto_getfs.py def channel_to_frequency(crval1, crpix1, cdelt1, vframe, nchan, nint, ndim=1): @@ -1663,33 +1648,30 @@ def do_fold(sig, ref, sig_freq, ref_freq, remove_wrap=False, shift_method="fft") _fold = kwargs.get("fold", False) _mode = 1 # 1: keep the sig else: keep the ref (not externally supported) nspect = self.nrows // 2 - self._calibrated = np.empty((nspect, self._nchan), dtype="d") + self._calibrated = np.ma.empty((nspect, self._nchan), dtype="d") self._tsys = np.empty(nspect, dtype="d") self._exposure = np.empty(nspect, dtype="d") # sig_freq = self._sigcalon[0] df_sig = self._sdfits.index(bintable=self._bintable_index).iloc[self._sigonrows] df_ref = self._sdfits.index(bintable=self._bintable_index).iloc[self._refonrows] - if self._debug: - print("df_sig", type(df_sig), len(df_sig)) + logger.debug(f"df_sig {type(df_sig)} len(df_sig)") sig_freq = index_frequency(df_sig) ref_freq = index_frequency(df_ref) chan_shift = abs(sig_freq[0, 0] - ref_freq[0, 0]) / np.abs(np.diff(sig_freq)).mean() - if self._debug: - print("FS: shift=%g nchan=%d" % (chan_shift, self._nchan)) + logger.debug(f"FS: shift={chan_shift:g} nchan={self._nchan:g}") # tcal is the same for REF and SIG, and the same for all integrations actually. - tcal = list(self._sdfits.index(bintable=self._bintable_index).iloc[self._sigonrows]["TCAL"]) - if self._debug: - print("TCAL:", len(tcal), tcal[0]) + tcal = self._sdfits.index(bintable=self._bintable_index).iloc[self._sigonrows]["TCAL"].to_numpy() + logger.debug(f"TCAL: {len(tcal)} {tcal[0]}") if len(tcal) != nspect: raise Exception(f"TCAL length {len(tcal)} and number of spectra {nspect} don't match") # @todo the nspect loop could be replaced with clever numpy? for i in range(nspect): tsys_sig = mean_tsys(calon=self._sigcalon[i], caloff=self._sigcaloff[i], tcal=tcal[i]) tsys_ref = mean_tsys(calon=self._refcalon[i], caloff=self._refcaloff[i], tcal=tcal[i]) - if i == 0 and self._debug: - print("Tsys(sig/ref)[0]=", tsys_sig, tsys_ref) + if i == 0: + logger.debug(f"Tsys(sig/ref)[0]={tsys_sig} / {tsys_ref}") tp_sig = 0.5 * (self._sigcalon[i] + self._sigcaloff[i]) tp_ref = 0.5 * (self._refcalon[i] + self._refcaloff[i]) # @@ -1779,6 +1761,9 @@ def timeaverage(self, weights="tsys"): ------- spectrum : :class:`~spectra.spectrum.Spectrum` The time-averaged spectrum + + .. note:: + Data that are masked will have values set to zero. This is a feature of `numpy.ma.average`. Data mask fill value is NaN (np.nan) """ if self._calibrated is None or len(self._calibrated) == 0: raise Exception("You can't time average before calibration.") @@ -1790,7 +1775,8 @@ def timeaverage(self, weights="tsys"): w = self.tsys_weight else: w = np.ones_like(self.tsys_weight) - self._timeaveraged._data = average(data, axis=0, weights=w) + self._timeaveraged._data = np.ma.average(data, axis=0, weights=w) + self._timeaveraged._data.set_fill_value(np.nan) non_blanks = find_non_blanks(data) self._timeaveraged.meta["MEANTSYS"] = np.mean(self._tsys[non_blanks]) self._timeaveraged.meta["WTTSYS"] = sq_weighted_avg(self._tsys[non_blanks], axis=0, weights=w[non_blanks]) @@ -1811,6 +1797,7 @@ class SubBeamNodScan(ScanBase): Whether or not to calibrate the data. smoothref: int the number of channels in the reference to boxcar smooth prior to calibration + apply_flags : boolean, optional. If True, apply flags before calibration. observer_location : `~astropy.coordinates.EarthLocation` Location of the observatory. See `~dysh.coordinates.Observatory`. This will be transformed to `~astropy.coordinates.ITRS` using the time of @@ -1831,6 +1818,7 @@ def __init__( reftp, calibrate=True, smoothref=1, + apply_flags=False, observer_location=Observatory["GBT"], **kwargs, ): @@ -1859,6 +1847,7 @@ def __init__( self._smoothref = smoothref if self._smoothref > 1: print(f"SubBeamNodScan smoothref={self._smoothref} not implemented yet") + self._apply_flags = apply_flags self._observer_location = observer_location self._calibrated = None if calibrate: @@ -1871,7 +1860,7 @@ def calibrate(self, **kwargs): self._tsys = np.empty(nspect, dtype=float) self._exposure = np.empty(nspect, dtype=float) self._delta_freq = np.empty(nspect, dtype=float) - self._calibrated = np.empty((nspect, self._nchan), dtype=float) + self._calibrated = np.ma.empty((nspect, self._nchan), dtype=float) for i in range(nspect): sig = self._sigtp[i].timeaverage(weights=kwargs["weights"]) @@ -1897,7 +1886,11 @@ def calibrated(self, i): rfq = restfrq * u.Unit(meta["CUNIT1"]) restfreq = rfq.to("Hz").value meta["RESTFRQ"] = restfreq # WCS wants no E - s = Spectrum.make_spectrum(self._calibrated[i] * u.K, meta=meta, observer_location=self._observer_location) + s = Spectrum.make_spectrum( + Masked(self._calibrated[i] * u.K, self._calibrated[i].mask), + meta=meta, + observer_location=self._observer_location, + ) s.merge_commentary(self) return s @@ -1910,18 +1903,36 @@ def delta_freq(self): return self._delta_freq def timeaverage(self, weights="tsys"): + r"""Compute the time-averaged spectrum for this scan. + + Parameters + ---------- + weights: str + 'tsys' or None. If 'tsys' the weight will be calculated as: + + :math:`w = t_{exp} \times \delta\nu/T_{sys}^2` + + Default: 'tsys' + Returns + ------- + spectrum : :class:`~spectra.spectrum.Spectrum` + The time-averaged spectrum + + .. note:: + Data that are masked will have values set to zero. This is a feature of `numpy.ma.average`. Data mask fill value is NaN (np.nan) + """ if self._calibrated is None or len(self._calibrated) == 0: raise Exception("You can't time average before calibration.") if self._npol > 1: raise Exception(f"Can't yet time average multiple polarizations {self._npol}") self._timeaveraged = deepcopy(self.calibrated(0)) data = self._calibrated - nchan = len(data[0]) if weights == "tsys": w = self.tsys_weight else: w = None - self._timeaveraged._data = average(data, axis=0, weights=w) + self._timeaveraged._data = np.ma.average(data, axis=0, weights=w) + self._timeaveraged._data.set_fill_value(np.nan) self._timeaveraged.meta["MEANTSYS"] = np.mean(self._tsys) self._timeaveraged.meta["WTTSYS"] = sq_weighted_avg(self._tsys, axis=0, weights=w) self._timeaveraged.meta["TSYS"] = self._timeaveraged.meta["WTTSYS"] diff --git a/src/dysh/spectra/spectrum.py b/src/dysh/spectra/spectrum.py index cf875aaf..5dfe3249 100644 --- a/src/dysh/spectra/spectrum.py +++ b/src/dysh/spectra/spectrum.py @@ -17,6 +17,7 @@ # from astropy.nddata.ccddata import fits_ccddata_writer from astropy.table import Table from astropy.time import Time +from astropy.utils.masked import Masked from astropy.wcs import WCS, FITSFixedWarning from ndcube import NDCube from specutils import Spectrum1D @@ -34,7 +35,7 @@ sanitize_skycoord, veldef_to_convention, ) -from ..log import HistoricalBase, log_call_to_history +from ..log import HistoricalBase, log_call_to_history, logger from ..plot import specplot as sp from ..util import minimum_string_match from . import baseline, get_spectral_equivalency @@ -67,11 +68,9 @@ class Spectrum(Spectrum1D, HistoricalBase): @log_call_to_history def __init__(self, *args, **kwargs): - # print(f"ARGS={args}") HistoricalBase.__init__(self) self._target = kwargs.pop("target", None) if self._target is not None: - # print(f"self._target is {self._target}") self._target = sanitize_skycoord(self._target) self._velocity_frame = self._target.frame.name else: @@ -163,11 +162,9 @@ def _toggle_sections(self, nchan, s): s1 = [] e = 0 # set this to 1 if you want to be exact complementary if s[0][0] == 0: - # print("toggle_sections: edged") for i in range(ns - 1): s1.append((s[i][1] + e, s[i + 1][0] - e)) else: - # print("toggle_sections: internal") s1.append((0, s[0][0])) for i in range(ns - 1): s1.append((s[i][1], s[i + 1][0])) @@ -489,7 +486,7 @@ def smooth(self, method="hanning", width=1, decimate=0, kernel=None): # All checks for smoothing should be completed by this point. # Create a new metadata dictionary to modify by smooth. new_meta = deepcopy(self.meta) - + md = np.ma.masked_array(self._data, self.mask) if this_method == "gaussian": if width <= self._resolution: raise ValueError( @@ -497,11 +494,17 @@ def smooth(self, method="hanning", width=1, decimate=0, kernel=None): ) kwidth = np.sqrt(width**2 - self._resolution**2) # Kernel effective width. stddev = kwidth / 2.35482 - s1 = core.smooth(self._data, this_method, stddev) + + s1 = core.smooth(md, this_method, stddev) else: kwidth = width - s1 = core.smooth(self._data, this_method, width) - + s1 = core.smooth(md, this_method, width) + # mask = np.full(s1.shape, False) + # in core.smooth, we fill masked values with np.nan. + # astropy.convolve does not return a new mask, so we recreate + # a decimated mask where values are nan + # mask[np.where(s1 == np.nan)] = True + # new_data = Masked(s1 * self.flux.unit, mask) new_data = s1 * self.flux.unit new_meta["FREQRES"] = np.sqrt((kwidth * self.meta["CDELT1"]) ** 2 + self.meta["FREQRES"] ** 2) @@ -744,7 +747,6 @@ def set_frame(self, toframe): actualframe = self.observer else: actualframe = astropy_frame_dict.get(toframe, toframe) - # print(f"actual frame is {actualframe} {type(actualframe)}") self._spectral_axis = self._spectral_axis.with_observer_stationary_relative_to(actualframe) self._meta["CTYPE1"] = change_ctype(self._meta["CTYPE1"], toframe) if isinstance(actualframe, str): @@ -1056,7 +1058,8 @@ def fake_spectrum(cls, nchan=1024, seed=None, **kwargs): @classmethod def make_spectrum(cls, data, meta, use_wcs=True, observer_location=None): # , shift_topo=False): - """Factory method to create a Spectrum object from a data and header. + """Factory method to create a Spectrum object from a data and header. The the data are masked, + the Spectrum mask will be set to the data mask. Parameters ---------- @@ -1122,7 +1125,6 @@ def make_spectrum(cls, data, meta, use_wcs=True, observer_location=None): savecomment = meta.pop("COMMENT", None) if savecomment is None: savecomment = meta.pop("comments", None) - # print(f"{meta=}") wcs = WCS(header=meta) if savehist is not None: meta["HISTORY"] = savehist @@ -1169,18 +1171,29 @@ def make_spectrum(cls, data, meta, use_wcs=True, observer_location=None): ) obsitrs = None - s = cls( - flux=data, - wcs=wcs, - meta=meta, - velocity_convention=vc, - radial_velocity=target.radial_velocity, - rest_value=meta["RESTFRQ"] * u.Hz, - observer=obsitrs, - target=target, - ) - # s._history = [] - # s._comments = [] + if np.ma.is_masked(data): + s = cls( + flux=data, + wcs=wcs, + meta=meta, + velocity_convention=vc, + radial_velocity=target.radial_velocity, + rest_value=meta["RESTFRQ"] * u.Hz, + observer=obsitrs, + target=target, + mask=data.mask, + ) + else: + s = cls( + flux=data, + wcs=wcs, + meta=meta, + velocity_convention=vc, + radial_velocity=target.radial_velocity, + rest_value=meta["RESTFRQ"] * u.Hz, + observer=obsitrs, + target=target, + ) # For some reason, Spectrum1D.spectral_axis created with WCS do not inherit # the radial velocity. In fact, they get no radial_velocity attribute at all! # This method creates a new spectral_axis with the given radial velocity. @@ -1261,7 +1274,6 @@ def __truediv__(self, other): return result def _add_meta(self, operand, operand2, **kwargs): - # print(kwargs) kwargs.setdefault("other_meta", True) meta = deepcopy(operand) if kwargs["other_meta"]: @@ -1356,7 +1368,9 @@ def wav2idx(wav, wcs, spectral_axis, coo, sto): # New Spectrum. return self.make_spectrum( - self.flux[start_idx:stop_idx], meta=meta, observer_location=Observatory[meta["TELESCOP"]] + Masked(self.flux[start_idx:stop_idx], self.mask[start_idx:stop_idx]), + meta=meta, + observer_location=Observatory[meta["TELESCOP"]], ) @@ -1514,8 +1528,8 @@ def spectrum_reader_gbtidl(fileobj, **kwargs): # registry.register_writer("mrt", Spectrum, spectrum_reader_mrt) -def average_spectra(spectra, equal_weights=False, align=False): - """ +def average_spectra(spectra, weights="tsys", align=False): + r""" Average `spectra`. The resulting `average` will have an exposure equal to the sum of the exposures, and coordinates and system temperature equal to the weighted average of the coordinates and system temperatures. @@ -1524,9 +1538,12 @@ def average_spectra(spectra, equal_weights=False, align=False): spectra : list of `Spectrum` Spectra to be averaged. They must have the same number of channels. No checks are done to ensure they are aligned. - equal_weights : bool - If `False` use the inverse of the variance, as computed from the radiometer equation, as weights. - If `True` all spectra have the same weight. + weights: str + 'tsys' or None. If 'tsys' the weight will be calculated as: + + :math:`w = t_{exp} \times \delta\nu/T_{sys}^2` + + Default: 'tsys' align : bool If `True` align the `spectra` to the first element. This uses `Spectrum.align_to`. @@ -1540,18 +1557,18 @@ def average_spectra(spectra, equal_weights=False, align=False): nspec = len(spectra) nchan = len(spectra[0].data) shape = (nspec, nchan) - data_array = np.empty(shape, dtype=float) - weights = np.empty(shape, dtype=float) + data_array = np.ma.empty(shape, dtype=float) + wts = np.empty(shape, dtype=float) exposures = np.empty(nspec, dtype=float) tsyss = np.empty(nspec, dtype=float) xcoos = np.empty(nspec, dtype=float) ycoos = np.empty(nspec, dtype=float) - + obs_location = spectra[0]._observer_location units = spectra[0].flux.unit for i, s in enumerate(spectra): if not isinstance(s, Spectrum): - raise ValueError(f"Element {i} of `spectra` is not a `Spectrum`.") + raise ValueError(f"Element {i} of `spectra` is not a `Spectrum`. {type(s)}") if units != s.flux.unit: raise ValueError( f"Element {i} of `spectra` has units {s.flux.unit}, but the first element has units {units}." @@ -1560,20 +1577,22 @@ def average_spectra(spectra, equal_weights=False, align=False): if i > 0: s = s.align_to(spectra[0]) data_array[i] = s.data - if not equal_weights: - weights[i] = core.tsys_weight(s.meta["EXPOSURE"], s.meta["CDELT1"], s.meta["TSYS"]) + data_array[i].mask = s.mask + + if weights == "tsys": + wts[i] = core.tsys_weight(s.meta["EXPOSURE"], s.meta["CDELT1"], s.meta["TSYS"]) else: - weights[i] = 1.0 + wts[i] = 1.0 exposures[i] = s.meta["EXPOSURE"] tsyss[i] = s.meta["TSYS"] xcoos[i] = s.meta["CRVAL2"] ycoos[i] = s.meta["CRVAL3"] - data_array = np.ma.MaskedArray(data_array, mask=np.isnan(data_array)) - data = np.ma.average(data_array, axis=0, weights=weights) - tsys = np.ma.average(tsyss, axis=0, weights=weights[:, 0]) - xcoo = np.ma.average(xcoos, axis=0, weights=weights[:, 0]) - ycoo = np.ma.average(ycoos, axis=0, weights=weights[:, 0]) + data_array = np.ma.MaskedArray(data_array, mask=np.isnan(data_array) | data_array.mask, fill_value=np.nan) + data = np.ma.average(data_array, axis=0, weights=wts) + tsys = np.ma.average(tsyss, axis=0, weights=wts[:, 0]) + xcoo = np.ma.average(xcoos, axis=0, weights=wts[:, 0]) + ycoo = np.ma.average(ycoos, axis=0, weights=wts[:, 0]) exposure = exposures.sum(axis=0) new_meta = deepcopy(spectra[0].meta) @@ -1582,6 +1601,6 @@ def average_spectra(spectra, equal_weights=False, align=False): new_meta["CRVAL2"] = xcoo new_meta["CRVAL3"] = ycoo - averaged = Spectrum.make_spectrum(data * units, meta=new_meta) + averaged = Spectrum.make_spectrum(Masked(data * units, data.mask), meta=new_meta, observer_location=obs_location) return averaged diff --git a/src/dysh/spectra/tests/test_scan.py b/src/dysh/spectra/tests/test_scan.py index 8afa0433..d5ea4972 100644 --- a/src/dysh/spectra/tests/test_scan.py +++ b/src/dysh/spectra/tests/test_scan.py @@ -61,10 +61,12 @@ def test_compare_with_GBTIDL_2(self, data_dir): data_path = f"{data_dir}/TGBT21A_501_11/NGC2782" sdf_file = f"{data_path}/TGBT21A_501_11_NGC2782.raw.vegas.A.fits" + print(f"{sdf_file=}") gbtidl_file = f"{data_path}/TGBT21A_501_11_getps_scans_156-158_ifnum_0_plnum_0_timeaverage.fits" sdf = gbtfitsload.GBTFITSLoad(sdf_file) ps_scans = sdf.getps(scan=[156, 158], ifnum=0, plnum=0) + print(np.shape(ps_scans[0]._calibrated), np.shape(ps_scans[1]._calibrated)) ta = ps_scans.timeaverage() hdu = fits.open(gbtidl_file) diff --git a/src/dysh/spectra/tests/test_spectrum.py b/src/dysh/spectra/tests/test_spectrum.py index 3a5f2fb2..b1081b36 100644 --- a/src/dysh/spectra/tests/test_spectrum.py +++ b/src/dysh/spectra/tests/test_spectrum.py @@ -50,10 +50,10 @@ def setup_method(self): data_dir = get_project_testdata() / "AGBT05B_047_01" sdf_file = data_dir / "AGBT05B_047_01.raw.acs" sdf = GBTFITSLoad(sdf_file) - getps0 = sdf.getps(scan=51, plnum=0) - self.ps0 = getps0.timeaverage() - getps1 = sdf.getps(scan=51, plnum=1) - self.ps1 = getps1.timeaverage() + self.getps0 = sdf.getps(scan=51, plnum=0) + self.ps0 = self.getps0.timeaverage() + self.getps1 = sdf.getps(scan=51, plnum=1) + self.ps1 = self.getps1.timeaverage() self.ss = self.ps0._copy() # Synthetic one. x = np.arange(0, len(self.ss.data)) fwhm = 5 @@ -254,7 +254,6 @@ def test_slice(self, mock_show, tmp_path): meta_ignore = ["CRPIX1", "CRVAL1"] spec_pars = ["_target", "_velocity_frame", "_observer", "_obstime", "_observer_location"] s = slice(1000, 1100, 1) - trimmed = self.ps0[s] assert trimmed.flux[0] == self.ps0.flux[s.start] assert trimmed.flux[-1] == self.ps0.flux[s.stop - 1] diff --git a/src/dysh/util/core.py b/src/dysh/util/core.py index 25b9825c..9413e98f 100644 --- a/src/dysh/util/core.py +++ b/src/dysh/util/core.py @@ -14,6 +14,8 @@ # import pandas as pd from astropy.time import Time +ALL_CHANNELS = "all channels" + def select_from(key, value, df): """ @@ -325,3 +327,51 @@ def ensure_ascii(text: Union[str, list[str]], check: bool = False) -> Union[str, for c in text: clean_text.append(_ensure_ascii_str(c)) return clean_text + + +def convert_array_to_mask(a, length, value=True): + """ + This method interprets a simple or compound array and returns a numpy mask + of length `length`. Single arrays/tuples will be treated as element index lists; + nested arrays will be treated as *inclusive* ranges, for instance: + + `` + # mask elements 1 and 10 + convert_array_to_mask([1,10]) + # mask elements 1 thru 10 inclusive + convert_array_to_mask([[1,10]]) + # mask ranges 1 thru 10 and 47 thru 56 inclusive, and element 75 + convert_array_to_mask([[1,10], [47,56], 75)]) + # tuples also work, though can be harder for a human to read + convert_array_to_mask(((1,10), [47,56], 75)) + `` + + Parameters + ---------- + a : number or array-like + The + length : int + The length of the mask to return, e.g. the number of channels in a spectrum. + + value : bool + The value to fill the mask with. True to mask data, False to unmask. + + Returns + ------- + mask : ~np.ndarray + A numpy array where the mask is True according to the rules above. + + """ + + if a == ALL_CHANNELS: + return np.full(length, value) + + mask = np.full(length, False) + + for v in a: + if isinstance(v, (tuple, list, np.ndarray)) and len(v) == 2: + # If there are just two numbers, interpret is as an inclusive range + mask[v[0] : v[1] + 1] = value + else: + mask[v] = value + return mask diff --git a/src/dysh/util/selection.py b/src/dysh/util/selection.py index f4de01d0..a7829c0b 100644 --- a/src/dysh/util/selection.py +++ b/src/dysh/util/selection.py @@ -15,7 +15,7 @@ from ..log import logger # from ..fits import default_sdfits_columns -from . import gbt_timestamp_to_time, generate_tag, keycase +from . import ALL_CHANNELS, gbt_timestamp_to_time, generate_tag, keycase default_aliases = { "freq": "crval1", @@ -631,14 +631,16 @@ def _base_select_within(self, tag=None, **kwargs): kw[k] = (v1, v2) self._base_select_range(tag, **kw) - def _base_select_channel(self, chan, tag=None): + def _base_select_channel(self, channel, tag=None): """ Select channels and/or channel ranges. These are NOT used in :meth:`final` but rather will be used to create a mask for calibration or flagging. Single arrays/tuples will be treated as channel lists; - nested arrays will be treated as ranges, for instance + nested arrays will be treated as *inclusive* ranges. For instance: `` + # select channel 24 + select_channel(24) # selects channels 1 and 10 select_channel([1,10]) # selects channels 1 thru 10 inclusive @@ -649,9 +651,11 @@ def _base_select_channel(self, chan, tag=None): select_channel(((1,10), [47,56], 75)) `` + *Note* : channel numbers start at zero. + Parameters ---------- - chan : number, or array-like + channel : number, or array-like The channels to select Returns @@ -667,9 +671,11 @@ def _base_select_channel(self, chan, tag=None): raise Exception( "You can only have one channel selection rule. Remove the old rule before creating a new one." ) - self._check_numbers(chan=chan) - self._channel_selection = chan - self._addrow({"CHAN": str(chan)}, dataframe=self, tag=tag) + self._check_numbers(chan=channel) + if isinstance(channel, numbers.Number): + channel = [int(channel)] + self._channel_selection = channel + self._addrow({"CHAN": str(channel)}, dataframe=self, tag=tag) # NB: using ** in doc here because `id` will make a reference to the # python built-in function. Arguably we should pick a different @@ -829,6 +835,21 @@ def __deepcopy__(self, memo): warnings.resetwarnings() return result + def get(self, key): + """Get the selection/flag rule by its ID + + Parameters + ---------- + key : int + The ID value. See :meth:`show`. + + Returns + ------- + ~pandas.DataFrame + The selection/flag rule + """ + return self._selection_rules[key] + class Selection(SelectionBase): """This class contains the methods for creating rules to select data from an SDFITS object. @@ -932,9 +953,11 @@ def select_channel(self, chan, tag=None): Select channels and/or channel ranges. These are NOT used in :meth:`final` but rather will be used to create a mask for calibration or flagging. Single arrays/tuples will be treated as channel lists; - nested arrays will be treated as ranges, for instance + nested arrays will be treated as *inclusive* ranges. For instance: `` + # select channel 24 + select_channel(24) # selects channels 1 and 10 select_channel([1,10]) # selects channels 1 thru 10 inclusive @@ -945,6 +968,8 @@ def select_channel(self, chan, tag=None): select_channel(((1,10), [47,56], 75)) `` + *Note* : channel numbers start at zero. + Parameters ---------- chan : number, or array-like @@ -992,7 +1017,9 @@ def flag(self, tag=None, **kwargs): """Add one or more exact flag rules, e.g., `key1 = value1, key2 = value2, ...` If `value` is array-like then a match to any of the array members will be flagged. For instance `flag(object=['3C273', 'NGC1234'])` will select data for either of those - objects and `flag(ifnum=[0,2])` will flag IF number 0 or IF number 2. + objects and `flag(ifnum=[0,2])` will flag IF number 0 or IF number 2. Channels for selected data + can be flagged using keyword `channel`, e.g., `flag(object='MBM12',channel=[0,23])` + will flag channels 0 through 23 *inclusive* for object MBM12. Parameters ---------- @@ -1005,23 +1032,29 @@ def flag(self, tag=None, **kwargs): The value to select """ - chan = kwargs.pop("chan", None) + chan = kwargs.pop("channel", None) if chan is not None: + if isinstance(chan, numbers.Number): + chan = [int(chan)] self._check_numbers(chan=chan) self._base_select(tag, **kwargs) # don't do this unless chan input is good. + idx = len(self._table) - 1 if chan is not None: - idx = len(self._table) - 1 self._table[idx]["CHAN"] = str(chan) self._flag_channel_selection[idx] = chan + else: + self._flag_channel_selection[idx] = ALL_CHANNELS - def flag_channel(self, chan, tag=None, **kwargs): + def flag_channel(self, channel, tag=None, **kwargs): """ - Flag channels and/or channel ranges for all rows. These are NOT used in :meth:`final` + Flag channels and/or channel ranges for *all data*. These are NOT used in :meth:`final` but rather will be used to create a mask for - flagging. Single arrays/tuples will be treated as channel lists; - nested arrays will be treated as ranges, for instance + flagging. Single arrays/tuples will be treated as *channel lists; + nested arrays will be treated as *inclusive* ranges. For instance: `` + # flag channel 24 + flag_channel(24) # flag channels 1 and 10 flag_channel([1,10]) # flags channels 1 thru 10 inclusive @@ -1032,9 +1065,12 @@ def flag_channel(self, chan, tag=None, **kwargs): flag_channel(((1,10), [47,56], 75)) `` - Parameters + *Note* : channel numbers start at zero + + + Parameters ---------- - chan : number, or array-like + channel : number, or array-like The channels to flag Returns @@ -1043,9 +1079,10 @@ def flag_channel(self, chan, tag=None, **kwargs): """ # okay to use base method because we are flagging all rows - self._base_select_channel(chan, tag, **kwargs) + self._base_select_channel(channel, tag, **kwargs) idx = len(self._table) - 1 - self._flag_channel_selection[idx] = chan + self._flag_channel_selection[idx] = channel + self._channel_selection = None # unused for flagging def flag_range(self, tag=None, **kwargs): """Flag a range of inclusive values for a given key(s).