from asap import rcParams, print_log, print_log_dec from asap import selector, scantable import matplotlib.axes from matplotlib.font_manager import FontProperties from matplotlib.text import Text import re class asapplotter: """ The ASAP plotter. By default the plotter is set up to plot polarisations 'colour stacked' and scantables across panels. Note: Currenly it only plots 'spectra' not Tsys or other variables. """ def __init__(self, visible=None , **kwargs): self._visible = rcParams['plotter.gui'] if visible is not None: self._visible = visible self._plotter = self._newplotter(**kwargs) self._panelling = None self._stacking = None self.set_panelling() self.set_stacking() self._rows = None self._cols = None self._autoplot = False self._minmaxx = None self._minmaxy = None self._datamask = None self._data = None self._lmap = None self._title = None self._ordinate = None self._abcissa = None self._abcunit = None self._usermask = [] self._maskselection = None self._selection = selector() self._hist = rcParams['plotter.histogram'] self._fp = FontProperties() def _translate(self, instr): keys = "s b i p t".split() if isinstance(instr, str): for key in keys: if instr.lower().startswith(key): return key return None def _newplotter(self, **kwargs): if self._visible: from asap.asaplotgui import asaplotgui as asaplot else: from asap.asaplot import asaplot return asaplot(**kwargs) @print_log_dec def plot(self, scan=None): """ Plot a scantable. Parameters: scan: a scantable Note: If a scantable was specified in a previous call to plot, no argument has to be given to 'replot' NO checking is done that the abcissas of the scantable are consistent e.g. all 'channel' or all 'velocity' etc. """ if self._plotter.is_dead: self._plotter = self._newplotter() self._plotter.hold() self._plotter.clear() from asap import scantable if not self._data and not scan: msg = "Input is not a scantable" if rcParams['verbose']: print msg return raise TypeError(msg) if isinstance(scan, scantable): if self._data is not None: if scan != self._data: self._data = scan # reset self._reset() else: self._data = scan self._reset() # ranges become invalid when unit changes if self._abcunit and self._abcunit != self._data.get_unit(): self._minmaxx = None self._minmaxy = None self._abcunit = self._data.get_unit() self._datamask = None self._plot(self._data) if self._minmaxy is not None: self._plotter.set_limits(ylim=self._minmaxy) self._plotter.release() self._plotter.tidy() self._plotter.show(hardrefresh=False) return def gca(self): return self._plotter.figure.gca() def refresh(self): """Do a soft refresh""" self._plotter.figure.show() def create_mask(self, nwin=1, panel=0, color=None): """ Interactively define a mask.It retruns a mask that is equivalent to the one created manually with scantable.create_mask. Parameters: nwin: The number of mask windows to create interactively default is 1. panel: Which panel to use for mask selection. This is useful if different IFs are spread over panels (default 0) """ if self._data is None: return [] outmask = [] self._plotter.subplot(panel) xmin, xmax = self._plotter.axes.get_xlim() marg = 0.05*(xmax-xmin) self._plotter.axes.set_xlim(xmin-marg, xmax+marg) self.refresh() def cleanup(lines=False, texts=False, refresh=False): if lines: del self._plotter.axes.lines[-1] if texts: del self._plotter.axes.texts[-1] if refresh: self.refresh() for w in xrange(nwin): wpos = [] self.text(0.05,1.0, "Add start boundary", coords="relative", fontsize=10) point = self._plotter.get_point() cleanup(texts=True) if point is None: continue wpos.append(point[0]) self.axvline(wpos[0], color=color) self.text(0.05,1.0, "Add end boundary", coords="relative", fontsize=10) point = self._plotter.get_point() cleanup(texts=True, lines=True) if point is None: self.refresh() continue wpos.append(point[0]) self.axvspan(wpos[0], wpos[1], alpha=0.1, edgecolor=color, facecolor=color) ymin, ymax = self._plotter.axes.get_ylim() outmask.append(wpos) self._plotter.axes.set_xlim(xmin, xmax) self.refresh() if len(outmask) > 0: return self._data.create_mask(*outmask) return [] # forwards to matplotlib axes def text(self, *args, **kwargs): if kwargs.has_key("interactive"): if kwargs.pop("interactive"): pos = self._plotter.get_point() args = tuple(pos)+args self._axes_callback("text", *args, **kwargs) text.__doc__ = matplotlib.axes.Axes.text.__doc__ def arrow(self, *args, **kwargs): if kwargs.has_key("interactive"): if kwargs.pop("interactive"): pos = self._plotter.get_region() dpos = (pos[0][0], pos[0][1], pos[1][0]-pos[0][0], pos[1][1] - pos[0][1]) args = dpos + args self._axes_callback("arrow", *args, **kwargs) arrow.__doc__ = matplotlib.axes.Axes.arrow.__doc__ def annotate(self, text, xy=None, xytext=None, **kwargs): if kwargs.has_key("interactive"): if kwargs.pop("interactive"): xy = self._plotter.get_point() xytext = self._plotter.get_point() if not kwargs.has_key("arrowprops"): kwargs["arrowprops"] = dict(arrowstyle="->") self._axes_callback("annotate", text, xy, xytext, **kwargs) annotate.__doc__ = matplotlib.axes.Axes.annotate.__doc__ def axvline(self, *args, **kwargs): if kwargs.has_key("interactive"): if kwargs.pop("interactive"): pos = self._plotter.get_point() args = (pos[0],)+args self._axes_callback("axvline", *args, **kwargs) axvline.__doc__ = matplotlib.axes.Axes.axvline.__doc__ def axhline(self, *args, **kwargs): if kwargs.has_key("interactive"): if kwargs.pop("interactive"): pos = self._plotter.get_point() args = (pos[1],)+args self._axes_callback("axhline", *args, **kwargs) axhline.__doc__ = matplotlib.axes.Axes.axhline.__doc__ def axvspan(self, *args, **kwargs): if kwargs.has_key("interactive"): if kwargs.pop("interactive"): pos = self._plotter.get_region() dpos = (pos[0][0], pos[1][0]) args = dpos + args self._axes_callback("axvspan", *args, **kwargs) # hack to preventy mpl from redrawing the patch # it seem to convert the patch into lines on every draw. # This doesn't happen in a test script??? #del self._plotter.axes.patches[-1] axvspan.__doc__ = matplotlib.axes.Axes.axvspan.__doc__ def axhspan(self, *args, **kwargs): if kwargs.has_key("interactive"): if kwargs.pop("interactive"): pos = self._plotter.get_region() dpos = (pos[0][1], pos[1][1]) args = dpos + args self._axes_callback("axhspan", *args, **kwargs) # hack to preventy mpl from redrawing the patch # it seem to convert the patch into lines on every draw. # This doesn't happen in a test script??? #del self._plotter.axes.patches[-1] axhspan.__doc__ = matplotlib.axes.Axes.axhspan.__doc__ def _axes_callback(self, axesfunc, *args, **kwargs): panel = 0 if kwargs.has_key("panel"): panel = kwargs.pop("panel") coords = None if kwargs.has_key("coords"): coords = kwargs.pop("coords") if coords.lower() == 'world': kwargs["transform"] = self._plotter.axes.transData elif coords.lower() == 'relative': kwargs["transform"] = self._plotter.axes.transAxes self._plotter.subplot(panel) self._plotter.axes.set_autoscale_on(False) getattr(self._plotter.axes, axesfunc)(*args, **kwargs) self._plotter.show(False) self._plotter.axes.set_autoscale_on(True) # end matplotlib.axes fowarding functions def set_mode(self, stacking=None, panelling=None): """ Set the plots look and feel, i.e. what you want to see on the plot. Parameters: stacking: tell the plotter which variable to plot as line colour overlays (default 'pol') panelling: tell the plotter which variable to plot across multiple panels (default 'scan' Note: Valid modes are: 'beam' 'Beam' 'b': Beams 'if' 'IF' 'i': IFs 'pol' 'Pol' 'p': Polarisations 'scan' 'Scan' 's': Scans 'time' 'Time' 't': Times """ msg = "Invalid mode" if not self.set_panelling(panelling) or \ not self.set_stacking(stacking): if rcParams['verbose']: print msg return else: raise TypeError(msg) if self._data: self.plot(self._data) return def set_panelling(self, what=None): mode = what if mode is None: mode = rcParams['plotter.panelling'] md = self._translate(mode) if md: self._panelling = md self._title = None return True return False def set_layout(self,rows=None,cols=None): """ Set the multi-panel layout, i.e. how many rows and columns plots are visible. Parameters: rows: The number of rows of plots cols: The number of columns of plots Note: If no argument is given, the potter reverts to its auto-plot behaviour. """ self._rows = rows self._cols = cols if self._data: self.plot(self._data) return def set_stacking(self, what=None): mode = what if mode is None: mode = rcParams['plotter.stacking'] md = self._translate(mode) if md: self._stacking = md self._lmap = None return True return False def set_range(self,xstart=None,xend=None,ystart=None,yend=None): """ Set the range of interest on the abcissa of the plot Parameters: [x,y]start,[x,y]end: The start and end points of the 'zoom' window Note: These become non-sensical when the unit changes. use plotter.set_range() without parameters to reset """ if xstart is None and xend is None: self._minmaxx = None else: self._minmaxx = [xstart,xend] if ystart is None and yend is None: self._minmaxy = None else: self._minmaxy = [ystart,yend] if self._data: self.plot(self._data) return def set_legend(self, mp=None, fontsize = None, mode = 0): """ Specify a mapping for the legend instead of using the default indices: Parameters: mp: a list of 'strings'. This should have the same length as the number of elements on the legend and then maps to the indeces in order. It is possible to uses latex math expression. These have to be enclosed in r'', e.g. r'$x^{2}$' fontsize: The font size of the label (default None) mode: where to display the legend Any other value for loc else disables the legend: 0: auto 1: upper right 2: upper left 3: lower left 4: lower right 5: right 6: center left 7: center right 8: lower center 9: upper center 10: center Example: If the data has two IFs/rest frequencies with index 0 and 1 for CO and SiO: plotter.set_stacking('i') plotter.set_legend(['CO','SiO']) plotter.plot() plotter.set_legend([r'$^{12}CO$', r'SiO']) """ self._lmap = mp self._plotter.legend(mode) if isinstance(fontsize, int): from matplotlib import rc as rcp rcp('legend', fontsize=fontsize) if self._data: self.plot(self._data) return def set_title(self, title=None, fontsize=None): """ Set the title of the plot. If multiple panels are plotted, multiple titles have to be specified. Example: # two panels are visible on the plotter plotter.set_title(["First Panel","Second Panel"]) """ self._title = title if isinstance(fontsize, int): from matplotlib import rc as rcp rcp('axes', titlesize=fontsize) if self._data: self.plot(self._data) return def set_ordinate(self, ordinate=None, fontsize=None): """ Set the y-axis label of the plot. If multiple panels are plotted, multiple labels have to be specified. Parameters: ordinate: a list of ordinate labels. None (default) let data determine the labels Example: # two panels are visible on the plotter plotter.set_ordinate(["First Y-Axis","Second Y-Axis"]) """ self._ordinate = ordinate if isinstance(fontsize, int): from matplotlib import rc as rcp rcp('axes', labelsize=fontsize) rcp('ytick', labelsize=fontsize) if self._data: self.plot(self._data) return def set_abcissa(self, abcissa=None, fontsize=None): """ Set the x-axis label of the plot. If multiple panels are plotted, multiple labels have to be specified. Parameters: abcissa: a list of abcissa labels. None (default) let data determine the labels Example: # two panels are visible on the plotter plotter.set_ordinate(["First X-Axis","Second X-Axis"]) """ self._abcissa = abcissa if isinstance(fontsize, int): from matplotlib import rc as rcp rcp('axes', labelsize=fontsize) rcp('xtick', labelsize=fontsize) if self._data: self.plot(self._data) return def set_colors(self, colmap): """ Set the colours to be used. The plotter will cycle through these colours when lines are overlaid (stacking mode). Parameters: colmap: a list of colour names Example: plotter.set_colors("red green blue") # If for example four lines are overlaid e.g I Q U V # 'I' will be 'red', 'Q' will be 'green', U will be 'blue' # and 'V' will be 'red' again. """ if isinstance(colmap,str): colmap = colmap.split() self._plotter.palette(0, colormap=colmap) if self._data: self.plot(self._data) # alias for english speakers set_colours = set_colors def set_histogram(self, hist=True, linewidth=None): """ Enable/Disable histogram-like plotting. Parameters: hist: True (default) or False. The fisrt default is taken from the .asaprc setting plotter.histogram """ self._hist = hist if isinstance(linewidth, float) or isinstance(linewidth, int): from matplotlib import rc as rcp rcp('lines', linewidth=linewidth) if self._data: self.plot(self._data) def set_linestyles(self, linestyles=None, linewidth=None): """ Set the linestyles to be used. The plotter will cycle through these linestyles when lines are overlaid (stacking mode) AND only one color has been set. Parameters: linestyles: a list of linestyles to use. 'line', 'dashed', 'dotted', 'dashdot', 'dashdotdot' and 'dashdashdot' are possible Example: plotter.set_colors("black") plotter.set_linestyles("line dashed dotted dashdot") # If for example four lines are overlaid e.g I Q U V # 'I' will be 'solid', 'Q' will be 'dashed', # U will be 'dotted' and 'V' will be 'dashdot'. """ if isinstance(linestyles,str): linestyles = linestyles.split() self._plotter.palette(color=0,linestyle=0,linestyles=linestyles) if isinstance(linewidth, float) or isinstance(linewidth, int): from matplotlib import rc as rcp rcp('lines', linewidth=linewidth) if self._data: self.plot(self._data) def set_font(self, **kwargs): """ Set font properties. Parameters: family: one of 'sans-serif', 'serif', 'cursive', 'fantasy', 'monospace' style: one of 'normal' (or 'roman'), 'italic' or 'oblique' weight: one of 'normal or 'bold' size: the 'general' font size, individual elements can be adjusted seperately """ from matplotlib import rc as rcp fdict = {} for k,v in kwargs.iteritems(): if v: fdict[k] = v self._fp = FontProperties(**fdict) if self._data: self.plot() def plot_lines(self, linecat=None, doppler=0.0, deltachan=10, rotate=90.0, location=None): """ Plot a line catalog. Parameters: linecat: the linecatalog to plot doppler: the velocity shift to apply to the frequencies deltachan: the number of channels to include each side of the line to determine a local maximum/minimum rotate: the rotation (in degrees) )for the text label (default 90.0) location: the location of the line annotation from the 'top', 'bottom' or alternate (None - the default) Notes: If the spectrum is flagged no line will be drawn in that location. """ if not self._data: raise RuntimeError("No scantable has been plotted yet.") from asap._asap import linecatalog if not isinstance(linecat, linecatalog): raise ValueError("'linecat' isn't of type linecatalog.") if not self._data.get_unit().endswith("Hz"): raise RuntimeError("Can only overlay linecatalogs when data is in frequency.") from numpy import ma for j in range(len(self._plotter.subplots)): self._plotter.subplot(j) lims = self._plotter.axes.get_xlim() for row in range(linecat.nrow()): # get_frequency returns MHz base = { "GHz": 1000.0, "MHz": 1.0, "Hz": 1.0e-6 } restf = linecat.get_frequency(row)/base[self._data.get_unit()] c = 299792.458 freq = restf*(1.0-doppler/c) if lims[0] < freq < lims[1]: if location is None: loc = 'bottom' if row%2: loc='top' else: loc = location maxys = [] for line in self._plotter.axes.lines: v = line._x asc = v[0] < v[-1] idx = None if not asc: if v[len(v)-1] <= freq <= v[0]: i = len(v)-1 while i>=0 and v[i] < freq: idx = i i-=1 else: if v[0] <= freq <= v[len(v)-1]: i = 0 while i len(v): upper = len(v) s = slice(lower, upper) y = line._y[s] maxy = ma.maximum(y) if isinstance( maxy, float): maxys.append(maxy) if len(maxys): peak = max(maxys) if peak > self._plotter.axes.get_ylim()[1]: loc = 'bottom' else: continue self._plotter.vline_with_label(freq, peak, linecat.get_name(row), location=loc, rotate=rotate) self._plotter.show(hardrefresh=False) def save(self, filename=None, orientation=None, dpi=None): """ Save the plot to a file. The know formats are 'png', 'ps', 'eps'. Parameters: filename: The name of the output file. This is optional and autodetects the image format from the file suffix. If non filename is specified a file called 'yyyymmdd_hhmmss.png' is created in the current directory. orientation: optional parameter for postscript only (not eps). 'landscape', 'portrait' or None (default) are valid. If None is choosen for 'ps' output, the plot is automatically oriented to fill the page. dpi: The dpi of the output non-ps plot """ self._plotter.save(filename,orientation,dpi) return def set_mask(self, mask=None, selection=None): """ Set a plotting mask for a specific polarization. This is useful for masking out "noise" Pangle outside a source. Parameters: mask: a mask from scantable.create_mask selection: the spectra to apply the mask to. Example: select = selector() select.setpolstrings("Pangle") plotter.set_mask(mymask, select) """ if not self._data: msg = "Can only set mask after a first call to plot()" if rcParams['verbose']: print msg return else: raise RuntimeError(msg) if len(mask): if isinstance(mask, list) or isinstance(mask, tuple): self._usermask = array(mask) else: self._usermask = mask if mask is None and selection is None: self._usermask = [] self._maskselection = None if isinstance(selection, selector): self._maskselection = {'b': selection.get_beams(), 's': selection.get_scans(), 'i': selection.get_ifs(), 'p': selection.get_pols(), 't': [] } else: self._maskselection = None self.plot(self._data) def _slice_indeces(self, data): mn = self._minmaxx[0] mx = self._minmaxx[1] asc = data[0] < data[-1] start=0 end = len(data)-1 inc = 1 if not asc: start = len(data)-1 end = 0 inc = -1 # find min index while start > 0 and data[start] < mn: start+= inc # find max index while end > 0 and data[end] > mx: end-=inc if end > 0: end +=1 if start > end: return end,start return start,end def _reset(self): self._usermask = [] self._usermaskspectra = None self.set_selection(None, False) def _plot(self, scan): savesel = scan.get_selection() sel = savesel + self._selection d0 = {'s': 'SCANNO', 'b': 'BEAMNO', 'i':'IFNO', 'p': 'POLNO', 'c': 'CYCLENO', 't' : 'TIME' } order = [d0[self._panelling],d0[self._stacking]] sel.set_order(order) scan.set_selection(sel) d = {'b': scan.getbeam, 's': scan.getscan, 'i': scan.getif, 'p': scan.getpol, 't': scan._gettime } polmodes = dict(zip(self._selection.get_pols(), self._selection.get_poltypes())) # this returns either a tuple of numbers or a length (ncycles) # convert this into lengths n0,nstack0 = self._get_selected_n(scan) if isinstance(n0, int): n = n0 else: n = len(n0) if isinstance(nstack0, int): nstack = nstack0 else: nstack = len(nstack0) maxpanel, maxstack = 16,16 if n > maxpanel or nstack > maxstack: from asap import asaplog maxn = 0 if nstack > maxstack: maxn = maxstack if n > maxpanel: maxn = maxpanel msg ="Scan to be plotted contains more than %d selections.\n" \ "Selecting first %d selections..." % (maxn, maxn) asaplog.push(msg) print_log() n = min(n,maxpanel) nstack = min(nstack,maxstack) if n > 1: ganged = rcParams['plotter.ganged'] if self._rows and self._cols: n = min(n,self._rows*self._cols) self._plotter.set_panels(rows=self._rows,cols=self._cols, nplots=n,ganged=ganged) else: self._plotter.set_panels(rows=n,cols=0,nplots=n,ganged=ganged) else: self._plotter.set_panels() r=0 nr = scan.nrow() a0,b0 = -1,-1 allxlim = [] allylim = [] newpanel=True panelcount,stackcount = 0,0 while r < nr: a = d[self._panelling](r) b = d[self._stacking](r) if a > a0 and panelcount < n: if n > 1: self._plotter.subplot(panelcount) self._plotter.palette(0) #title xlab = self._abcissa and self._abcissa[panelcount] \ or scan._getabcissalabel() ylab = self._ordinate and self._ordinate[panelcount] \ or scan._get_ordinate_label() self._plotter.set_axes('xlabel', xlab) self._plotter.set_axes('ylabel', ylab) lbl = self._get_label(scan, r, self._panelling, self._title) if isinstance(lbl, list) or isinstance(lbl, tuple): if 0 <= panelcount < len(lbl): lbl = lbl[panelcount] else: # get default label lbl = self._get_label(scan, r, self._panelling, None) self._plotter.set_axes('title',lbl) newpanel = True stackcount =0 panelcount += 1 if (b > b0 or newpanel) and stackcount < nstack: y = [] if len(polmodes): y = scan._getspectrum(r, polmodes[scan.getpol(r)]) else: y = scan._getspectrum(r) m = scan._getmask(r) from numpy import logical_not, logical_and if self._maskselection and len(self._usermask) == len(m): if d[self._stacking](r) in self._maskselection[self._stacking]: m = logical_and(m, self._usermask) x = scan._getabcissa(r) from numpy import ma, array y = ma.masked_array(y,mask=logical_not(array(m,copy=False))) if self._minmaxx is not None: s,e = self._slice_indeces(x) x = x[s:e] y = y[s:e] if len(x) > 1024 and rcParams['plotter.decimate']: fac = len(x)/1024 x = x[::fac] y = y[::fac] llbl = self._get_label(scan, r, self._stacking, self._lmap) if isinstance(llbl, list) or isinstance(llbl, tuple): if 0 <= stackcount < len(llbl): # use user label llbl = llbl[stackcount] else: # get default label llbl = self._get_label(scan, r, self._stacking, None) self._plotter.set_line(label=llbl) plotit = self._plotter.plot if self._hist: plotit = self._plotter.hist if len(x) > 0: plotit(x,y) xlim= self._minmaxx or [min(x),max(x)] allxlim += xlim ylim= self._minmaxy or [ma.minimum(y),ma.maximum(y)] allylim += ylim stackcount += 1 # last in colour stack -> autoscale x if stackcount == nstack: allxlim.sort() self._plotter.axes.set_xlim([allxlim[0],allxlim[-1]]) # clear allxlim =[] newpanel = False a0=a b0=b # ignore following rows if (panelcount == n) and (stackcount == nstack): # last panel -> autoscale y if ganged if rcParams['plotter.ganged']: allylim.sort() self._plotter.set_limits(ylim=[allylim[0],allylim[-1]]) break r+=1 # next row #reset the selector to the scantable's original scan.set_selection(savesel) if self._fp is not None: for o in self._plotter.figure.findobj(Text): o.set_fontproperties(self._fp) def set_selection(self, selection=None, refresh=True, **kw): if selection is None: # reset if len(kw) == 0: self._selection = selector() else: # try keywords for k in kw: if k not in selector.fields: raise KeyError("Invalid selection key '%s', valid keys are %s" % (k, selector.fields)) self._selection = selector(**kw) elif isinstance(selection, selector): self._selection = selection else: raise TypeError("'selection' is not of type selector") d0 = {'s': 'SCANNO', 'b': 'BEAMNO', 'i':'IFNO', 'p': 'POLNO', 'c': 'CYCLENO', 't' : 'TIME' } order = [d0[self._panelling],d0[self._stacking]] self._selection.set_order(order) if self._data and refresh: self.plot(self._data) def _get_selected_n(self, scan): d1 = {'b': scan.getbeamnos, 's': scan.getscannos, 'i': scan.getifnos, 'p': scan.getpolnos, 't': scan.ncycle } d2 = { 'b': self._selection.get_beams(), 's': self._selection.get_scans(), 'i': self._selection.get_ifs(), 'p': self._selection.get_pols(), 't': self._selection.get_cycles() } n = d2[self._panelling] or d1[self._panelling]() nstack = d2[self._stacking] or d1[self._stacking]() return n,nstack def _get_label(self, scan, row, mode, userlabel=None): if isinstance(userlabel, list) and len(userlabel) == 0: userlabel = " " pms = dict(zip(self._selection.get_pols(),self._selection.get_poltypes())) if len(pms): poleval = scan._getpollabel(scan.getpol(row),pms[scan.getpol(row)]) else: poleval = scan._getpollabel(scan.getpol(row),scan.poltype()) d = {'b': "Beam "+str(scan.getbeam(row)), 's': scan._getsourcename(row), 'i': "IF"+str(scan.getif(row)), 'p': poleval, 't': str(scan.get_time(row)) } return userlabel or d[mode] def plotazel(self): """ plot azimuth and elevation versus time of a scantable """ from matplotlib import pylab as PL from matplotlib.dates import DateFormatter, timezone from matplotlib.dates import HourLocator, MinuteLocator,SecondLocator, DayLocator from matplotlib.ticker import MultipleLocator from numpy import array, pi dates = self._data.get_time(asdatetime=True) t = PL.date2num(dates) tz = timezone('UTC') PL.cla() PL.ioff() PL.clf() tdel = max(t) - min(t) ax = PL.subplot(2,1,1) el = array(self._data.get_elevation())*180./pi PL.ylabel('El [deg.]') dstr = dates[0].strftime('%Y/%m/%d') if tdel > 1.0: dstr2 = dates[len(dates)-1].strftime('%Y/%m/%d') dstr = dstr + " - " + dstr2 majloc = DayLocator() minloc = HourLocator(range(0,23,12)) timefmt = DateFormatter("%b%d") elif tdel > 24./60.: timefmt = DateFormatter('%H:%M') majloc = HourLocator() minloc = MinuteLocator(30) else: timefmt = DateFormatter('%H:%M') majloc = MinuteLocator(interval=5) minloc = SecondLocator(30) PL.title(dstr) PL.plot_date(t,el,'b,', tz=tz) ax.yaxis.grid(True) if tdel > 1.0: labels = ax.get_xticklabels() # PL.setp(labels, fontsize=10, rotation=45) PL.setp(labels, fontsize=10) # Az plot az = array(self._data.get_azimuth())*180./pi if min(az) < 0: for irow in range(len(az)): if az[irow] < 0: az[irow] += 360.0 ax2 = ax.figure.add_subplot(2,1,2, sharex=ax) ax2.set_xlabel('Time (UT)') ax2.set_ylabel('Az [deg.]') ax2.plot_date(t,az,'b,', tz=tz) ax2.yaxis.grid(True) # set this last as x axis is shared ax.xaxis.set_major_formatter(timefmt) ax.xaxis.set_major_locator(majloc) ax.xaxis.set_minor_locator(minloc) PL.ion() PL.draw() def plotpointing(self): """ plot telescope pointings """ from matplotlib import pylab as PL from numpy import array dir = array(self._data.get_directionval()).transpose() ra = dir[0]*180./pi dec = dir[1]*180./pi PL.cla() PL.ioff() PL.clf() ax = PL.axes([0.1,0.1,0.8,0.8]) ax = PL.axes([0.1,0.1,0.8,0.8]) ax.set_aspect('equal') PL.plot(ra, dec, 'b,') PL.xlabel('RA [deg.]') PL.ylabel('Declination [deg.]') PL.title('Telescope pointings') [xmin,xmax,ymin,ymax] = PL.axis() PL.axis([xmax,xmin,ymin,ymax]) PL.ion() PL.draw()