from asap.parameters import rcParams
from asap.selector import selector
from asap.scantable import scantable
from asap.logging import asaplog, asaplog_post_dec
import matplotlib.axes
from matplotlib.font_manager import FontProperties
from matplotlib.text import Text
from matplotlib import _pylab_helpers

import re

def new_asaplot(visible=None,**kwargs):
    """
    Returns a new asaplot instance based on the backend settings.
    """
    if visible == None:
        visible = rcParams['plotter.gui']

    backend=matplotlib.get_backend()
    if not visible:
        from asap.asaplot import asaplot
    elif backend == 'TkAgg':
        from asap.asaplotgui import asaplotgui as asaplot
    elif backend == 'Qt4Agg':
        from asap.asaplotgui_qt4 import asaplotgui as asaplot
    elif backend == 'GTkAgg':
        from asap.asaplotgui_gtk import asaplotgui as asaplot
    else:
        from asap.asaplot import asaplot
    return asaplot(**kwargs)

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 = None
        self._inikwg = kwargs

        self._panelling = None
        self._stacking = None
        self.set_panelling()
        self.set_stacking()
        self._rows = None
        self._cols = None
        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()
        self._margins = self.set_margin(refresh=False)
        self._offset = None
        self._startrow = 0
        self._ipanel = -1
        self._panelrows = []
        self._headtext={'string': None, 'textobj': None}
        self._colormap = None
        self._linestyles = None
        self._legendloc = None

    def _translate(self, instr):
        keys = "s b i p t r".split()
        if isinstance(instr, str):
            for key in keys:
                if instr.lower().startswith(key):
                    return key
        return None

    @asaplog_post_dec
    def _reload_plotter(self):
        if self._plotter is not None:
            #if not self._plotter.is_dead:
            #    # clear lines and axes
            #    try:
            #        self._plotter.clear()
            #    except: # Can't remove when already killed.
            #        pass
            if self.casabar_exists():
                del self._plotter.figmgr.casabar
            self._plotter.quit()
            del self._plotter
        asaplog.push('Loading new plotter')
        self._plotter = new_asaplot(self._visible,**self._inikwg)
        self._plotter.figmgr.casabar=self._new_custombar()
        # just to make sure they're set
        self._plotter.palette(color=0,colormap=self._colormap,
                              linestyle=0,linestyles=self._linestyles)
        self._plotter.legend(self._legendloc)

    def _new_custombar(self):
        backend=matplotlib.get_backend()
        if not self._visible:
            return None
        elif backend == "TkAgg":
            from asap.customgui_tkagg import CustomToolbarTkAgg
            return CustomToolbarTkAgg(self)
        elif backend == "Qt4Agg":
            from asap.customgui_qt4agg import CustomToolbarQT4Agg
            return CustomToolbarQT4Agg(self)
        return None

    def casabar_exists(self):
        if not hasattr(self._plotter.figmgr,'casabar'):
            return False
        elif self._plotter.figmgr.casabar:
            return True
        return False

    def _assert_plotter(self,action="status",errmsg=None):
        """
        Check plot window status. Returns True if plot window is alive.
        Parameters
            action:    An action to take if the plotter window is not alive.
                       ['status'|'reload'|'halt']
                       The action 'status' simply returns False if asaplot
                       is not alive. When action='reload', plot window is
                       reloaded and the method returns True. Finally, an
                       error is raised when action='halt'.
            errmsg:    An error (warning) message to send to the logger,
                       when plot window is not alive.
        """
        isAlive = (self._plotter is not None) and self._plotter._alive()
        # More tests
        #if isAlive:
        #    if self._plotter.figmgr:
        #        figmgr = self._plotter.figmgr
        #        figid = figmgr.num
        #        # Make sure figid=0 is what asapplotter expects.
        #        # It might be already destroied/overridden by matplotlib
        #        # commands or other plotting methods using asaplot.
        #        isAlive = _pylab_helpers.Gcf.has_fignum(figid) and \
        #                  (figmgr == \
        #                   _pylab_helpers.Gcf.get_fig_manager(figid))
        #    else:
        #        isAlive = False
            
        if isAlive:
            return True
        # Plotter is not alive.
        haltmsg = "Plotter window has not yet been loaded or is closed."
        if type(errmsg)==str and len(errmsg) > 0:
            haltmsg = errmsg
        
        if action.upper().startswith("R"):
            # reload plotter
            self._reload_plotter()
            return True
        elif action.upper().startswith("H"):
            # halt
            asaplog.push(haltmsg)
            asaplog.post("ERROR")
            raise RuntimeError(haltmsg)
        else:
            if errmsg:
                asaplog.push(errmsg)
                asaplog.post("WARN")
            return False


    @asaplog_post_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 not self._data and not scan:
            msg = "Input is not a scantable"
            raise TypeError(msg)
        self._startrow = 0
        self._ipanel = -1
        self._reset_header()
        self._panelrows = []

        self._assert_plotter(action="reload")
        if self.casabar_exists():
            self._plotter.figmgr.casabar.set_pagecounter(1)

        self._plotter.hold()
        #self._plotter.clear()
        if scan: 
            self.set_data(scan, refresh=False)
        self._plotter.palette(color=0,colormap=self._colormap,
                              linestyle=0,linestyles=self._linestyles)
        self._plotter.legend(self._legendloc)

        self._plot(self._data)
        if self._minmaxy is not None:
            self._plotter.set_limits(ylim=self._minmaxy)
        if self.casabar_exists(): self._plotter.figmgr.casabar.enable_button()
        self._plotter.release()
        self._plotter.tidy()
        self._plotter.show(hardrefresh=False)
        return

    def gca(self):
        errmsg = "No axis to retun. Need to plot first."
        if not self._assert_plotter(action="status",errmsg=errmsg):
            return None
        return self._plotter.figure.gca()

    def refresh(self):
        """Do a soft refresh"""
        errmsg = "No figure to re-plot. Need to plot first."
        self._assert_plotter(action="halt",errmsg=errmsg)

        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)
        """
        ## this method relies on already plotted figure
        if not self._assert_plotter(action="status") or (self._data is None):
            msg = "Cannot create mask interactively on plot. Can only create mask after plotting."
            asaplog.push( msg )
            asaplog.post( "ERROR" )
            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):
        self._assert_plotter(action="reload")
        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):
        self._assert_plotter(action="reload")
        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):
        self._assert_plotter(action="reload")
        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):
        self._assert_plotter(action="reload")
        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):
        self._assert_plotter(action="reload")
        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):
        self._assert_plotter(action="reload")
        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):
        self._assert_plotter(action="reload")
        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):
        self._assert_plotter(action="reload")
        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

    @asaplog_post_dec
    def set_data(self, scan, refresh=True):
        """
        Set a scantable to plot.
        Parameters:
            scan:      a scantable
            refresh:   True (default) or False. If True, the plot is
                       replotted based on the new parameter setting(s).
                       Otherwise,the parameter(s) are set without replotting.
        Note:
           The user specified masks and data selections will be reset
           if a new scantable is set. This method should be called before
           setting data selections (set_selection) and/or masks (set_mask).
        """
        from asap import scantable
        if isinstance(scan, scantable):
            if (self._data is not None) and (scan != self._data):
                del self._data
                msg = "A new scantable is set to the plotter. "\
                      "The masks and data selections are reset."
                asaplog.push( msg )
            self._data = scan
            # reset
            self._reset()
        else:
            msg = "Input is not a scantable"
            raise TypeError(msg)

        # 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
        if refresh: self.plot()

    @asaplog_post_dec
    def set_mode(self, stacking=None, panelling=None, refresh=True):
        """
        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'
            refresh:      True (default) or False. If True, the plot is
                          replotted based on the new parameter setting(s).
                          Otherwise,the parameter(s) are set without replotting.
        Note:
            Valid modes are:
                 'beam' 'Beam' 'b':     Beams
                 'if' 'IF' 'i':         IFs
                 'pol' 'Pol' 'p':       Polarisations
                 'scan' 'Scan' 's':     Scans
                 'time' 'Time' 't':     Times
                 'row' 'Row' 'r':       Rows
            When either 'stacking' or 'panelling' is set to 'row',
            the other parameter setting is ignored.
        """
        msg = "Invalid mode"
        if not self.set_panelling(panelling) or \
               not self.set_stacking(stacking):
            raise TypeError(msg)
        #if self._panelling == 'r':
        #    self._stacking = '_r'
        #if self._stacking == 'r':
        #    self._panelling = '_r'
        if refresh and self._data: self.plot(self._data)
        return

    def set_panelling(self, what=None):
        """Set the 'panelling' mode i.e. which type of spectra should be
        spread across different panels.
        """

        mode = what
        if mode is None:
             mode = rcParams['plotter.panelling']
        md = self._translate(mode)
        if md:
            self._panelling = md
            self._title = None
            #if md == 'r':
            #    self._stacking = '_r'
            # you need to reset counters for multi page plotting
            self._reset_counters()
            return True
        return False

    def set_layout(self,rows=None,cols=None,refresh=True):
        """
        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
             refresh:  True (default) or False. If True, the plot is
                       replotted based on the new parameter setting(s).
                       Otherwise,the parameter(s) are set without replotting.
        Note:
             If no argument is given, the potter reverts to its auto-plot
             behaviour.
        """
        self._rows = rows
        self._cols = cols
        if refresh and self._data: self.plot(self._data)
        return

    def set_stacking(self, what=None):
        """Set the 'stacking' mode i.e. which type of spectra should be
        overlayed.
        """
        mode = what
        if mode is None:
             mode = rcParams['plotter.stacking']
        md = self._translate(mode)
        if md:
            self._stacking = md
            self._lmap = None
            #if md == 'r':
            #    self._panelling = '_r'
            # you need to reset counters for multi page plotting
            self._reset_counters()
            return True
        return False

    def _reset_counters(self):
        self._startrow = 0
        self._ipanel = -1
        self._panelrows = []

    def set_range(self,xstart=None,xend=None,ystart=None,yend=None,refresh=True, offset=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
            refresh:  True (default) or False. If True, the plot is
                      replotted based on the new parameter setting(s).
                      Otherwise,the parameter(s) are set without replotting.
            offset:   shift the abcissa by the given amount. The abcissa label will
                      have '(relative)' appended to it.
        Note:
            These become non-sensical when the unit changes.
            use plotter.set_range() without parameters to reset

        """
        self._offset = offset
        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 refresh and self._data: self.plot(self._data)
        return

    def set_legend(self, mp=None, fontsize = None, mode = 0, refresh=True):
        """
        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
            refresh:    True (default) or False. If True, the plot is
                        replotted based on the new parameter setting(s).
                        Otherwise,the parameter(s) are set without replotting.

        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)
        self._legendloc = mode
        if isinstance(fontsize, int):
            from matplotlib import rc as rcp
            rcp('legend', fontsize=fontsize)
        if refresh and self._data: self.plot(self._data)
        return

    def set_title(self, title=None, fontsize=None, refresh=True):
        """
        Set the title of sub-plots. If multiple sub-plots are plotted,
        multiple titles have to be specified.
        Parameters:
            title:      a list of titles of sub-plots.
            fontsize:   a font size of titles (integer)
            refresh:    True (default) or False. If True, the plot is
                        replotted based on the new parameter setting(s).
                        Otherwise,the parameter(s) are set without replotting.
        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 refresh and self._data: self.plot(self._data)
        return

    def set_ordinate(self, ordinate=None, fontsize=None, refresh=True):
        """
        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
            fontsize:    a font size of vertical axis labels (integer)
            refresh:     True (default) or False. If True, the plot is
                         replotted based on the new parameter setting(s).
                         Otherwise,the parameter(s) are set without replotting.
        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 refresh and self._data: self.plot(self._data)
        return

    def set_abcissa(self, abcissa=None, fontsize=None, refresh=True):
        """
        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
            fontsize:    a font size of horizontal axis labels (integer)
            refresh:     True (default) or False. If True, the plot is
                         replotted based on the new parameter setting(s).
                         Otherwise,the parameter(s) are set without replotting.
        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 refresh and self._data: self.plot(self._data)
        return

    def set_colors(self, colmap, refresh=True):
        """
        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
            refresh:    True (default) or False. If True, the plot is
                        replotted based on the new parameter setting(s).
                        Otherwise,the parameter(s) are set without replotting.
        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)
        self._colormap = colmap
        if refresh and self._data: self.plot(self._data)

    # alias for english speakers
    set_colours = set_colors

    def set_histogram(self, hist=True, linewidth=None, refresh=True):
        """
        Enable/Disable histogram-like plotting.
        Parameters:
            hist:        True (default) or False. The fisrt default
                         is taken from the .asaprc setting
                         plotter.histogram
            linewidth:   a line width
            refresh:     True (default) or False. If True, the plot is
                         replotted based on the new parameter setting(s).
                         Otherwise,the parameter(s) are set without replotting.
        """
        self._hist = hist
        if isinstance(linewidth, float) or isinstance(linewidth, int):
            from matplotlib import rc as rcp
            rcp('lines', linewidth=linewidth)
        if refresh and self._data: self.plot(self._data)

    def set_linestyles(self, linestyles=None, linewidth=None, refresh=True):
        """
        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
            linewidth:       a line width
            refresh:         True (default) or False. If True, the plot is
                             replotted based on the new parameter setting(s).
                             Otherwise,the parameter(s) are set without replotting.
        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)
        self._linestyles = linestyles
        if isinstance(linewidth, float) or isinstance(linewidth, int):
            from matplotlib import rc as rcp
            rcp('lines', linewidth=linewidth)
        if refresh and self._data: self.plot(self._data)

    def set_font(self, refresh=True,**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
            refresh:   True (default) or False. If True, the plot is
                       replotted based on the new parameter setting(s).
                       Otherwise,the parameter(s) are set without replotting.
        """
        from matplotlib import rc as rcp
        fdict = {}
        for k,v in kwargs.iteritems():
            if v:
                fdict[k] = v
        self._fp = FontProperties(**fdict)
        if refresh and self._data: self.plot(self._data)

    def set_margin(self,margin=[],refresh=True):
        """
        Set margins between subplots and plot edges.
        Parameters:
            margin:   a list of margins in figure coordinate (0-1),
                      i.e., fraction of the figure width or height.
                      The order of elements should be:
                      [left, bottom, right, top, horizontal space btw panels,
                      vertical space btw panels].
            refresh:  True (default) or False. If True, the plot is
                      replotted based on the new parameter setting(s).
                      Otherwise,the parameter(s) are set without replotting.
        Note
        * When margin is not specified, the values are reset to the defaults
          of matplotlib.
        * If any element is set to be None, the current value is adopted.
        """
        if margin == []: self._margins=self._reset_margin()
        else:
            self._margins=[None]*6
            self._margins[0:len(margin)]=margin
        #print "panel margin set to ",self._margins
        if refresh and self._data: self.plot(self._data)

    def _reset_margin(self):
        ks=map(lambda x: 'figure.subplot.'+x,
               ['left','bottom','right','top','hspace','wspace'])
        return map(matplotlib.rcParams.get,ks)

    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.
        """
        errmsg = "Cannot plot spectral lines. Need to plot scantable first."
        self._assert_plotter(action="halt",errmsg=errmsg)
        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) and v[i] < freq:
                                    idx = i
                                    i+=1
                        if idx is not None:
                            lower = idx - deltachan
                            upper = idx + deltachan
                            if lower < 0: lower = 0
                            if upper > 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 known 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
        """
        errmsg = "Cannot save figure. Need to plot first."
        self._assert_plotter(action="halt",errmsg=errmsg)
        
        self._plotter.save(filename,orientation,dpi)
        return

    @asaplog_post_dec
    def set_mask(self, mask=None, selection=None, refresh=True):
        """
        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.
             refresh:        True (default) or False. If True, the plot is
                             replotted based on the new parameter setting(s).
                             Otherwise,the parameter(s) are set without replotting.
        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()"
            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
        if refresh: 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
        minind=start
        for ind in xrange(start,end+inc,inc):
            if data[ind] > mn: break
            minind=ind
        # find max index
        #while end > 0 and data[end] > mx:
        #    end-=inc
        #if end > 0: end +=1
        maxind=end
        for ind in xrange(end,start-inc,-inc):
            if data[ind] < mx: break
            maxind=ind
        start=minind
        end=maxind
        if start > end:
            return end,start+1
        elif start < end:
            return start,end+1
        else:
            return start,end

    def _reset(self):
        self._usermask = []
        self._usermaskspectra = None
        self._offset = None
        self.set_selection(None, False)
        self._reset_header()

    def _reset_header(self):
        self._headtext={'string': None, 'textobj': None}

    def _plot(self, scan):
        savesel = scan.get_selection()
        sel = savesel +  self._selection
        order = self._get_sortstring([self._panelling,self._stacking])
        if order:
            sel.set_order(order)
        scan.set_selection(sel)
        d = {'b': scan.getbeam, 's': scan.getscan,
             'i': scan.getif, 'p': scan.getpol, 't': scan.get_time,
             'r': int}#, '_r': int}

        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)
        # In case of row stacking
        rowstack = False
        titlemode = self._panelling
        if self._stacking == "r" and self._panelling != "r":
            rowstack = True
            titlemode = '_r'
        nptot = n
        maxpanel, maxstack = 16,16
        if nstack > maxstack:
            msg ="Scan to be overlayed contains more than %d selections.\n" \
                  "Selecting first %d selections..." % (maxstack, maxstack)
            asaplog.push(msg)
            asaplog.post('WARN')
            nstack = min(nstack,maxstack)
        #n = min(n-self._ipanel-1,maxpanel)
        n = n-self._ipanel-1

        ganged = False
        if n > 1:
            ganged = rcParams['plotter.ganged']
            if self._panelling == 'i':
                ganged = False
            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,margin=self._margins,ganged=ganged)
            else:
                n = min(n,maxpanel)
                self._plotter.set_panels(rows=n,cols=0,nplots=n,margin=self._margins,ganged=ganged)
        else:
            self._plotter.set_panels(margin=self._margins)
        #r = 0
        r = self._startrow
        nr = scan.nrow()
        a0,b0 = -1,-1
        allxlim = []
        allylim = []
        #newpanel=True
        newpanel=False
        panelcount,stackcount = 0,0
        # If this is not the first page
        if r > 0:
            # panelling value of the prev page 
            a0 = d[self._panelling](r-1)
            # set the initial stackcount large not to plot
            # the start row automatically
            stackcount = nstack

        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()
                if self._offset and not self._abcissa:
                    xlab += " (relative)"
                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)
                lbl = self._get_label(scan, r, titlemode, 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)
                        lbl = self._get_label(scan, r, titlemode, None)
                self._plotter.set_axes('title',lbl)
                newpanel = True
                stackcount = 0
                panelcount += 1
                # save the start row to plot this panel for future revisit.
                if self._panelling != 'r' and \
                       len(self._panelrows) < self._ipanel+1+panelcount:
                    self._panelrows += [r]
                    
            #if (b > b0 or newpanel) and stackcount < nstack:
            if stackcount < nstack and (newpanel or rowstack or (a == a0 and b > b0)):
                y = []
                if len(polmodes):
                    y = scan._getspectrum(r, polmodes[scan.getpol(r)])
                else:
                    y = scan._getspectrum(r)
                # flag application
                mr = scan._getflagrow(r)
                from numpy import ma, array
                if mr:
                    y = ma.masked_array(y,mask=mr)
                else:
                    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)
                    y = ma.masked_array(y,mask=logical_not(array(m,copy=False)))

                x = array(scan._getabcissa(r))
                if self._offset:
                    x += self._offset
                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 and not mr:
                    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
                else:
                    xlim = self._minmaxx or []
                    allxlim += xlim
                    ylim= self._minmaxy or []
                    allylim += ylim
                stackcount += 1
                a0=a
                b0=b
                # last in colour stack -> autoscale x
                if stackcount == nstack and len(allxlim) > 0:
                    allxlim.sort()
                    self._plotter.subplots[panelcount-1]['axes'].set_xlim([allxlim[0],allxlim[-1]])
                    if ganged:
                        allxlim = [allxlim[0],allxlim[-1]]
                    else:
                        # clear
                        allxlim =[]

            newpanel = False
            #a0=a
            #b0=b
            # ignore following rows
            if (panelcount == n and stackcount == nstack) or (r == nr-1):
                # last panel -> autoscale y if ganged
                #if rcParams['plotter.ganged'] and len(allylim) > 0:
                if ganged and len(allylim) > 0:
                    allylim.sort()
                    self._plotter.set_limits(ylim=[allylim[0],allylim[-1]])
                break
            r+=1 # next row

        # save the current counter for multi-page plotting
        self._startrow = r+1
        self._ipanel += panelcount
        if self.casabar_exists():
            if self._ipanel >= nptot-1:
                self._plotter.figmgr.casabar.disable_next()
            else:
                self._plotter.figmgr.casabar.enable_next()
            if self._ipanel + 1 - panelcount > 0:
                self._plotter.figmgr.casabar.enable_prev()
            else:
                self._plotter.figmgr.casabar.disable_prev()

        #reset the selector to the scantable's original
        scan.set_selection(savesel)

        #temporary switch-off for older matplotlib
        #if self._fp is not None:
        if self._fp is not None and getattr(self._plotter.figure,'findobj',False):
            for o in self._plotter.figure.findobj(Text):
                o.set_fontproperties(self._fp)

    def _get_sortstring(self, lorders):
        d0 = {'s': 'SCANNO', 'b': 'BEAMNO', 'i':'IFNO',
              'p': 'POLNO', 'c': 'CYCLENO', 't' : 'TIME', 'r':None, '_r':None }
        if not (type(lorders) == list) and not (type(lorders) == tuple):
            return None
        if len(lorders) > 0:
            lsorts = []
            for order in lorders:
                if order == "r":
                    # don't sort if row panelling/stacking
                    return None
                ssort = d0[order]
                if ssort:
                    lsorts.append(ssort)
            return lsorts
        return None

    def set_selection(self, selection=None, refresh=True, **kw):
        """
        Parameters:
            selection:  a selector object (default unset the selection)
            refresh:    True (default) or False. If True, the plot is
                        replotted based on the new parameter setting(s).
                        Otherwise,the parameter(s) are set without replotting.
        """
        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")

        order = self._get_sortstring([self._panelling,self._stacking])
        if order:
            self._selection.set_order(order)
        if refresh and self._data: 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,
             'r': scan.nrow}#, '_r': False}
        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(),
               'r': False}#, '_r': 1}
        n =  d2[self._panelling] or d1[self._panelling]()
        nstack = d2[self._stacking] or d1[self._stacking]()
        # handle row panelling/stacking
        if self._panelling == 'r':
            nstack = 1
        elif self._stacking == 'r':
            n = 1
        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),
             's': "Scan "+str(scan.getscan(row))+\
                  " ("+str(scan._getsourcename(row))+")",
             'i': "IF"+str(scan.getif(row)),
             'p': poleval,
             't': str(scan.get_time(row)),
             'r': "row "+str(row),
             #'_r': str(scan.get_time(row))+",\nIF"+str(scan.getif(row))+", "+poleval+", Beam"+str(scan.getbeam(row)) }
             '_r': "" }
        return userlabel or d[mode]

    def plotazel(self, scan=None, outfile=None):
        """
        plot azimuth and elevation versus time of a scantable
        """
        visible = rcParams['plotter.gui']
        from matplotlib import pylab as PL
        from matplotlib.dates import DateFormatter
        from pytz import timezone
        from matplotlib.dates import HourLocator, MinuteLocator,SecondLocator, DayLocator
        from matplotlib.ticker import MultipleLocator
        from numpy import array, pi
        if not visible or not self._visible:
            PL.ioff()
            from matplotlib.backends.backend_agg import FigureCanvasAgg
            PL.gcf().canvas.switch_backends(FigureCanvasAgg)
        self._data = scan
        dates = self._data.get_time(asdatetime=True)
        t = PL.date2num(dates)
        tz = timezone('UTC')
        PL.cla()
        PL.ioff()
        PL.clf()
        # Adjust subplot margins
        if not self._margins or len(self._margins) != 6:
            self.set_margin(refresh=False)
        lef, bot, rig, top, wsp, hsp = self._margins
        PL.gcf().subplots_adjust(left=lef,bottom=bot,right=rig,top=top,
                                 wspace=wsp,hspace=hsp)

        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)
        if tdel == 0.0:
            th = (t - PL.floor(t))*24.0
            PL.plot(th,el,'o',markersize=2, markerfacecolor='b', markeredgecolor='b')
        else:
            PL.plot_date(t,el,'o', markersize=2, markerfacecolor='b', markeredgecolor='b',tz=tz)
            #ax.grid(True)
            ax.xaxis.set_major_formatter(timefmt)
            ax.xaxis.set_major_locator(majloc)
            ax.xaxis.set_minor_locator(minloc)
        ax.yaxis.grid(True)
        yloc = MultipleLocator(30)
        ax.set_ylim(0,90)
        ax.yaxis.set_major_locator(yloc)
        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 = PL.subplot(2,1,2)
        #PL.xlabel('Time (UT [hour])')
        PL.ylabel('Az [deg.]')
        if tdel == 0.0:
            PL.plot(th,az,'o',markersize=2, markeredgecolor='b',markerfacecolor='b')
        else:
            PL.plot_date(t,az,'o', markersize=2,markeredgecolor='b',markerfacecolor='b',tz=tz)
            ax2.xaxis.set_major_formatter(timefmt)
            ax2.xaxis.set_major_locator(majloc)
            ax2.xaxis.set_minor_locator(minloc)
        #ax2.grid(True)
        ax2.set_ylim(0,360)
        ax2.yaxis.grid(True)
        #hfmt = DateFormatter('%H')
        #hloc = HourLocator()
        yloc = MultipleLocator(60)
        ax2.yaxis.set_major_locator(yloc)
        if tdel > 1.0:
            labels = ax2.get_xticklabels()
            PL.setp(labels, fontsize=10)
            PL.xlabel('Time (UT [day])')
        else:
            PL.xlabel('Time (UT [hour])')

        PL.ion()
        PL.draw()
        if matplotlib.get_backend() == 'Qt4Agg': PL.gcf().show()
        if (outfile is not None):
           PL.savefig(outfile)

    def plotpointing(self, scan=None, outfile=None):
        """
        plot telescope pointings
        """
        visible = rcParams['plotter.gui']
        from matplotlib import pylab as PL
        from numpy import array, pi
        if not visible or not self._visible:
            PL.ioff()
            from matplotlib.backends.backend_agg import FigureCanvasAgg
            PL.gcf().canvas.switch_backends(FigureCanvasAgg)
        self._data = scan
        dir = array(self._data.get_directionval()).transpose()
        ra = dir[0]*180./pi
        dec = dir[1]*180./pi
        PL.cla()
        #PL.ioff()
        PL.clf()
        # Adjust subplot margins
        if not self._margins or len(self._margins) != 6:
            self.set_margin(refresh=False)
        lef, bot, rig, top, wsp, hsp = self._margins
        PL.gcf().subplots_adjust(left=lef,bottom=bot,right=rig,top=top,
                                 wspace=wsp,hspace=hsp)
        ax = PL.gca()
        #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()
        if matplotlib.get_backend() == 'Qt4Agg': PL.gcf().show()
        if (outfile is not None):
           PL.savefig(outfile)

    # plot total power data
    # plotting in time is not yet implemented..
    @asaplog_post_dec
    def plottp(self, scan=None):
        self._assert_plotter(action="reload")
        self._plotter.hold()
        self._plotter.clear()
        from asap import scantable
        if not self._data and not scan:
            msg = "Input is not a scantable"
            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 abcissa 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

        # Adjust subplot margins
        if not self._margins or len(self._margins) !=6:
            self.set_margin(refresh=False)
        lef, bot, rig, top, wsp, hsp = self._margins
        self._plotter.figure.subplots_adjust(
            left=lef,bottom=bot,right=rig,top=top,wspace=wsp,hspace=hsp)
        if self.casabar_exists(): self._plotter.figmgr.casabar.disable_button()
        self._plottp(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 _plottp(self,scan):
        """
        private method for plotting total power data
        """
        from numpy import ma, array, arange, logical_not
        r=0
        nr = scan.nrow()
        a0,b0 = -1,-1
        allxlim = []
        allylim = []
        y=[]
        self._plotter.set_panels()
        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()
        xlab = self._abcissa or 'row number' #or Time
        ylab = self._ordinate or scan._get_ordinate_label()
        self._plotter.set_axes('xlabel',xlab)
        self._plotter.set_axes('ylabel',ylab)
        lbl = self._get_label(scan, r, 's', 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)
        y=array(scan._get_column(scan._getspectrum,-1))
        m = array(scan._get_column(scan._getmask,-1))
        y = ma.masked_array(y,mask=logical_not(array(m,copy=False)))
        x = arange(len(y))
        # try to handle spectral data somewhat...
        l,m = y.shape
        if m > 1:
            y=y.mean(axis=1)
        plotit = self._plotter.plot
        llbl = self._get_label(scan, r, self._stacking, None)
        self._plotter.set_line(label=llbl)
        if len(x) > 0:
            plotit(x,y)


    # forwards to matplotlib.Figure.text
    def figtext(self, *args, **kwargs):
        """
        Add text to figure at location x,y (relative 0-1 coords).
        This method forwards *args and **kwargs to a Matplotlib method,
        matplotlib.Figure.text.
        See the method help for detailed information.
        """
        self._assert_plotter(action="reload")
        self._plotter.text(*args, **kwargs)
    # end matplotlib.Figure.text forwarding function


    # printing header information
    @asaplog_post_dec
    def print_header(self, plot=True, fontsize=9, logger=False, selstr='', extrastr=''):
        """
        print data (scantable) header on the plot and/or logger.
        To plot the header on the plot, this method should be called after
        plotting spectra by the method, asapplotter.plot.
        Parameters:
            plot:      whether or not print header info on the plot.
            fontsize:  header font size (valid only plot=True)
            logger:    whether or not print header info on the logger.
            selstr:    additional selection string (not verified)
            extrastr:  additional string to print at the beginning (not verified)
        """
        if not plot and not logger:
            return
        if not self._data:
            raise RuntimeError("No scantable has been set yet.")
        # Now header will be printed on plot and/or logger.
        # Get header information and format it.
        ssum=self._data._list_header()
        # Print Observation header to the upper-left corner of plot
        headstr=[ssum[0:ssum.find('Obs. Type:')]]
        headstr.append(ssum[ssum.find('Obs. Type:'):ssum.find('Flux Unit:')])
        if extrastr != '':
            headstr[0]=extrastr+'\n'+headstr[0]
            self._headtext['extrastr'] = extrastr
        if selstr != '':
            selstr += '\n'
            self._headtext['selstr'] = selstr
        ssel=(selstr+self._data.get_selection().__str__()+self._selection.__str__() or 'none')
        headstr.append('***Selections***\n'+ssel)

        if plot:
            errmsg = "Can plot header only after the first call to plot()."
            self._assert_plotter(action="halt",errmsg=errmsg)
            self._plotter.hold()
            self._header_plot(headstr,fontsize=fontsize)
            import time
            self._plotter.figure.text(0.99,0.01,
                            time.strftime("%a %d %b %Y  %H:%M:%S %Z"),
                            horizontalalignment='right',
                            verticalalignment='bottom',fontsize=8)
            self._plotter.release()
        if logger:
            selstr = "Selections:    "+ssel
            asaplog.push("----------------\n  Plot Summary\n----------------")
            asaplog.push(extrastr)
            asaplog.push(ssum[0:ssum.find('Selection:')]\
                         + selstr)
        self._headtext['string'] = headstr
        del ssel, ssum, headstr

    def _header_plot(self, texts, fontsize=9):
        self._headtext['textobj']=[]
        nstcol=len(texts)
        for i in range(nstcol):
            self._headtext['textobj'].append(
                self._plotter.figure.text(0.03+float(i)/nstcol,0.98,
                                          texts[i],
                                          horizontalalignment='left',
                                          verticalalignment='top',
                                          fontsize=fontsize))

    def clear_header(self):
        if not self._headtext['textobj']:
            asaplog.push("No header has been plotted. Exit without any operation")
            asaplog.post("WARN")
        elif self._assert_plotter(action="status"):
            self._plotter.hold()
            for textobj in self._headtext['textobj']:
                #if textobj.get_text() in self._headstring:
                try:
                    textobj.remove()
                except NotImplementedError:
                    self._plotter.figure.texts.pop(self._plotter.figure.texts.index(textobj))
            self._plotter.release()
        self._reset_header()

    # plot spectra by pointing
    @asaplog_post_dec
    def plotgrid(self, scan=None,center=None,spacing=None,rows=None,cols=None):
        """
        Plot spectra based on direction.
        
        Parameters:
            scan:      a scantable to plot
            center:    the grid center direction (a list) of plots in the
                       unit of DIRECTION column.
                       (default) the center of map region
            spacing:   a list of horizontal (R.A.) and vertical (Dec.)
                       spacing in the unit of DIRECTION column.
                       (default) Calculated by the extent of map region and
                       the number of rows and cols to cover
            rows:      number of panels (grid points) in horizontal direction
            cols:      number of panels (grid points) in vertical direction

        Note:
        - Only the first IFNO, POLNO, and BEAM in the scantable will be
        plotted.
        - This method doesn't re-grid and average spectra in scantable. Use
        asapgrid module to re-grid spectra before plotting with this method.
        Only the first spectrum is plotted in case there are multiple
        spectra which belong to a grid.
        """
        from asap import scantable
        from numpy import array, ma, cos
        if not self._data and not scan:
            msg = "No scantable is specified to plot"
            raise TypeError(msg)
        if scan: 
            self.set_data(scan, refresh=False)
            del scan

        # Rows and cols
        if rows:
            self._rows = int(rows)
        else:
            msg = "Number of rows to plot are not specified. "
            if self._rows:
                msg += "Using previous value = %d" % (self._rows)
                asaplog.push(msg)
            else:
                self._rows = 1
                msg += "Setting rows = %d" % (self._rows)
                asaplog.post()
                asaplog.push(msg)
                asaplog.post("WARN")
        if cols:
            self._cols = int(cols)
        else:
            msg = "Number of cols to plot are not specified. "
            if self._cols:
                msg += "Using previous value = %d" % (self._cols)
                asaplog.push(msg)
            else:
                self._cols = 1
                msg += "Setting cols = %d" % (self._cols)
                asaplog.post()
                asaplog.push(msg)
                asaplog.post("WARN")

        # Center and spacing
        if center is None:
            #asaplog.post()
            asaplog.push("Grid center is not specified. Automatically calculated from pointing center.")
            #asaplog.post("WARN")
            dirarr = array(self._data.get_directionval()).transpose()
            #center = [dirarr[0].mean(), dirarr[1].mean()]
            center = [0.5*(dirarr[0].max() + dirarr[0].min()),
                      0.5*(dirarr[1].max() + dirarr[1].min())]
            del dirarr
        elif (type(center) in (list, tuple)) and len(center) > 1:
            center = center[0:2]
        else:
            msg = "Direction of grid center should be a list of float (R.A., Dec.)"
            raise ValueError, msg
        asaplog.push("Grid center: (%f, %f) " % (center[0],center[1]))

        if spacing is None:
            #asaplog.post()
            asaplog.push("Grid spacing not specified. Automatically calculated from map coverage")
            #asaplog.post("WARN")
            # automatically get spacing
            dirarr = array(self._data.get_directionval()).transpose()
            wx = 2. * max(abs(dirarr[0].max()-center[0]),
                          abs(dirarr[0].min()-center[0]))
            wy = 2. * max(abs(dirarr[1].max()-center[1]),
                          abs(dirarr[1].min()-center[1]))
            ## slightly expand area to plot the edges
            #wx *= 1.01
            #wy *= 1.01
            #xgrid = wx/self._cols
            #ygrid = wy/self._rows
            xgrid = wx/max(self._cols-1.,1.)
            ygrid = wy/max(self._rows-1.,1.)
            #print "Pointing range: (x, y) = (%f - %f, %f - %f)" %\
            #  (dirarr[0].min(),dirarr[0].max(),dirarr[1].min(),dirarr[1].max())
            # identical R.A. and/or Dec. for all spectra.
            if xgrid == 0:
                xgrid = 1.
            if ygrid == 0:
                ygrid = 1.
            # spacing should be negative to transpose plot
            spacing = [- xgrid, - ygrid]
            del dirarr, xgrid, ygrid
        #elif isinstance(spacing, str):
        #    # spacing is a quantity
        elif (type(spacing) in (list, tuple)) and len(spacing) > 1:
            for i in xrange(2):
                val = spacing[i]
                if not isinstance(val, float):
                    raise TypeError("spacing should be a list of float")
                if val > 0.:
                    spacing[i] = -val
            spacing = spacing[0:2]
            # Correction of Dec. effect
            spacing[0] /= cos(center[1])
        else:
            msg = "Invalid spacing."
            raise TypeError(msg)
        asaplog.push("Spacing: (%f, %f) (projected)" % (spacing[0],spacing[1]))

        ntotpl = self._rows * self._cols
        minpos = [center[0]-spacing[0]*self._cols/2.,
                  center[1]-spacing[1]*self._rows/2.]
        #print "Plot range: (x, y) = (%f - %f, %f - %f)" %\
        #      (minpos[0],minpos[0]+spacing[0]*self._cols,
        #       minpos[1],minpos[1]+spacing[1]*self._rows)
        ifs = self._data.getifnos()
        if len(ifs) > 1:
            msg = "Found multiple IFs in scantable. Only the first IF (IFNO=%d) will be plotted." % ifs[0]
            asaplog.post()
            asaplog.push(msg)
            asaplog.post("WARN")
        pols = self._data.getpolnos()
        if len(pols) > 1:
            msg = "Found multiple POLs in scantable. Only the first POL (POLNO=%d) will be plotted." % pols[0]
            asaplog.post()
            asaplog.push(msg)
            asaplog.post("WARN")
        beams = self._data.getbeamnos()
        if len(beams) > 1:
            msg = "Found multiple BEAMs in scantable. Only the first BEAM (BEAMNO=%d) will be plotted." % beams[0]
            asaplog.post()
            asaplog.push(msg)
            asaplog.post("WARN")
        self._data.set_selection(ifs=[ifs[0]],pols=[pols[0]],beams=[beams[0]])
        if self._data.nrow() > ntotpl:
            msg = "Scantable is finely sampled than plotting grids. "\
                  + "Only the first spectrum is plotted in each grid."
            asaplog.post()
            asaplog.push(msg)
            asaplog.post("WARN")
        
        self._assert_plotter(action="reload")
        self._plotter.hold()
        self._plotter.clear()
        self._plotter.legend()
        
        # Adjust subplot margins
        if not self._margins or len(self._margins) !=6:
            self.set_margin(refresh=False)
        self._plotter.set_panels(rows=self._rows,cols=self._cols,
                                 nplots=ntotpl,margin=self._margins,ganged=True)
        if self.casabar_exists():
            self._plotter.figmgr.casabar.set_pagecounter(1)
            self._plotter.figmgr.casabar.enable_button()
        # Actual plot
        npl = 0
        for irow in range(self._data.nrow()):
            pos = self._data.get_directionval(irow)
            ix = int((pos[0] - minpos[0])/spacing[0])
            if ix < 0 or ix >= self._cols:
                #print "Row %d : Out of X-range (x = %f) ... skipped" % (irow, pos[0])
                continue
            iy = int((pos[1]- minpos[1])/spacing[1])
            if iy < 0 or iy >= self._cols:
                #print "Row %d : Out of Y-range (y = %f) ... skipped" % (irow,pos[1])
                continue
            ipanel = ix + iy*self._cols
            if len(self._plotter.subplots[ipanel]['lines']) > 0:
                #print "Row %d : panel %d lready plotted ... skipped" % (irow,ipanel)
                # a spectrum already plotted in the panel
                continue
            # Plotting this row
            #print "PLOTTING row %d (panel=%d)" % (irow, ipanel)
            npl += 1
            self._plotter.subplot(ipanel)
            self._plotter.palette(0,colormap=self._colormap, \
                                  linestyle=0,linestyles=self._linestyles)
            xlab = self._abcissa and self._abcissa[ipanel] \
                   or self._data._getabcissalabel(irow)
            if self._offset and not self._abcissa:
                xlab += " (relative)"
            ylab = self._ordinate and self._ordinate[ipanel] \
                   or self._data._get_ordinate_label()
            self._plotter.set_axes('xlabel', xlab)
            self._plotter.set_axes('ylabel', ylab)
            #from numpy import pi
            #lbl = "(%f, %f)" % (self._data.get_directionval(irow)[0]*180/pi,self._data.get_directionval(irow)[1]*180./pi)
            lbl = self._data.get_direction(irow)
            self._plotter.set_axes('title',lbl)

            y = self._data._getspectrum(irow)
            # flag application
            mr = self._data._getflagrow(irow)
            if mr:  # FLAGROW=True
                y = ma.masked_array(y,mask=mr)
            else:
                m = self._data._getmask(irow)
                from numpy import logical_not, logical_and
                ### user mask is not available so far
                #if self._maskselection and len(self._usermask) == len(m):
                #    if d[self._stacking](irow) in self._maskselection[self._stacking]:
                #            m = logical_and(m, self._usermask)
                y = ma.masked_array(y,mask=logical_not(array(m,copy=False)))

            x = array(self._data._getabcissa(irow))
            if self._offset:
                x += self._offset
            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]
            self._plotter.set_line(label=lbl)
            plotit = self._plotter.plot
            if self._hist: plotit = self._plotter.hist
            if len(x) > 0 and not mr:
                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
#             else:
#                 xlim = self._minmaxx or []
#                 allxlim += xlim
#                 ylim= self._minmaxy or []
#                 allylim += ylim
            
            if npl >= ntotpl:
                break
            
        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
