text: from .. import metaarray as metaarray
  from ..Qt import QtCore
  from .GraphicsObject import GraphicsObject
  from .PlotCurveItem import PlotCurveItem
  from .ScatterPlotItem import ScatterPlotItem
  import numpy as np
  from .. import functions as fn
  from .. import debug as debug
  from .. import getConfigOption
  
  class PlotDataItem(GraphicsObject):
      """
      **Bases:** :class:`GraphicsObject <pyqtgraph.GraphicsObject>`
      
      GraphicsItem for displaying plot curves, scatter plots, or both. 
      While it is possible to use :class:`PlotCurveItem <pyqtgraph.PlotCurveItem>` or
      :class:`ScatterPlotItem <pyqtgraph.ScatterPlotItem>` individually, this class
      provides a unified interface to both. Instances of :class:`PlotDataItem` are 
      usually created by plot() methods such as :func:`pyqtgraph.plot` and
      :func:`PlotItem.plot() <pyqtgraph.PlotItem.plot>`.
      
      ============================== ==============================================
      **Signals:**
      sigPlotChanged(self)           Emitted when the data in this item is updated.  
      sigClicked(self)               Emitted when the item is clicked.
      sigPointsClicked(self, points) Emitted when a plot point is clicked
                                     Sends the list of points under the mouse.
      ============================== ==============================================
      """
      
      sigPlotChanged = QtCore.Signal(object)
      sigClicked = QtCore.Signal(object)
      sigPointsClicked = QtCore.Signal(object, object)
      
      def __init__(self, *args, **kargs):
          """
          There are many different ways to create a PlotDataItem:
          
          **Data initialization arguments:** (x,y data only)
          
              =================================== ======================================
              PlotDataItem(xValues, yValues)      x and y values may be any sequence (including ndarray) of real numbers
              PlotDataItem(yValues)               y values only -- x will be automatically set to range(len(y))
              PlotDataItem(x=xValues, y=yValues)  x and y given by keyword arguments
              PlotDataItem(ndarray(Nx2))          numpy array with shape (N, 2) where x=data[:,0] and y=data[:,1]
              =================================== ======================================
          
          **Data initialization arguments:** (x,y data AND may include spot style)
          
              ===========================   =========================================
              PlotDataItem(recarray)        numpy array with dtype=[('x', float), ('y', float), ...]
              PlotDataItem(list-of-dicts)   [{'x': x, 'y': y, ...},   ...] 
              PlotDataItem(dict-of-lists)   {'x': [...], 'y': [...],  ...}           
              PlotDataItem(MetaArray)       1D array of Y values with X sepecified as axis values 
                                            OR 2D array with a column 'y' and extra columns as needed.
              ===========================   =========================================
          
          **Line style keyword arguments:**
  
              ==========   ==============================================================================
              connect      Specifies how / whether vertexes should be connected. See
                           :func:`arrayToQPath() <pyqtgraph.arrayToQPath>`
              pen          Pen to use for drawing line between points.
                           Default is solid grey, 1px width. Use None to disable line drawing.
                           May be any single argument accepted by :func:`mkPen() <pyqtgraph.mkPen>`
              shadowPen    Pen for secondary line to draw behind the primary line. disabled by default.
                           May be any single argument accepted by :func:`mkPen() <pyqtgraph.mkPen>`
              fillLevel    Fill the area between the curve and fillLevel
              fillBrush    Fill to use when fillLevel is specified. 
                           May be any single argument accepted by :func:`mkBrush() <pyqtgraph.mkBrush>`
              stepMode     If True, two orthogonal lines are drawn for each sample
                           as steps. This is commonly used when drawing histograms.
                           Note that in this case, `len(x) == len(y) + 1`
                           (added in version 0.9.9)
              ==========   ==============================================================================
          
          **Point style keyword arguments:**  (see :func:`ScatterPlotItem.setData() <pyqtgraph.ScatterPlotItem.setData>` for more information)
          
              ============   =====================================================
              symbol         Symbol to use for drawing points OR list of symbols, 
                             one per point. Default is no symbol.
                             Options are o, s, t, d, +, or any QPainterPath
              symbolPen      Outline pen for drawing points OR list of pens, one 
                             per point. May be any single argument accepted by 
                             :func:`mkPen() <pyqtgraph.mkPen>`
              symbolBrush    Brush for filling points OR list of brushes, one per 
                             point. May be any single argument accepted by 
                             :func:`mkBrush() <pyqtgraph.mkBrush>`
              symbolSize     Diameter of symbols OR list of diameters.
              pxMode         (bool) If True, then symbolSize is specified in 
                             pixels. If False, then symbolSize is 
                             specified in data coordinates.
              ============   =====================================================
          
          **Optimization keyword arguments:**
          
              ================ =====================================================================
              antialias        (bool) By default, antialiasing is disabled to improve performance.
                               Note that in some cases (in particluar, when pxMode=True), points 
                               will be rendered antialiased even if this is set to False.
              decimate         deprecated.
              downsample       (int) Reduce the number of samples displayed by this value
              downsampleMethod 'subsample': Downsample by taking the first of N samples. 
                               This method is fastest and least accurate.
                               'mean': Downsample by taking the mean of N samples.
                               'peak': Downsample by drawing a saw wave that follows the min 
                               and max of the original data. This method produces the best 
                               visual representation of the data but is slower.
              autoDownsample   (bool) If True, resample the data before plotting to avoid plotting
                               multiple line segments per pixel. This can improve performance when
                               viewing very high-density data, but increases the initial overhead 
                               and memory usage.
              clipToView       (bool) If True, only plot data that is visible within the X range of
                               the containing ViewBox. This can improve performance when plotting
                               very large data sets where only a fraction of the data is visible
                               at any time.
              identical        *deprecated*
              ================ =====================================================================
          
          **Meta-info keyword arguments:**
          
              ==========   ================================================
              name         name of dataset. This would appear in a legend
              ==========   ================================================
          """
          GraphicsObject.__init__(self)
          self.setFlag(self.ItemHasNoContents)
          self.xData = None
          self.yData = None
          self.xDisp = None
          self.yDisp = None
          #self.dataMask = None
          #self.curves = []
          #self.scatters = []
          self.curve = PlotCurveItem()
          self.scatter = ScatterPlotItem()
          self.curve.setParentItem(self)
          self.scatter.setParentItem(self)
          
          self.curve.sigClicked.connect(self.curveClicked)
          self.scatter.sigClicked.connect(self.scatterClicked)
          
          
          #self.clear()
          self.opts = {
              'connect': 'all',
              
              'fftMode': False,
              'logMode': [False, False],
              'alphaHint': 1.0,
              'alphaMode': False,
              
              'pen': (200,200,200),
              'shadowPen': None,
              'fillLevel': None,
              'fillBrush': None,
              'stepMode': None, 
              
              'symbol': None,
              'symbolSize': 10,
              'symbolPen': (200,200,200),
              'symbolBrush': (50, 50, 150),
              'pxMode': True,
              
              'antialias': getConfigOption('antialias'),
              'pointMode': None,
              
              'downsample': 1,
              'autoDownsample': False,
              'downsampleMethod': 'peak',
              'autoDownsampleFactor': 5.,  # draw ~5 samples per pixel
              'clipToView': False,
              
              'data': None,
          }
          self.setData(*args, **kargs)
      
      def implements(self, interface=None):
          ints = ['plotData']
          if interface is None:
              return ints
          return interface in ints
      
      def name(self):
          return self.opts.get('name', None)
      
      def boundingRect(self):
          return QtCore.QRectF()  ## let child items handle this
  
      def setAlpha(self, alpha, auto):
          if self.opts['alphaHint'] == alpha and self.opts['alphaMode'] == auto:
              return
          self.opts['alphaHint'] = alpha
          self.opts['alphaMode'] = auto
          self.setOpacity(alpha)
          #self.update()
          
      def setFftMode(self, mode):
          if self.opts['fftMode'] == mode:
              return
          self.opts['fftMode'] = mode
          self.xDisp = self.yDisp = None
          self.xClean = self.yClean = None
          self.updateItems()
          self.informViewBoundsChanged()
      
      def setLogMode(self, xMode, yMode):
          if self.opts['logMode'] == [xMode, yMode]:
              return
          self.opts['logMode'] = [xMode, yMode]
          self.xDisp = self.yDisp = None
          self.xClean = self.yClean = None
          self.updateItems()
          self.informViewBoundsChanged()
      
      def setPointMode(self, mode):
          if self.opts['pointMode'] == mode:
              return
          self.opts['pointMode'] = mode
          self.update()
          
      def setPen(self, *args, **kargs):
          """
          | Sets the pen used to draw lines between points.
          | *pen* can be a QPen or any argument accepted by :func:`pyqtgraph.mkPen() <pyqtgraph.mkPen>`
          """
          pen = fn.mkPen(*args, **kargs)
          self.opts['pen'] = pen
          #self.curve.setPen(pen)
          #for c in self.curves:
              #c.setPen(pen)
          #self.update()
          self.updateItems()
          
      def setShadowPen(self, *args, **kargs):
          """
          | Sets the shadow pen used to draw lines between points (this is for enhancing contrast or 
            emphacizing data). 
          | This line is drawn behind the primary pen (see :func:`setPen() <pyqtgraph.PlotDataItem.setPen>`)
            and should generally be assigned greater width than the primary pen.
          | *pen* can be a QPen or any argument accepted by :func:`pyqtgraph.mkPen() <pyqtgraph.mkPen>`
          """
          pen = fn.mkPen(*args, **kargs)
          self.opts['shadowPen'] = pen
          #for c in self.curves:
              #c.setPen(pen)
          #self.update()
          self.updateItems()
          
      def setFillBrush(self, *args, **kargs):
          brush = fn.mkBrush(*args, **kargs)
          if self.opts['fillBrush'] == brush:
              return
          self.opts['fillBrush'] = brush
          self.updateItems()
          
      def setBrush(self, *args, **kargs):
          return self.setFillBrush(*args, **kargs)
      
      def setFillLevel(self, level):
          if self.opts['fillLevel'] == level:
              return
          self.opts['fillLevel'] = level
          self.updateItems()
  
      def setSymbol(self, symbol):
          if self.opts['symbol'] == symbol:
              return
          self.opts['symbol'] = symbol
          #self.scatter.setSymbol(symbol)
          self.updateItems()
          
      def setSymbolPen(self, *args, **kargs):
          pen = fn.mkPen(*args, **kargs)
          if self.opts['symbolPen'] == pen:
              return
          self.opts['symbolPen'] = pen
          #self.scatter.setSymbolPen(pen)
          self.updateItems()
          
      
      
      def setSymbolBrush(self, *args, **kargs):
          brush = fn.mkBrush(*args, **kargs)
          if self.opts['symbolBrush'] == brush:
              return
          self.opts['symbolBrush'] = brush
          #self.scatter.setSymbolBrush(brush)
          self.updateItems()
      
      
      def setSymbolSize(self, size):
          if self.opts['symbolSize'] == size:
              return
          self.opts['symbolSize'] = size
          #self.scatter.setSymbolSize(symbolSize)
          self.updateItems()
  
      def setDownsampling(self, ds=None, auto=None, method=None):
          """
          Set the downsampling mode of this item. Downsampling reduces the number
          of samples drawn to increase performance. 
          
          ==============  =================================================================
          **Arguments:**
          ds              (int) Reduce visible plot samples by this factor. To disable,
                          set ds=1.
          auto            (bool) If True, automatically pick *ds* based on visible range
          mode            'subsample': Downsample by taking the first of N samples.
                          This method is fastest and least accurate.
                          'mean': Downsample by taking the mean of N samples.
                          'peak': Downsample by drawing a saw wave that follows the min
                          and max of the original data. This method produces the best
                          visual representation of the data but is slower.
          ==============  =================================================================
          """
          changed = False
          if ds is not None:
              if self.opts['downsample'] != ds:
                  changed = True
                  self.opts['downsample'] = ds
                  
          if auto is not None and self.opts['autoDownsample'] != auto:
              self.opts['autoDownsample'] = auto
              changed = True
                  
          if method is not None:
              if self.opts['downsampleMethod'] != method:
                  changed = True
                  self.opts['downsampleMethod'] = method
          
          if changed:
              self.xDisp = self.yDisp = None
              self.updateItems()
          
      def setClipToView(self, clip):
          if self.opts['clipToView'] == clip:
              return
          self.opts['clipToView'] = clip
          self.xDisp = self.yDisp = None
          self.updateItems()
          
          
      def setData(self, *args, **kargs):
          """
          Clear any data displayed by this item and display new data.
          See :func:`__init__() <pyqtgraph.PlotDataItem.__init__>` for details; it accepts the same arguments.
          """
          #self.clear()
          profiler = debug.Profiler()
          y = None
          x = None
          if len(args) == 1:
              data = args[0]
              dt = dataType(data)
              if dt == 'empty':
                  pass
              elif dt == 'listOfValues':
                  y = np.array(data)
              elif dt == 'Nx2array':
                  x = data[:,0]
                  y = data[:,1]
              elif dt == 'recarray' or dt == 'dictOfLists':
                  if 'x' in data:
                      x = np.array(data['x'])
                  if 'y' in data:
                      y = np.array(data['y'])
              elif dt ==  'listOfDicts':
                  if 'x' in data[0]:
                      x = np.array([d.get('x',None) for d in data])
                  if 'y' in data[0]:
                      y = np.array([d.get('y',None) for d in data])
                  for k in ['data', 'symbolSize', 'symbolPen', 'symbolBrush', 'symbolShape']:
                      if k in data:
                          kargs[k] = [d.get(k, None) for d in data]
              elif dt == 'MetaArray':
                  y = data.view(np.ndarray)
                  x = data.xvals(0).view(np.ndarray)
              else:
                  raise Exception('Invalid data type %s' % type(data))
              
          elif len(args) == 2:
              seq = ('listOfValues', 'MetaArray', 'empty')
              dtyp = dataType(args[0]), dataType(args[1])
              if dtyp[0] not in seq or dtyp[1] not in seq:
                  raise Exception('When passing two unnamed arguments, both must be a list or array of values. (got %s, %s)' % (str(type(args[0])), str(type(args[1]))))
              if not isinstance(args[0], np.ndarray):
                  #x = np.array(args[0])
                  if dtyp[0] == 'MetaArray':
                      x = args[0].asarray()
                  else:
                      x = np.array(args[0])
              else:
                  x = args[0].view(np.ndarray)
              if not isinstance(args[1], np.ndarray):
                  #y = np.array(args[1])
                  if dtyp[1] == 'MetaArray':
                      y = args[1].asarray()
                  else:
                      y = np.array(args[1])
              else:
                  y = args[1].view(np.ndarray)
              
          if 'x' in kargs:
              x = kargs['x']
          if 'y' in kargs:
              y = kargs['y']
  
          profiler('interpret data')
          ## pull in all style arguments. 
          ## Use self.opts to fill in anything not present in kargs.
          
          if 'name' in kargs:
              self.opts['name'] = kargs['name']
          if 'connect' in kargs:
              self.opts['connect'] = kargs['connect']
  
          ## if symbol pen/brush are given with no symbol, then assume symbol is 'o'
          
          if 'symbol' not in kargs and ('symbolPen' in kargs or 'symbolBrush' in kargs or 'symbolSize' in kargs):
              kargs['symbol'] = 'o'
              
          if 'brush' in kargs:
              kargs['fillBrush'] = kargs['brush']
              
          for k in list(self.opts.keys()):
              if k in kargs:
                  self.opts[k] = kargs[k]
                  
          #curveArgs = {}
          #for k in ['pen', 'shadowPen', 'fillLevel', 'brush']:
              #if k in kargs:
                  #self.opts[k] = kargs[k]
              #curveArgs[k] = self.opts[k]
              
          #scatterArgs = {}
          #for k,v in [('symbolPen','pen'), ('symbolBrush','brush'), ('symbol','symbol')]:
              #if k in kargs:
                  #self.opts[k] = kargs[k]
              #scatterArgs[v] = self.opts[k]
          
  
          if y is None:
              return
          if y is not None and x is None:
              x = np.arange(len(y))
          
          if isinstance(x, list):
              x = np.array(x)
          if isinstance(y, list):
              y = np.array(y)
          
          self.xData = x.view(np.ndarray)  ## one last check to make sure there are no MetaArrays getting by
          self.yData = y.view(np.ndarray)
          self.xClean = self.yClean = None
          self.xDisp = None
          self.yDisp = None
          profiler('set data')
          
          self.updateItems()
          profiler('update items')
          
          self.informViewBoundsChanged()
          #view = self.getViewBox()
          #if view is not None:
              #view.itemBoundsChanged(self)  ## inform view so it can update its range if it wants
          
          self.sigPlotChanged.emit(self)
          profiler('emit')
  
      def updateItems(self):
          
          curveArgs = {}
          for k,v in [('pen','pen'), ('shadowPen','shadowPen'), ('fillLevel','fillLevel'), ('fillBrush', 'brush'), ('antialias', 'antialias'), ('connect', 'connect'), ('stepMode', 'stepMode')]:
              curveArgs[v] = self.opts[k]
          
          scatterArgs = {}
          for k,v in [('symbolPen','pen'), ('symbolBrush','brush'), ('symbol','symbol'), ('symbolSize', 'size'), ('data', 'data'), ('pxMode', 'pxMode'), ('antialias', 'antialias')]:
              if k in self.opts:
                  scatterArgs[v] = self.opts[k]
          
          x,y = self.getData()
          #scatterArgs['mask'] = self.dataMask
          
          if curveArgs['pen'] is not None or (curveArgs['brush'] is not None and curveArgs['fillLevel'] is not None):
              self.curve.setData(x=x, y=y, **curveArgs)
              self.curve.show()
          else:
              self.curve.hide()
          
          if scatterArgs['symbol'] is not None:
              self.scatter.setData(x=x, y=y, **scatterArgs)
              self.scatter.show()
          else:
              self.scatter.hide()
  
  
      def getData(self):
          if self.xData is None:
              return (None, None)
          
          #if self.xClean is None:
              #nanMask = np.isnan(self.xData) | np.isnan(self.yData) | np.isinf(self.xData) | np.isinf(self.yData)
              #if nanMask.any():
                  #self.dataMask = ~nanMask
                  #self.xClean = self.xData[self.dataMask]
                  #self.yClean = self.yData[self.dataMask]
              #else:
                  #self.dataMask = None
                  #self.xClean = self.xData
                  #self.yClean = self.yData
              
          if self.xDisp is None:
              x = self.xData
              y = self.yData
              
              
              #ds = self.opts['downsample']
              #if isinstance(ds, int) and ds > 1:
                  #x = x[::ds]
                  ##y = resample(y[:len(x)*ds], len(x))  ## scipy.signal.resample causes nasty ringing
                  #y = y[::ds]
              if self.opts['fftMode']:
                  x,y = self._fourierTransform(x, y)
              if self.opts['logMode'][0]:
                  x = np.log10(x)
              if self.opts['logMode'][1]:
                  y = np.log10(y)
              #if any(self.opts['logMode']):  ## re-check for NANs after log
                  #nanMask = np.isinf(x) | np.isinf(y) | np.isnan(x) | np.isnan(y)
                  #if any(nanMask):
                      #self.dataMask = ~nanMask
                      #x = x[self.dataMask]
                      #y = y[self.dataMask]
                  #else:
                      #self.dataMask = None
                      
              ds = self.opts['downsample']
              if not isinstance(ds, int):
                  ds = 1
                  
              if self.opts['autoDownsample']:
                  # this option presumes that x-values have uniform spacing
                  range = self.viewRect()
                  if range is not None:
                      dx = float(x[-1]-x[0]) / (len(x)-1)
                      x0 = (range.left()-x[0]) / dx
                      x1 = (range.right()-x[0]) / dx
                      width = self.getViewBox().width()
                      if width != 0.0:
                          ds = int(max(1, int((x1-x0) / (width*self.opts['autoDownsampleFactor']))))
                      ## downsampling is expensive; delay until after clipping.
              
              if self.opts['clipToView']:
                  view = self.getViewBox()
                  if view is None or not view.autoRangeEnabled()[0]:
                      # this option presumes that x-values have uniform spacing
                      range = self.viewRect()
                      if range is not None and len(x) > 1:
                          dx = float(x[-1]-x[0]) / (len(x)-1)
                          # clip to visible region extended by downsampling value
                          x0 = np.clip(int((range.left()-x[0])/dx)-1*ds , 0, len(x)-1)
                          x1 = np.clip(int((range.right()-x[0])/dx)+2*ds , 0, len(x)-1)
                          x = x[x0:x1]
                          y = y[x0:x1]
                      
              if ds > 1:
                  if self.opts['downsampleMethod'] == 'subsample':
                      x = x[::ds]
                      y = y[::ds]
                  elif self.opts['downsampleMethod'] == 'mean':
                      n = len(x) // ds
                      x = x[:n*ds:ds]
                      y = y[:n*ds].reshape(n,ds).mean(axis=1)
                  elif self.opts['downsampleMethod'] == 'peak':
                      n = len(x) // ds
                      x1 = np.empty((n,2))
                      x1[:] = x[:n*ds:ds,np.newaxis]
                      x = x1.reshape(n*2)
                      y1 = np.empty((n,2))
                      y2 = y[:n*ds].reshape((n, ds))
                      y1[:,0] = y2.max(axis=1)
                      y1[:,1] = y2.min(axis=1)
                      y = y1.reshape(n*2)
                  
                      
              self.xDisp = x
              self.yDisp = y
          #print self.yDisp.shape, self.yDisp.min(), self.yDisp.max()
          #print self.xDisp.shape, self.xDisp.min(), self.xDisp.max()
          return self.xDisp, self.yDisp
  
      def dataBounds(self, ax, frac=1.0, orthoRange=None):
          """
          Returns the range occupied by the data (along a specific axis) in this item.
          This method is called by ViewBox when auto-scaling.
  
          =============== =============================================================
          **Arguments:**
          ax              (0 or 1) the axis for which to return this item's data range
          frac            (float 0.0-1.0) Specifies what fraction of the total data 
                          range to return. By default, the entire range is returned.
                          This allows the ViewBox to ignore large spikes in the data
                          when auto-scaling.
          orthoRange      ([min,max] or None) Specifies that only the data within the
                          given range (orthogonal to *ax*) should me measured when 
                          returning the data range. (For example, a ViewBox might ask
                          what is the y-range of all data with x-values between min
                          and max)
          =============== =============================================================
          """
          
          range = [None, None]
          if self.curve.isVisible():
              range = self.curve.dataBounds(ax, frac, orthoRange)
          elif self.scatter.isVisible():
              r2 = self.scatter.dataBounds(ax, frac, orthoRange)
              range = [
                  r2[0] if range[0] is None else (range[0] if r2[0] is None else min(r2[0], range[0])),
                  r2[1] if range[1] is None else (range[1] if r2[1] is None else min(r2[1], range[1]))
                  ]
          return range
      
      def pixelPadding(self):
          """
          Return the size in pixels that this item may draw beyond the values returned by dataBounds().
          This method is called by ViewBox when auto-scaling.
          """
          pad = 0
          if self.curve.isVisible():
              pad = max(pad, self.curve.pixelPadding())
          elif self.scatter.isVisible():
              pad = max(pad, self.scatter.pixelPadding())
          return pad
          
  
      def clear(self):
          #for i in self.curves+self.scatters:
              #if i.scene() is not None:
                  #i.scene().removeItem(i)
          #self.curves = []
          #self.scatters = []
          self.xData = None
          self.yData = None
          #self.xClean = None
          #self.yClean = None
          self.xDisp = None
          self.yDisp = None
          self.curve.setData([])
          self.scatter.setData([])
              
      def appendData(self, *args, **kargs):
          pass
      
      def curveClicked(self):
          self.sigClicked.emit(self)
          
      def scatterClicked(self, plt, points):
          self.sigClicked.emit(self)
          self.sigPointsClicked.emit(self, points)
      
      def viewRangeChanged(self):
          # view range has changed; re-plot if needed
          if self.opts['clipToView'] or self.opts['autoDownsample']:
              self.xDisp = self.yDisp = None
              self.updateItems()
              
      def _fourierTransform(self, x, y):
          ## Perform fourier transform. If x values are not sampled uniformly,
          ## then use np.interp to resample before taking fft.
          dx = np.diff(x)
          uniform = not np.any(np.abs(dx-dx[0]) > (abs(dx[0]) / 1000.))
          if not uniform:
              x2 = np.linspace(x[0], x[-1], len(x))
              y = np.interp(x2, x, y)
              x = x2
          f = np.fft.fft(y) / len(y)
          y = abs(f[1:len(f)/2])
          dt = x[-1] - x[0]
          x = np.linspace(0, 0.5*len(x)/dt, len(y))
          return x, y
      
  def dataType(obj):
      if hasattr(obj, '__len__') and len(obj) == 0:
          return 'empty'
      if isinstance(obj, dict):
          return 'dictOfLists'
      elif isSequence(obj):
          first = obj[0]
          
          if (hasattr(obj, 'implements') and obj.implements('MetaArray')):
              return 'MetaArray'
          elif isinstance(obj, np.ndarray):
              if obj.ndim == 1:
                  if obj.dtype.names is None:
                      return 'listOfValues'
                  else:
                      return 'recarray'
              elif obj.ndim == 2 and obj.dtype.names is None and obj.shape[1] == 2:
                  return 'Nx2array'
              else:
                  raise Exception('array shape must be (N,) or (N,2); got %s instead' % str(obj.shape))
          elif isinstance(first, dict):
              return 'listOfDicts'
          else:
              return 'listOfValues'
          
          
  def isSequence(obj):
      return hasattr(obj, '__iter__') or isinstance(obj, np.ndarray) or (hasattr(obj, 'implements') and obj.implements('MetaArray'))
      
              
              
  #class TableData:
      #"""
      #Class for presenting multiple forms of tabular data through a consistent interface.
      #May contain:
          #- numpy record array
          #- list-of-dicts (all dicts are _not_ required to have the same keys)
          #- dict-of-lists
          #- dict (single record)
                 #Note: if all the values in this record are lists, it will be interpreted as multiple records
          
      #Data can be accessed and modified by column, by row, or by value
          #data[columnName]
          #data[rowId]
          #data[columnName, rowId] = value
          #data[columnName] = [value, value, ...]
          #data[rowId] = {columnName: value, ...}
      #"""
      
      #def __init__(self, data):
          #self.data = data
          #if isinstance(data, np.ndarray):
              #self.mode = 'array'
          #elif isinstance(data, list):
              #self.mode = 'list'
          #elif isinstance(data, dict):
              #types = set(map(type, data.values()))
              ### dict may be a dict-of-lists or a single record
              #types -= set([list, np.ndarray]) ## if dict contains any non-sequence values, it is probably a single record.
              #if len(types) != 0:
                  #self.data = [self.data]
                  #self.mode = 'list'
              #else:
                  #self.mode = 'dict'
          #elif isinstance(data, TableData):
              #self.data = data.data
              #self.mode = data.mode
          #else:
              #raise TypeError(type(data))
          
          #for fn in ['__getitem__', '__setitem__']:
              #setattr(self, fn, getattr(self, '_TableData'+fn+self.mode))
          
      #def originalData(self):
          #return self.data
      
      #def toArray(self):
          #if self.mode == 'array':
              #return self.data
          #if len(self) < 1:
              ##return np.array([])  ## need to return empty array *with correct columns*, but this is very difficult, so just return None
              #return None
          #rec1 = self[0]
          #dtype = functions.suggestRecordDType(rec1)
          ##print rec1, dtype
          #arr = np.empty(len(self), dtype=dtype)
          #arr[0] = tuple(rec1.values())
          #for i in xrange(1, len(self)):
              #arr[i] = tuple(self[i].values())
          #return arr
              
      #def __getitem__array(self, arg):
          #if isinstance(arg, tuple):
              #return self.data[arg[0]][arg[1]]
          #else:
              #return self.data[arg]
              
      #def __getitem__list(self, arg):
          #if isinstance(arg, basestring):
              #return [d.get(arg, None) for d in self.data]
          #elif isinstance(arg, int):
              #return self.data[arg]
          #elif isinstance(arg, tuple):
              #arg = self._orderArgs(arg)
              #return self.data[arg[0]][arg[1]]
          #else:
              #raise TypeError(type(arg))
          
      #def __getitem__dict(self, arg):
          #if isinstance(arg, basestring):
              #return self.data[arg]
          #elif isinstance(arg, int):
              #return dict([(k, v[arg]) for k, v in self.data.iteritems()])
          #elif isinstance(arg, tuple):
              #arg = self._orderArgs(arg)
              #return self.data[arg[1]][arg[0]]
          #else:
              #raise TypeError(type(arg))
  
      #def __setitem__array(self, arg, val):
          #if isinstance(arg, tuple):
              #self.data[arg[0]][arg[1]] = val
          #else:
              #self.data[arg] = val
  
      #def __setitem__list(self, arg, val):
          #if isinstance(arg, basestring):
              #if len(val) != len(self.data):
                  #raise Exception("Values (%d) and data set (%d) are not the same length." % (len(val), len(self.data)))
              #for i, rec in enumerate(self.data):
                  #rec[arg] = val[i]
          #elif isinstance(arg, int):
              #self.data[arg] = val
          #elif isinstance(arg, tuple):
              #arg = self._orderArgs(arg)
              #self.data[arg[0]][arg[1]] = val
          #else:
              #raise TypeError(type(arg))
          
      #def __setitem__dict(self, arg, val):
          #if isinstance(arg, basestring):
              #if len(val) != len(self.data[arg]):
                  #raise Exception("Values (%d) and data set (%d) are not the same length." % (len(val), len(self.data[arg])))
              #self.data[arg] = val
          #elif isinstance(arg, int):
              #for k in self.data:
                  #self.data[k][arg] = val[k]
          #elif isinstance(arg, tuple):
              #arg = self._orderArgs(arg)
              #self.data[arg[1]][arg[0]] = val
          #else:
              #raise TypeError(type(arg))
  
      #def _orderArgs(self, args):
          ### return args in (int, str) order
          #if isinstance(args[0], basestring):
              #return (args[1], args[0])
          #else:
              #return args
          
      #def __iter__(self):
          #for i in xrange(len(self)):
              #yield self[i]
  
      #def __len__(self):
          #if self.mode == 'array' or self.mode == 'list':
              #return len(self.data)
          #else:
              #return max(map(len, self.data.values()))
  
      #def columnNames(self):
          #"""returns column names in no particular order"""
          #if self.mode == 'array':
              #return self.data.dtype.names
          #elif self.mode == 'list':
              #names = set()
              #for row in self.data:
                  #names.update(row.keys())
              #return list(names)
          #elif self.mode == 'dict':
              #return self.data.keys()
              
      #def keys(self):
          #return self.columnNames()
  