text: # -*- coding: utf-8 -*-
  import warnings
  import numpy as np
  from .. import metaarray as metaarray
  from ..Qt import QtCore
  from .GraphicsObject import GraphicsObject
  from .PlotCurveItem import PlotCurveItem
  from .ScatterPlotItem import ScatterPlotItem
  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, ev)                Emitted when the item is clicked.
      sigPointsClicked(self, points, ev)  Emitted when a plot point is clicked
                                          Sends the list of points under the mouse.
      sigPointsHovered(self, points, ev)  Emitted when a plot point is hovered over.
                                          Sends the list of points under the mouse.
      ==================================  ==============================================
      """
  
      sigPlotChanged = QtCore.Signal(object)
      sigClicked = QtCore.Signal(object, object)
      sigPointsClicked = QtCore.Signal(object, object, object)
      sigPointsHovered = QtCore.Signal(object, 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
  
              fillOutline  (bool) If True, an outline surrounding the *fillLevel* area is drawn.
              fillBrush    Fill to use when fillLevel is specified.
                           May be any single argument accepted by :func:`mkBrush() <pyqtgraph.mkBrush>`
              stepMode     (str or None) If "center", a step is drawn using the x
                           values as boundaries and the given y values are
                           associated to the mid-points between the boundaries of
                           each step. This is commonly used when drawing
                           histograms. Note that in this case, len(x) == len(y) + 1
                           If "left" or "right", the step is drawn assuming that
                           the y value is associated to the left or right boundary,
                           respectively. In this case len(x) == len(y)
                           If not passed or an empty string or None is passed, the
                           step mode is not enabled.
                           Passing True is a deprecated equivalent to "center".
                           (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.
              dynamicRangeLimit (float or None) Limit off-screen positions of data points at large
                                magnification to avoids display errors. Disabled if None.
              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.scatter.sigHovered.connect(self.scatterHovered)
  
          self._viewRangeWasChanged = False
          self._styleWasChanged = True # force initial update
  
          self._dataRect = None
          self._drlLastClip = (0.0, 0.0) # holds last clipping points of dynamic range limiter
          #self.clear()
          self.opts = {
              'connect': 'all',
  
              'fftMode': False,
              'logMode': [False, False],
              'derivativeMode': False,
              'phasemapMode': False,
              'alphaHint': 1.0,
              'alphaMode': False,
  
              'pen': (200,200,200),
              'shadowPen': None,
              'fillLevel': None,
              'fillOutline': False,
              '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,
              'dynamicRangeLimit': 1e6,
              'dynamicRangeHyst': 3.0,
  
              'data': None,
          }
          self.setCurveClickable(kargs.get('clickable', False))
          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 setCurveClickable(self, s, width=None):
          self.curve.setClickable(s, width)
  
      def curveClickable(self):
          return self.curve.clickable
  
      def boundingRect(self):
          return QtCore.QRectF()  ## let child items handle this
  
      def setPos(self, x, y):
          GraphicsObject.setPos(self, x, y)
          # to update viewRect:
          self.viewTransformChanged()
          # to update displayed point sets, e.g. when clipping (which uses viewRect):
          self.viewRangeChanged()
  
      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.updateItems(styleUpdate=False)
          self.informViewBoundsChanged()
  
      def setLogMode(self, xMode, yMode):
          """
          To enable log scaling for y<0 and y>0, the following formula is used:
          
              scaled = sign(y) * log10(abs(y) + eps)
  
          where eps is the smallest unit of y.dtype.
          This allows for handling of 0. values, scaling of large values,
          as well as the typical log scaling of values in the range -1 < x < 1.
          Note that for values within this range, the signs are inverted.
          """
          if self.opts['logMode'] == [xMode, yMode]:
              return
          self.opts['logMode'] = [xMode, yMode]
          self.xDisp = self.yDisp = None
          self.updateItems(styleUpdate=False)
          self.informViewBoundsChanged()
  
  
      def setDerivativeMode(self, mode):
          if self.opts['derivativeMode'] == mode:
              return
          self.opts['derivativeMode'] = mode
          self.xDisp = self.yDisp = None
          self.updateItems(styleUpdate=False)
          self.informViewBoundsChanged()
  
      def setPhasemapMode(self, mode):
          if self.opts['phasemapMode'] == mode:
              return
          self.opts['phasemapMode'] = mode
          self.xDisp = self.yDisp = None
          self.updateItems(styleUpdate=False)
          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(styleUpdate=True)
  
      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(styleUpdate=True)
  
      def setFillBrush(self, *args, **kargs):
          brush = fn.mkBrush(*args, **kargs)
          if self.opts['fillBrush'] == brush:
              return
          self.opts['fillBrush'] = brush
          self.updateItems(styleUpdate=True)
  
      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(styleUpdate=True)
  
      def setSymbol(self, symbol):
          if self.opts['symbol'] == symbol:
              return
          self.opts['symbol'] = symbol
          #self.scatter.setSymbol(symbol)
          self.updateItems(styleUpdate=True)
  
      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(styleUpdate=True)
  
      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(styleUpdate=True)
  
  
      def setSymbolSize(self, size):
          if self.opts['symbolSize'] == size:
              return
          self.opts['symbolSize'] = size
          #self.scatter.setSymbolSize(symbolSize)
          self.updateItems(styleUpdate=True)
  
      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(styleUpdate=False)
  
      def setClipToView(self, clip):
          if self.opts['clipToView'] == clip:
              return
          self.opts['clipToView'] = clip
          self.xDisp = self.yDisp = None
          self.updateItems(styleUpdate=False)
  
      def setDynamicRangeLimit(self, limit=1e06, hysteresis=3.):
          """
          Limit the off-screen positions of data points at large magnification
          This avoids errors with plots not displaying because their visibility is incorrectly determined. The default setting repositions far-off points to be within +-1E+06 times the viewport height.
  
          =============== ================================================================
          **Arguments:**
          limit           (float or None) Any data outside the range of limit * hysteresis
                          will be constrained to the limit value limit.
                          All values are relative to the viewport height.
                          'None' disables the check for a minimal increase in performance.
                          Default is 1E+06.
                          
          hysteresis      (float) Hysteresis factor that controls how much change
                          in zoom level (vertical height) is allowed before recalculating
                          Default is 3.0
          =============== ================================================================
          """
          if hysteresis < 1.0: 
              hysteresis = 1.0
          self.opts['dynamicRangeHyst']  = hysteresis
  
          if limit == self.opts['dynamicRangeLimit']:
              return # avoid update if there is no change
          self.opts['dynamicRangeLimit'] = limit # can be None
          self.xDisp = self.yDisp = None
          self.updateItems(styleUpdate=False)
  
      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()
          if kargs.get("stepMode", None) is True:
              warnings.warn(
                  'stepMode=True is deprecated, use stepMode="center" instead',
                  DeprecationWarning, stacklevel=3
              )
          if 'decimate' in kargs.keys():
              warnings.warn(
                  'decimate kwarg has been deprecated, it has no effect',
                  DeprecationWarning, stacklevel=2
              )
          
          if 'identical' in kargs.keys():
              warnings.warn(
                  'identical kwarg has been deprecated, it has no effect',
                  DeprecationWarning, stacklevel=2
              )
          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 dataType(x) == 'MetaArray':
                  x = x.asarray()
          if 'y' in kargs:
              y = kargs['y']
              if dataType(y) == 'MetaArray':
                  y = y.asarray()
  
          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']
              self._styleWasChanged = True
  
          if 'connect' in kargs:
              self.opts['connect'] = kargs['connect']
              self._styleWasChanged = True
  
          ## if symbol pen/brush are given with no previously set symbol, then assume symbol is 'o'
          if 'symbol' not in kargs and ('symbolPen' in kargs or 'symbolBrush' in kargs or 'symbolSize' in kargs):
              if self.opts['symbol'] is None: 
                  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]
                  self._styleWasChanged = True
          #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 or len(y) == 0: # empty data is represented as None
              self.yData = None
          else: # actual data is represented by ndarray
              if not isinstance(y, np.ndarray):
                  y = np.array(y)
              self.yData = y.view(np.ndarray)
              if x is None:
                  x = np.arange(len(y))
                  
          if x is None or len(x)==0: # empty data is represented as None
              self.xData = None
          else: # actual data is represented by ndarray
              if not isinstance(x, np.ndarray):
                  x = np.array(x)
              self.xData = x.view(np.ndarray)  # one last check to make sure there are no MetaArrays getting by
          self._dataRect = None
          self.xDisp = None
          self.yDisp = None
          profiler('set data')
  
          self.updateItems( styleUpdate = self._styleWasChanged )
          self._styleWasChanged = False # items have been updated
          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, styleUpdate=True):
          # override styleUpdate request and always enforce update until we have a better solution for
          # - ScatterPlotItem losing per-point style information
          # - PlotDataItem performing multiple unnecessary setData call on initialization
          styleUpdate=True
          
          curveArgs = {}
          scatterArgs = {}
  
          if styleUpdate: # repeat style arguments only when changed
              for k,v in [('pen','pen'), ('shadowPen','shadowPen'), ('fillLevel','fillLevel'), ('fillOutline', 'fillOutline'), ('fillBrush', 'brush'), ('antialias', 'antialias'), ('connect', 'connect'), ('stepMode', 'stepMode')]:
                  if k in self.opts:
                      curveArgs[v] = self.opts[k]
  
              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 self.opts['pen'] is not None or (self.opts['fillBrush'] is not None and self.opts['fillLevel'] is not None): # draw if visible...
              self.curve.setData(x=x, y=y, **curveArgs)
              self.curve.show()
          else: # ...hide if not.
              self.curve.hide()
  
          if self.opts['symbol'] is not None: # draw if visible...
              ## check against `True` too for backwards compatibility
              if self.opts.get('stepMode', False) in ("center", True):
                  x = 0.5 * (x[:-1] + x[1:])                
              self.scatter.setData(x=x, y=y, **scatterArgs)
              self.scatter.show()
          else: # ...hide if not.
              self.scatter.hide()
  
  
      def getData(self):
          if self.xData is None:
              return (None, None)
  
          if self.xDisp is None or self._viewRangeWasChanged:
              x = self.xData
              y = self.yData
  
              if self.opts['fftMode']:
                  x,y = self._fourierTransform(x, y)
                  # Ignore the first bin for fft data if we have a logx scale
                  if self.opts['logMode'][0]:
                      x=x[1:]
                      y=y[1:]
  
              if self.opts['derivativeMode']:  # plot dV/dt
                  y = np.diff(self.yData)/np.diff(self.xData)
                  x = x[:-1]
              if self.opts['phasemapMode']:  # plot dV/dt vs V
                  x = self.yData[:-1]
                  y = np.diff(self.yData)/np.diff(self.xData)
                      
              with np.errstate(divide='ignore'):
                  if self.opts['logMode'][0]:
                      x = np.log10(x)
                  if self.opts['logMode'][1]:
                      if np.issubdtype(y.dtype, np.floating):
                          eps = np.finfo(y.dtype).eps
                      else:
                          eps = 1
                      y = np.sign(y) * np.log10(np.abs(y)+eps)
  
              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 and len(x) > 1:
                      dx = float(x[-1]-x[0]) / (len(x)-1)
                      if dx != 0.0:
                          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 are in increasing order
                      range = self.viewRect()
                      if range is not None and len(x) > 1:
                          # clip to visible region extended by downsampling value, assuming
                          # uniform spacing of x-values, has O(1) performance
                          dx = float(x[-1]-x[0]) / (len(x)-1)
                          # workaround for slowdown from numpy deprecation issues in 1.17 to 1.20+
                          # 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)
                          x0 = fn.clip_scalar(int((range.left()-x[0])/dx) - 1*ds, 0, len(x)-1)
                          x1 = fn.clip_scalar(int((range.right()-x[0])/dx) + 2*ds, 0, len(x)-1)
  
                          # if data has been clipped too strongly (in case of non-uniform
                          # spacing of x-values), refine the clipping region as required
                          # worst case performance: O(log(n))
                          # best case performance: O(1)
                          if x[x0] > range.left():
                              x0 = np.searchsorted(x, range.left()) - 1*ds
                              x0 = fn.clip_scalar(x0, 0, len(x)) # workaround
                              # x0 = np.clip(x0, 0, len(x))
                          if x[x1] < range.right():
                              x1 = np.searchsorted(x, range.right()) + 2*ds
                              x1 = fn.clip_scalar(x1, 0, len(x))
                              # x1 = np.clip(x1, 0, len(x))
                          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)
  
              if self.opts['dynamicRangeLimit'] is not None:
                  view_range = self.viewRect()
                  if view_range is not None:
                      data_range = self.dataRect()
                      if data_range is not None:
                          view_height = view_range.height()
                          limit = self.opts['dynamicRangeLimit']
                          hyst  = self.opts['dynamicRangeHyst']
                          # never clip data if it fits into +/- (extended) limit * view height
                          if ( # note that "bottom" is the larger number, and "top" is the smaller one.
                              not data_range.bottom() < view_range.top()     # never clip if all data is too small to see
                              and not data_range.top() > view_range.bottom() # never clip if all data is too large to see
                              and data_range.height() > 2 * hyst * limit * view_height
                          ):
                              cache_is_good = False
                              # check if cached display data can be reused:
                              if self.yDisp is not None: # top is minimum value, bottom is maximum value
                                  # how many multiples of the current view height does the clipped plot extend to the top and bottom?
                                  top_exc =-(self._drlLastClip[0]-view_range.bottom()) / view_height
                                  bot_exc = (self._drlLastClip[1]-view_range.top()   ) / view_height
                                  # print(top_exc, bot_exc, hyst)
                                  if (    top_exc >= limit / hyst and top_exc <= limit * hyst
                                      and bot_exc >= limit / hyst and bot_exc <= limit * hyst ):
                                      # restore cached values
                                      x = self.xDisp
                                      y = self.yDisp
                                      cache_is_good = True
                              if not cache_is_good:
                                  min_val = view_range.bottom() - limit * view_height
                                  max_val = view_range.top()    + limit * view_height
                                  if( self.yDisp is not None              # Do we have an existing cache? 
                                      and min_val >= self._drlLastClip[0] # Are we reducing it further?
                                      and max_val <= self._drlLastClip[1] ):
                                      # if we need to clip further, we can work in-place on the output buffer
                                      # print('in-place:', end='')
                                      # workaround for slowdown from numpy deprecation issues in 1.17 to 1.20+ :
                                      # np.clip(self.yDisp, out=self.yDisp, a_min=min_val, a_max=max_val)
                                      fn.clip_array(self.yDisp, min_val, max_val, out=self.yDisp)
                                      self._drlLastClip = (min_val, max_val)
                                      # print('{:.1e}<->{:.1e}'.format( min_val, max_val ))
                                      x = self.xDisp
                                      y = self.yDisp
                                  else:
                                      # if none of the shortcuts worked, we need to recopy from the full data
                                      # print('alloc:', end='')
                                      # workaround for slowdown from numpy deprecation issues in 1.17 to 1.20+ :
                                      # y = np.clip(y, a_min=min_val, a_max=max_val)
                                      y = fn.clip_array(y, min_val, max_val)
                                      self._drlLastClip = (min_val, max_val)
                                      # print('{:.1e}<->{:.1e}'.format( min_val, max_val ))
  
              self.xDisp = x
              self.yDisp = y
              self._viewRangeWasChanged = False
          return self.xDisp, self.yDisp
  
      def dataRect(self):
          """
          Returns a bounding rectangle (as QRectF) for the full set of data.
          Will return None if there is no data or if all values (x or y) are NaN.
          """
          if self._dataRect is not None:
              return self._dataRect
          if self.xData is None or self.yData is None:
              return None
          if len(self.xData) == 0: # avoid crash if empty data is represented by [] instead of None
              return None
          with warnings.catch_warnings(): 
              # All-NaN data is handled by returning None; Explicit numpy warning is not needed.
              warnings.simplefilter("ignore")
              ymin = np.nanmin(self.yData)
              if np.isnan( ymin ):
                  return None # most likely case for all-NaN data
              xmin = np.nanmin(self.xData)
              if np.isnan( xmin ):
                  return None # less likely case for all-NaN data
              ymax = np.nanmax(self.yData)
              xmax = np.nanmax(self.xData)
          self._dataRect = QtCore.QRectF(
              QtCore.QPointF(xmin,ymin),
              QtCore.QPointF(xmax,ymax) )
          return self._dataRect
  
      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.xDisp = None
          self.yDisp = None
          self._dataRect = None
          self.curve.clear()
          self.scatter.clear()
  
      def appendData(self, *args, **kargs):
          pass
  
      def curveClicked(self, curve, ev):
          self.sigClicked.emit(self, ev)
  
      def scatterClicked(self, plt, points, ev):
          self.sigClicked.emit(self, ev)
          self.sigPointsClicked.emit(self, points, ev)
  
      def scatterHovered(self, plt, points, ev):
          self.sigPointsHovered.emit(self, points, ev)
  
      def viewRangeChanged(self):
          # view range has changed; re-plot if needed
          self._viewRangeWasChanged = True
          if( self.opts['clipToView']
              or self.opts['autoDownsample']
          ):
              self.xDisp = self.yDisp = None
              self.updateItems(styleUpdate=False)
          elif self.opts['dynamicRangeLimit'] is not None:
              # update, but do not discard cached display data
              self.updateItems(styleUpdate=False)
  
      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
          n = y.size
          f = np.fft.rfft(y) / n
          d = float(x[-1]-x[0]) / (len(x)-1)
          x = np.fft.rfftfreq(n, d)
          y = np.abs(f)
          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.items()])
          #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()
  