def _show_stream_items()

in tensorwatch/mpl/line_plot.py [0:0]

• 62 lines of code
• 21 McCabe index (conditional complexity)

    def _show_stream_items(self, stream_vis, stream_items):
        """Paint the given stream_items in to visualizer. If visualizer is dirty then return False else True.
        """

        vals = self._extract_vals(stream_items)
        if not len(vals):
            return True # not dirty

        line = stream_vis.ax.get_lines()[-1]
        if not self.is_3d:
            xdata, ydata = line.get_data()
            zdata = []
        else:
            xdata, ydata, zdata = line._verts3d # in future use get_data_3d(), these are numpy arrays
            xdata, ydata, zdata = list(xdata), list(ydata), list(zdata)
        anndata, txtdata, clrdata = [], [], []
        lows, highs = [], [] # confidence interval

        unpacker = lambda a0=None,a1=None,a2=None,a3=None,a4=None,a5=None,a6=None,a7=None,*_:\
            (a0,a1,a2,a3,a4,a5,a6,a7)

        # add each value in trace data
        # each value is of the form:
        # 2D graphs:
        #   y
        #   x [, y [,low, [, high [,annotation [, text [, color]]]]]]
        #   y
        #   x [, y [, z, [,low, [, high [annotation [, text [, color]]]]]
        for val in vals:
            # set defaults
            x, y, z =  len(xdata), None, None
            low, high = None, None
            ann, txt, clr = None, None, None

            # if val turns out to be array-like, extract x,y
            val_l = utils.is_scaler_array(val)
            if val_l >= 0:
                if self.is_3d:
                    x, y, z, low, high, ann, txt, clr = unpacker(*val)
                else:
                    x, y, low, high, ann, txt, clr, _ = unpacker(*val)
            elif isinstance(val, PointData):
                x, y, z, low, high, ann, txt, clr = val.x, val.y, val.z, \
                    val.low, val.high, val.annotation, val.text, val.color
            else:
                y = val

            if ann is not None:
                ann = str(ann)
            if txt is not None:
                txt = str(txt)

            xdata.append(x)
            ydata.append(y)
            zdata.append(z)
            if low is not None:
                lows.append(low)
            if high is not None:
                highs.append(high)
            if txt is not None:
                txtdata.append(txt)
            if clr is not None:
                clrdata.append(clr)
            if ann: #TODO: yref should be y2 for different y axis
                anndata.append(dict(x=x, y=y, xref='x', yref='y', text=ann, showarrow=False))

        if not self.is_3d:
            line.set_data(xdata, ydata)
        else:
            # in future use set_data_3d
            line._verts3d = (xdata, ydata, zdata)
            line.stale = True

        if stream_vis.fill_between_col is not None:
            stream_vis.fill_between_col.remove()
        if len(lows) > 0 and len(highs) > 0:
            stream_vis.ax.fill_between(xdata, highs, lows, color=stream_vis.color, alpha=0.2)
        for ann in anndata:
            stream_vis.xylabel_refs.append(stream_vis.ax.text( \
                ann['x'], ann['y'], ann['text']))

        stream_vis.ax.relim()
        stream_vis.ax.autoscale_view(True,True,True)

        return False # dirty