Python pyqtgraph.plot() Examples

def update():
    markerGen.update()
    markerIn.update()
    betaGen.update(task=markerIn.task)  # Rate-limiting step. Will time.sleep as needed.
    signal, tvec = betaIn.update()

    if haspyqtgraph:
        plot = qwindow.getPlotItem()
        graphs = plot.listDataItems()
        if not graphs:
            # create graphs
            for i in range(signal.shape[1]):
                plot.plot(tvec, signal[:, i])
        else:
            # update graphs
            for i in range(signal.shape[1]):
                graphs[i].setData(signal[:, i], x=tvec) 

def update_plot(self):
        self.cycle += 1
        if self.cycle == 1:
            self.starttime = time.time()
        if self.cycle == self.cycles:
            self.endtime = time.time()
        if self.REDPITAYA:
            t = self.r.scope.times
            #y1 = self.r.scope.curve(ch=1, timeout=0)
            #y2 = self.r.scope.curve(ch=2, timeout=0)
            #self.r.scope.setup()
            y1 = self.r.scope._data_ch1_current
            y2 = self.r.scope._data_ch2_current
        else:
            t = self.t0 + (time.time()-self.starttime)
            phi = 2.0*np.pi*self.frequency*t
            y1 = np.sin(phi)
            y2 = np.cos(phi)
        if self.cycle == 1:
            self.c1 = self.plotWidget.plot(t, y1, pen='g')
            self.c2 = self.plotWidget.plot(t, y2, pen='r')
        else:
            self.c1.setData(t, y1)
            self.c2.setData(t, y2) 

def clicked(self):
	l = float(self.edit1.text())
	r = float(self.edit2.text())
	s = float(self.edit3.text())
	x = np.arange(l,r,s)
	print(x)
	s = calculator_class.var
	print("In Second")
	print(s)
	y = eval(s)
	pg.setConfigOption('background', 'w')      # sets background white
    	pg.setConfigOption('foreground', 'k')      # sets axis color to black
	pw = pg.plot(x,y,pen = 'k')
	pw.setTitle('y = f(x)')
   	pw.setLabel('bottom', 'x -->')           	# x-label
    	pw.setLabel('left', 'y = f(x) -->')             # y-label 

def test_CSVExporter():
    tempfilename = tempfile.NamedTemporaryFile(suffix='.csv').name
    print("using %s as a temporary file" % tempfilename)
    
    plt = pg.plot()
    y1 = [1,3,2,3,1,6,9,8,4,2]
    plt.plot(y=y1, name='myPlot')
    
    y2 = [3,4,6,1,2,4,2,3,5,3,5,1,3]
    x2 = pg.np.linspace(0, 1.0, len(y2))
    plt.plot(x=x2, y=y2)
    
    y3 = [1,5,2,3,4,6,1,2,4,2,3,5,3]
    x3 = pg.np.linspace(0, 1.0, len(y3)+1)
    plt.plot(x=x3, y=y3, stepMode=True)
    
    ex = pg.exporters.CSVExporter(plt.plotItem)
    ex.export(fileName=tempfilename)

    r = csv.reader(open(tempfilename, 'r'))
    lines = [line for line in r]
    header = lines.pop(0)
    assert header == ['myPlot_x', 'myPlot_y', 'x0001', 'y0001', 'x0002', 'y0002']
    
    i = 0
    for vals in lines:
        vals = list(map(str.strip, vals))
        assert (i >= len(y1) and vals[0] == '') or approxeq(float(vals[0]), i) 
        assert (i >= len(y1) and vals[1] == '') or approxeq(float(vals[1]), y1[i]) 
        
        assert (i >= len(x2) and vals[2] == '') or approxeq(float(vals[2]), x2[i])
        assert (i >= len(y2) and vals[3] == '') or approxeq(float(vals[3]), y2[i])
        
        assert (i >= len(x3) and vals[4] == '') or approxeq(float(vals[4]), x3[i])
        assert (i >= len(y3) and vals[5] == '') or approxeq(float(vals[5]), y3[i])
        i += 1

    os.unlink(tempfilename) 

def test_InfiniteLine():
    # Test basic InfiniteLine API
    plt = pg.plot()
    plt.setXRange(-10, 10)
    plt.setYRange(-10, 10)
    plt.resize(600, 600)
    
    # seemingly arbitrary requirements; might need longer wait time for some platforms..
    QtTest.QTest.qWaitForWindowShown(plt)
    QtTest.QTest.qWait(100)
    
    vline = plt.addLine(x=1)
    assert vline.angle == 90
    br = vline.mapToView(QtGui.QPolygonF(vline.boundingRect()))
    assert br.containsPoint(pg.Point(1, 5), QtCore.Qt.OddEvenFill)
    assert not br.containsPoint(pg.Point(5, 0), QtCore.Qt.OddEvenFill)
    hline = plt.addLine(y=0)
    assert hline.angle == 0
    assert hline.boundingRect().contains(pg.Point(5, 0))
    assert not hline.boundingRect().contains(pg.Point(0, 5))

    vline.setValue(2)
    assert vline.value() == 2
    vline.setPos(pg.Point(4, -5))
    assert vline.value() == 4
    
    oline = pg.InfiniteLine(angle=30)
    plt.addItem(oline)
    oline.setPos(pg.Point(1, -1))
    assert oline.angle == 30
    assert oline.pos() == pg.Point(1, -1)
    assert oline.value() == [1, -1]
    
    # test bounding rect for oblique line
    br = oline.mapToScene(oline.boundingRect())
    pos = oline.mapToScene(pg.Point(2, 0))
    assert br.containsPoint(pos, QtCore.Qt.OddEvenFill)
    px = pg.Point(-0.5, -1.0 / 3**0.5)
    assert br.containsPoint(pos + 5 * px, QtCore.Qt.OddEvenFill)
    assert not br.containsPoint(pos + 7 * px, QtCore.Qt.OddEvenFill) 

def test_CSVExporter():
    tempfilename = tempfile.NamedTemporaryFile(suffix='.csv').name
    print("using %s as a temporary file" % tempfilename)
    
    plt = pg.plot()
    y1 = [1,3,2,3,1,6,9,8,4,2]
    plt.plot(y=y1, name='myPlot')
    
    y2 = [3,4,6,1,2,4,2,3,5,3,5,1,3]
    x2 = pg.np.linspace(0, 1.0, len(y2))
    plt.plot(x=x2, y=y2)
    
    y3 = [1,5,2,3,4,6,1,2,4,2,3,5,3]
    x3 = pg.np.linspace(0, 1.0, len(y3)+1)
    plt.plot(x=x3, y=y3, stepMode=True)
    
    ex = pg.exporters.CSVExporter(plt.plotItem)
    ex.export(fileName=tempfilename)

    r = csv.reader(open(tempfilename, 'r'))
    lines = [line for line in r]
    header = lines.pop(0)
    assert header == ['myPlot_x', 'myPlot_y', 'x0001', 'y0001', 'x0002', 'y0002']
    
    i = 0
    for vals in lines:
        vals = list(map(str.strip, vals))
        assert (i >= len(y1) and vals[0] == '') or approxeq(float(vals[0]), i) 
        assert (i >= len(y1) and vals[1] == '') or approxeq(float(vals[1]), y1[i]) 
        
        assert (i >= len(x2) and vals[2] == '') or approxeq(float(vals[2]), x2[i])
        assert (i >= len(y2) and vals[3] == '') or approxeq(float(vals[3]), y2[i])
        
        assert (i >= len(x3) and vals[4] == '') or approxeq(float(vals[4]), x3[i])
        assert (i >= len(y3) and vals[5] == '') or approxeq(float(vals[5]), y3[i])
        i += 1

    os.unlink(tempfilename) 

def test_InfiniteLine():
    # Test basic InfiniteLine API
    plt = pg.plot()
    plt.setXRange(-10, 10)
    plt.setYRange(-10, 10)
    plt.resize(600, 600)
    
    # seemingly arbitrary requirements; might need longer wait time for some platforms..
    QtTest.QTest.qWaitForWindowShown(plt)
    QtTest.QTest.qWait(100)
    
    vline = plt.addLine(x=1)
    assert vline.angle == 90
    br = vline.mapToView(QtGui.QPolygonF(vline.boundingRect()))
    assert br.containsPoint(pg.Point(1, 5), QtCore.Qt.OddEvenFill)
    assert not br.containsPoint(pg.Point(5, 0), QtCore.Qt.OddEvenFill)
    hline = plt.addLine(y=0)
    assert hline.angle == 0
    assert hline.boundingRect().contains(pg.Point(5, 0))
    assert not hline.boundingRect().contains(pg.Point(0, 5))

    vline.setValue(2)
    assert vline.value() == 2
    vline.setPos(pg.Point(4, -5))
    assert vline.value() == 4
    
    oline = pg.InfiniteLine(angle=30)
    plt.addItem(oline)
    oline.setPos(pg.Point(1, -1))
    assert oline.angle == 30
    assert oline.pos() == pg.Point(1, -1)
    assert oline.value() == [1, -1]
    
    # test bounding rect for oblique line
    br = oline.mapToScene(oline.boundingRect())
    pos = oline.mapToScene(pg.Point(2, 0))
    assert br.containsPoint(pos, QtCore.Qt.OddEvenFill)
    px = pg.Point(-0.5, -1.0 / 3**0.5)
    assert br.containsPoint(pos + 5 * px, QtCore.Qt.OddEvenFill)
    assert not br.containsPoint(pos + 7 * px, QtCore.Qt.OddEvenFill) 

def pull_and_plot(self, plot_time: float, plt: pg.PlotItem):
        """Pull data from the inlet and add it to the plot.
        :param plot_time: lowest timestamp that's still visible in the plot
        :param plt: the plot the data should be shown on
        """
        # We don't know what to do with a generic inlet, so we skip it.
        pass 

def pull_and_plot(self, plot_time, plt):
        # pull the data
        _, ts = self.inlet.pull_chunk(timeout=0.0,
                                      max_samples=self.buffer.shape[0],
                                      dest_obj=self.buffer)
        # ts will be empty if no samples were pulled, a list of timestamps otherwise
        if ts:
            ts = np.asarray(ts)
            y = self.buffer[0:ts.size, :]
            this_x = None
            old_offset = 0
            new_offset = 0
            for ch_ix in range(self.channel_count):
                # we don't pull an entire screen's worth of data, so we have to
                # trim the old data and append the new data to it
                old_x, old_y = self.curves[ch_ix].getData()
                # the timestamps are identical for all channels, so we need to do
                # this calculation only once
                if ch_ix == 0:
                    # find the index of the first sample that's still visible,
                    # i.e. newer than the left border of the plot
                    old_offset = old_x.searchsorted(plot_time)
                    # same for the new data, in case we pulled more data than
                    # can be shown at once
                    new_offset = ts.searchsorted(plot_time)
                    # append new timestamps to the trimmed old timestamps
                    this_x = np.hstack((old_x[old_offset:], ts[new_offset:]))
                # append new data to the trimmed old data
                this_y = np.hstack((old_y[old_offset:], y[new_offset:, ch_ix] - ch_ix))
                # replace the old data
                self.curves[ch_ix].setData(this_x, this_y) 

def setup(self):
        self.t0 = np.linspace(0, self.duration, self.N)
        self.plotWidget = pg.plot(title="Realtime plotting benchmark")
        self.cycle = 0
        self.starttime = time.time()  # not yet the actual starttime, but needed for timeout
        if self.REDPITAYA:
            self.r.scope.setup(trigger_source='immediately', duration=self.duration)
        self.timer = QtCore.QTimer()
        self.timer.setInterval(1000*self.dt)
        self.timer.timeout.connect(self.update_plot)
        self.timer.start() 

def test_CSVExporter():
    tempfilename = tempfile.NamedTemporaryFile(suffix='.csv').name
    print("using %s as a temporary file" % tempfilename)
    
    plt = pg.plot()
    y1 = [1,3,2,3,1,6,9,8,4,2]
    plt.plot(y=y1, name='myPlot')
    
    y2 = [3,4,6,1,2,4,2,3,5,3,5,1,3]
    x2 = pg.np.linspace(0, 1.0, len(y2))
    plt.plot(x=x2, y=y2)
    
    y3 = [1,5,2,3,4,6,1,2,4,2,3,5,3]
    x3 = pg.np.linspace(0, 1.0, len(y3)+1)
    plt.plot(x=x3, y=y3, stepMode=True)
    
    ex = pg.exporters.CSVExporter(plt.plotItem)
    ex.export(fileName=tempfilename)

    r = csv.reader(open(tempfilename, 'r'))
    lines = [line for line in r]
    header = lines.pop(0)
    assert header == ['myPlot_x', 'myPlot_y', 'x0001', 'y0001', 'x0002', 'y0002']
    
    i = 0
    for vals in lines:
        vals = list(map(str.strip, vals))
        assert (i >= len(y1) and vals[0] == '') or approxeq(float(vals[0]), i) 
        assert (i >= len(y1) and vals[1] == '') or approxeq(float(vals[1]), y1[i]) 
        
        assert (i >= len(x2) and vals[2] == '') or approxeq(float(vals[2]), x2[i])
        assert (i >= len(y2) and vals[3] == '') or approxeq(float(vals[3]), y2[i])
        
        assert (i >= len(x3) and vals[4] == '') or approxeq(float(vals[4]), x3[i])
        assert (i >= len(y3) and vals[5] == '') or approxeq(float(vals[5]), y3[i])
        i += 1

    os.unlink(tempfilename) 

def updateHDF5Plot(self):
        if self.hdf5 is None:
            self.setData([])
            return
        
        vb = self.getViewBox()
        if vb is None:
            return  # no ViewBox yet
        
        # Determine what data range must be read from HDF5
        xrange = vb.viewRange()[0]
        start = max(0,int(xrange[0])-1)
        stop = min(len(self.hdf5), int(xrange[1]+2))
        
        # Decide by how much we should downsample 
        ds = int((stop-start) / self.limit) + 1
        
        if ds == 1:
            # Small enough to display with no intervention.
            visible = self.hdf5[start:stop]
            scale = 1
        else:
            # Here convert data into a down-sampled array suitable for visualizing.
            # Must do this piecewise to limit memory usage.        
            samples = 1 + ((stop-start) // ds)
            visible = np.zeros(samples*2, dtype=self.hdf5.dtype)
            sourcePtr = start
            targetPtr = 0
            
            # read data in chunks of ~1M samples
            chunkSize = (1000000//ds) * ds
            while sourcePtr < stop-1: 
                chunk = self.hdf5[sourcePtr:min(stop,sourcePtr+chunkSize)]
                sourcePtr += len(chunk)
                
                # reshape chunk to be integral multiple of ds
                chunk = chunk[:(len(chunk)//ds) * ds].reshape(len(chunk)//ds, ds)
                
                # compute max and min
                chunkMax = chunk.max(axis=1)
                chunkMin = chunk.min(axis=1)
                
                # interleave min and max into plot data to preserve envelope shape
                visible[targetPtr:targetPtr+chunk.shape[0]*2:2] = chunkMin
                visible[1+targetPtr:1+targetPtr+chunk.shape[0]*2:2] = chunkMax
                targetPtr += chunk.shape[0]*2
            
            visible = visible[:targetPtr]
            scale = ds * 0.5
            
        self.setData(visible) # update the plot
        self.setPos(start, 0) # shift to match starting index
        self.resetTransform()
        self.scale(scale, 1)  # scale to match downsampling 

def test_mouseInteraction():
    plt = pg.plot()
    plt.scene().minDragTime = 0  # let us simulate mouse drags very quickly.
    vline = plt.addLine(x=0, movable=True)
    plt.addItem(vline)
    hline = plt.addLine(y=0, movable=True)
    hline2 = plt.addLine(y=-1, movable=False)
    plt.setXRange(-10, 10)
    plt.setYRange(-10, 10)
    
    # test horizontal drag
    pos = plt.plotItem.vb.mapViewToScene(pg.Point(0,5)).toPoint()
    pos2 = pos - QtCore.QPoint(200, 200)
    mouseMove(plt, pos)
    assert vline.mouseHovering is True and hline.mouseHovering is False
    mouseDrag(plt, pos, pos2, QtCore.Qt.LeftButton)
    px = vline.pixelLength(pg.Point(1, 0), ortho=True)
    assert abs(vline.value() - plt.plotItem.vb.mapSceneToView(pos2).x()) <= px

    # test missed drag
    pos = plt.plotItem.vb.mapViewToScene(pg.Point(5,0)).toPoint()
    pos = pos + QtCore.QPoint(0, 6)
    pos2 = pos + QtCore.QPoint(-20, -20)
    mouseMove(plt, pos)
    assert vline.mouseHovering is False and hline.mouseHovering is False
    mouseDrag(plt, pos, pos2, QtCore.Qt.LeftButton)
    assert hline.value() == 0

    # test vertical drag
    pos = plt.plotItem.vb.mapViewToScene(pg.Point(5,0)).toPoint()
    pos2 = pos - QtCore.QPoint(50, 50)
    mouseMove(plt, pos)
    assert vline.mouseHovering is False and hline.mouseHovering is True
    mouseDrag(plt, pos, pos2, QtCore.Qt.LeftButton)
    px = hline.pixelLength(pg.Point(1, 0), ortho=True)
    assert abs(hline.value() - plt.plotItem.vb.mapSceneToView(pos2).y()) <= px

    # test non-interactive line
    pos = plt.plotItem.vb.mapViewToScene(pg.Point(5,-1)).toPoint()
    pos2 = pos - QtCore.QPoint(50, 50)
    mouseMove(plt, pos)
    assert hline2.mouseHovering == False
    mouseDrag(plt, pos, pos2, QtCore.Qt.LeftButton)
    assert hline2.value() == -1 

def test_mouseInteraction():
    plt = pg.plot()
    plt.scene().minDragTime = 0  # let us simulate mouse drags very quickly.
    vline = plt.addLine(x=0, movable=True)
    plt.addItem(vline)
    hline = plt.addLine(y=0, movable=True)
    hline2 = plt.addLine(y=-1, movable=False)
    plt.setXRange(-10, 10)
    plt.setYRange(-10, 10)
    
    # test horizontal drag
    pos = plt.plotItem.vb.mapViewToScene(pg.Point(0,5)).toPoint()
    pos2 = pos - QtCore.QPoint(200, 200)
    mouseMove(plt, pos)
    assert vline.mouseHovering is True and hline.mouseHovering is False
    mouseDrag(plt, pos, pos2, QtCore.Qt.LeftButton)
    px = vline.pixelLength(pg.Point(1, 0), ortho=True)
    assert abs(vline.value() - plt.plotItem.vb.mapSceneToView(pos2).x()) <= px

    # test missed drag
    pos = plt.plotItem.vb.mapViewToScene(pg.Point(5,0)).toPoint()
    pos = pos + QtCore.QPoint(0, 6)
    pos2 = pos + QtCore.QPoint(-20, -20)
    mouseMove(plt, pos)
    assert vline.mouseHovering is False and hline.mouseHovering is False
    mouseDrag(plt, pos, pos2, QtCore.Qt.LeftButton)
    assert hline.value() == 0

    # test vertical drag
    pos = plt.plotItem.vb.mapViewToScene(pg.Point(5,0)).toPoint()
    pos2 = pos - QtCore.QPoint(50, 50)
    mouseMove(plt, pos)
    assert vline.mouseHovering is False and hline.mouseHovering is True
    mouseDrag(plt, pos, pos2, QtCore.Qt.LeftButton)
    px = hline.pixelLength(pg.Point(1, 0), ortho=True)
    assert abs(hline.value() - plt.plotItem.vb.mapSceneToView(pos2).y()) <= px

    # test non-interactive line
    pos = plt.plotItem.vb.mapViewToScene(pg.Point(5,-1)).toPoint()
    pos2 = pos - QtCore.QPoint(50, 50)
    mouseMove(plt, pos)
    assert hline2.mouseHovering == False
    mouseDrag(plt, pos, pos2, QtCore.Qt.LeftButton)
    assert hline2.value() == -1 

def main():
    # firstly resolve all streams that could be shown
    inlets: List[Inlet] = []
    print("looking for streams")
    streams = pylsl.resolve_streams()

    # Create the pyqtgraph window
    pw = pg.plot(title='LSL Plot')
    plt = pw.getPlotItem()
    plt.enableAutoRange(x=False, y=True)

    # iterate over found streams, creating specialized inlet objects that will
    # handle plotting the data
    for info in streams:
        if info.type() == 'Markers':
            if info.nominal_srate() != pylsl.IRREGULAR_RATE \
                    or info.channel_format() != pylsl.cf_string:
                print('Invalid marker stream ' + info.name())
            print('Adding marker inlet: ' + info.name())
            inlets.append(MarkerInlet(info))
        elif info.nominal_srate() != pylsl.IRREGULAR_RATE \
                and info.channel_format() != pylsl.cf_string:
            print('Adding data inlet: ' + info.name())
            inlets.append(DataInlet(info, plt))
        else:
            print('Don\'t know what to do with stream ' + info.name())

    def scroll():
        """Move the view so the data appears to scroll"""
        # We show data only up to a timepoint shortly before the current time
        # so new data doesn't suddenly appear in the middle of the plot
        fudge_factor = pull_interval * .002
        plot_time = pylsl.local_clock()
        pw.setXRange(plot_time - plot_duration + fudge_factor, plot_time - fudge_factor)

    def update():
        # Read data from the inlet. Use a timeout of 0.0 so we don't block GUI interaction.
        mintime = pylsl.local_clock() - plot_duration
        # call pull_and_plot for each inlet.
        # Special handling of inlet types (markers, continuous data) is done in
        # the different inlet classes.
        for inlet in inlets:
            inlet.pull_and_plot(mintime, plt)

    # create a timer that will move the view every update_interval ms
    update_timer = QtCore.QTimer()
    update_timer.timeout.connect(scroll)
    update_timer.start(update_interval)

    # create a timer that will pull and add new data occasionally
    pull_timer = QtCore.QTimer()
    pull_timer.timeout.connect(update)
    pull_timer.start(pull_interval)

    import sys

    # Start Qt event loop unless running in interactive mode or using pyside.
    if (sys.flags.interactive != 1) or not hasattr(QtCore, 'PYQT_VERSION'):
        QtGui.QApplication.instance().exec_() 

def updateHDF5Plot(self):
        if self.hdf5 is None:
            self.setData([])
            return
        
        vb = self.getViewBox()
        if vb is None:
            return  # no ViewBox yet
        
        # Determine what data range must be read from HDF5
        xrange = vb.viewRange()[0]
        start = max(0,int(xrange[0])-1)
        stop = min(len(self.hdf5), int(xrange[1]+2))
        
        # Decide by how much we should downsample 
        ds = int((stop-start) / self.limit) + 1
        
        if ds == 1:
            # Small enough to display with no intervention.
            visible = self.hdf5[start:stop]
            scale = 1
        else:
            # Here convert data into a down-sampled array suitable for visualizing.
            # Must do this piecewise to limit memory usage.        
            samples = 1 + ((stop-start) // ds)
            visible = np.zeros(samples*2, dtype=self.hdf5.dtype)
            sourcePtr = start
            targetPtr = 0
            
            # read data in chunks of ~1M samples
            chunkSize = (1000000//ds) * ds
            while sourcePtr < stop-1: 
                chunk = self.hdf5[sourcePtr:min(stop,sourcePtr+chunkSize)]
                sourcePtr += len(chunk)
                
                # reshape chunk to be integral multiple of ds
                chunk = chunk[:(len(chunk)//ds) * ds].reshape(len(chunk)//ds, ds)
                
                # compute max and min
                chunkMax = chunk.max(axis=1)
                chunkMin = chunk.min(axis=1)
                
                # interleave min and max into plot data to preserve envelope shape
                visible[targetPtr:targetPtr+chunk.shape[0]*2:2] = chunkMin
                visible[1+targetPtr:1+targetPtr+chunk.shape[0]*2:2] = chunkMax
                targetPtr += chunk.shape[0]*2
            
            visible = visible[:targetPtr]
            scale = ds * 0.5
            
        self.setData(visible) # update the plot
        self.setPos(start, 0) # shift to match starting index
        self.resetTransform()
        self.scale(scale, 1)  # scale to match downsampling