Hi,

If you already have your camera/processing etc working as a standalone 
module with its own acquisition thread, then it might be easy enough just 
to use one of the scrolling plot examples above. Get your acquisition 
thread to store the data (say, in a circular buffer or expanding array). On 
the QTimer update from the scrolling plot examples, reach into the buffer 
to and curve.setData(). Just ensure you make it threadsafe, for example by 
surrounding any reads or writes to the buffer with locks 
<https://docs.python.org/3/library/threading.html#lock-objects>.

Patrick

On Friday, 18 May 2018 08:03:19 UTC+9:30, Kaisar Khatak wrote:
>
> Thank you for responding. I am a noob to pyqtgraph.
>
> The capture rate will match the frame rate and processing. So, I expect 
> there to be 25-30 (frame rate) values per second. Its a very simple python 
> app. I do not think I even need  date/time variable on the second (x) axis 
> - maybe a nice to have. I just want to show eye aspect ratio (fluctuations) 
> on the y axis over time (e.g. 60 second demo). I can do this in matplotlib, 
> but the graphs don't look as cool as pyqtgraph. 
>
> I have attached a similar style script which captures yawns (e.g. 
> lip_distance). So, in this case, I want to store lip_distance into an array 
> and while program writes/appends/extends to this lip_distance [] array, I 
> want the plot to show the new values as they are captured. Has anyone built 
> something similar? I saw a few posts in this group around scrolling plots.
>
> So, is scrolling plots the recommended route?  Or is there another python 
> visualization tool that can solve this?
>
> On Thursday, May 17, 2018 at 12:52:11 PM UTC-4, Luke Campagnola wrote:
>>
>> My suggestion would have been to look at the scrolling plot examples, but 
>> you have already done that. Have you tried using any of those? If so, why 
>> didn't they work for you?
>>
>> On Wed, May 16, 2018, 23:39 Kaisar Khatak <kaisar...@gmail.com> wrote:
>>
>>> I have a real time application that uses a camera to measure eye aspect 
>>> ratio. The ratio will be an integer value and will fluctuate between 1-10 
>>> (example).
>>>
>>> What is the easiest way to capture that value and display a scrolling 
>>> plot (best approach?) real time? Do I need threading? Just looking for 
>>> simplest solution to start off with...
>>>
>>> I have read the scrollingPlots.py examples...
>>>
>>> I have installled pyqtgraph and am running python 2/3 on ubuntu 16.
>>>
>>>
>>> Thanks.
>>>
>>>
>>> ----------------------------------------------------------------------------
>>> # -*- coding: utf-8 -*-
>>> """
>>> Various methods of drawing scrolling plots.
>>> """
>>> import initExample ## Add path to library (just for examples; you do not 
>>> need this)
>>>
>>> import pyqtgraph as pg
>>> from pyqtgraph.Qt import QtCore, QtGui
>>> import numpy as np
>>>
>>> win = pg.GraphicsLayoutWidget(show=True)
>>> win.setWindowTitle('pyqtgraph example: Scrolling Plots')
>>>
>>>
>>> # 1) Simplest approach -- update data in the array such that plot 
>>> appears to scroll
>>> #    In these examples, the array size is fixed.
>>> p1 = win.addPlot()
>>> p2 = win.addPlot()
>>> data1 = np.random.normal(size=300)
>>> curve1 = p1.plot(data1)
>>> curve2 = p2.plot(data1)
>>> ptr1 = 0
>>> def update1():
>>>     global data1, ptr1
>>>     data1[:-1] = data1[1:]  # shift data in the array one sample left
>>>                             # (see also: np.roll)
>>>     data1[-1] = np.random.normal()
>>>     curve1.setData(data1)
>>>     
>>>     ptr1 += 1
>>>     curve2.setData(data1)
>>>     curve2.setPos(ptr1, 0)
>>>     
>>>
>>> # 2) Allow data to accumulate. In these examples, the array doubles in 
>>> length
>>> #    whenever it is full. 
>>> win.nextRow()
>>> p3 = win.addPlot()
>>> p4 = win.addPlot()
>>> # Use automatic downsampling and clipping to reduce the drawing load
>>> p3.setDownsampling(mode='peak')
>>> p4.setDownsampling(mode='peak')
>>> p3.setClipToView(True)
>>> p4.setClipToView(True)
>>> p3.setRange(xRange=[-100, 0])
>>> p3.setLimits(xMax=0)
>>> curve3 = p3.plot()
>>> curve4 = p4.plot()
>>>
>>> data3 = np.empty(100)
>>> ptr3 = 0
>>>
>>> def update2():
>>>     global data3, ptr3
>>>     data3[ptr3] = np.random.normal()
>>>     ptr3 += 1
>>>     if ptr3 >= data3.shape[0]:
>>>         tmp = data3
>>>         data3 = np.empty(data3.shape[0] * 2)
>>>         data3[:tmp.shape[0]] = tmp
>>>     curve3.setData(data3[:ptr3])
>>>     curve3.setPos(-ptr3, 0)
>>>     curve4.setData(data3[:ptr3])
>>>
>>>
>>> # 3) Plot in chunks, adding one new plot curve for every 100 samples
>>> chunkSize = 100
>>> # Remove chunks after we have 10
>>> maxChunks = 10
>>> startTime = pg.ptime.time()
>>> win.nextRow()
>>> p5 = win.addPlot(colspan=2)
>>> p5.setLabel('bottom', 'Time', 's')
>>> p5.setXRange(-10, 0)
>>> curves = []
>>> data5 = np.empty((chunkSize+1,2))
>>> ptr5 = 0
>>>
>>> def update3():
>>>     global p5, data5, ptr5, curves
>>>     now = pg.ptime.time()
>>>     for c in curves:
>>>         c.setPos(-(now-startTime), 0)
>>>     
>>>     i = ptr5 % chunkSize
>>>     if i == 0:
>>>         curve = p5.plot()
>>>         curves.append(curve)
>>>         last = data5[-1]
>>>         data5 = np.empty((chunkSize+1,2))        
>>>         data5[0] = last
>>>         while len(curves) > maxChunks:
>>>             c = curves.pop(0)
>>>             p5.removeItem(c)
>>>     else:
>>>         curve = curves[-1]
>>>     data5[i+1,0] = now - startTime
>>>     data5[i+1,1] = np.random.normal()
>>>     curve.setData(x=data5[:i+2, 0], y=data5[:i+2, 1])
>>>     ptr5 += 1
>>>
>>>
>>> # update all plots
>>> def update():
>>>     update1()
>>>     update2()
>>>     update3()
>>> timer = pg.QtCore.QTimer()
>>> timer.timeout.connect(update)
>>> timer.start(50)
>>>
>>>
>>>
>>> ## Start Qt event loop unless running in interactive mode or using 
>>> pyside.
>>> if __name__ == '__main__':
>>>     import sys
>>>     if (sys.flags.interactive != 1) or not hasattr(QtCore, 
>>> 'PYQT_VERSION'):
>>>         QtGui.QApplication.instance().exec_()
>>>
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