pmod_dac_adc


DAC-ADC Pmod Examples using Matplotlib and Widget



Pmod DAC-ADC Feedback

This example shows how to use the PmodDA4 DAC and the PmodAD2 ADC on the PYNQ-Z1 board, using the baord's two Pmod interfaces. The notebook then compares the DAC output to the ADC input and tracks the errors.

The errors are plotted using Matplotlib and an XKCD version of the plot is produced (for fun). Finally a slider widget is introduced to control the number of samples diaplayed in the error plot.

Note: The output of the DAC (pin A) must be connected with a wire to the input of the ADC (V1 input).

1. Import hardware libraries and classes


In [1]:
from pynq import Overlay
from pynq.iop import Pmod_ADC, Pmod_DAC

2. Program the ZYNQ PL


In [2]:
ol = Overlay('base.bit')
ol.download()

3. Instantiate the Pmod peripherals as Python objects


In [3]:
adc = Pmod_ADC(1)
dac = Pmod_DAC(2)

4. Write to DAC, read from ADC, print result


In [4]:
dac.write(0.35)
sample = adc.read()
print(sample)


[0.3418]

Tracking the IO Error

Report DAC-ADC Pmod Loopback Measurement Error.


In [5]:
from math import ceil
from time import sleep
import numpy as np
import matplotlib.pyplot as plt

from pynq import Overlay
from pynq.iop import Pmod_ADC, Pmod_DAC

ol = Overlay('base.bit')
ol.download()

adc = Pmod_ADC(1)
dac = Pmod_DAC(2)

delay = 0.0
values = np.linspace(0, 2, 20)
samples = []
for value in values:
    dac.write(value)
    sleep(delay)
    sample = adc.read()
    samples.append(sample[0])
    print('Value written: {:4.2f}\tSample read: {:4.2f}\tError: {:+4.4f}'.
          format(value, sample[0], sample[0]-value))


Value written: 0.00	Sample read: 0.00	Error: +0.0020
Value written: 0.11	Sample read: 0.10	Error: -0.0037
Value written: 0.21	Sample read: 0.21	Error: +0.0004
Value written: 0.32	Sample read: 0.30	Error: -0.0111
Value written: 0.42	Sample read: 0.43	Error: +0.0086
Value written: 0.53	Sample read: 0.50	Error: -0.0278
Value written: 0.63	Sample read: 0.62	Error: -0.0144
Value written: 0.74	Sample read: 0.73	Error: -0.0102
Value written: 0.84	Sample read: 0.81	Error: -0.0335
Value written: 0.95	Sample read: 0.93	Error: -0.0177
Value written: 1.05	Sample read: 1.03	Error: -0.0253
Value written: 1.16	Sample read: 1.12	Error: -0.0368
Value written: 1.26	Sample read: 1.24	Error: -0.0210
Value written: 1.37	Sample read: 1.31	Error: -0.0598
Value written: 1.47	Sample read: 1.43	Error: -0.0401
Value written: 1.58	Sample read: 1.53	Error: -0.0516
Value written: 1.68	Sample read: 1.62	Error: -0.0631
Value written: 1.79	Sample read: 1.75	Error: -0.0434
Value written: 1.89	Sample read: 1.81	Error: -0.0842
Value written: 2.00	Sample read: 1.93	Error: -0.0664

Error plot with Matplotlib

This example shows plots in notebook (rather than in separate window).


In [6]:
%matplotlib inline
    
X = np.arange(len(values))
plt.bar(X + 0.0, values, facecolor='blue', 
        edgecolor='white', width=0.5, label="Written_to_DAC")
plt.bar(X + 0.25, samples, facecolor='red', 
        edgecolor='white', width=0.5, label="Read_from_ADC")

plt.title('DAC-ADC Linearity')
plt.xlabel('Sample_number')
plt.ylabel('Volts')
plt.legend(loc='upper left', frameon=False)

plt.show()


XKCD Plot

Same data plotted in XKCD format ...

(http://xkcd.com)


In [7]:
%matplotlib inline
    
# xkcd comic book style plots
with plt.xkcd():
    X = np.arange(len(values))
    plt.bar(X + 0.0, values, facecolor='blue', 
            edgecolor='white', width=0.5, label="Written_to_DAC")
    plt.bar(X + 0.25, samples, facecolor='red', 
            edgecolor='white', width=0.5, label="Read_from_ADC")

    plt.title('DAC-ADC Linearity')
    plt.xlabel('Sample_number')
    plt.ylabel('Volts')
    plt.legend(loc='upper left', frameon=False)

plt.show()


Widget controlled plot

In this example, we extend the IO plot with a slider widget to control the number of samples appearing in the output plot.

We use the ipwidgets library and the simple interact() method to launch a slider bar.

The interact function (ipywidgets.interact) automatically creates user interface (UI) controls for exploring code and data interactively. It is the easiest way to get started using IPython’s widgets.

For more details see Using ipwidgets interact()


In [8]:
from math import ceil
from time import sleep
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline

from ipywidgets import interact
import ipywidgets as widgets

from pynq import Overlay
from pynq.iop import Pmod_ADC, Pmod_DAC

ol = Overlay('base.bit')
ol.download()

dac = Pmod_DAC(2)
adc = Pmod_ADC(1)

def capture_samples(nmbr_of_samples):    
    # Write to DAC, read from ADC, write to OLED
    delay = 0.0
    values = np.linspace(0, 2, nmbr_of_samples)
    samples = []
    for value in values:
        dac.write(value)
        sleep(delay)
        sample = adc.read()
        samples.append(sample[0])

    X = np.arange(nmbr_of_samples)
    plt.bar(X + 0.0, values[:nmbr_of_samples+1], 
            facecolor='blue', edgecolor='white', 
            width=0.5, label="Written_to_DAC")
    plt.bar(X + 0.25, samples[:nmbr_of_samples+1], 
            facecolor='red', edgecolor='white', 
            width=0.5, label="Read_from_ADC")

    plt.title('DAC-ADC Linearity')
    plt.xlabel('Sample_number')
    plt.ylabel('Volts')
    plt.legend(loc='upper left', frameon=False)
   
interact(capture_samples, 
         nmbr_of_samples=widgets.IntSlider(
            min=5, max=30, step=5,
            value=10, continuous_update=False));

plt.show()