Introduction

Since June 1st 2022 it is possible to travel within the whole country of Germany by public transportation system with a flat-rate ticket which costs only 9 Euros per month. The ticket is time-limited and valid only during the current month. It applies only to the German public transportation system (e. g. trains of German Railways such as DB RegionalBahn, RegionalExpress, S-Bahn, also metropolitan transportation  systems such as U-Bahn, busses & trams). The ticket doesn’t apply to inter-regional trains such as ICE, IC or EC and doesn’t cover 1st class travelling in any of the mentioned transportation systems.

It was proposed by the German Government as a time-limited experiment in order to motivate people to drive less cars and as a financial relief due to very high energy and gasoline prices. The 9 Euro ticket will be obtainable during months June, July and August in 2022. The financial losses to the transportation companies due to this very cheap ticket will be compensated by the taxpayers. I think this is a good idea although its execution wasn’t very well planned. Nevertheless, it’s a step in the right direction! The German public transportation system is “OK” but isn’t able to handle – let’s say – sudden passenger increases of 20% or more very easily.

My Trip from Braunschweig to Meschede

I’ve bought myself a 9 Euro ticket and went on a trip from Braunschweig to a small city called Meschede. I’ve been living and working in Meschede for about 1.5 years before moving to Braunschweig. Although its location is in mid-western Germany and it’s only 260ish kilometers distant, it is a remote location because it’s surrounded by forests and hills of the Sauerland region. There is no easy way to get inside or outside of Sauerland 😉 My trip planner calculated a total travel time of about 5 hours and 21 minutes with approx. 1 hour of exchange durations. Travelling from Braunschweig to Meschede and back by train will be at least a 10 hr and 42 minute trip (which was the case).

I’ve started from my apartment at roughly 6:30 in the morning and took the bus to the central station. My first train went from Braunschweig to Bielefeld, a ~2hr trip. The two floor train was very clean and there was enough room for passengers and bicycle users. Changing train in Bielefeld offered a different image. The train was heading in direction to Dortmund. It was pretty full with passengers and commuters but I was able to get a seat in the bike wagon. People standing around me were 10 drunk ladies (bachelor party) which were very funny and entertaining. In Dortmund, the train filled up to its maximum capacity where I felt sorry for the passengers. They were stuffed like canned tuna. I’ve spent about 20 minutes on the Dortmund central station gathering some fresh air. Nothing special: cops everywhere, fewer-than-usual soccer fans, LGBTQ pride flag carriers, occasionally religious fanatics and beggars. It’s a wild place but somehow appealing. The last section of the route was from Dortmund to Meschede. The train was OK – clean, not too full, a little bit loud due to its diesel engines. I found a seat near a group of drunk guys which were also very funny and entertaining. At one of the train stations, another drunk guy joined and collected refundable bottles. This was so unreal, he looked like a 70 year old smashed alcoholic, barely able to walk but the drunk party donated like 10-12 empty bottles of beer instantly to him 😀

To my surprise, all trains were on time and there were no major delays. I arrived in Meschede at 12:40 so the total travelling time starting from my apartment was approx. 6 hr 10 min. I couldn’t stay for a long time in Meschede because of the 6h return trip so I planned to stay for about 4.5 hours. This is one thing to consider when using 9 Euro ticket: the travel duration killed most of the day and reduced the visit duration significantly.

However, I was able to visit my old workplace and say hello to my former colleagues and some old friends. The Open-Door Day at University of Applied Sciences in Meschede offered a rich program of science, engineering, entertainment and food. The weather was excellent and very inviting.

The return journey was very tiring: the train from Dortmund to Bielefeld was 15 minutes late and full to the max. We were standing for about 30 minutes without any room to move until a portion of passengers left the train. Luckily due to long exchange duration, the meanwhile 20 minute delay didn’t affect the connecting train from Bielefeld to Braunschweig. The section from Bielefeld to Braunschweig was OK – no problems whatsoever. About 90% of people inside of trains were wearing a face mask due to COVID-19 pandemic which was very nice. The current infections are very low in the region of 350 per 100 000 inhabitants but one has to be careful anyways. There may be a new infection wave in the autumn this year…

Summary and conclusion

I’ve spent most of my day travelling in trains. It was cheap but also do-able. I was able to travel from Braunschweig to Meschede and back in one day for 9 Euros! This trip would have cost at least 50 Euros instead without much improvement in travel quality. I’ve been to Meschede few weeks before this trip by car and the gasoline alone cost me 80 Euros.  The quality of trains (size, cleanliness) and its connection vary significantly from region to region. One has to consider that trains will be full during commuting hours and empty outside of them. During weekends, bicyclists take up a lot of space in the trains so this can be an issue, too. The people are usually friendly and very helpful but there are also exceptions: drunk and loud people, some of them smell badly due to sweat or even urine. The toilets are in an acceptable condition but don’t rely on this – try to take Number One/Number Two at a train station instead inside of a train toilet.

The 9 Euro ticket is definitely worth it and should be used to visit all kinds of places in Germany. There are wonderful towns and regions which should be visited during the summer time. I haven’t been to cities such as Rostock, Schwerin, Konstanz, Kiel, Flensburg, Saarbrücken and many others so I’ll try to catch up with my visits.

Introduction

Taking measurements with electronic equipment doesn’t require much skill and effort: turn the device on, set up the measurement parameters such as input channel/range/sensitivity, trigger the measurement and read the numbers on the screen (if there are any). The same procedure can be applied in everyday situations – whether you have to read the time from a clock or check the temperature in your fridge/oven.

But what if one does have to take thousands or even million of measurements  every day? What happens if measurements have to be performed 24/7? There surely are applications where it is necessary  to take measurements manually but putting a human behind a bench and performing boring and repetitive measurements during a 8-hour-workday is prone to exhaustion and errors. This is where automated test equipment (ATE) kicks in. It is possible to let a computer do the “boring stuff” while the human operator may spend his or her precious time on solving problems instead of doing repetitive work. I’ve successfully stolen borrowed and tested foreign Python code and want to demonstrate how to control a digital multimeter and a function generator via General-Purpose Interface Bus (GPIB) and Python’s pyvisa module.

GPIB

GPIB is a 8-bit parallel interface bus which has been invented in the late 1960’s by Hewlett-Packard (see Wikipedia article). Prior to its standardization in the late 1970’s as IEEE 488, it was known as HP-IB (Hewlett-Packard Interface Bus). It allows interconnection between instruments and controllers. It is system independent of device functions. This de facto standard has been around for a while and is well established in the Test & Measurement industry. Fast forward to the year 2022, GPIB is still present in test equipment although in recent two decades, communication with instruments have been enabled with modern-day interfaces such as USB or Ethernet. GPIB allows a very diverse topology – one can connect all instruments either in a Daisy Chain or parallel configuration or even in a combination of both of them. There are certain GPIB-specific limitations concerning cable lengths, maximum number of connected devices, data transfer speeds and others which have to be considered when automating test equipment.

Experimental Setup

I’ve connected a HP 34401A digital multimeter to a HP 3325B function generator in order to generate a sinusoidal test signal, which can be represented by an equation \(v(t) = v_0 \cdot \sin (\omega t) \) with \(\omega = 2\pi f\). Both instruments are connected to a GPIB controller National Instruments GPIB-ENET/100. The GPIB-ENET controller acts as a bridge  and allows communication to GPIB devices via ethernet, which is highly convenient in my lab. It allows me to control the instruments via LAN from any of my computers. This device requires drivers from National Instruments so I’ve chosen NI MAX v17.5 along with NI-488.2 driver in order to be able to use the legacy GIPB-ENET/100 controller. I’m using Windows 10 along with the WinPython distribution. WinPython comes along with Python 3.9.10 and hundreds of useful modules, such as numpy, pandas, matplotlib etc. (which are optional). The pyvisa module needs to be installed manually via pip, see PyVISA’s project website for further installation instructions.

Important notice for new users

Many of the GPIB-USB adapters sold on eBay are fakes or counterfeits. They are sold very cheap (different brands such as National Instruments, Agilent/Keysight, Keithley) in the price range of 100 EUR to 120 EUR per piece whereas the genuine GPIB-USB adapters often cost more than 400 EUR. Buying a new unit directly from the manufacturer will cost in the order of 900 -1200EUR which is kinda crazy! Nevertheless, some of the fake GPIB-USB adapters seem to work pretty well and are recognized by the official drivers. However, some EEVBlog users also report having problems with fake adapters… So it’s really a gamble.

I’ve bought a 2nd hand “fake” GBIB-USB-HS for cheap which didn’t work for the previous owner but works perfectly on my Windows 10 machine. I haven’t tested it with Raspberry PI yet which will be done in a future project. The GPIB-ENET/100 unit is a little bit pricy (~200ish EUR) but the most affordable interface cards are the PCI-GPIB types. I’ve seen genuine units being sold on eBay for 40-80 EUR. No need to spend hundreds of Euros for a GPIB interface. Microcontroller-based GPIB-USB projects also exist where one can assemble a GPIB-USB or GPIB-LAN controller for cheap (which may not work always but gets the job done). Proper GPIB-cables can be expensive, too, but I’ve seen them being sold for approx. 7 … 12 EUR per piece occasionally. Custom-made GPIB cables out of ribbon cable and Centronics/Amphenol connectors may also work and get the job done.

Example Code

First of all we’re gonna need to import some Python modules. pyvisa is mandatory, other modules will be useful.

# Import Python modules
import pyvisa
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import datetime
import time

Next step: we need to open the pyvisa Resource Manager. We need to know the GPIB addresses of our instruments. For example, I’ve assigned GPIB address No. 4 to my digital multimeter dmm and the frequency generator fgen has the GPIB address No. 18. GPIB 22 is another digital multimeter which isn’t used in this example.

# Open pyvisa Resource Manager, print out a list with active devices
rm = pyvisa.ResourceManager()
print(rm.list_resources())

The print(rm.list_resources()) statement gives us the GPIB addresses of the connected devices, such as: ('ASRL3::INSTR', 'GPIB0::4::INSTR', 'GPIB0::18::INSTR', 'GPIB0::22::INSTR').  Now we can open a connection to the resources.

# Assign GPIB address 4 to dmm and open resource
dmm = rm.open_resource('GPIB0::4::INSTR')
dmm.read_termination = '\n'
dmm.write_termination = '\n'

# Assign GPIB address 18 to fgen and open resource
fgen = rm.open_resource('GPIB0::18::INSTR')
fgen.read_termination = '\n'
fgen.write_termination = '\n'

The dmm and fgen objects have been created. The statements dmm.read_termination = '\n' deal with line feed and carriage returns. The next step will set the DMM settings and call an identify string via a SCPI command *IDN?

# Set the DMM for measurements
print(dmm.query('*IDN?'))                      # Identify string
print(dmm.write('CONF:VOLT:DC 10,0.001'))      # Set DC voltage range and 4.5 digits of resolution

The same commands are applied to our frequency generator. The output of the DMM looks something like (name, model, firmware version):
HEWLETT-PACKARD,34401A,0,4-1-1
22

# Set the FGEN for measurements according to the manual
print(fgen.query('*IDN?'))             # Identify string
print(fgen.write('AM 2.0 VO'))         # Set amplitude to 2 V_pp
print(fgen.write('FR 0.1 HZ'))         # Set frequency to 0.1 Hz

The parameters such as measurement range, frequency and amplitude have been set. Now let’s take a single measurement. This is done via query command. A query is a short representation of dmm.write() and dmm.read() commands. The READ? command fetches the current measurement value from the digital multimeter.

# Example 1: Take a single measurement
print('Measured value: {} Volts.'.format(dmm.query('READ?')))

Measured value: -9.97000000E-02 Volts.

We have measured the voltage! Yaay! Its value is -99.7 mV, written in the engineering notation. Our next code example will be used to take 2048 measurements and timestamp them. I’ve used the pandas DataFrame as a convenient structure to store the data. This could have been done with numpy arrays for sure! OK, after setting the Number of acquired samples to N = 2048, I’ve created an empty pandas DataFrame. It will be filled with our precious data. The next line (6.) prints out the date and time. The t₀ variable in line (7.) is used to get the timestamp value at the beginning of the measurement. This value will be subtracted from the timestamps generated by time.time().

The for loop starts at the value 0 and calls two commands according to line (9.). It writes a timestamp and measured value into the DataFrame df at index 0. After execution of line (9.), the for loop is repeated N-1 = 2047 times. As soon as the for loop finishes, we need to convert the string-datatype voltages into floating point numbers, otherwise Python will have difficulties handling  them with further calculations.

# Example 2: Take N numbers of measurements
N = 2048                               # Number of samples to acquire
df = pd.DataFrame(columns=['t', 'V'])  # Create an empty pandas DataFrame

# Print date and time, write data into the DataFrame
print(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S.%f'))
t0 = time.time()
for i in range(N):
    df.loc[i] = [time.time()-t0, dmm.query('READ?')]

# Convert dmm readout strings to a float number
df.V = df.V.astype(float)

That’s it! We’ve acquired 2048 measurements. Just imagine sitting there and typing all these numbers in Excel! Such a waste of time 😉 The next line of code extracts the total measurement time from the DataFrame timestamps. The measurement took approx. 25.8 seconds to complete. This corresponds to approx 79.2 samples per second at 4.5 digits of precision.

# Show elapsed time in seconds
df.iloc[N-1].t

25.846375942230225

# Calculate the sampling rate in Samples per Second
str(np.round((len(df)-1)/df.iloc[N-1].t, 3))

'79.199'

The last step of this example will be to plot the results into a graph figure. First we create an empty figure called fig with sizes 11.6 x 8.2 inches at 300 dpi resolution. The plt.plot() command needs the \(x\)– and \(y\)– values (or time and voltage values) as function arguments in order to plot a nice diagram. We label the axes, set a title and grid. The figure is then saved as PNG file and displayed. See picture below!

# Plot the results, annotate the graph, show figure and export as PNG file
fig = plt.figure(figsize = (11.6, 8.2), dpi=300, facecolor='w', edgecolor='k')
plt.plot(df.t, df.V, 'k.')
plt.xlabel('Time (s)')
plt.ylabel('Voltage (V)')
plt.title('HP 34401A measurement (Range: 10 V (DC), Resolution: \n 4.5 digits @ ' + str(np.round((len(df)-1)/df.iloc[N-1].t, 3)) + ' Sa/s, $f$ = 0.1 Hz, $v_0$ = 2 V$_{pp}$)')
plt.grid(True)
plt.savefig('Measurement_result.png')
plt.show()

Now we could do some statistics or fit a sine function to it. I’ll explain this in a future blog post. A small bonus can be achieved via the following command:

dmm.write('DISP:TEXT "73 DE DH7DN"')

I shot some oscilloscope images yesterday with my smartphone and I was really wondering about the bad image quality of the 10 MHz sine wave. Today I tried to repeat the experiment and unfortunately I got the same results. As it seems, the bad image quality is the result of using the “Night Mode” and simultaneous digital zoom-in. The image shot took 5-8 seconds in a motion stabilized environment. However, the heavy compression artifacts are still there.

After tinkering around with the camera app, I discovered the “Pro” Mode where the user is in control of camera parameters such as ISO, focus, camera lens and exposure time. The results are much better – sharper image and no compression artifacts. The phone is a Samsung Galaxy S22 with Android 11 operating system.

The results are comparable to my Nikon D7200. Unfortunately, the workflow from shooting photographs to transferring to the PC is a horror. I’m currently trying to figure out how to shoot quick photographs, process them and upload them as quickly as possible.

During the past year and a half, I’ve build my electronics lab. I’m able to measure many electrical quantities like voltage, current, resistance, capacitance, inductance, frequency, spectra and much more. I surely am suffering to the Gear Acquisition Syndrome and I’ve already posted some pictures inside of the TEA-thread (Test Equipment Anonymous) of the EEVBlog. I wasn’t kidding when I posted that I have sold my soul for Tektronix. I’d like to start restoring/repairing/calibrating/maintaining the older Test Equipment in order to do be able to do some exciting electronics projects in the years to come!

After half a decade of changing jobs, cities, apartments, relationships, … — I think it’s time to finally follow my passions and share them with like-minded people 🙂

Happy New Year 2022 and see ya around!

vy 73 de Denis (DH7DN)

Siffing through junk, I found few memorabilia.

…

Lost & FoundRead More »

Hello there!

During the past few days I’ve made some progress concerning the design and functionality of this blog. I think I’ll use the current dark theme called “Astra”. Although the “Pro” version of this theme is quite expensive, the functionality is far better than other minimalistic dark themes.

LaTeX and Python code

Let’s test some Python code highlighting. For example…

# Useless code snippet
k = [1, 2, 3]
for i in k:
    print('Hello World!')

And now one famous equation written in \(\LaTeX\)

Alright, this stuff seems to work! 😀

I’m testing some stuff with piwigo, a very nice and easy to use photo gallery written in php.

Let’s see, if the picture is parsed correctly:

Unfortunately, the WordPress Media Library is not suitable for sophisticated photo management. I’m planning to publish high quality photographs of test equipment and repairs.

Another nice picture…