QRSS is an experimental radio mode that uses frequency-shift-keyed (FSK) continuous wave (CW) Morse code to transmit messages that can be decoded visually by inspecting the radio frequency spectrogram. The name “QRSS” is a derivation of the Q code “QRS”, a phrase Morse code operators send to indicate the transmitter needs to slow down. The extra “S” means slow way, way down, and at the typical speed of 6 second dots and 18 second dashes most QRSS operators have just enough time to send their call sign once every ten minutes (as required by federal law). These slow Morse code messages can be decoded by visual inspection of spectrograms created by computer software processing the received audio. A QRSS grabber is a radio/computer setup configured to upload the latest radio spectrogram to the internet every 10 minutes. QRSS Plus is an automatically-updating list of active QRSS grabbers around the world, allowing the QRSS community to see QRSS transmitters being detected all over the world.
TLDR: Get Started with QRSS
- Tune your radio to 10.140 MHz (10.1387 MHz USB)
- Install spectrogram software like FSKview
- Inspect the spectrogram to decode callsigns visually
- Join the QRSS Knights mailing list to learn what’s new
- Go to QRSS Plus to see QRSS signals around the world
- Design and build a circuit (or buy a kit) to transmit QRSS
QRSS allows miniscule amounts of power to send messages enormous distances. For example, 200 mW QRSS transmitters are routinely spotted on QRSS grabbers thousands of miles away. The key to this resilience lies in the fact that spectrograms can be designed which average several seconds of audio into each pixel. By averaging audio in this way, the level of the noise (which is random and averages toward zero) falls below the level of the signal, allowing visualization of signals on the spectrogram which are too deep in the noise to be heard by ear.
If you have a radio and a computer, you can view QRSS! Connect your radio to your computer’s microphone, then run a spectrogram like FSKview to visualize that audio as a spectrogram. The most QRSS activity is on 30m within 100 Hz of 10.140 MHz, so set your radio to upper sideband (USB) mode and tune to 10.1387 MHz so QRSS audio will be captured as 1.4 kHz audio tones.
FSKview is radio frequency spectrogram software for viewing QRSS and WSPR simultaneously. I wrote FSKview to be simple and easy to use, but it’s worth noting that Spectrum Lab, Argo, LOPORA, and QRSSpig are also popular spectrogram software projects used for QRSS, with the last two supporting Linux and suitable for use on the Raspberry Pi.
QRSS transmitters can be extraordinarily simple because they just transmit a single tone which shifts between two frequencies. The simplicity of QRSS transmitters makes them easy to assemble as a kits, or inexpensively designed and built by those first learning about RF circuit design. The simplest designs use a crystal oscillator (typically a Colpitts configuration) followed by a buffer stage and a final amplifier (often Class C configuration using a 2N7000 N-channel MOSFET or 2N2222 NPN transistor). Manual frequency adjustments are achieved using a variable capacitor, supplemented in this case with twisted wire to act as a simple but effective variable capacitor for fine frequency tuning within the 100 Hz QRSS band. Frequency shift keying to transmit call signs is typically achieved using a microcontroller to adjust voltage on a reverse-biased diode (acting as a varactor) to modulate capacitance and shift resonant frequency of the oscillator. Following a low-pass filter (typically a 3-pole Chebyshev design) the signal is then sent to an antenna.
QRP Labs is a great source for QRSS kits. The kit pictured above and below is one of their earliest kits (the 30/40/80/160m QRSS Kit), but they have created many impressive new products in the last several years. Some of their more advanced QRSS kits leverage things like direct digital synthesis (DDS), GPS time synchronization, and the ability to transmit additional digital modes like Hellschreiber and WSPR.
Atmospheric phenomena and other special conditions can often be spotted in QRSS spectrograms. One of the most common special cases are radio frequency reflections off of airplanes resulting in the radio waves arriving at the receiver simultaneously via two different paths (a form of multipath propagation). Due to the Doppler shift from the airplane approaching the receiver the signal from the reflected path appears higher frequency than the direct path, and as the airplane flies over and begins heading away the signal from the reflected path decreases in frequency relative to the signal of the direct path. The image below is one of my favorites, captured by Andy (G0FTD) in the 10m QRSS band. QRSS de W4HBK is a website that has many blog posts about rare and special grabs, demonstrating effects of meteors and coronal mass ejections on QRSS signals.
Radio beacons send continuous, automated, unattended, one-way transmissions without specific reception targets. In contrast, QRSS transmitters are only intended to be transmitting when the control operator is available to control them, and the recipients are known QRSS grabbers around the world. To highlight the distinction from radio beacons, QRSS transmitters are termed Manned Experimental Propagation Transmitters (MEPTs). Users in the United States will recall that the FCC (in Part 97.203) confines operation of radio beacons to specific regions of the radio spectrum and disallows operation of beacons below 28 MHz. Note that amateur radio beacons typically operate up to 100 W which is a power level multiple orders of magnitude greater than QRSS transmitters. MEPTs, in contrast, can transmit in any portion of the radio frequency spectrum where CW operation is permitted.
QRSS was first mentioned in epsisode 28 of The Soldersmoke Podcast on July 30, 2006. It was discussed in several episodes over the next few years, and a 2009 post about QRSS on Hack-A-Day brought it to my attention. In the early days of QRSS the only way to transmit QRSS was to design and build your own transmitter. David Hassall (WA5DJJ), Bill Houghton (W4HBK), Hans Summers (G0UPL), and others would post their designs on their personal websites along with notes about where their transmitters had been spotted. In the following years the act of creating QRSS grabbers became streamlined, and websites like I2NDT’s QRSS Grabber Compendium and QRSS Plus made it easier to see QRSS signals around the world. Hans Summers (G0UPL) began selling QRSS transmitter kits at amateur radio conventions, then later through the QRP Labs website. As more people started selling and buying kits (and documenting their experiences) it became easier and easier to get started with QRSS. Before QRSS kits were easy to obtain the only way to participate in the hobby was to design and build a transmitter from scratch, representing a high barrier to entry for those potentially interested in this fascinating hobby. Now with the availability of high quality QRSS transmitter kits and the ubiquity of internet tools and software to facilitate QRSS reception, it’s easier than ever to get involved in this exciting field! For these reasons I believe we have entered into a New Age of QRSS.
To receive QRSS, configure your radio for uper sideband using a dial frequency from the table below and QRSS will be audible as 1-2 kHz audio tones. This radio configuration is identical to the recommendation by WSPRnet for receiving WSPR using WSJT-X software, allowing QRSS and WSPR to be monitored simultaneously.
The entire QRSS band is approximately 200 Hz wide centered on the QRSS frequencies listed in the table below. Testing and experimentation is encouraged in the 100 Hz below the listed frequency, and the upper portion is typically used for more stable transmitters. An exception is the 10m band where the QRSS band is 400 Hz wide, extending ±200 Hz from the center frequency listed below.
| Band |
QRSS Frequency (±100 Hz) |
USB Dial Frequency (Hz) |
Audio Frequency (Hz) |
| 600m |
476,100 |
474,200 |
1,900 |
| 160m |
1,837,900 |
1,836,600 |
1,300 |
| 80m |
3,569,900 ⭐ popular |
3,568,600 |
1,300 |
| 60m |
5,288,550 |
5,287,200 |
1,350 |
| 40m |
7,039,900 ⭐ popular |
7,038,600 |
1,300 |
| 30m |
10,140,000 🌟 most popular |
10,138,700 |
1,300 |
| 22m |
13,555,400 |
13,553,900 |
1,300 |
| 20m |
14,096,900 ⭐ popular |
14,095,600 |
1,300 |
| 17m |
18,105,900 |
18,104,600 |
1,300 |
| 15m |
21,095,900 |
21,094,600 |
1,300 |
| 12m |
24,925,900 |
24,924,600 |
1,300 |
| 10m |
28,125,700 |
28,124,600 |
1,100 |
| 6m |
50,294,300 |
50,293,000 |
1,300 |
Similar QRSS frequency tables:
⚠️ WARNING: It may not be legal for you to transmit on these frequencies. Check license requirements and regulations for your region before transmitting QRSS.
⚠️ WARNING: These frequencies sometimes change based upon community discussion. Frequency tables can be found on the Knights QRSS Wiki. Outdated or alternate frequencies include 160m (1,843,200 Hz), 80m (3,593,900 Hz), 12m (24,890,800 Hz), 10m (28,000,800 Hz and 28,322,000 ±500 Hz), and 6m (50,000,900 Hz). Experimentation on 10m is encouraged in the 100Hz above the band.
When tuning your radio your dial frequency may be lower than the QRSS frequency. If you are using upper-sideband (USB) mode, you have to tune your radio dial 1.4 kHz below the QRSS band to hear QRSS signals as a 1.4 kHz tone. Recommended dial frequencies in the table above are suitable for receiving QRSS and WSPR.
The QRSS Knights is a group of QRSS enthusiasts who coordinate events and discuss experiments over email. The group is kind and welcoming to newcomers, and those interested in learning more about QRSS are encouraged to join the mailing list.
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This page can be found at https://swharden.com/qrss
-
QRSS Plus is an automatically-updating active QRSS grabber list
-
What is QRSS by M0AYF is a classic summary of QRSS, with many links to detailed schematics and design consideration notes. Notably the sections for receiving QRSS and transmitting QRSS are great places to learn more.
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QRSS and You by KA7OEI is another classic summary of QRSS.
-
Weak Signal Propagation Reporter (WSPR) is a low power radio protocol that typically operates adjacent to the QRSS bands and provides automated decoding of callsign, power, and location information. Read more at http://wsprnet.org
-
The QRSS Adventure by Dave Hassall (WA5DJJ) has circuit designs and commentary spanning far back into the early days of QRSS. His 1,164,000,000 Miles per Watt Test is extraordinary!
-
QRSS de W4HBK website by Bill Houghton (W4HBK) contains many useful blog posts about advanced QRSS topics. The website also has many examples of special grabs depicting rare events and atmospheric phenomena.
-
Hans Summers’ website (the founder of QRP Labs) has many excellent resources related to RF design and early work in the QRSS space.
-
Simple QRP Equipment by Onno (PA2OHH) is a collection of fantastic resources related to QRSS transmission, reception, and software design.
-
Electronics & HAM Radio Blog by Eldon Brown (WA0UWH) has many fantastic articles about QRSS. Eldon’s SMT band-edge transmitter inspired me to make a SMT QRSS transmitter many years later.
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Dave Richards, AA7EE has a fantastic website documenting many amateur radio topics including QRSS. This website has the prettiest pictures of circuit boards you’ll ever see.
-
Andy, G0FTD has an excellent website with many pages about radio transmitters and QRSS including a gallery of interesting QRSS grabs
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My QRSS Hardware GitHub page collects notes and resources related to QRSS transmitter and receiver design.
This page describes a simple circuit which produces ECG-like waveform. The waveform is not very detailed, but it contains a sharp depolarizing (rising) component, a slower hyperpolarizing (falling) component, and a repetition rate of approximately one beat per second making it potentially useful for testing heartbeat detection circuitry.
In 2019 I released a YouTube video and blog post showing how to build an ECG machine using an AD8232 interfaced to a computer’s sound card. At the end of the video I discussed how to use a 555 timer to create a waveform roughly like an ECG signal, but I didn’t post the circuit at the end of that video. I get questions about it from time to time, so I’ll share my best guess at what that circuit was here using LTSpice to simulate it.
-
The 555 timer generates pulses about once per second.
-
The diode (D1) causes the 555 to produce very short pulses. The duty of the pulses is controlled by the resistance in series with the diode (R3), with higher resistances resulting in larger duty.
-
The main purpose of the first op-amp is to invert polarity of the signal emitted by the 555. The signal is a square wave at about 1Hz, but it is mostly high with brief low pulses.
-
The second op-amp serves as a voltage buffer to stabilize the output, and the final series capacitor shifts the voltage so it’s centered around zero.
-
Unity gain op-amps should have some feedback resistance to improve small-signal stability in production applications, but for messing around here I felt fine omitting them.
This page shows how to use the LM741 op-amp model file in LTSpice. This is surprisingly un-intuitive, but is a good thing to know how to do. Model files can often be downloaded by vendor sites, but LTSpice only comes pre-loaded with models of common LT components.
I found LM741.MOD available on the TI’s LM741 product page.
Save it wherever you want, but you will need to know the full path to this file later.
Open the model file in a text editor and look for the line starting with .SUBCKT. The top of LM741.MOD looks like this:
* connections: non-inverting input
* | inverting input
* | | positive power supply
* | | | negative power supply
* | | | | output
* | | | | |
* | | | | |
.SUBCKT LM741/NS 1 2 99 50 28
The last line tells us the name of this model’s sub-circuit is LM741/NS
Click the “.op” button on the toolbar, then add .include followed by the full path to the model file. After clicking OK place the text somewhere on your LTSpice circuit diagram.
We know the part we are including is a 5-pin op-amp, so we can start by placing a generic component. Notice the description says you must give the value a name and include this file. We will do this in the next step.
Right-click the op-amp and update its Value to match the name of the subcircuit we read from the model file earlier.
Your new component will run using the properties of the model you downloaded.
Fitting an exponential curve to data is a common task and in this example we’ll use Python and SciPy to determine parameters for a curve fitted to arbitrary X/Y points. You can follow along using the fit.ipynb Jupyter notebook.
import numpy as np
import scipy.optimize
import matplotlib.pyplot as plt
xs = np.arange(12) + 7
ys = np.array([304.08994, 229.13878, 173.71886, 135.75499,
111.096794, 94.25109, 81.55578, 71.30187,
62.146603, 54.212032, 49.20715, 46.765743])
plt.plot(xs, ys, '.')
plt.title("Original Data")
To fit an arbitrary curve we must first define it as a function. We can then call scipy.optimize.curve_fit which will tweak the arguments (using arguments we provide as the starting parameters) to best fit the data. In this example we will use a single exponential decay function.
def monoExp(x, m, t, b):
return m * np.exp(-t * x) + b
In biology / electrophysiology biexponential functions are often used to separate fast and slow components of exponential decay which may be caused by different mechanisms and occur at different rates. In this example we will only fit the data to a method with a exponential component (a monoexponential function), but the idea is the same.
# perform the fit
p0 = (2000, .1, 50) # start with values near those we expect
params, cv = scipy.optimize.curve_fit(monoExp, xs, ys, p0)
m, t, b = params
sampleRate = 20_000 # Hz
tauSec = (1 / t) / sampleRate
# determine quality of the fit
squaredDiffs = np.square(ys - monoExp(xs, m, t, b))
squaredDiffsFromMean = np.square(ys - np.mean(ys))
rSquared = 1 - np.sum(squaredDiffs) / np.sum(squaredDiffsFromMean)
print(f"R² = {rSquared}")
# plot the results
plt.plot(xs, ys, '.', label="data")
plt.plot(xs, monoExp(xs, m, t, b), '--', label="fitted")
plt.title("Fitted Exponential Curve")
# inspect the parameters
print(f"Y = {m} * e^(-{t} * x) + {b}")
print(f"Tau = {tauSec * 1e6} µs")
Y = 2666.499 * e^(-0.332 * x) + 42.494
Tau = 150.422 µs
R² = 0.999107330342064
We can use the calculated parameters to extend this curve to any position by passing X values of interest into the function we used during the fit.
The value at time 0 is simply m + b because the exponential component becomes e^(0) which is 1.
xs2 = np.arange(25)
ys2 = monoExp(xs2, m, t, b)
plt.plot(xs, ys, '.', label="data")
plt.plot(xs2, ys2, '--', label="fitted")
plt.title("Extrapolated Exponential Curve")
What if we know our data decays to 0? It’s not best to fit to an exponential decay function that lets the b component be whatever it wants. Indeed, our fit from earlier calculated the ideal b to be 42.494 but what if we know it should be 0? The solution is to fit using an exponential function where b is constrained to 0 (or whatever value you know it to be).
def monoExpZeroB(x, m, t):
return m * np.exp(-t * x)
# perform the fit using the function where B is 0
p0 = (2000, .1) # start with values near those we expect
paramsB, cv = scipy.optimize.curve_fit(monoExpZeroB, xs, ys, p0)
mB, tB = paramsB
sampleRate = 20_000 # Hz
tauSec = (1 / tB) / sampleRate
# inspect the results
print(f"Y = {mB} * e^(-{tB} * x)")
print(f"Tau = {tauSec * 1e6} µs")
# compare this curve to the original
ys2B = monoExpZeroB(xs2, mB, tB)
plt.plot(xs, ys, '.', label="data")
plt.plot(xs2, ys2, '--', label="fitted")
plt.plot(xs2, ys2B, '--', label="zero B")
Y = 1245.580 * e^(-0.210 * x)
Tau = 237.711 µs
The curves produced are very different at the extremes (especially when time is 0), even though they appear to both fit the data points nicely. Which curve is more accurate? That depends on your application. A hint can be gained by inspecting the time constants of these two curves.
| Parameter |
Fitted B |
Fixed B |
| m |
2666.499 |
1245.580 |
| t |
0.332 |
0.210 |
| Tau |
150.422 µs |
237.711 µs |
| b |
42.494 |
0 |
By inspecting Tau I can gain insight into which method may be better for me to use in my application. I expect Tau to be near 250 µs, leading me to trust the fixed-B method over the fitted B method. Choosing the correct method has great implications on the value of m (which is also the value of the curve when time is 0).
How to apply low-pass, high-pass, and band-pass filters with Python
This page describes how to perform low-pass, high-pass, and band-pass filtering in Python. I favor SciPy’s filtfilt function because the filtered data it produces is the same length as the source data and it has no phase offset, so the output always aligns nicely with the input. The sosfiltfilt function is even more convenient because it consumes filter parameters as a single object which makes them easier work with.
import numpy as np
import scipy.signal
import scipy.io.wavfile
import matplotlib.pyplot as plt
def lowpass(data: np.ndarray, cutoff: float, sample_rate: float, poles: int = 5):
sos = scipy.signal.butter(poles, cutoff, 'lowpass', fs=sample_rate, output='sos')
filtered_data = scipy.signal.sosfiltfilt(sos, data)
return filtered_data
# Load sample data from a WAV file
sample_rate, data = scipy.io.wavfile.read('ecg.wav')
times = np.arange(len(data))/sample_rate
# Apply a 50 Hz low-pass filter to the original data
filtered = lowpass(data, 50, sample_rate)
# Code used to display the result
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 3), sharex=True, sharey=True)
ax1.plot(times, data)
ax1.set_title("Original Signal")
ax1.margins(0, .1)
ax1.grid(alpha=.5, ls='--')
ax2.plot(times, filtered)
ax2.set_title("Low-Pass Filter (50 Hz)")
ax2.grid(alpha=.5, ls='--')
plt.tight_layout()
plt.show()
import numpy as np
import scipy.signal
import scipy.io.wavfile
import matplotlib.pyplot as plt
def highpass(data: np.ndarray, cutoff: float, sample_rate: float, poles: int = 5):
sos = scipy.signal.butter(poles, cutoff, 'highpass', fs=sample_rate, output='sos')
filtered_data = scipy.signal.sosfiltfilt(sos, data)
return filtered_data
# Load sample data from a WAV file
sample_rate, data = scipy.io.wavfile.read('ecg.wav')
times = np.arange(len(data))/sample_rate
# Apply a 20 Hz high-pass filter to the original data
filtered = highpass(data, 20, sample_rate)
# Code used to display the result
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 3), sharex=True, sharey=True)
ax1.plot(times, data)
ax1.set_title("Original Signal")
ax1.margins(0, .1)
ax1.grid(alpha=.5, ls='--')
ax2.plot(times, filtered)
ax2.set_title("High-Pass Filter (20 Hz)")
ax2.grid(alpha=.5, ls='--')
plt.tight_layout()
plt.show()
import numpy as np
import scipy.signal
import scipy.io.wavfile
import matplotlib.pyplot as plt
def bandpass(data: np.ndarray, edges: list[float], sample_rate: float, poles: int = 5):
sos = scipy.signal.butter(poles, edges, 'bandpass', fs=sample_rate, output='sos')
filtered_data = scipy.signal.sosfiltfilt(sos, data)
return filtered_data
# Load sample data from a WAV file
sample_rate, data = scipy.io.wavfile.read('ecg.wav')
times = np.arange(len(data))/sample_rate
# Apply a 10-50 Hz high-pass filter to the original data
filtered = bandpass(data, [10, 50], sample_rate)
# Code used to display the result
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 3), sharex=True, sharey=True)
ax1.plot(times, data)
ax1.set_title("Original Signal")
ax1.margins(0, .1)
ax1.grid(alpha=.5, ls='--')
ax2.plot(times, filtered)
ax2.set_title("Band-Pass Filter (10-50 Hz)")
ax2.grid(alpha=.5, ls='--')
plt.tight_layout()
plt.show()
This code evaluates the same signal low-pass filtered using different cutoff frequencies:
import numpy as np
import scipy.signal
import scipy.io.wavfile
import matplotlib.pyplot as plt
# Load sample data from a WAV file
sample_rate, data = scipy.io.wavfile.read('ecg.wav')
times = np.arange(len(data))/sample_rate
# Plot the original signal
plt.plot(times, data, '.-', alpha=.5, label="original signal")
# Plot the signal low-pass filtered using different cutoffs
for cutoff in [10, 20, 30, 50]:
sos = scipy.signal.butter(5, cutoff, 'lowpass', fs=sample_rate, output='sos')
filtered = scipy.signal.sosfiltfilt(sos, data)
plt.plot(times, filtered, label=f"low-pass {cutoff} Hz")
plt.legend()
plt.grid(alpha=.5, ls='--')
plt.axis([0.35, 0.5, None, None])
plt.show()
Artifacts may appear in the smooth signal if the first or last data point differs greatly from their adjacent points. This is because, in an effort to ensure the filtered signal length is the same as the input signal, the input signal is “padded” with data on each side prior to filtering. The default behavior is to pad the data by duplicating the first and last data points, but this causes artifacts in the smoothed signal if the first or last points contain an extreme value. An alternative strategy is Gustafsson’s Method, described in a 1996 paper by Fredrik Gustafsson in which “initial conditions are chosen for the forward and backward passes so that the forward-backward filter gives the same result as the backward-forward filter.” Interestingly, the original publication demonstrates the method by filtering noise out of an ECG recording.
import numpy as np
import scipy.signal
import scipy.io.wavfile
import matplotlib.pyplot as plt
# Load sample data from a WAV file
sample_rate, data = scipy.io.wavfile.read('ecg.wav')
times = np.arange(len(data))/sample_rate
# Isolate a small portion of data to inspect
segment = data[350:400]
# Create a 5-pole low-pass filter with an 80 Hz cutoff
b, a = scipy.signal.butter(5, 80, fs=sample_rate)
# Apply the filter using the default edge method (padding)
filtered_pad = scipy.signal.filtfilt(b, a, segment)
# Apply the filter using Gustafsson's method
filtered_gust = scipy.signal.filtfilt(b, a, segment, method="gust")
# Display the Results
plt.plot(segment, '.-', alpha=.5, label="data")
plt.plot(filtered_pad, 'k--', label="Default (Padding)")
plt.plot(filtered_gust, 'k', label="Gustafsson's Method")
plt.legend()
plt.grid(alpha=.5, ls='--')
plt.title("Padded Data vs. Gustafsson’s Method")
plt.show()
An alternative strategy to low-pass a signal is to use convolution. In this method you create a kernel (typically a bell-shaped curve) and convolve the kernel with the signal. The wider the window is the smoother the output signal will be. Also, the window must be normalized so its sum is 1 to preserve the amplitude of the input signal. Note that this method exclusively uses NumPy and does not require SciPy.
There are different for handling data at the edges of the signal, but setting mode to valid deletes insufficiently filtered points at the edges to produce an output signal that is fully filtered but slightly shorter than the input signal. See numpy.convolve documentation for additional information.
The kernel shape affects the spectral properties of the filter. Commonly called window functions, these different shapes produce filtered signals with different frequency response characteristics. The Hanning window is preferred for most general purpose signal processing applications. See FftSharp for additional information about the pros and cons of common window functions.
import numpy as np
import scipy.io.wavfile
import matplotlib.pyplot as plt
# Load sample data from a WAV file
sample_rate, data = scipy.io.wavfile.read('ecg.wav')
times = np.arange(len(data))/sample_rate
# create a Hanning kernel 1/50th of a second wide
kernel_width_seconds = 1.0/50
kernel_size_points = int(kernel_width_seconds * sample_rate)
kernel = np.hanning(kernel_size_points)
# normalize the kernel
kernel = kernel / kernel.sum()
# Create a filtered signal by convolving the kernel with the original data
filtered = np.convolve(kernel, data, mode='valid')
# Display the result
fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(10, 3))
ax1.plot(np.arange(len(kernel))/sample_rate, kernel, '.-')
ax1.set_title("Kernel (1/50 sec wide)")
ax1.grid(alpha=.5, ls='--')
ax2.plot(np.arange(len(data))/sample_rate, data)
ax2.set_title("Original Signal")
ax2.margins(0, .1)
ax2.grid(alpha=.5, ls='--')
ax3.plot(np.arange(len(filtered))/sample_rate, filtered)
ax3.set_title("Convolved Signal")
ax3.margins(0, .1)
ax3.grid(alpha=.5, ls='--')
plt.tight_layout()
plt.show()
Azure Pipelines makes it easy to run tests in the cloud, but I found that a new React projects made with create-react-app fail to properly test in the cloud using the simple npm test command. Attempting this would display No tests found related to files changed since last commit but hang forever.
I solved this problem and got my React app to test properly in the cloud by adding -- --watchAll=false after npm test. This is my final azure-pipelines.yml file:
trigger:
- master
pool:
vmImage: "ubuntu-latest"
steps:
- task: NodeTool@0
inputs:
versionSpec: "10.x"
displayName: "Install Node.js"
- script: npm install
displayName: "Install NPM"
- script: npm run build
displayName: "Build"
- script: npm test -- --watchAll=false
displayName: "Test"
A working React app that tests properly with Azure Pipelines is GitHub.com/swharden/AliCalc
This week my website was removed from the Wayback Machine. The Wayback Machine is an impressive website that lets you view what a website looked like years ago. As part of Archive.org Internet Archive, this website is truly impressive and holds entertainingly-old versions of most webpages. Just look at Amazon.com in the year 2000 for a good laugh.
I started this blog as a child twenty years ago and after seeing what the Wayback Machine pulled-up I realized that it may be best that the thoughts I had as a child stay in the past. I have personal copies of all my old blog posts, but with the wisdom of age and hindsight I’d much prefer that that material stay off the internet. Luckily I was able to get my website removed from the Wayback Machine, and this post documents how I did it.
For those of you wanting to do the same, this is how I did it: I sent an email to info@archive.org stating the following:
Please remove my website [MY URL] from the Wayback Machine.
[MY URL]/robots.txt has been updated to indicate I do not wish
this website to be archived.
https://lookup.icann.org/ shows that [MY URL] points to
[HOSTING COMPANY] nameservers, and I have attached a recent
invoice from [HOSTING COMPANY] as evidence that I own this domain.
If additional evidence or action is required (e.g., DMCA takedown
notice) please let me know.
Thank you!
Scott
I’m not sure if editing robots.txt was necessary, but I felt it gave credence to the fact that I had control over the content of this domain. That file contains the following text. In the past I read this was all it took to get your website de-listed from the wayback machine, but I added this same file to another domain name of mine and it has not been de-listed.
User-agent: archive.org_bot
Disallow: /
User-agent: ia_archiver
Disallow: /
I attached an invoice from the present year showing a credit card payment to my hosting company for the domain as a PDF. Interestingly I did not have to show a history of domain ownership. I downloaded the invoice from my hosting company’s billing page that day, and it displays my home address but not my email address.
Six days later, my site was removed. This is the email I received:
FROM: Office Manager (Internet Archive)
Hello,
The following has now been submitted for exclusion from the
Wayback Machine at web.archive.org: [MY SITE]
Please allow up to a day for the automated portions of the process
to run their course and for the changes to take effect.
– The Internet Archive Team
I reviewed a lot of websites before reaching my strategy. I was surprised to see some people using issuing DMCA takedown notices notices to Archive.org, and was happy to find this was not required in my case. Here are some of the resources I found helpful:
⚠️ WARNING: This may not be permanent. I’m not sure what will happen if I lose my domain name (and robots.txt file) in the future. It is possible that my site is still being archived, while not being available on the wayback machine, and that some time in the future my site will be re-listed.
If you have updated information send me an email so I can update this page! In the mean time, I hope this information will be useful for others interested in curating their historical online presence.
