Hi fellow engineers,
I made a python package for S-transform with Hyperbolic window (Hyperbolic S-transform or HSTransform package). This is my first time publishing a python package, so the project is still far from stable and still under beta release.
This transformation is applied to signal processing, analyzing transient changes of a signal during very short-time. Some special use case can be in power system signal, or Geophysical signal analysis, or MRI ... Also, the comparison with Wavelet Transform has been shown. (which shows more potential in detecting transient changes)
I would highly appreciate some feedback, before progressing further. Thanks a lot.
HSTransform is available on pypi.
Link to source code in github

Quick Usage

import numpy as np from hstransform import HSTransform # Create input signal (for example: Voltage signal) t = np.linspace(0, 10, 100) # timeseries V_m = 220*np.sqrt(2) # peak voltage f_V = 50 # frequency phi_V = 0 # phase V_clean = V_m * np.sin(2 * np.pi * f_V * t + phi_V) # Create voltage sag/dip (80% of the nominal voltage for 0.15 second) V_sag = np.where((t >= 2) & (t <= 3.5), 0.5 * V_clean, V_clean) # Create an instance of HSTransform hs = HSTransform() # Perform the transform signal = V_sag S_transformed = hs.fit_transform(t, signal) 

Hi fellow engineers,
I made a python package for S-transform with Hyperbolic window (Hyperbolic S-transform or HSTransform package). This is my first time publishing a python package, so the project is still far from stable and still under beta release.
This transformation is applied to signal processing, analyzing transient changes of a signal during very short-time. Some special use case can be in power system signal, or Geophysical signal analysis, or MRI ... Also, the comparison with Wavelet Transform has been shown. (which shows more potential in detecting transient changes)
I would highly appreciate some feedback, before progressing further. Thanks a lot.
HSTransform is available on pypi.
Link to source code in github

Quick Usage

import numpy as np from hstransform import HSTransform # Create input signal (for example: Voltage signal) t = np.linspace(0, 10, 100) # timeseries V_m = 220*np.sqrt(2) # peak voltage f_V = 50 # frequency phi_V = 0 # phase V_clean = V_m * np.sin(2 * np.pi * f_V * t + phi_V) # Create voltage sag/dip (80% of the nominal voltage for 0.15 second) V_sag = np.where((t >= 2) & (t <= 3.5), 0.5 * V_clean, V_clean) # Create an instance of HSTransform hs = HSTransform() # Perform the transform signal = V_sag S_transformed = hs.fit_transform(t, signal) 

I made a python package for S-transform with Hyperbolic window (Hyperbolic S-transform or HSTransform package). This is my first time publishing a python package, so the project is still far from stable and still under beta release.
This transformation is applied to signal processing, analyzing transient changes of a signal during very short-time. Some special use case can be in power system signal, or Geophysical signal analysis, or MRI ... Also, the comparison with Wavelet Transform has been shown. (which shows more potential in detecting transient changes)
I would highly appreciate some feedback, before progressing further. Thanks a lot.
HSTransform is available on pypi.
Link to source code in github

Quick Usage

import numpy as np from hstransform import HSTransform # Create input signal (for example: Voltage signal) t = np.linspace(0, 10, 100) # timeseries V_m = 220*np.sqrt(2) # peak voltage f_V = 50 # frequency phi_V = 0 # phase V_clean = V_m * np.sin(2 * np.pi * f_V * t + phi_V) # Create voltage sag/dip (80% of the nominal voltage for 0.15 second) V_sag = np.where((t >= 2) & (t <= 3.5), 0.5 * V_clean, V_clean) # Create an instance of HSTransform hs = HSTransform() # Perform the transform signal = V_sag S_transformed = hs.fit_transform(t, signal) 

I made a python package for S-transform with Hyperbolic window (Hyperbolic S-transform or HSTransform package). This is my first time publishing a python package, so the project is still far from stable and still under beta release.

• Key features: This transformation is applied to signal processing, convert time-series to frequency domain, analyzing transient changes of a signal during very short-time. Some special use case can be in power system signal, or Geophysical signal analysis, or MRI ...
• Target audience: anyone who is interested in signal processing or power system analysis or geographical analysis.
• Comparison: The comparison with Wavelet Transform has been shown. (which shows more potential in detecting transient changes)

I would highly appreciate some feedback, before progressing further. Looking forward to feedback on code, suggestions for improvements, bug reports, or unit test ...
HSTransform is available on pypi.
Link to source code in github

Quick Usage

import numpy as np from hstransform import HSTransform # Create input signal (for example: Voltage signal) t = np.linspace(0, 10, 100) # timeseries V_m = 220*np.sqrt(2) # peak voltage f_V = 50 # frequency phi_V = 0 # phase V_clean = V_m * np.sin(2 * np.pi * f_V * t + phi_V) # Create voltage sag/dip (80% of the nominal voltage for 0.15 second) V_sag = np.where((t >= 2) & (t <= 3.5), 0.5 * V_clean, V_clean) # Create an instance of HSTransform hs = HSTransform() # Perform the transform signal = V_sag S_transformed = hs.fit_transform(t, signal) 

I made a python package for S-transform with Hyperbolic window (Hyperbolic S-transform or HSTransform package). This is my first time publishing a python package, so the project is still far from stable and still under beta release.

• What my project does: This transformation is applied to signal processing, analyzing transient changes of a signal during very short-time. Some special use case can be in power system signal, or Geophysical signal analysis, or MRI ...
• Target audience: anyone who is interested in signal processing or power system analysis or geographical analysis.
• Comparison: The comparison with Wavelet Transform has been shown. (which shows more potential in detecting transient changes)

I would highly appreciate some feedback, before progressing further.
HSTransform is available on pypi.
Link to source code in github

Quick Usage

import numpy as np from hstransform import HSTransform # Create input signal (for example: Voltage signal) t = np.linspace(0, 10, 100) # timeseries V_m = 220*np.sqrt(2) # peak voltage f_V = 50 # frequency phi_V = 0 # phase V_clean = V_m * np.sin(2 * np.pi * f_V * t + phi_V) # Create voltage sag/dip (80% of the nominal voltage for 0.15 second) V_sag = np.where((t >= 2) & (t <= 3.5), 0.5 * V_clean, V_clean) # Create an instance of HSTransform hs = HSTransform() # Perform the transform signal = V_sag S_transformed = hs.fit_transform(t, signal) 

I made a python package for S-transform with Hyperbolic window (Hyperbolic S-transform or HSTransform package). This is my first time publishing a python package, so the project is still far from stable and still under beta release.

• What my project does: This transformation is applied to signal processing, analyzing transient changes of a signal during very short-time. Some special use case can be in power system signal, or Geophysical signal analysis, or MRI ...
• Target audience: anyone who is interested in signal processing or power system analysis or geographical analysis.
• Comparison: The comparison with Wavelet Transform has been shown. (which shows more potential in detecting transient changes)

I would highly appreciate some feedback, before progressing further.
HSTransform is available on pypi.
Link to source code in github

Quick Usage

import numpy as np from hstransform import HSTransform # Create input signal (for example: Voltage signal) t = np.linspace(0, 10, 100) # timeseries V_m = 220*np.sqrt(2) # peak voltage f_V = 50 # frequency phi_V = 0 # phase V_clean = V_m * np.sin(2 * np.pi * f_V * t + phi_V) # Create voltage sag/dip (80% of the nominal voltage for 0.15 second) V_sag = np.where((t >= 2) & (t <= 3.5), 0.5 * V_clean, V_clean) # Create an instance of HSTransform hs = HSTransform() # Perform the transform signal = V_sag S_transformed = hs.fit_transform(t, signal) 

I made a python package for S-transform with Hyperbolic window (Hyperbolic S-transform or HSTransform package). This is my first time publishing a python package, so the project is still far from stable and still under beta release.

• What my project does: This transformation is applied to signal processing, analyzing transient changes of a signal during very short-time. Some special use case can be in power system signal, or Geophysical signal analysis, or MRI ...
• Target audience: anyone who is interested in signal processing or power system analysis or geographical analysis.
• Comparison: The comparison with Wavelet Transform has been shown. (which shows more potential in detecting transient changes)

I would highly appreciate some feedback, before progressing further.
HSTransform is available on pypi.
Link to source code in github

Quick Usage

import numpy as np from hstransform import HSTransform # Create input signal (for example: Voltage signal) t = np.linspace(0, 10, 100) # timeseries V_m = 220*np.sqrt(2) # peak voltage f_V = 50 # frequency phi_V = 0 # phase V_clean = V_m * np.sin(2 * np.pi * f_V * t + phi_V) # Create voltage sag/dip (80% of the nominal voltage for 0.15 second) V_sag = np.where((t >= 2) & (t <= 3.5), 0.5 * V_clean, V_clean) # Create an instance of HSTransform hs = HSTransform() # Perform the transform signal = V_sag S_transformed = hs.fit_transform(t, signal) 

I’ve searched some stuff up about phi beta kappa, but I’m still skeptical. Is it really worth joining. UCI apparently covers the fee.

Hello everyone,
Is it still possible to set a custom "min-cache-ttl" in, for example, /etc/dnsmasq.d/yourname.conf using Pi-hole 6? At the beginning of this beta, this entry was ignored and flagged as an issue.
The "dns.cache.optimizer" setting does not meet my needs for modifying the TTL.
Thank you for your help!

I just wanted to share a prometheus exporter I've been working on that uses the new API in the version 6 beta of pi-hole:
https://github.com/bazmonk/pihole6_exporter
I've also got a dashboard that uses most of the metrics. I just updated the screenshots but the dashboard listing is slow to update. If you click on it in the next few hours and only see one screenshot, that's the old one.

Why another pihole exporter? People have written a bunch already...

Two reasons: first, I couldn't find one that uses the new API in the upcoming version 6. It's undergone a lot of changes. This one uses HTTPS and possibly an app token.
Second, an annoying thing I find in a lot of the existing exporters is that they only provide the per-24h stats that the admin dashboard mostly uses. Tracking that number over time is useless. You see a lot of the "ads blocked over time" graphs are jagged lines instead of increasing ones... that's because they're actually reporting the ads blocked over the last 24h (to the hour), which makes it jump down every hour and never go up overall. With only rolling 24h stats it's difficult/impossible to derive what was actually happening over time.
So, in addition to reporting those numbers (for use as quick-stats), I'm also collecting the counts of queries by [client, type, status, response, upstream destination] per minute, so that you can actually look up how many queries of some kind happened over arbitrary amounts of time, as opposed to merely what the 24h count was at that time. You can look at the cumulative stats and aggregate stats-over-time for the hour, the week, etc. It's simply more useful data.

What's Prometheus?

It's a system for gathering metrics (numerical data) off computers and putting it into a time-series database for analyzing and looking for trends/problems/etc. Usually it's used along with grafana (a web app for making pretty graphs and dashboards based off that data), and it's often used alongside Loki (a similar system for gathering log data). Folks also use it to generate alerts (e.g. I get slack alerts if I have updates available, or if a user is logged in, or if available memory is too low or CPU pressure too high too long).
While you can host your own prometheus/loki/grafana servers (it's open-source), Grafana also offers a cloud service that hosts it for you. The free plan (free-free: don't gotta give them payment method or anything) is plenty of space for a few raspberry pi and their logs. They also offer Grafana Alloy, an agent that handles sending up your data securely so you don't have to deal with that aspect. AND, they have a pre-built Raspberry Pi integration that is pre-configured and ready to go. If you go that route, in the README I include a sample blurb you can put into the Alloy configuration to use this exporter along with it.
Aaaanyway, any feedback is welcome!

Does being in Phi Beta Kappa really do anything in terms of admission?

Just wanted to show how fast I could make a Phi-ntagon!! Take that long winded blowhards!!
https://youtu.be/s1BifR3j9cc

Hi guys! So I have this vector [-3,4,-1] in Cartesian (Rectangular). Usually, when changing Cartesian to Cylindrical for phi, you use arctan (y/x). With this formula, you would do arctan (4/(-3)). When I use the Ti function to convert from Cartesian to Rectangular, however, the calculator gives me a result of arctan(3/4) + pi/2. My question is, where does this come from!? Is it correct? Thx!