Usually, I can figure this stuff out, google/YouTube tutorial or a book but for the life of me I cant figure this one out…Either sleep deprived or missing something very very obvious…and I am using the Microsoft Office Pro plus 2016 version for reference purposes.

In this link https://imgur.com/a/7ZqlXXW you can see the first screenshot where I have a list of cryptocurrencies combined with their value and percentages. To the right I have a small box with percentages (top two 61% etc).

Problem: When I adjust my filter (seen in the second screenshot) to low-high or a-z the names/values/percentages move in the list (as they should because they are linked). But my small box on the right with top 2 (which should still be 61%) also adjusts since it doesn’t move with the rest but is still referring to cells D16&D17.

How do I solve the issue that these cells aren’t fixed but also move with my filter? Feel free to laugh, give advice or any other form of support. Thanks in advance 😊

I would like to filter my dataframe so that it does not contain the condition below. I tried using the '~' operator, but what it does it removes all instances of the first boolean instead of the combined operation.

df[~(df['Tactic 1'] == 'Tactical communications') & (df['Tactic 2'] == 0)]

I expected this to return a dataframe which removes all rows where both of those conditions are met, i..e column 1 is tactical comms and column 2 is 0. I do not want this combination in my df.

Any help? Thanks.

It would be nice to have an option to remove traits on the pop out trait slider, rather than select them. Could just be a small checkbox like auto blitz sim.

I’d like to quickly check my entire roster excluding mutants, for example.

Currently if you choose bio it locks out tech and skill.

If I use .filter(Boolean) on an array, it returns a new array with only the true values. How would I go about doing essentially the same thing to return only false values?

Cheers.

EDIT: Solved, thanks to PiasaChimera who pointed out that (i) was wrong, I had written "output" where it should have said "input" -_-

Hi!

I'm working on a project where I'm trying to design a "variable slope" filter by chaining IIR low pass and inverse low pass filters with increasing cutoff, so that they almost but not quite cancel out, and depending on the cutoff distance, different slopes can be achieved. Something is wrong as I get nan values due to overflow, I'll write each step below and if anyone would like to "proofread" it would be much appretiated. I'll try to keep it as light weight as possible

Initially I just want to use the most basic low pass and its inverse.

From the transfer function

H(z) =  alpha / ( 1 + (alpha - 1) z^-1 )

where alpha is a function of the cutoff angular frequency omega_c,

alpha = cos(omega_c)  - 1 + sqrt( cos(omega_c)^2 - 4 * cos(omega_c) + 3 )

where

omega_c = pi * 2 * cutoff frequency * sample rate^-1

I get the recurrence relation

output[n] = output[n - 1] + alpha*(input[n] - output[n-1] )

I get the inverse by just switching the numerator and denominator in the transfer function

G(z) = ( 1 + (alpha - 1) z^-1 ) / alpha

which gives the recurrence relation

output[n] = alpha^-1 * (input[n] + output[n - 1] * (alpha - 1))               (i)

Now, chaining several of these together, I keep track of layers of signals, as the input of filter m is the output of filter m - 1 . I want to be able to this sample by sample in real time, but that should just mean passing the output[n] _m as input[n] _(m + 1) right?

Is this generally right or am I making some big conceptual miss? Or should my errors just be due to some dumb mistake? All input is much appreciated!

Right now I'm working in a jupyter python notebook, but I've been learning JUCE to then implement it in an VST / audio unit further on

https://preview.redd.it/bs4yz8uuily51.png?width=547&format=png&auto=webp&s=2ead2d56750c0b464e63b10cf0bee67fcc9a4a37

Hi!

As part of a project course in my engineering degree I'm implementing a filter design that involves series of low pass and inverse low pass filters. I'm learning JUCE to implement it as a VST plugin, however right now I'm just working in Python in a Jupyter Notebook testing filter equations and looking at Bode plots.

In the end, what I need is difference equations for a low pass and an inverse low pass where I can specify cut off in Hz and that behaves as a typical one pole filter (and its inverse) in audio applications.

I have previously taken a transform course and a control theory course, but neither of these dealt with z-transform, and it was a couple of years ago.

I've been trying to find the most simple low pass filter (that is still usable) to implement but I'm somewhat confused about how the regular transfer function in the s - domain relates to the transfer function in the z - domain. Further, the inverse filter has the inverse transfer function, so I need to be able to find the transfer function of the regular low pass, invert it, and then find the difference equation from this, if I cant find the inverse difference equation stated explicitly.

This is one common description of the most simple low pass

y[n] = y[n-1] + (x[n]-y[n-1]) * omega_c (1)

where omega_c is the cut off. This would then have the z-transform transfer function

Y = Y * z^-1 - Y * z^-1 * omega_c + X * omega_c

Y = Y * z^-1 (1 - omega_c) + X * omega_c

Y ( 1 - (1 - omega_c) ) z^-1 ) = X * omega_c

Y / X = omega_c / (z^-1 + omega_c * z^-1 )

This seems erroneous though, I was expecting

Y / X = omega_c / (z^-1 + omega_c)

Anyway, invering this gives

Y / X = (z^-1 + omega_c * z^-1 ) / omega_c

=>

Y = omega_c^-1 * X * (z^-1 + omega_c * z^-1 )

=>

y[n] = omega_c^-1 * (x[n - 1] + omega_c * x[n -1])

However, in the book "VA Filter Design" by Vadim Zavalishin the author describes (1) as "naive" implementation as having a bad frequency response for high cut off values. He recommends using a trapezoid design, described in pseudo code as

omega_d = (2 / T) * arctan(omega_c * (T / 2) )

g = omega_d * T / 2

G = g/(1 + g)

s = 0

loop over samples x and do {

v = (x - s) * G

y = v + s

s = y + v }

This supposedly "stretches" the correct part of the frequency response of the naive implementation to cover the range up until the Nyquist frequency. However, this equation is arrived to via block representation, and I am unsure how to derive the invers

Hello! I've assigned to create a Matlab script where it loads a RGB image, separates the channels, applies the fourier transform function and low pass filter to each channel, inverse fourier transform and merge all of them for my digital control class, the problem is that It's one of my first times using Matlab and I'm practically new to it, I've created the script but I don't think the final image output is the correct one, once I obtain the inverse fourier transform I use a function to get only the real part and the image of that process it's practically full dark, when I show the complex image output I get also a dark image but with a few lines, tried to remove all the full black pixels to visualize better but I don't know how to work with a image with complex numbers and Matlab won't let me. Most of the work I did was following this 'class' http://www.cs.uregina.ca/Links/class-info/425/Lab5/ .

The way my script works: First separate image into R, G, B channel and then convert each color channel to grayscale, then I process each grayscale layer applying the fourier transform, low pass filter, inverse fourier transform and merge all the results with the cat function.

Here's everything I've done. https://drive.google.com/open?id=1aHaFsAGEIBUPOCNftN_7uYWTFiA9UQdH

Been having a discussion with some friends about what Herald games looked like and how people performed in said games. Does anyone know of a way to somehow watch Herald games in the in-game client? Or does anyone have a replay ID of a friend or someone that is in Herald that I could look at? It would be greatly appreciated.

UPDATE: Error in OP title!

SAYS: "inverse Bloom lookup filters"

SHOULD sAY: "the opposite of a Bloom lookup filter"

Sorry about the confusion, I'm still studying this stuff!

I'm not a specialist in Bloom lookup filters yet, and I'm still researching them, but I think the following facts can be established at this point.

Facts

• A Bloom filter was proposed / submitted by Mike Hearn for addition to Bitcoin in November 2015, as part of the "thin blocks" feature, which would have been a major solution to one of the most important constraints in the whole "Bitcoin scaling debate", by providing an 85% reduction in bandwidth needed for nodes relaying blocks and transactions.

• Three weeks after it was submitted, this feature was removed by Gregory Maxwell, on November 26, 2015:

https://np.reddit.com/r/btc/comments/43iup7/mike_hearn_implemented_a_test_version_of_thin/

• "Bloom" filters work by using a hash table to provide significant saving of space when testing set-membership, and they are commonly used in networking situations where throughput capacity constraints are an issue (eg, Bitoin nodes relaying blocks containing transactions)

• There are different types of "generalized" Bloom filters which can provide various properties / guarantees (ie, some types provide freedom from false positives, while other types provide freedom from false negatives), and the choice of which kind of Bloom filter to use would of course depend on whether it was more important to avoid false positives or false negatives in a particular application (eg, a node deciding whether another node did or did not include a particular transaction in a block).

Hypothesis

There most likely ais some form of Bloom filter (either a "normal" one, or its "opposite" form) which would have worked fine for the particular use case "thin blocks" - Bitcoin nodes relaying blocks and transactions, and the need to reduce the traffic between nodes when testing whether a transaction is included in a particular block.

Conclusion

If the above hypothesis turns out to be true, then it appears that Gregory Maxwell has been quietly trying to suppress a proposed enhancement to Bitcoin which could have provided an 85% reduction in the amount of traffic required for nodes to relay blocks and transactions.

Literature

https://www.google.de/search?q=inverted+bloom+lookup+%22false+negative%22

PDF - http://www.eecs.harvard.edu/~michaelm/postscripts/tempim3.pdf

>

I'm just starting out with image restoration and feel confused about some concepts Is an inverse filter same as deconvolution? I wanted to apply a blur or an image(using a kernel) and then fourier the distorted image as well as kernel and divide so as to get the fourier of original image. Now, applying an inverse filter yeilds the original image. Is this approach correct?

Could someone post a link to any available python code for the same(without using fft function directly)? (I tried googling)

Thanks :)

It would be awesome if we could inverse our filters for finding nests! Is this possible?

I know we can't upload custom images for iPhones, but would it be possible to customize the color of the bubble? Or even have an option to set really rare stuff (or whatever you want) to blink?

Loveeee this app!!!

I'm wondering what's the "proper" way to use the Inverse Telecine filter in Squeeze 8. Basically, when I use the Inverse Telecine filter, do I need to manually set the framerate of the encode to 24FPS or will leaving the preset at 1:1 do the trick? Because my concern is that when the encoder makes its run over the file, and it reads the output framerate of 1:1 it'll think "oh, well the input is 29.97 FPS, so I should output to that," and then I've basically undone what I did.

The specific example I have in mind is the search box on the All Upgrades page in the guild section. I'd like the ability to filter the search results to show me all upgrades that DO NOT contain the word scribing.

Thanks in advance.

I use the inversion method. 2 minutes then I put on the filter cap with a paper filter and press out that liquid goodness.

This morning for no reason at all, I put the filter cap with a paper filter on the Aeropress at the beginning of the 2 minutes. I realized that I had done it this way as I was finishing my coffee. I did not notice any flavor difference, but then I was not looking for any.

Has anyone looked into this and determined if it makes a difference? Does it help or hinder flavor if you let the brewing coffee breath? Does the filter not make a difference either way? Have I been watching too many AeroPress World Competition videos?

I will attempt to analyze the periodicity of GME’s price using the Fast Fourier Transform (FFT). This is a method to translate a function (in our case: price:time) from the time domain into the frequency domain, giving insights into the various periodicities present in the signal as well as their power. This DD does not REQUIRE you to have an understanding of the FFT, but it will help you to understand the analysis better, so I suggest a cursory overview of the Fourier Transform Wikipedia page.

In ape speak: Fourier = big brain. Instead of “wen happen” makes “how much repeat”. I look at “how much repeat” and try predict “wen happen”.

Let’s start by calculating GME’s FFT, operating on the HIGH edge of each daily candle for the past 225 days, i.e starting from the March 2021 pop.

FFT of GME Price for the last 225 days

The above chart is the FFT. On the X-axis we have frequency, and on the Y axis we have power. In other words, the position of the line indicates period: if a line is further to the left that means it has a shorter period, i.e the associated phenomenon occurs more frequently. Similarly – as we move to the right edge of the chart the phenomenon occurs less frequently.

In ape speak: Line left mean happens lotsa times. Line right mean happen not lotsa times.

The height of the lines indicates the amount of power they have in the signal. Thus the height indicates dominance/significance of the corresponding frequency in the time domain. Think of it like this: The original purpose of Fourier shenanigans was to decompose arbitrary functions into an infinite sum of sines and cosines. However, you don’t need infinite terms to APPROXIMATE an arbitrary function. It is enough to take the HIGH ENERGY components, as they would contribute the most to the approximation. Since these high energy components contribute most, that means they have a stronger descriptive power about the original signal.

In ape speak: big line mean important. Smol line mean useless garbage

We observe a massively dominant component at a period of 0.01785 1/D , which is 648.407511 📷

In other words, the most dominant periodicity for the past 225 days is 56 days. So, something happens every 56 days (or close to it). Let’s see what that is…

[ GME Chart with 7-4-1 cycle overlay and marked Pk-Pk durations ](https://preview.redd.it/dfu5wk

#Greetings, Peasants.

##CURRENT MAINLINE PATCH: e1.6.5

##CURRENT BETA PATCH: e1.7.0

##LATEST DEV UPDATE: Future Plans

Welcome to the patch thread, we make these threads to save us another pin space for other things.

##Taleworlds forum bug reporting thread

##Mount and Blade Discord

##Link to wiki - Previous patches and threads inside

#Main e1.6.5

#Latest Changes:

##Singleplayer

###Crashes

Fixed a crash that occurred when inviting a party that was involved in a siege to an army.

Fixed a crash that occurred if the player party consisted of companions alone.

Fixed a crash that occurred when making peace through a kingdom decision while in an encounter.

Fixed a crash that occurred when a companion fought in a common area.

Fixed a crash that occurred while generating the "Scout Enemy Garrison" quest.

Fixed a crash that occurred due to corrupt issue data when settlement ownership changed.

Fixed a crash that occurred when transferring prisoners.

###Fixes

Fixed a bug that prevented emissaries from gaining charm experience after relation gain.

Fixed a bug that allowed the player to assign fugitive and recently released heroes as governors.

###Changes

Chinese language updates.

##Previous Beta Hotfixes:

##24/11/21

##Singleplayer

###Crashes

Fixed a crash that occurred when joining a battle.

Fixed a crash that occurred when loading a save game that was created just before an alley fight.

Fixed a crash related to the lowest graphics settings preset.

Fixed a crash related to issue generation that occurred upon load.

###Fixes

Fixed a DLSS issue that caused the screen to be partially rendered.

Fixed a menu bug with the tournament prize reroll.

###Changes

A 15 days cooldown was added for tournaments in the same city.

Regular tournament prize values are scaled according to the number of nobles that participated in the tournament.

##Modding

Tournament participants and prizes are now moddable.

##12/11/21

##Singleplayer

###Crashes

Fixed a crash that occurred if the player navigated b

I am pleased to announce that after nearly a year working on it, Mekanism 10.1 for MC 1.16.5 is finally out (requires forge 36.2.21 or higher).

Before I get into the specifics of what has changed or been added in 10.1, there are a few things I would like to mention; starting with a reminder that while everything in the world should update smoothly from recent 10.0.x versions, it is definitely recommended to make a backup of your world first, as once you update your world to 10.1 if you decide to downgrade your world back to 10.0.x, some of the data will not be properly loadable (and some things will even cause crashes). Additionally, as a lot of Mekanism’s internals have changed there is a chance that any addons or mods that are directly accessing them (rather than asking us to expose things to the API) might break and if they do you should report the crash to them and not us.

I would also like to give a brief shout out and thank you to u/Ridanisaurus for creating the majority of the new textures and models.

In terms of the plans for updating to 1.18 (just to try and reduce the number of people asking), my current plans now that Mekanism 10.1 has been released are as follows: fix a bunch of bugs that got reported to ProjectE in its 1.16.5 lifetime, then work on porting ProjectE to 1.18 as it is a smaller mod and will let me get a better sense of what has changed in 1.17 and 1.18, and finally switch back and work on porting Mekanism to 1.18.

Now with all that out of the way, let’s get to the part you all have been waiting for… Finding out the larger and more noteworthy changes that Mekanism 10.1 is bringing. Do note that the below lists are not the complete set of changes; there have been many bug fixes, performance improvements, and other minor changes, so if you want to see a more detailed list of changes, please see the changelog on CurseForge that is generated from the commits that went into making 10.1, but be warned it is very long!

New Features:

• A new crafting window system for the QIO (with full support for JEI’s recipe transfer system)

https://reddit.com/link/rmcthj/video/owaccz8jl3781/player

• Added a new (API exposed) skin system for Robits

https://reddit.com/link/rmcthj/video/bovtj8cgj3781/player

• Added an HDPE Reinforced Elytra and an Elytra Unit for the MekaSuit (that can be used in combination with the Jetpack Unit to achieve a similar effect as using firework rockets)

https://reddit.com/link/rmcthj/video/zohqjr8jj3781/pl

I am trying to replicate a signal processing technique and struggled on reconstructing the experimental impulse response data that I have. I will appreciate any help I can get here.

I am basing my work on "High Resolution Modal Analysis" published at Journal of Sound and Vibration and the link below contains excerpts related to my problem:

https://drive.google.com/file/d/10Z1SzvIcKVviqkScjtTUW4ssb9Y4hhfz/view?usp=sharing

As explained in the text, the first step is to find an FIR filter g that transforms the force signal into a Dirac function. I suppose in discrete form, it will hence be a Kronecker delta function. I have no idea how to find g. I tried to deconvolute the delta function (0 everywhwere, 1 at particular point) with my force data but I am not getting anything that looks like the example shown. Any help in finding this g will be greatly appreciated.

p/s: I am working this on MATLAB, but will appreciate any suggestions. I'll figure out how to code the solutions in MATLAB on my own. Thanks in advance!

Preface: The biggest controversy surrounding GME is the question: 'Have the shorts covered?". There are countless DDs on this subreddit that can answer that question. However, this DD wants to offer a different perspective. Say we travel to a parallel universe to Earth #666, where we know for a fact the shorts covered ceteris paribus (everything else about GME remains constant). TA on GME involving supply/demand can still prove the stock at the current status quo is bullish.

Happy New Year fellow smooth brain apes from another universe. Two years ago DFV enlightened us to the song of GME's rebirth. First verse came in a "short squeeze" last January. Now January has arrived again, and I can feel in my balls that the second verse is starting.

Could also be a STI from New Year's eve, cuz it really hurts...

"The squeeze squoze moron. What will be the catalyst even be this time?"
Fuck would I know what Ryan Cohen is cooking. (NFT Marketplace???) But this voodoo astrology can smell the tendies he is about to serve.

Here. Check a look at the amazing TA with GME sitting comfortably on the 2021 YTD VWAP.

Look at all those triangles. Mooooooon tomorrow!!!

Jokes aside, instead of displaying to you how two lines on a chart form a triangle, this DD will draw you lines but wrapped with theoretical bullshit containing Wyckoff Method, IV percentile, and Nancy Pelosi.

First, go educate yourself about the Wyckoff Method.

Yeah, I wouldn't read that long ass article either, but it would help you understand this section better.

In short, the market can be understood and anticipated through analysis of supply and demand. Supply and demand is the invisible hand that determines price - not fundamentals or technicals. Price cycles through accumulation, markup, distribution, and then markdown. Accumulation and distribution events can be visualized in certain patterns.

Picture

This method was developed by Richard Wyckoff a century ago, who realized that stock price trends were driven primarily by institutional and other large operators who manipulate stock prices in their favor. Using Wyckoff's method, one can invest in stocks by capitalizing on the intentions of the large “smart money” interests, rather than being caught on the wrong side of the market.

Two patterns to know that your brain will

I've been using a chiral inverter at home (in my garage, don't want to deal with any leaks!) to make artificial sweetener from maple syrup (I know I should be using something more refined, but I like the flavor!), but the process is incredibly slow, I'm wondering if anyone else here has tried to build a macroscopic chiral inversion rig.