LPF | Low Pass Filter for JavaScript | Video Utils library
kandi X-RAY | LPF Summary
kandi X-RAY | LPF Summary
Lightweight algorithm to for smoothing series of values. Low Pass Filter muffles fast (high-frequency) changes to the signal. For more information visit
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QUESTION
print(df.sample(50)):
match_datetime country league home_team away_team home_odds draw_odds away_odds run_time home_score away_score
72170 2021-10-17 12:30:00 Ukraine Persha Liga Alliance Uzhhorod 1.22 5.62 9.71 2021-10-17 09:22:20.212731 NaN NaN
100398 2021-11-02 14:35:00 Saudi Arabia Division 1 Al Qadisiya Bisha 1.61 3.61 4.94 2021-11-02 09:13:18.768604 2.0 1.0
33929 2021-09-11 23:00:00 Panama LPF Veraguas Plaza Amador 2.75 2.75 2.71 2021-09-10 23:47:54.682982 1.0 1.0
12328 2021-08-15 15:30:00 Poland Ekstraklasa Slask Wroclaw Leczna 1.74 3.74 4.59 2021-08-14 22:44:26.136608 0.0 0.0
81500 2021-10-24 13:00:00 Italy Serie D - Group A Caronnese Saluzzo 1.69 3.60 4.28 2021-10-23 13:37:16.920175 2.0 2.0
143370 2021-12-05 14:00:00 Poland Division 1 Chrobry Glogow Widzew Lodz 3.36 3.17 2.15 2021-11-30 17:40:24.833519 0.0 0.0
175061 2022-01-08 18:00:00 Spain Primera RFEF - Group 1 R. Union Extremadura UD 1.26 4.40 18.00 2022-01-08 17:00:46.662761 0.0 1.0
21293 2021-08-29 16:00:00 Italy Serie B Cittadella Crotone 2.32 3.11 3.31 2021-08-26 18:04:46.221393 4.0 2.0
97427 2021-11-01 17:00:00 Israel Leumit League M. Nazareth Beitar Tel Aviv 1.92 3.26 3.75 2021-10-30 09:40:08.966330 4.0 2.0
177665 2022-01-13 12:30:00 Egypt Division 2 - Group C Said El Mahalla Al Magd 4.12 3.08 1.94 2022-01-12 17:53:33.570126 0.0 0.0
69451 2021-10-17 05:00:00 South Korea K League 1 Gangwon Gwangju FC 2.06 3.38 3.65 2021-10-15 09:55:54.578112 NaN NaN
4742 2021-08-10 20:30:00 Peru Liga 2 Deportivo Coopsol Grau 3.14 3.49 2.06 2021-08-10 18:14:01.996860 0.0 2.0
22266 2021-08-29 13:00:00 France Ligue 1 Angers Rennes 2.93 3.27 2.56 2021-08-27 12:26:34.904374 2.0 0.0
46412 2021-09-26 04:00:00 Japan J2 League Okayama Blaublitz 2.24 2.90 3.63 2021-09-23 09:08:26.979783 1.0 1.0
133207 2021-11-27 21:15:00 Bolivia Division Profesional Palmaflor Blooming 1.51 4.05 5.10 2021-11-25 18:22:28.275844 3.0 0.0
140825 2021-11-28 11:00:00 Spain Tercera RFEF - Group 6 Valencia B Torrellano 1.58 3.56 5.26 2021-11-28 19:54:40.066637 2.0 0.0
226985 2022-03-04 00:30:00 Argentina Copa de la Liga Profesional Central Cordoba Rosario Central 2.36 3.26 2.86 2022-03-02 17:23:10.014424 0.0 1.0
137226 2021-11-28 12:45:00 Greece Super League 2 Apollon Pontou PAOK B 3.37 3.25 2.01 2021-11-27 15:13:05.937815 0.0 3.0
182756 2022-01-22 10:30:00 Turkey 1. Lig Umraniyespor Menemenspor 1.40 4.39 7.07 2022-01-19 17:25:27.128331 2.0 1.0
89895 2021-10-28 16:45:00 Netherlands KNVB Beker Ajax Cambuur 9.10 5.55 1.26 2021-10-27 07:46:56.253996 0.0 5.0
227595 2022-03-06 17:00:00 Israel Ligat ha'Al Ashdod Maccabi Petah Tikva 2.30 3.21 3.05 2022-03-02 17:23:10.014424 NaN NaN
57568 2021-10-02 13:00:00 Estonia Meistriliiga Kalju Legion 1.58 4.10 4.84 2021-10-02 10:55:35.287359 2.0 2.0
227035 2022-03-04 19:00:00 Denmark Superliga FC Copenhagen Randers FC 1.70 3.84 5.06 2022-03-02 17:23:10.014424 NaN NaN
108668 2021-11-07 13:30:00 Germany Oberliga Mittelrhein Duren Freialdenhoven 1.35 5.20 6.35 2021-11-06 17:37:37.629603 2.0 0.0
86270 2021-10-25 18:00:00 Belgium Pro League U21 Lommel SK U21 Lierse K. U21 3.23 3.84 1.92 2021-10-26 01:22:31.111441 0.0 0.0
89437 2021-11-01 02:10:00 Colombia Primera A America De Cali Petrolera 1.86 2.92 4.60 2021-10-27 07:41:24.427246 NaN NaN
13986 2021-08-21 13:00:00 France Ligue 2 Dijon Toulouse 3.92 3.51 1.94 2021-08-16 13:22:02.749887 2.0 4.0
105179 2021-11-06 15:00:00 England NPL Premier Division Atherton South Shields 3.90 3.42 1.82 2021-11-05 10:01:28.567328 1.0 1.0
142821 2021-12-01 12:30:00 Bulgaria Vtora liga Marek Septemvri Simitli 1.79 3.38 4.35 2021-11-30 17:40:24.833519 2.0 2.0
45866 2021-09-24 00:30:00 Venezuela Primera Division Dep. Tachira Portuguesa 1.96 3.60 3.22 2021-09-23 09:08:26.979783 4.0 1.0
76100 2021-10-22 16:30:00 Denmark 1st Division Hvidovre IF Koge 1.91 3.56 3.81 2021-10-21 08:43:12.445245 NaN NaN
115896 2021-11-14 16:00:00 Spain Tercera RFEF - Group 6 Olimpic Xativa Torrellano 2.78 2.89 2.39 2021-11-13 12:21:45.955738 1.0 0.0
156159 2021-12-12 16:00:00 Spain Segunda RFEF - Group 1 Marino de Luanco Coruxo FC 2.19 3.27 3.07 2021-12-10 09:26:45.001977 0.0 0.0
18240 2021-08-21 12:00:00 Germany Regionalliga West Rodinghausen Fortuna Koln 3.25 3.60 2.00 2021-08-21 03:30:43.193978 NaN NaN
184913 2022-01-22 10:00:00 World Club Friendly Zilina B Trinec 3.56 4.14 1.78 2022-01-22 16:44:32.650325 0.0 3.0
16782 2021-08-22 23:05:00 Colombia Primera A Petrolera Dep. Cali 3.01 3.00 2.44 2021-08-19 18:24:24.966505 2.0 3.0
63847 2021-10-10 09:30:00 Spain Tercera RFEF - Group 7 Carabanchel RSD Alcala 4.39 3.42 1.75 2021-10-09 12:03:50.720013 NaN NaN
7254 2021-08-12 16:45:00 Europe Europa Conference League Hammarby Cukaricki 1.72 3.87 4.13 2021-08-11 23:48:31.958394 NaN NaN
82727 2021-10-24 14:00:00 Lithuania I Lyga Zalgiris 2 Neptunas 1.76 3.78 3.35 2021-10-24 12:02:06.306279 1.0 3.0
43074 2021-09-22 18:00:00 Ukraine Super Cup Shakhtar Donetsk Dyn. Kyiv 2.57 3.49 2.59 2021-09-19 09:39:56.624504 NaN NaN
65187 2021-10-11 18:45:00 World World Cup Norway Montenegro 1.56 4.17 6.28 2021-10-11 10:56:09.973470 NaN NaN
120993 2021-11-18 00:00:00 USA NISA Maryland Bobcats California Utd. 2.76 3.23 2.39 2021-11-17 20:36:26.562731 1.0 1.0
201469 2022-02-12 15:00:00 England League One AFC Wimbledon Sunderland 3.30 3.48 2.17 2022-02-10 17:47:36.501159 1.0 1.0
142180 2021-12-01 19:45:00 Scotland Premiership St. Mirren Ross County 2.06 3.25 3.85 2021-11-29 18:28:22.249662 0.0 0.0
4681 2021-08-10 18:30:00 Europe Champions League Young Boys CFR Cluj 1.48 4.29 6.92 2021-08-10 18:14:01.996860 3.0 1.0
67321 2021-10-17 13:00:00 Spain LaLiga Rayo Vallecano Elche 1.78 3.64 4.99 2021-10-13 11:22:34.979378 NaN NaN
27499 2021-09-04 14:00:00 Iceland Inkasso-deildin Kordrengir Fjolnir 2.18 3.66 2.82 2021-09-02 23:28:49.414126 1.0 4.0
48962 2021-09-25 21:00:00 Mexico Liga Premier Serie B Uruapan Lobos Huerta 1.83 3.69 3.70 2021-09-25 13:02:58.238466 NaN NaN
65636 2021-10-16 17:00:00 Switzerland Super League Young Boys Luzern 1.26 6.04 9.43 2021-10-11 10:56:09.973470 NaN NaN
17333 2021-08-21 14:00:00 Finland Kakkonen Group A Atlantis Kiffen 1.57 4.29 4.42 2021-08-20 12:41:03.159846 1.0 1.0
ANSWER
Answered 2022-Mar-05 at 01:38If I understand correctly, you would like to filter the dataframe to retain, for each run_time, the last two rows (or up to two rows) by match_datetime.
Simplified answer
This can be done relatively easily without any join, using GroupBy.tail(). (Note, my original answer was using GroupBy.rank(), but this is simpler, although slower):
QUESTION
I have a data set on police killings that you can find on Kaggle. There's some missing data in several columns:
...
ANSWER
Answered 2021-Jul-29 at 11:47Your approach of encoding categorical values first and then imputing missing values is prone to problems and thus, not recommended.
Some imputing strategies, like IterativeImputer, will not guarantee that the output contains only previously known numeric values . This can result in imputed values which are unknown to the encoder and will cause an error upon the inverse transformation (which is exactly your case).
It is better to first impute the missing values for both, numeric and categorical features, and then encode the categorical features. One option would be to use SimpleImputer and replacing missing values with the most frequent category or a new constant value.
Also, a note on LabelEncoder: it is clearly mentioned in its documentation that:
This transformer should be used to encode target values, i.e.
y, and not the inputX.
If you insist on an encoding strategy like LabelEncoder, you can use OrdinalEncoder which does the same but is actually meant for feature encoding. However, you should be aware that such an encoding strategy might falsely suggest an ordinal relationship between each category of a feature, which might lead to undesired consequences. You should therefore consider other encoding strategies as well.
QUESTION
The Pan Tompkins algorithm1 for removing noise from an ECG/EKG is cited often. They use a low pass filter, followed by a high pass filter. The output of the high pass filter looks great. But (depending on starting conditions) the output of the low pass filter will continuously increase or decrease. Given enough time, your numbers will eventually get to a size that the programming language cannot handle and rollover. If I run this on an Arduino (which uses a variant of C), it rolls over on the order of 10 seconds. Not ideal. Is there a way to get rid of this bias? I've tried messing with initial conditions, but I'm fresh out of ideas. The advantage of this algorithm is that it's not very computationally intensive and will run comfortably on a modest microprocessor.
1 Pan, Jiapu; Tompkins, Willis J. (March 1985). "A Real-Time QRS Detection Algorithm". IEEE Transactions on Biomedical Engineering. BME-32 (3): 230–236.
Python code to illustrate problem. Uses numpy and matplotlib:
...ANSWER
Answered 2021-Jun-24 at 04:54The trick seems to be the initial conditions. Load the first 13 values of input and output of low pass filter to zero and the bias goes away.
QUESTION
ANSWER
Answered 2021-May-03 at 09:00I was actually quite surprised, how good the result already is, seeing this noticable skew. But, that's not the actual problem with the last line, but the shadow! This is your thresholded image:
So, pytesseract has no chance to properly detect anything meaningful from the last line. Let's try to remove the shadow, following Dan Mašek's answer here, and let Otsu do the thresholding:
QUESTION
I need to apply HPF and LPF to the Fourier Image and perform the inverse transformation, and compare them. I do the following algorithm, but nothing comes out:
...ANSWER
Answered 2021-Apr-04 at 20:10You use a white circle black background and apply it to the FFT magnitude to do a low pass filter. The high pass filter is the reverse polarity of the low pass filter -- black circle on white background. You can mitigate the "ringing" effect in the result by applying a Gaussian filter to the circle. Here is an example of a low pass filter.
Input:
QUESTION
Iam trying to create low pass filter in matlab
I've a matrix with size 12x6
...ANSWER
Answered 2021-Mar-29 at 08:55You are applying the filter to the region of your image where all values are set. That means you're cropping two columns on the left, two columns on the right, two rows on the top and two rows on the bottom. There are two bugs in your code
- You're cropping one row and one column more than necessary.
- You're implicitly padding the result with zeros on the left and on the top.
Additionally, you should preallocate your matrices.
Here is a fix
QUESTION
sock = socket(AF_PACKET, SOCK_RAW, htons(ETH_P_ALL));
setsockopt(sock, SOL_SOCKET, SO_ATTACH_FILTER, &f, sizeof (f))
ANSWER
Answered 2020-Nov-12 at 11:14The "bug" you're describing is real and I've seen it at multiple companies in my career. There is something like an "oral tradition" around this bug that is passed from one network engineer to another. Here are the common fixes:
- Just call
recvon the socket until it is empty - Double-filter by filtering packets in usermode as well as using the bpf
- Use the zero-bpf technique just like libpcap where you apply an empty bpf first, then empty the socket, and then apply the real bpf.
I've written about this problem extensively on my blog to try and codify the oral tradition around this bug into a concrete recommendation and best-practice.
QUESTION
I have following class file
...ANSWER
Answered 2020-Jun-01 at 06:26Try using dependsOn or use a priority in your test annotations. @Test methods are not always executed in the same order as they are written. Also if the Tests are run in parallel this would be an issue as well. Since logOut is completely dependent on the login method, try using
QUESTION
I am facing difficulty solving this question in C++. My initial thought was to find the lowest prime factor of all the elements from 2 to 10^6 as it's the given constrain. Then, I used two-pointers and a set. My algorithm is as follows:
...ANSWER
Answered 2020-Apr-02 at 16:50You can't consider only the lowest prime factor, you have to consider all of them. For example if you have an array A = [10, 15] your code will mistakenly output 2, because it thinks LCM(10,15) = 10*15 even though they share the factor 5. The rest of your two pointers approach is fine, but at each step you should remove/add all prime factors to the set S instead of just the lowest one.
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