transfer.sh | Easy and fast file sharing from the command-line | Continuous Deployment library
kandi X-RAY | transfer.sh Summary
kandi X-RAY | transfer.sh Summary
Easy and fast file sharing from the command-line. This code contains the server with everything you need to create your own instance. Transfer.sh currently supports the s3 (Amazon S3), gdrive (Google Drive), storj (Storj) providers, and local file system (local).
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QUESTION
I'm trying to deploy a docker container to my Kubernetes cluster, but I'm running into an issue with passing the required command-line arguments to the container. I need to pass two arguments called --provider local
and --basedir /tmp
. Here is what the docker run command looks like (I can run this without any issues on my docker host):
ANSWER
Answered 2022-Mar-12 at 02:46Try:
QUESTION
I'm trying to detect specific pattern in the Real time data (Time Series). For the visualization, I'll show the data in two parts here.
Pattern
: I'm trying to search for in time series,
DataWindow
: data buffer(window) I slide in real time to keep track of history.
Here is my recorded data(red boxes shows the pattern that I want to detect), but this can be different since it is Real Time:
The above data doesn't have a lot of noise (at least for this collection) - as far as I look at the resolutions, peaks (maybe I would say sinusoidal peaks) are distinguishable at first glance. That is why applying a moving average filter does not help me at all.
The below image shows some samples from real time data but in the saved data, plotter applies extrapolation to draw continous plot. In general, data samples look like the image below or maybe with more resolution than this image.
For the initial start, I've tried Spike Detection in a Time-Seriesusing moving average and did not work as I expected. I've also tried some solutions here from this thread Detecting patterns from two arrays of data in Python and the results are not good enough for me to raise a flag in the patterns during run-time(there are many false positives)
Also, as you might realize from the saved real time data that, patterns can have different scale and most importantly can have different offset. That is the problem I guess for me to apply above solutions on my problem to get distinguishable results.
To give some example to try out, these can be used for the Pattern
and DataWindow
Pattern = [5.9, 5.6, 4.08, 2.57, 2.78, 4.78, 7.3, 7.98, 4.81, 5.57, 4.7]
SampleTarget = [4.74, 4.693, 4.599, 4.444, 3.448, 2.631, 1.845, 2.032, 2.415, 3.714, 5.184, 5.82, 5.61, 4.841, 3.802, 3.11]
SampleTarget2 = [5.898, 5.91, 5.62, 5.25, 4.72, 4.09, 3.445, 2.91, 2.7, 2.44, 2.515, 2.79, 3.25, 3.915,4.72, 5.65, 6.28, 7.15, 7.81, 8.2, 7.9, 7.71, 7.32, 6.88, 6.44, 6.0,5.58, 5.185, 4.88, 4.72, 4.69, 4.82]
I am trying to solve this problem on Python for PoC. UPDATE: Dataset is added, includes first two red boxes and a bit wider side as well, which is shown in the saved real time data.dataset
...ANSWER
Answered 2022-Jan-25 at 15:38You can compute the gradient of the data and use a threshold to identify the features. Here I use a triple mask to get the down/up/down feature.
I commented the code to give you the main steps, so I hope it is comprehensive.
QUESTION
I have a byte array named buf
, that contains a single video frame in YUV I420 format obtained from a framebuffer. For every video frame I also have the following information:
ANSWER
Answered 2021-Oct-10 at 23:07The layout of the data seems odd but using the given offsets and strides, this is decodable as YUV.
First there are 384 * 180 bytes of luma.
Following are the chroma lines, each being 192 bytes long... but U and V lines take turns! This is accounted for by the strange offsets. U offset points exactly to after luma. V offset is 192 bytes further... and reading would leapfrog by 384 bytes.
Here's code that extracts those planes and assembles them as I420, for decoding with cvtColor
:
QUESTION
I have a database with several tables, the ones involved in this query that I want to optimize are only 4.
albums
, songs
, genres
, genre_song
A song can have many genres, and a genre many songs. An album can have many songs. An album is related to genres through songs.
The objective is to be able to recommend albums related to the genre of the album.
So that led me to have this query.
...ANSWER
Answered 2020-Aug-29 at 18:37FWIW, I find the following easier to understand, so I would want to see the EXPLAIN for this:
QUESTION
SELECT user.id
,user.emp_id
,user.name,COUNT(transfer.record_name) AS current_rank
,ROW_NUMBER() OVER () AS count
FROM transfer AS transfer
LEFT JOIN users AS user ON user.emp_id= transfer.user_id
WHERE transfer.lobs= 0
AND transfer.shift_date = '2020-03-27'
GROUP BY user.emp_id
ORDER BY current_rank DESC
...ANSWER
Answered 2020-Mar-29 at 17:08The result of ROW_NUMBER()
is not affected by the sort in the query.
Instead, it accepts an ORDER BY
option within its OVER
clause: without it, the ordering of ROW_NUMBER()
is undefined, meaning that the database is free to order the rows as it likes. You may observe that the same ordering is used over consecutive executions, but the database does not guarantee it.
So, do add an ORDER BY
to the window function:
QUESTION
Transfer.sh is a service that you can upload files by curl. I'm getting this error when I try to upload some file:
...ANSWER
Answered 2020-Jan-15 at 11:09The service seems to be unstable.
You can use https://file.io
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