sift | A visualization application for satellite imagery | Computer Vision library
kandi X-RAY | sift Summary
kandi X-RAY | sift Summary
Satellite Information Familiarization Tool (SIFT) was designed by the Space Science and Engineering Center (SSEC) at the University of Wisconsin - Madison to support scientists during forecaster training events. It provides a graphical interface for visualization and basic analysis of geostationary satellite data. SIFT is built on open source technologies like Python, OpenGL, and PyQt5. It can be run from Mac, Windows, and Linux. The SIFT application is provided as a python library called "uwsift". It can also be installed as a standalone application. SIFT's main website is The Git repository where you can find SIFT's source code, issue tracker, and other documentation is on GitHub: The project wiki with some in-depth usage and installation instructions can also be found on GitHub: Developer documentation can be found on
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Top functions reviewed by kandi - BETA
- Start the import process
- Get metadata from a GeoTIFF file
- Checks the geotiff metatiff metadata
- Add an item to the list
- Setup the UI
- Translate the UI
- Generate a heatmap from anaw map
- Return the awips colormap colors from the map
- Start importing products
- Creates a PUG file object
- Connect signals to the document
- Removes the layer from the document list
- Updates the display information for all the selected layers
- Clone a colormap
- Creates a new product
- Get metadata for ahi dataset
- Create a test image
- Generate a matplotlib plot
- Return the data for the given index
- Merge product metadata
- Drops the given mime data
- Update the text for a given point probe
- Viewbox event handler
- Extract information from AHI image
- Collect the metadata for the given paths
- Run the current version
sift Key Features
sift Examples and Code Snippets
Community Discussions
Trending Discussions on sift
QUESTION
The code for_siftup
at github - python/cpython/Lib/heapq.py has a final call to _siftdown
:
...
ANSWER
Answered 2022-Mar-28 at 10:22This is the consequence of a particular choice the authors made in the algorithm.
More common is an algorithm where this final _siftdown()
is not necessary, but then the loop must stop when newitem < heap[childpos]
, after which pos
will be a valid spot for newitem
and no more sifting is needed.
In this version however, the loop continues until a leaf is found, and newitem
is placed at a leaf spot. This may not be a valid spot for newitem
, so the extra call is needed to go back up to a valid spot.
In the comment block that precedes this function, the authors have explained why they made this choice, which at first seems to be less efficient, but in practice turns out to result in fewer comparisons:
We could break out of the loop as soon as we find a
pos
wherenewitem
<= both its children, but turns out that's not a good idea, and despite that many books write the algorithm that way. During a heap pop, the last array element is sifted in, and that tends to be large, so that comparing it against values starting from the root usually doesn't pay (= usually doesn't get us out of the loop early). See Knuth, Volume 3, where this is explained and quantified in an exercise.
See also Wikipedia - bottom-up heapsort:
The change improves the linear-time heap-building phase somewhat, but is more significant in the second phase. Like ordinary heapsort, each iteration of the second phase extracts the top of the heap,
* The article has "smallest" and "minimum" since it discusses a max-heap, not a min-heap as is whata[0]
, and fills the gap it leaves witha[end]
, then sifts this latter element down the heap. But this element comes from the lowest level of the heap, meaning it is one of the [greatest]* elements in the heap, so the sift-down will likely take many steps to move it back down. In ordinary heapsort, each step of the sift-down requires two comparisons, to find the [maximum]* of three elements: the new node and its two children.heapq
provides.
It is a pitty that Wikipedia discusses this in the context of heapsort, since it applies to heap interactions even when the heap does not serve a heapsort process.
QUESTION
I am comparing images and I have used BFMatcher
to perform feature matching
My actual code is:
...ANSWER
Answered 2022-Mar-15 at 23:03I have finally done this, which seems to work well:
QUESTION
I need to combine SIFT
and ORB
descriptors of an image.
As you know, SIFT
descriptors are of 128-length and ORB
descriptors are of 32-length.
At this moment what I do is:
- Reshaping
SIFT
descriptors to 32-length. For instance, reshape a (135, 128) descriptor to a (540, 32) descriptor - Concatenating
SIFT
andORB
descriptors (since at this moment both have 32-length)
Code:
...ANSWER
Answered 2022-Mar-15 at 22:59In case someone is interested, what I have finally done is to use ORB in order to detect
the images keypoints and use SIFT to compute
descriptors from that keypoints
Code:
QUESTION
I'm trying to create a dynamic website that loads a header component at random on every refresh. No matter which approach I take, it works fine on the initial load and then throws this error every refresh after:
...ANSWER
Answered 2021-Dec-24 at 22:51I noticed that you already tried editing babelrc file, but can you try add this
QUESTION
I am trying to troubleshoot my data, and check whether a certain name appears in two different columns in the same row (same observation):
...ANSWER
Answered 2021-Dec-24 at 15:21I include a dplyr approach:
QUESTION
Empirically it seems that whenever you set_index
on a Dask dataframe, Dask will always put rows with equal indexes into a single partition, even if it results in wildly imbalanced partitions.
Here is a demonstration:
...ANSWER
Answered 2021-Oct-19 at 10:45Is it the case that a single index can never be in two different partitions?
IIUC, the answer for practical purposes is yes.
A dask dataframe will in general have multiple partitions and dask may or may not know about the index values associated with each partition (see Partitions
). If dask does know which partition contains which index range, then this will be reflected in df.divisions
output (if not, the result of this call will be None
).
When running .set_index
, dask will compute divisions and it seems that in determining the divisions it will require that divisions are sequential and unique (except for the last element). The relevant code is here.
So two potential follow-up questions: why not allow any non-sequential indexing, and as a specific case of the previous, why not allow duplicate indexes in partitions.
With regards to the first question: for smallish data it might be feasible to think about a design that allows non-sorted indexing, but you can imagine that a general non-sorted indexing won't scale well, since dask will need to store indexes for each partition somehow.
With regards to the second question: it seems that this should be possible, but it also seems that right now it's not implemented correctly. See the snippet below:
QUESTION
git
and a workflow where I have many loose changes that are not intended for check-in. Is there a good git
way to manage those not-for-check-in modified files?
In my project, we have about 700,000 source files. I'd call it a larger project.
When I am working on fixing a bug or implementing a feature, I will quite frequently end up with many files that I have made ancillary edits. Such as debugging instrumentation, or alternative implementation, or an expensive check for a never-happen situation that once appears to have happened in the wild and I want to catch it if it ever happens on my machine, or clang-format
because the original had goofy formatting.
To commit my good changes, I'll branch, I carefully add the relevant files and commit those. (Followed by a push of my changes. Make a PR. Get code review approval. Jenkins builds on all the dozen different target platforms, and runs the test suite. Then I merge my branch into main.)
Probably a fairly typical workflow... except for that I have many (1000+) not-for-check-in files that I want to keep modified in my worktree, but not merge those into main. That latter part is probably atypical.
With Perforce, I would add my not-for-check-in files into a not-for-check-in changelist and park them there. They'd be out of the way, and I could not accidentally pull one of those "tainted" files without taking steps to move it out of the not-for-check-in changelist.
So far, my git
tactic of being super-duper careful has worked, but seems fraught with peril. I maintain a stash.txt
file that has a list of my not-for-check-in files, and frequently stash
them to temporarily get them out of the way, do my git
things (making branches, fetch, merge, push, whatever), and stash pop
them back in my worktree. Seems janky, manual, and error prone; high cognitive load. Has to be a better way.
(I have not run into the scenario when I have a single file that has both good changes and not-for-check-in changes. If/when I do, I am aware of how to add-and-commit hunks of changes.)
I have tried the tactic of making a branch, add-and-commit both my good changes and not-for-check-in changes. Then cherry pick the good changes for what should go into main. That scales poorly with the 1000s of not-for-check-in files that need to be sifted through.
Any advice or guidance is appreciated.
...ANSWER
Answered 2021-Nov-11 at 15:36Using git worktree
, I would work with two separate working tree (from the same cloned repository: no need to clone twice)
- one for the work in progress, with many files not to be added
- one for reporting the work which needs to be added: no stash to maintain in this one.
Does Git support multiple concurrent index (or staging), which would be the analog to Perforce changelist?
Not really: it would be easier to make multiple commits:
- one your PR
- one for the rest
And push only the first commit (for PR).
From the discussion:
"How can I make Git "forget" about a file that was tracked, but is now in .gitignore?" uses
git update-index --skip-worktree
, which I don't find very practical, or easier thangit stash
.git rebase -i
follwoed bygit push :
should be enough
QUESTION
Even after sifting through many related posts I can't seem to find a suitable answer. I have a winsock2 application (code for server setup is adapted for my needs from the microsoft documentation) and I simply want to display the server IPv4 address after binding. This is the code I have so far (placed after binding to the ListenSocket):
...ANSWER
Answered 2021-Nov-10 at 09:36Example code from Microsoft
QUESTION
I've been looking for a few weeks and nowhere have i found anything that could help me with this specific problem.
I got a large output from an API call (Meraki) i'm looking to extract certain features out of the list.
Task: read output from API call, loop through output until status 'failed' is detected and print the interface and networkId of that item turn the networkId into a network name from a predefined list, and continue to print all "failed interfaces" till end of output.
The API call gets the entire organisation and i want to match the list of networkid's with Network names (since they aren't added in the same API call) so its readable what network has which interface that failed.
The output contains a lot of data , and i don't need all of those output values like IP, gateway, DNS, etc.
an example of the output from the API call:
...ANSWER
Answered 2021-Oct-28 at 13:11Based on your sample output, looks like you have got the network ID only once in response and interface and is seen many times as part of Uplink attribute, Hence, you can parse the API response as a Json object and have the network names - network ID mapping in a dictionary and do something like below to get the failed status
QUESTION
I have a pandas dataframe of a standard shape:
...ANSWER
Answered 2021-Jul-31 at 22:41You can use select_dtypes
to get only the columns in a dataframe that match a specific type. For example, to get just the float columns you'd use:
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