stumpy | scalable Python library for modern time series analysis | Time Series Database library
kandi X-RAY | stumpy Summary
kandi X-RAY | stumpy Summary
STUMPY is a powerful and scalable Python library for modern time series analysis
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Top functions reviewed by kandi - BETA
- Helper function for Stompop
- Check if P is larger than threshold
- Calculate the squared distance matrix for a given profile
- R Calculates the squared distance between the squared distances
- Perform the ostinato algorithm
- Calculate the distance between the nearest neighbors
- Calculate the central motif
- Do the OSTinato algorithm
- Pan image
- Pan
- Wraps the driver_not_not_found function
- Called when the driver is not found
- Get the package name
- Error if driver is not found
- Wrapper for GPU driver_not_not found
- Wrapper for driver_notification
- Return list of extras
- Convert perf text to time
- Calculate the nanmax of an array
- Calculate the Aampo - style Aampo - T
- Decorator for non - normalization
- Calculate Mueen - distance profile
- Stamp a time series of mass frequencies
- Convert index matrix to MP
- Calculate mass distance matrix
- Wrapper for driver_notations
stumpy Key Features
stumpy Examples and Code Snippets
Lines of Code : 20License : Strong Copyleft (CC BY-SA 4.0)
Copyfor row in range(1, image.shape[0]):
forward_energy[(1,),(1, image.shape[1])](row, imd, ed, md)
cuda.synchronize()
def foward_energy(row, im, energy, m):
# algo stuff
threads_per_blfor i in range(len(SampleTarget)):
# Iterate over the list and check if the number matchs the first
# one we are checking agaisnt for our pattern
if SampleTarget[i] == Pattern[0]:
# Hey this index might be the start of context = ssl.SSLContext() # Ignore SSL certificate verification for simplicity
url = "https://www.cs.ucr.edu/~eamonn/iSAX/steamgen.dat"
raw_bytes = urllib.request.urlopen(url, context=context).read()
data = io.BytesIO(raw_bytes)
<pip install stumpy
conda install -c conda-forge stumpy
self.hitbox = pygame.Rect(self.x + 5, self.y +3, 67, 65)
if player.hitbox.colliderect(obstacle.hitbox):
print('hit')
import stumpy
import numpy as np
your_time_series = np.random.rand(10000)
window_size = 50 # Approximately, how many data points might be found in a pattern
matrix_profile = stumpy.stump(your_time_series, m=window_size)
subseq_len = 50In [1]: a = np.random.rand(10).astype(np.float64)
In [2]: b = np.random.rand(10).astype(np.float32)
In [3]: np.issubdtype(a.dtype,np.floating)
Out[3]: True
In [4]: np.issubdtype(b.dtype,np.floating)
Out[4]: True
Community Discussions
Trending Discussions on stumpy
QUESTION
Some background story!
I have been working with git to contribute to an open source package, and I am somewhat novice in this area. I know some basic stuff. Furthermore, I also read that if merge conflict happens, we can go through the file and the markers can help us to find the block of codes that have conflict with each other in two versions of the same file.
Recently, I confronted a merge conflict for the first time, and it is on Jupyter notebook file (.ipynb). However, the markers of merge conflict make the notebook unreadable by Jupyter. So, I tried JSON editor and VS CODE and Notepad++. Although I can now see the file, it is really messy because it not only has all metadata of Jupyter notebook but also it shows conflict even for the number of execution. In addition, if there is a figure as an output of a cell in the notebook, it is converted to a lot of characters and scrolling down and go through them is headache.
After searching google and stackoverflow and discuss the issue with the owner of the project, we decided to take another approach. But, it doesn't work. (I explained it below, but first I need to provide further info. Please bear with me)
According to the history of the commits, I should be the one who created such conflicts locally by probably changing the same file on the PARENT branch as nobody changes it in the upstream (in the originAL repo)
Therefore, please let me first briefly walk you through the branches and some changes I did on files.
- After forking and cloning, I created a branch
branch-A - I changed the notebook 'notebook-file`
- I did Pull Request (PR)
- I created a sub-branch of A, lets call it
sub-branch-of-A - After switching to sub-branch, I changed two .py files and then also change that
notebook-file - merge
sub-branch-of-Aintobranch-A
Inside my branch-A, I did: git push origin branch-A but I got non-fast-forward error. Which means a divergence happened. Right? So, I did git pull origin branch-A to resolve it, but I get merge conflict for the notebook-file.
Alternative Solution
So, I was told that I can copy the file to somewhere outside my local git repo, then do git checkout notebook-file to get the file in the parent node where divergence happened. Right? Then, if I do git pull ... there should be no problem (then I can include the changes of that copied file)
BUT...
I, again, got the merge conflict error. I went crazy and tried several things and still nothing.
I attached the git log below.
The branch I am talking about is Snippets_Tutorial, And its sub-branch is Snippets_Regime. If I remember correctly, I used git checkout -b Snippets_Regime Snippets_Tutorial to create that sub-branch. As I mentioned earlier, I switched to the sub-branch Snippets_Regime and did some changes to somefile.py files and the notebook-file. Then, I merge it into Snippets_Tutorial.
ANSWER
Answered 2021-Jul-03 at 13:44What matters in a merge conflict resolution is the two tips and the base, and the conflict. Everything else is noise.
QUESTION
For getting the list of installed libraries, I run the following command in Jupyter Notebook:
...ANSWER
Answered 2020-Nov-17 at 11:03We can use os module to create the pip list, then we use pandas.read_csv with \s+ as seperator to read the pip list into a dataframe:
QUESTION
I'm trying to detect patterns from open-high-low-close (OHLC) data, so here is what I did:
- Find local minima and maxima on the dataset
- Normalize my data by converting the array of local minima and maxima to an array of numbers, where every number is the variation from the previous point.
Until now, everything works, but I got stuck on the following part. I defined an array of data, which is a pattern, that when is plotted on a chart will have a certain shape. I'm now trying to find, on other datasets, shapes that are similar to the pattern I specified.
Here is the pattern specified by me:
...ANSWER
Answered 2020-Jul-23 at 04:24Here's a rather improvised solution that assumes that you are looking for an exact match, its just brute-forcing match checks by iterating over the entire list, if it finds a match it checks the next pos and so on so forth. It also assumes Pattern[0] is not repeated within the Pattern list however that could easily be coded out with a bit more bedazzling
QUESTION
I have the following set of OHLC data:
...ANSWER
Answered 2020-Jul-09 at 01:31Stumpy will work for you.
Basic MethodologyThe basic gist of the algorithm is to compute a matrix profile of a data stream, and then use that to find areas that are similar. (You can think of the matrix profile as a sliding window that gives a rating of how closely two patters match using Z-normalized Euclidean Distance).
This article explains matrix profiles in a pretty straightforward way. Here's an excerpt that explains what you want:
Simply put, a motif is a repeated pattern in a time series and a discord is an anomaly. With the Matrix Profile computed, it is simple to find the top-K number of motifs or discords. The Matrix Profile stores the distances in Euclidean space meaning that a distance close to 0 is most similar to another sub-sequence in the time series and a distance far away from 0, say 100, is unlike any other sub-sequence. Extracting the lowest distances gives the motifs and the largest distances gives the discords.
The benefits of using a matrix profile can be found here.
The gist of what you want to do is compute the matrix profile, then look for minima. Minima mean the sliding window matched another place well.
This example shows how to use it to find repeating patterns in one data set:
To reproduce their results myself, I navigated to the DAT file and downloaded it myself, then opened and read it instead of using their broken urllib calls to get the data.
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Install stumpy
You can use stumpy like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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