| Name | knarrow JSON |
| Version |
0.8.0
JSON |
| download |
| home_page | None |
| Summary | Shoot a knarrow to the knee |
| upload_time | 2023-07-01 17:11:12 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | None |
| keywords |
elbow
knee
kneedle
optimization
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# knarrow
[](https://github.com/psf/black)
Shoot a `knarrow` to the knee ;)
_(The lib is better than this pun, I swear.)_
Detect knee points in various scenarios using a plethora of methods
## Usage
Just plug in your values in a `list`, `tuple` or an `np.ndarray` and watch `knarrow` hit the knee:
```pycon
>>> from knarrow import find_knee
>>> find_knee([1, 2, 3, 4, 6]) # use a list
3
>>> find_knee((1, 2, 3, 4, 6)) # or a tuple
3
>>> import numpy as np
>>> y = np.array([1.0, 1.05, 1.15, 1.28, 1.30, 2.5, 3.6, 4.9])
>>> find_knee(y) # provide just the values
4
>>> x = np.arange(8)
>>> find_knee(x, y) # or both x and y
4
>>> A = np.vstack((x, y))
>>> A
array([[0. , 1. , 2. , 3. , 4. , 5. , 6. , 7. ],
[1. , 1.05, 1.15, 1.28, 1.3 , 2.5 , 3.6 , 4.9 ]])
>>> find_knee(A) # works with x in first row, y in the second
4
>>> A.T
array([[0. , 1. ],
[1. , 1.05],
[2. , 1.15],
[3. , 1.28],
[4. , 1.3 ],
[5. , 2.5 ],
[6. , 3.6 ],
[7. , 4.9 ]])
>>> find_knee(A.T) # also works with x in the first column, y in the second column
4
>>> find_knee(x, y, smoothing=0.01) # for better results use cubic spline smoothing
4
```
### CLI
This library can also come with a handy CLI if you install it with the `cli` extra:
```shell
$ pip install "knarrow[cli]"
$ cat data.txt | knarrow -
<stdin> 11
$ cat data.txt | knarrow -o value -
<stdin> 59874.14171519781845532648
$ knarrow --sort -d ',' -o value shuf_delim.txt
shuf_delim.txt 20
```
_(the `-` for stdin is, unfortunately, mandatory)_
Try writing `knarrow --help` for more info.
## Similar projects
While I've come up with most of these methods by myself, I am not the only one. Here is a (non-comprehensive) list of projects I've found that implement a similar functionality and may have been an inspiration for me:
- [mariolpantunes/knee](https://github.com/mariolpantunes/knee)
Note: this project was bootstrapped by [python-blueprint](https://github.com/johnthagen/python-blueprint). Since then, it has been heavily modified, though.
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"description": "# knarrow\n\n\n\n\n\n\n\n\n\n[](https://github.com/psf/black)\n\nShoot a `knarrow` to the knee ;)\n\n_(The lib is better than this pun, I swear.)_\n\nDetect knee points in various scenarios using a plethora of methods\n\n\n## Usage\nJust plug in your values in a `list`, `tuple` or an `np.ndarray` and watch `knarrow` hit the knee:\n\n```pycon\n>>> from knarrow import find_knee\n>>> find_knee([1, 2, 3, 4, 6]) # use a list\n3\n>>> find_knee((1, 2, 3, 4, 6)) # or a tuple\n3\n>>> import numpy as np\n>>> y = np.array([1.0, 1.05, 1.15, 1.28, 1.30, 2.5, 3.6, 4.9])\n>>> find_knee(y) # provide just the values\n4\n>>> x = np.arange(8)\n>>> find_knee(x, y) # or both x and y\n4\n>>> A = np.vstack((x, y))\n>>> A\narray([[0. , 1. , 2. , 3. , 4. , 5. , 6. , 7. ],\n [1. , 1.05, 1.15, 1.28, 1.3 , 2.5 , 3.6 , 4.9 ]])\n>>> find_knee(A) # works with x in first row, y in the second\n4\n>>> A.T\narray([[0. , 1. ],\n [1. , 1.05],\n [2. , 1.15],\n [3. , 1.28],\n [4. , 1.3 ],\n [5. , 2.5 ],\n [6. , 3.6 ],\n [7. , 4.9 ]])\n>>> find_knee(A.T) # also works with x in the first column, y in the second column\n4\n>>> find_knee(x, y, smoothing=0.01) # for better results use cubic spline smoothing\n4\n```\n\n### CLI\nThis library can also come with a handy CLI if you install it with the `cli` extra:\n```shell\n$ pip install \"knarrow[cli]\"\n$ cat data.txt | knarrow -\n<stdin> 11\n$ cat data.txt | knarrow -o value -\n<stdin> 59874.14171519781845532648\n$ knarrow --sort -d ',' -o value shuf_delim.txt\nshuf_delim.txt 20\n```\n_(the `-` for stdin is, unfortunately, mandatory)_\nTry writing `knarrow --help` for more info.\n\n## Similar projects\n\nWhile I've come up with most of these methods by myself, I am not the only one. Here is a (non-comprehensive) list of projects I've found that implement a similar functionality and may have been an inspiration for me:\n- [mariolpantunes/knee](https://github.com/mariolpantunes/knee)\n\nNote: this project was bootstrapped by [python-blueprint](https://github.com/johnthagen/python-blueprint). Since then, it has been heavily modified, though.\n",
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