| Name | carabiner-tools JSON |
| Version |
0.0.2
JSON |
| download |
| home_page | None |
| Summary | Useful utilities. |
| upload_time | 2024-04-23 08:52:32 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | MIT License Copyright (c) [year] [fullname] Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
bioinformatics
machine-learning
data
|
| VCS |
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| requirements |
No requirements were recorded.
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|
# 🪨 carabiner
A ragtag collection of useful Python functions and classes.
- [Installation](#installation)
- [Fast and flexible reading and random access of very large files](#fast-and-flexible-reading-and-random-access-of-very-large-files)
- [Reading tabular data](#reading-tabular-data)
- [Utilities to simplify building command-line apps](#utilities-to-simplify-building-command-line-apps)
- [Reservoir sampling](#reservoir-sampling)
- [Multikey dictionaries](#multikey-dictionaries)
- [Decorators](#decorators)
- [Vectorized functions](#vectorized-functions)
- [Return `None` instead of error](#return-none-instead-of-error)
- [Decorators with parameters](#decorators-with-parameters)
- [Colorblind palette](#colorblind-palette)
- [Grids with sensible defaults in Matplotlib](#grids-with-sensible-defaults-in-matplotlib)
- [Fast indicator matrix x dense matrix multiplication in Tensorflow](#fast-indicator-matrix-x-dense-matrix-multiplication-in-tensorflow)
- [Issues, problems, suggestions](#issues-problems-suggestions)
- [Documentation](#documentation)
## Installation
### The easy way
Install the pre-compiled version from GitHub:
```bash
$ pip install carabiner-tools
```
If you want to use the `tensorflow`, `pandas`, or `matplotlib` utilities, these must be installed separately
or together:
```bash
$ pip install carabiner-tools[deep]
# or
$ pip install carabiner-tools[pd]
# or
$ pip install carabiner-tools[mpl]
# or
$ pip install carabiner-tools[all]
```
### From source
Clone the repository, then `cd` into it. Then run:
```bash
$ pip install -e .
```
## Fast and flexible reading and random access of very large files
Subsets of lines from very large, optionally compressed, files can be read quickly
into memory. for example, we can read the first 10,000 lines of an arbitrarily large
file:
```python
>>> from carabiner.io import get_lines
>>> get_lines("big-table.tsv.gz", lines=10_000)
```
Or random access of specific lines. Hundreds of millions of lines can be
parsed per minute.
```python
>>> get_lines("big-table.tsv.gz", lines=[999999, 10000000, 100000001])
```
This pattern will allow sampling a random subset:
```python
>>> from random import sample
>>> from carabiner.io import count_lines, get_lines
>>> number_of_lines = count_lines("big-table.tsv.gz")
>>> line_sample = sample(range(number_of_lines), k=1000)
>>> get_lines("big-table.tsv.gz", lines=line_sample)
```
### Reading tabular data
With this backend, we can read subsets of very large files more quickly
and flexibly than plain `pandas.read_csv`. Formats (delimiters) including Excel
are inferred from file extensions, but can also be over-ridden with the `format`
parameter.
```python
>>> from carabiner.pd import read_table
>>> read_table("big-table.tsv.gz", lines=10_000)
```
The same fast random access is availavble as for reading lines. Hundreds of
millions of records can be looped through per minute.
```python
>>> from random import sample
>>> from carabiner.io import count_lines, get_lines
>>> number_of_lines = count_lines("big-table.tsv.gz")
>>> line_sample = sample(range(number_of_lines), k=1000)
>>> read_table("big-table.tsv.gz", lines=line_sample)
```
## Utilities to simplify building command-line apps
The standard library `argparse` is robust but verbose when building command-line apps with several sub-commands, each with many options. `carabiner.cliutils` smooths this process. Apps are built by defining `CLIOptions` which are then assigned to `CLICommands` directing the functions to run when called, which then form part of a `CLIApp`.
First define the options:
```python
inputs = CLIOption('inputs',
type=str,
default=[],
nargs='*',
help='')
output = CLIOption('--output', '-o',
type=FileType('w'),
default=sys.stdout,
help='Output file. Default: STDOUT')
formatting = CLIOption('--format', '-f',
type=str,
default='TSV',
choices=['TSV', 'CSV', 'tsv', 'csv'],
help='Format of files. Default: %(default)s')
```
Then the commands:
```python
test = CLICommand("test",
description="Test CLI subcommand using Carabiner utilities.",
options=[inputs, output, formatting],
main=_main)
```
The same options can be assigned to multiple commands if necessary.
Fianlly, define the app and run it:
```python
app = CLIApp("Carabiner",
version=__version__,
description="Test CLI app using Carabiner utilities.",
commands=[test])
app.run()
```
## Reservoir sampling
If you need to sample a random subset from an iterator of unknown length by looping through only once, you can use this pure python implementation of [reservoir sampling](https://en.wikipedia.org/wiki/Reservoir_sampling).
An important limitation is that while the population to be sampled is not necessarily in memory, the sampled population must fit in memory.
Originally written in [Python Bugs](https://bugs.python.org/issue41311).
Based on [this GitHub Gist](https://gist.github.com/oscarbenjamin/4c1b977181f34414a425f68589e895d1).
```python
>>> from carabiner.random import sample_iter
>>> from string import ascii_letters
>>> from itertools import chain
>>> from random import seed
>>> seed(1)
>>> sample_iter(chain.from_iterable(ascii_letters for _ in range(1000000)), 10)
['X', 'c', 'w', 'q', 'T', 'e', 'u', 'w', 'E', 'h']
>>> seed(1)
>>> sample_iter(chain.from_iterable(ascii_letters for _ in range(1000000)), 10, shuffle_output=False)
['T', 'h', 'u', 'X', 'E', 'e', 'w', 'q', 'c', 'w']
```
## Multikey dictionaries
Conveniently return the values of multiple keys from a dictionary without manually looping.
```python
>>> from carabiner.collections import MultiKeyDict
>>> d = MultiKeyDict(a=1, b=2, c=3)
>>> d
{'a': 1, 'b': 2, 'c': 3}
>>> d['c']
{'c': 3}
>>> d['a', 'b']
{'a': 1, 'b': 2}
```
## Decorators
`carabiner` provides several decorators to facilitate functional programming.
### Vectorized functions
In scientific programming frameworks like `numpy` we are used to functions which take a scalar or vector and apply to every element. It is occasionally useful to convert functions from arbitrary packages to behave in a vectorized manner on Python iterables.
Scalar functions can be converted to a vectorized form easily using `@vectorize`.
```python
>>> @vectorize
... def vector_adder(x): return x + 1
...
>>> list(vector_adder(range(3)))
[1, 2, 3]
>>> list(vector_adder((4, 5, 6)))
[5, 6, 7]
>>> vector_adder([10])
11
>>> vector_adder(10)
11
```
### Return `None` instead of error
When it is useful for a function to not fail, but have a testable indicator of success, you can wrap in `@return_none_on_error`.
```python
>>> def error_maker(x): raise KeyError
...
>>> @return_none_on_error
... def error_maker2(x): raise KeyError
...
>>> @return_none_on_error(exception=ValueError)
... def error_maker3(x): raise KeyError
...
>>> error_maker('a') # Causes an error
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<stdin>", line 1, in error_maker
KeyError
>>> error_maker2('a') # Wrapped returns None
>>> error_maker3('a') # Only catches ValueError
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File ".../carabiner/decorators.py", line 59, in wrapped_function
File "<stdin>", line 2, in error_maker3
KeyError
```
### Decorators with parameters
Sometimes a decorator has optional parameters to control its behavior. It's convenient to use it in the form `@decorator` when you want the default behavior, or `@decorator(*kwargs)` when you want to custmize the behavior. Usually this requires some convoluted code, but this has been packed up into `@decorator_with_params`, to decorate your decorator definitions!
```python
>>> def decor(f, suffix="World"):
... return lambda x: f(x + suffix)
...
>>> @decor
... def printer(x):
... print(x)
...
# doesn't work, raises an error!
>>> @decor(suffix="everyone")
... def printer2(x):
... print(x)
...
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: decor() missing 1 required positional argument: 'f'
# decorate the decorator!
>>> @decorator_with_params
... def decor2(f, suffix="World"):
... return lambda x: f(x + suffix)
...
# Now it works!
>>> @decor2(suffix="everyone")
... def printer3(x):
... print(x)
...
>>> printer("Hello ")
Hello World
>>> printer3("Hello ")
Hello everyone
```
## Colorblind palette
Here's a qualitative palette that's colorblind friendly.
```python
>>> from carabiner import colorblind_palette
>>> colorblind_palette()
('#EE7733', '#0077BB', '#33BBEE', '#EE3377', '#CC3311', '#009988', '#BBBBBB', '#000000')
# subsets
>>> colorblind_palette(range(2))
('#EE7733', '#0077BB')
>>> colorblind_palette(slice(3, 6))
('#EE3377', '#CC3311', '#009988')
```
## Grids with sensible defaults in Matplotlib
While `plt.subplots()` is very flexible, it requires many defaults to be defined. Instead, `carabiner.mpl.grid()` generates the `fig, ax` tuple with sensible defaults of a 1x1 grid with panel size 3 and a `constrained` layout.
```python
from carabiner.mpl import grid
fig, ax = grid() # 1x1 grid
fig, ax = grid(ncol=3) # 1x3 grid; figsize expands appropriately
fig, ax = grid(ncol=3, nrow=2, sharex=True) #additional parameters are passed to `plt.subplots()`
```
## Fast indicator matrix x dense matrix multiplication in Tensorflow
If you want to multiply an indicator matrix, i.e. a sparse matrix of zeros and ones with the same number of non-zero entries per row (as in linear models), as part of a Tensorflow model, this pattern will be faster than using `tensorflow.SparseMatrix` if you convert the indicator matrix to a `[n x 1]` matrix providing the index of the non-zero element per row.
## Issues, problems, suggestions
Add to the [issue tracker](https://www.github.com/carabiner/issues).
## Documentation
Available at [ReadTheDocs](https://carabiner-docs.readthedocs.org).
Raw data
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"description": "# \ud83e\udea8 carabiner\n\n\n\n\n\nA ragtag collection of useful Python functions and classes.\n\n- [Installation](#installation)\n- [Fast and flexible reading and random access of very large files](#fast-and-flexible-reading-and-random-access-of-very-large-files)\n - [Reading tabular data](#reading-tabular-data)\n- [Utilities to simplify building command-line apps](#utilities-to-simplify-building-command-line-apps)\n- [Reservoir sampling](#reservoir-sampling)\n- [Multikey dictionaries](#multikey-dictionaries)\n- [Decorators](#decorators)\n - [Vectorized functions](#vectorized-functions)\n - [Return `None` instead of error](#return-none-instead-of-error)\n - [Decorators with parameters](#decorators-with-parameters)\n- [Colorblind palette](#colorblind-palette)\n- [Grids with sensible defaults in Matplotlib](#grids-with-sensible-defaults-in-matplotlib)\n- [Fast indicator matrix x dense matrix multiplication in Tensorflow](#fast-indicator-matrix-x-dense-matrix-multiplication-in-tensorflow)\n- [Issues, problems, suggestions](#issues-problems-suggestions)\n- [Documentation](#documentation)\n\n## Installation\n\n### The easy way\n\nInstall the pre-compiled version from GitHub:\n\n```bash\n$ pip install carabiner-tools\n```\n\nIf you want to use the `tensorflow`, `pandas`, or `matplotlib` utilities, these must be installed separately\nor together:\n\n```bash\n$ pip install carabiner-tools[deep]\n# or\n$ pip install carabiner-tools[pd]\n# or\n$ pip install carabiner-tools[mpl]\n# or\n$ pip install carabiner-tools[all]\n```\n\n### From source\n\nClone the repository, then `cd` into it. Then run:\n\n```bash\n$ pip install -e .\n```\n\n## Fast and flexible reading and random access of very large files\n\nSubsets of lines from very large, optionally compressed, files can be read quickly \ninto memory. for example, we can read the first 10,000 lines of an arbitrarily large \nfile:\n\n```python\n>>> from carabiner.io import get_lines\n\n>>> get_lines(\"big-table.tsv.gz\", lines=10_000)\n```\n\nOr random access of specific lines. Hundreds of millions of lines can be \nparsed per minute.\n\n```python\n>>> get_lines(\"big-table.tsv.gz\", lines=[999999, 10000000, 100000001])\n```\n\nThis pattern will allow sampling a random subset:\n\n```python\n>>> from random import sample\n>>> from carabiner.io import count_lines, get_lines\n\n>>> number_of_lines = count_lines(\"big-table.tsv.gz\")\n>>> line_sample = sample(range(number_of_lines), k=1000)\n>>> get_lines(\"big-table.tsv.gz\", lines=line_sample)\n```\n\n### Reading tabular data\n\nWith this backend, we can read subsets of very large files more quickly \nand flexibly than plain `pandas.read_csv`. Formats (delimiters) including Excel \nare inferred from file extensions, but can also be over-ridden with the `format` \nparameter.\n\n```python\n>>> from carabiner.pd import read_table\n\n>>> read_table(\"big-table.tsv.gz\", lines=10_000)\n```\n\nThe same fast random access is availavble as for reading lines. Hundreds of \nmillions of records can be looped through per minute.\n\n```python\n>>> from random import sample\n>>> from carabiner.io import count_lines, get_lines\n\n>>> number_of_lines = count_lines(\"big-table.tsv.gz\")\n>>> line_sample = sample(range(number_of_lines), k=1000)\n>>> read_table(\"big-table.tsv.gz\", lines=line_sample)\n```\n\n## Utilities to simplify building command-line apps\n\nThe standard library `argparse` is robust but verbose when building command-line apps with several sub-commands, each with many options. `carabiner.cliutils` smooths this process. Apps are built by defining `CLIOptions` which are then assigned to `CLICommands` directing the functions to run when called, which then form part of a `CLIApp`.\n\nFirst define the options:\n```python\ninputs = CLIOption('inputs',\n type=str,\n default=[],\n nargs='*',\n help='')\noutput = CLIOption('--output', '-o', \n type=FileType('w'),\n default=sys.stdout,\n help='Output file. Default: STDOUT')\nformatting = CLIOption('--format', '-f', \n type=str,\n default='TSV',\n choices=['TSV', 'CSV', 'tsv', 'csv'],\n help='Format of files. Default: %(default)s')\n```\n\nThen the commands:\n\n```python\ntest = CLICommand(\"test\",\n description=\"Test CLI subcommand using Carabiner utilities.\",\n options=[inputs, output, formatting],\n main=_main)\n```\n\nThe same options can be assigned to multiple commands if necessary.\n\nFianlly, define the app and run it:\n\n```python\n\napp = CLIApp(\"Carabiner\", \n version=__version__,\n description=\"Test CLI app using Carabiner utilities.\",\n commands=[test])\n\napp.run()\n```\n## Reservoir sampling\n\nIf you need to sample a random subset from an iterator of unknown length by looping through only once, you can use this pure python implementation of [reservoir sampling](https://en.wikipedia.org/wiki/Reservoir_sampling).\n\nAn important limitation is that while the population to be sampled is not necessarily in memory, the sampled population must fit in memory.\n\nOriginally written in [Python Bugs](https://bugs.python.org/issue41311).\n\nBased on [this GitHub Gist](https://gist.github.com/oscarbenjamin/4c1b977181f34414a425f68589e895d1).\n\n```python\n>>> from carabiner.random import sample_iter\n>>> from string import ascii_letters\n>>> from itertools import chain\n>>> from random import seed\n>>> seed(1)\n>>> sample_iter(chain.from_iterable(ascii_letters for _ in range(1000000)), 10)\n['X', 'c', 'w', 'q', 'T', 'e', 'u', 'w', 'E', 'h']\n>>> seed(1)\n>>> sample_iter(chain.from_iterable(ascii_letters for _ in range(1000000)), 10, shuffle_output=False)\n['T', 'h', 'u', 'X', 'E', 'e', 'w', 'q', 'c', 'w']\n\n```\n\n## Multikey dictionaries\n\nConveniently return the values of multiple keys from a dictionary without manually looping.\n\n```python\n>>> from carabiner.collections import MultiKeyDict\n>>> d = MultiKeyDict(a=1, b=2, c=3)\n>>> d\n{'a': 1, 'b': 2, 'c': 3}\n>>> d['c']\n{'c': 3}\n>>> d['a', 'b']\n{'a': 1, 'b': 2} \n```\n\n## Decorators\n\n`carabiner` provides several decorators to facilitate functional programming.\n\n### Vectorized functions\n\nIn scientific programming frameworks like `numpy` we are used to functions which take a scalar or vector and apply to every element. It is occasionally useful to convert functions from arbitrary packages to behave in a vectorized manner on Python iterables.\n\nScalar functions can be converted to a vectorized form easily using `@vectorize`.\n\n```python\n>>> @vectorize\n... def vector_adder(x): return x + 1\n...\n>>> list(vector_adder(range(3)))\n[1, 2, 3]\n>>> list(vector_adder((4, 5, 6)))\n[5, 6, 7]\n>>> vector_adder([10])\n11\n>>> vector_adder(10)\n11\n```\n\n### Return `None` instead of error\n\nWhen it is useful for a function to not fail, but have a testable indicator of success, you can wrap in `@return_none_on_error`.\n\n```python\n>>> def error_maker(x): raise KeyError\n... \n>>> @return_none_on_error\n... def error_maker2(x): raise KeyError\n... \n>>> @return_none_on_error(exception=ValueError)\n... def error_maker3(x): raise KeyError\n... \n\n>>> error_maker('a') # Causes an error\nTraceback (most recent call last):\nFile \"<stdin>\", line 1, in <module>\nFile \"<stdin>\", line 1, in error_maker\nKeyError\n\n>>> error_maker2('a') # Wrapped returns None\n\n>>> error_maker3('a') # Only catches ValueError\nTraceback (most recent call last):\nFile \"<stdin>\", line 1, in <module>\nFile \".../carabiner/decorators.py\", line 59, in wrapped_function\n \nFile \"<stdin>\", line 2, in error_maker3\nKeyError\n```\n\n### Decorators with parameters\n\nSometimes a decorator has optional parameters to control its behavior. It's convenient to use it in the form `@decorator` when you want the default behavior, or `@decorator(*kwargs)` when you want to custmize the behavior. Usually this requires some convoluted code, but this has been packed up into `@decorator_with_params`, to decorate your decorator definitions!\n\n```python\n>>> def decor(f, suffix=\"World\"): \n... return lambda x: f(x + suffix)\n...\n>>> @decor\n... def printer(x): \n... print(x)\n... \n\n# doesn't work, raises an error!\n>>> @decor(suffix=\"everyone\") \n... def printer2(x): \n... print(x)\n... \nTraceback (most recent call last):\nFile \"<stdin>\", line 1, in <module>\nTypeError: decor() missing 1 required positional argument: 'f'\n\n# decorate the decorator!\n>>> @decorator_with_params\n... def decor2(f, suffix=\"World\"): \n... return lambda x: f(x + suffix)\n... \n\n# Now it works!\n>>> @decor2(suffix=\"everyone\") \n... def printer3(x): \n... print(x)\n... \n\n>>> printer(\"Hello \")\nHello World\n>>> printer3(\"Hello \")\nHello everyone\n```\n\n## Colorblind palette\n\nHere's a qualitative palette that's colorblind friendly.\n\n```python\n>>> from carabiner import colorblind_palette\n\n>>> colorblind_palette()\n('#EE7733', '#0077BB', '#33BBEE', '#EE3377', '#CC3311', '#009988', '#BBBBBB', '#000000')\n\n# subsets\n>>> colorblind_palette(range(2))\n('#EE7733', '#0077BB')\n>>> colorblind_palette(slice(3, 6))\n('#EE3377', '#CC3311', '#009988')\n```\n\n## Grids with sensible defaults in Matplotlib\n\nWhile `plt.subplots()` is very flexible, it requires many defaults to be defined. Instead, `carabiner.mpl.grid()` generates the `fig, ax` tuple with sensible defaults of a 1x1 grid with panel size 3 and a `constrained` layout.\n\n```python\nfrom carabiner.mpl import grid\nfig, ax = grid() # 1x1 grid\nfig, ax = grid(ncol=3) # 1x3 grid; figsize expands appropriately\nfig, ax = grid(ncol=3, nrow=2, sharex=True) #additional parameters are passed to `plt.subplots()`\n```\n\n## Fast indicator matrix x dense matrix multiplication in Tensorflow\n\nIf you want to multiply an indicator matrix, i.e. a sparse matrix of zeros and ones with the same number of non-zero entries per row (as in linear models), as part of a Tensorflow model, this pattern will be faster than using `tensorflow.SparseMatrix` if you convert the indicator matrix to a `[n x 1]` matrix providing the index of the non-zero element per row.\n\n## Issues, problems, suggestions\n\nAdd to the [issue tracker](https://www.github.com/carabiner/issues).\n\n## Documentation\n\nAvailable at [ReadTheDocs](https://carabiner-docs.readthedocs.org).\n",
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