path: root/python-k1lib.spec

%global _empty_manifest_terminate_build 0
Name:		python-k1lib
Version:	1.4
Release:	1
Summary:	Some nice ML overhaul
License:	MIT
URL:		https://k1lib.com
Source0:	https://mirrors.aliyun.com/pypi/web/packages/0b/5f/ab2685cce2319e4e7ded3533c7888d1018341d6bee67c8636fb649fbb534/k1lib-1.4.linux-x86_64.tar.gz
BuildArch:	noarch

Requires:	python3-numpy
Requires:	python3-matplotlib
Requires:	python3-dill
Requires:	python3-forbiddenfruit
Requires:	python3-wurlitzer
Requires:	python3-validators
Requires:	python3-graphviz
Requires:	python3-torchvision
Requires:	python3-pillow
Requires:	python3-scikit-image
Requires:	python3-pyperclip
Requires:	python3-k1a
Requires:	python3-torch

%description
# k1lib

This library enables piping in Python, and has a lot of prebuilt piping tools
to support this workflow.

## Installation

```bash
  pip install k1lib[all]
```

This will install lots of heavy dependencies like PyTorch. If you want to install
the leanest version of the library, do this instead:

```bash
  pip install k1lib
```

To use it in a notebook, do this:

```python
  from k1lib.imports import *
```

Check out the source code for "k1lib.imports" if you're curious what it's
importing. If you hate \* imports for whatever reason, you can import cli
tools individually, like this::

```python
  from k1lib.cli import ls, cat, grep, apply, batched, display
```

## Examples

```python
# returns [0, 1, 4, 9, 16], kinda like map
range(5) | apply(lambda x: x**2) | deref()

# plotting the function y = x^2
x = np.linspace(-2, 2); y = x**2
plt.plot(x, y)         # normal way
[x, y] | ~aS(plt.plot) # pipe way

# plotting the functions y = x**2, y = x**3, y = x**4
x = np.linspace(-2, 2)
[2, 3, 4] | apply(lambda exp: [x, x**exp]) | ~apply(plt.plot) | deref()

# loading csv file and displaying first 10 rows in a nice table
cat("abc.csv") | apply(lambda x: x.split(",")) | display()

# searching for "gene_name: ..." lines in a file and display a nice overview of just the gene names alone
cat("abc.txt") | grep("gene_name: ") | apply(lambda x: x.split(": ")[1]) | batched(4) | display()

# manipulate numpy arrays and pytorch tensors
a = np.random.randn(3, 4, 5)
a | transpose()     | shape() # returns (4, 3, 5)
a | transpose(0, 2) | shape() # returns (5, 4, 3)

# loading images from categories and splitting them into train and valid sets. Image url: dataset/categoryA/image1.jpg
train, valid = ls("dataset") | apply(ls() | splitW()) | transpose() | deref()
# shape of output is (train/valid, category, image url). It was (category, train/valid, image url) before going through transpose()

# executing task in multiple processes
range(10_000_000) | batched(1_000_000) | applyMp(toSum()) | toSum()
# this splits numbers from 0 to 10M into 10 batches, and then sum each batch in parallel, and then sum the results of each batch

# executing task in multiple processes on multiple computers
range(10_000_000) | batched(1_000_000) | applyCl(toSum()) | toSum()
```

You can combine these "cli tools" together in really complex ways to do really complex
manipulation really fast and with little code. Hell, you can even create a full blown
PyTorch dataloader from scratch where you're in control of every detail, operating in 7
dimensions, in multiple processes on multiple nodes, in just 6 lines of code. Check over
the basics of it here: [k1lib.cli](https://k1lib.com/latest/cli/index.html).

After doing that, you can check out the tutorials to get a large overview of how everything
integrates together nicely.

## Some details

- Repo: https://github.com/157239n/k1lib/
- Docs: https://k1lib.com

## Contacts?

If you found bugs, open a new issue on the repo itself. If you want to have a chat, then email me at 157239q@gmail.com

If you want to get an overview of how the repo is structured, read [contributing.md](contributing.md)


%package -n python3-k1lib
Summary:	Some nice ML overhaul
Provides:	python-k1lib
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-k1lib
# k1lib

This library enables piping in Python, and has a lot of prebuilt piping tools
to support this workflow.

## Installation

```bash
  pip install k1lib[all]
```

This will install lots of heavy dependencies like PyTorch. If you want to install
the leanest version of the library, do this instead:

```bash
  pip install k1lib
```

To use it in a notebook, do this:

```python
  from k1lib.imports import *
```

Check out the source code for "k1lib.imports" if you're curious what it's
importing. If you hate \* imports for whatever reason, you can import cli
tools individually, like this::

```python
  from k1lib.cli import ls, cat, grep, apply, batched, display
```

## Examples

```python
# returns [0, 1, 4, 9, 16], kinda like map
range(5) | apply(lambda x: x**2) | deref()

# plotting the function y = x^2
x = np.linspace(-2, 2); y = x**2
plt.plot(x, y)         # normal way
[x, y] | ~aS(plt.plot) # pipe way

# plotting the functions y = x**2, y = x**3, y = x**4
x = np.linspace(-2, 2)
[2, 3, 4] | apply(lambda exp: [x, x**exp]) | ~apply(plt.plot) | deref()

# loading csv file and displaying first 10 rows in a nice table
cat("abc.csv") | apply(lambda x: x.split(",")) | display()

# searching for "gene_name: ..." lines in a file and display a nice overview of just the gene names alone
cat("abc.txt") | grep("gene_name: ") | apply(lambda x: x.split(": ")[1]) | batched(4) | display()

# manipulate numpy arrays and pytorch tensors
a = np.random.randn(3, 4, 5)
a | transpose()     | shape() # returns (4, 3, 5)
a | transpose(0, 2) | shape() # returns (5, 4, 3)

# loading images from categories and splitting them into train and valid sets. Image url: dataset/categoryA/image1.jpg
train, valid = ls("dataset") | apply(ls() | splitW()) | transpose() | deref()
# shape of output is (train/valid, category, image url). It was (category, train/valid, image url) before going through transpose()

# executing task in multiple processes
range(10_000_000) | batched(1_000_000) | applyMp(toSum()) | toSum()
# this splits numbers from 0 to 10M into 10 batches, and then sum each batch in parallel, and then sum the results of each batch

# executing task in multiple processes on multiple computers
range(10_000_000) | batched(1_000_000) | applyCl(toSum()) | toSum()
```

You can combine these "cli tools" together in really complex ways to do really complex
manipulation really fast and with little code. Hell, you can even create a full blown
PyTorch dataloader from scratch where you're in control of every detail, operating in 7
dimensions, in multiple processes on multiple nodes, in just 6 lines of code. Check over
the basics of it here: [k1lib.cli](https://k1lib.com/latest/cli/index.html).

After doing that, you can check out the tutorials to get a large overview of how everything
integrates together nicely.

## Some details

- Repo: https://github.com/157239n/k1lib/
- Docs: https://k1lib.com

## Contacts?

If you found bugs, open a new issue on the repo itself. If you want to have a chat, then email me at 157239q@gmail.com

If you want to get an overview of how the repo is structured, read [contributing.md](contributing.md)


%package help
Summary:	Development documents and examples for k1lib
Provides:	python3-k1lib-doc
%description help
# k1lib

This library enables piping in Python, and has a lot of prebuilt piping tools
to support this workflow.

## Installation

```bash
  pip install k1lib[all]
```

This will install lots of heavy dependencies like PyTorch. If you want to install
the leanest version of the library, do this instead:

```bash
  pip install k1lib
```

To use it in a notebook, do this:

```python
  from k1lib.imports import *
```

Check out the source code for "k1lib.imports" if you're curious what it's
importing. If you hate \* imports for whatever reason, you can import cli
tools individually, like this::

```python
  from k1lib.cli import ls, cat, grep, apply, batched, display
```

## Examples

```python
# returns [0, 1, 4, 9, 16], kinda like map
range(5) | apply(lambda x: x**2) | deref()

# plotting the function y = x^2
x = np.linspace(-2, 2); y = x**2
plt.plot(x, y)         # normal way
[x, y] | ~aS(plt.plot) # pipe way

# plotting the functions y = x**2, y = x**3, y = x**4
x = np.linspace(-2, 2)
[2, 3, 4] | apply(lambda exp: [x, x**exp]) | ~apply(plt.plot) | deref()

# loading csv file and displaying first 10 rows in a nice table
cat("abc.csv") | apply(lambda x: x.split(",")) | display()

# searching for "gene_name: ..." lines in a file and display a nice overview of just the gene names alone
cat("abc.txt") | grep("gene_name: ") | apply(lambda x: x.split(": ")[1]) | batched(4) | display()

# manipulate numpy arrays and pytorch tensors
a = np.random.randn(3, 4, 5)
a | transpose()     | shape() # returns (4, 3, 5)
a | transpose(0, 2) | shape() # returns (5, 4, 3)

# loading images from categories and splitting them into train and valid sets. Image url: dataset/categoryA/image1.jpg
train, valid = ls("dataset") | apply(ls() | splitW()) | transpose() | deref()
# shape of output is (train/valid, category, image url). It was (category, train/valid, image url) before going through transpose()

# executing task in multiple processes
range(10_000_000) | batched(1_000_000) | applyMp(toSum()) | toSum()
# this splits numbers from 0 to 10M into 10 batches, and then sum each batch in parallel, and then sum the results of each batch

# executing task in multiple processes on multiple computers
range(10_000_000) | batched(1_000_000) | applyCl(toSum()) | toSum()
```

You can combine these "cli tools" together in really complex ways to do really complex
manipulation really fast and with little code. Hell, you can even create a full blown
PyTorch dataloader from scratch where you're in control of every detail, operating in 7
dimensions, in multiple processes on multiple nodes, in just 6 lines of code. Check over
the basics of it here: [k1lib.cli](https://k1lib.com/latest/cli/index.html).

After doing that, you can check out the tutorials to get a large overview of how everything
integrates together nicely.

## Some details

- Repo: https://github.com/157239n/k1lib/
- Docs: https://k1lib.com

## Contacts?

If you found bugs, open a new issue on the repo itself. If you want to have a chat, then email me at 157239q@gmail.com

If you want to get an overview of how the repo is structured, read [contributing.md](contributing.md)


%prep
%autosetup -n k1lib.linux-x86_64-1.4

%build
%py3_build

%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
	find usr/lib -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
	find usr/lib64 -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
	find usr/bin -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
	find usr/sbin -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
	find usr/share/man -type f -printf "\"/%h/%f.gz\"\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .

%files -n python3-k1lib -f filelist.lst
%dir %{python3_sitelib}/*

%files help -f doclist.lst
%{_docdir}/*

%changelog
* Fri Jun 09 2023 Python_Bot <Python_Bot@openeuler.org> - 1.4-1
- Package Spec generated