path: root/python-bcolz-zipline.spec

%global _empty_manifest_terminate_build 0
Name:		python-bcolz-zipline
Version:	1.2.6
Release:	1
Summary:	Columnar and compressed data containers.
License:	Copyright Notice and Statement for bcolz Software Library and Utilities:  Copyright (c) 2010-2011 by Francesc Alted Copyright (c) 2012 by Continuum Analytics Copyright (c) 2013 by Francesc Alted Copyright (c) 2014-2018 by Francesc Alted and the bcolz contributors All rights reserved.  Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:  a. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.  b. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.  c. Neither the name of Francesc Alted nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. 
URL:		https://pypi.org/project/bcolz-zipline/
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/df/a2/8cb034b0cd58841eca00256b1e662ecd8e30397d1850203759e98cbe0148/bcolz-zipline-1.2.6.tar.gz

Requires:	python3-numpy
Requires:	python3-numexpr
Requires:	python3-dask
Requires:	python3-pandas
Requires:	python3-tables
Requires:	python3-pytest
Requires:	python3-pytest-cov
Requires:	python3-coveralls

%description
# bcolz: columnar and compressed data containers

<p align="center">
    <a href="https://gitter.im/Blosc/bcolz?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge" target="_blank">
        <img alt="Gitter" src="https://badges.gitter.im/Blosc/bcolz.svg" />
    </a>
    <a href="https://pypi.org/project/bcolz-zipline/" target="_blank">
        <img alt="Version" src="https://img.shields.io/pypi/v/bcolz-zipline.svg?cacheSeconds=2592000" />
    </a>
    <a href="https://bcolz.readthedocs.io/en/latest/" target="_blank">
        <img alt="Documentation" src="https://img.shields.io/badge/documentation-yes-brightgreen.svg" />
    </a>
    <img alt="GitHub Workflow Status" src="https://img.shields.io/github/workflow/status/stefan-jansen/bcolz-zipline/Tests?label=tests"><a href='https://coveralls.io/github/stefan-jansen/bcolz-zipline?branch=main'><img src='https://coveralls.io/repos/github/stefan-jansen/bcolz-zipline/badge.svg?branch=main' alt='Coverage Status' /></a>
    <img alt="GitHub Workflow Status" src="https://img.shields.io/github/workflow/status/stefan-jansen/bcolz-zipline/Build%20Wheels?label=PyPI"><a href="#" target="_blank">
    <img alt="GitHub Workflow Status" src="https://img.shields.io/github/workflow/status/stefan-jansen/bcolz-zipline/Build%20conda%20distribution?label=Anaconda">
    <img alt="License: BSD" src="https://img.shields.io/badge/License-BSD-yellow.svg" />
    </a>
    <a href="https://twitter.com/ml4trading" target="_blank">
        <img alt="Twitter: @ml4t" src="https://img.shields.io/twitter/follow/ml4trading.svg?style=social" />
    </a>
    <a href="http://blosc.org" target="_blank">
        <img alt="Blosc" src="http://b.repl.ca/v1/Powered--By-Blosc-blue.png" />
    </a>
</p>


<p align="center">
<a href="https://www.amazon.com/Machine-Learning-Algorithmic-Trading-alternative/dp/1839217715?pf_rd_r=GZH2XZ35GB3BET09PCCA&pf_rd_p=c5b6893a-24f2-4a59-9d4b-aff5065c90ec&pd_rd_r=91a679c7-f069-4a6e-bdbb-a2b3f548f0c8&pd_rd_w=2B0Q0&pd_rd_wg=GMY5S&ref_=pd_gw_ci_mcx_mr_hp_d">
<img src="https://imgur.com/g8emkEZ.png" width="35%">
</a>
</p>

bcolz provides columnar, chunked data containers that can be compressed either in-memory and on-disk. Column storage allows for efficiently querying tables, as well as for cheap column addition and removal. It is based on [NumPy](http://www.numpy.org), and uses it as the standard data container to communicate with bcolz objects, but it also comes with support for import/export facilities to/from [HDF5/PyTables tables](http://www.pytables.org) and [pandas dataframes](http://pandas.pydata.org).

bcolz objects are compressed by default not only for reducing memory/disk storage, but also to improve I/O speed. The compression process is carried out internally by [Blosc](http://blosc.org), a high-performance, multithreaded meta-compressor that is optimized for binary data (although it works with text data just fine too).

bcolz can also use [numexpr](https://github.com/pydata/numexpr)
internally (it does that by default if it detects numexpr installed) or
[dask](https://github.com/dask/dask) so as to accelerate many vector and query operations (although it can use pure NumPy for doing so too). numexpr/dask can optimize the memory usage and use multithreading for doing the computations, so it is blazing fast. This, in combination with carray/ctable disk-based, compressed containers, can be used for performing out-of-core computations efficiently, but most importantly
*transparently*.

Just to whet your appetite, [here is an example](http://nbviewer.ipython.org/github/Blosc/movielens-bench/blob/master/querying-ep14.ipynb)
with real data, where bcolz is already fulfilling the promise of accelerating memory I/O by using compression.

## Rationale

By using compression, you can deal with more data using the same amount of memory, which is very good on itself. But in case you are wondering about the price to pay in terms of performance, you should know that nowadays memory access is the most common bottleneck in many computational scenarios, and that CPUs spend most of its time waiting for data. Hence, having data compressed in memory can reduce the stress of the memory subsystem as well.

Furthermore, columnar means that the tabular datasets are stored column-wise order, and this turns out to offer better opportunities to improve compression ratio. This is because data tends to expose more similarity in elements that sit in the same column rather than those in the same row, so compressors generally do a much better job when data is aligned in such column-wise order. In addition, when you have to deal with tables with a large number of columns and your operations only involve some
of them, a columnar-wise storage tends to be much more effective because minimizes the amount of data that travels to CPU caches.

So, the ultimate goal for bcolz is not only reducing the memory needs of large arrays/tables, but also making bcolz operations to go faster than using a traditional data container like those in NumPy or Pandas. That is actually already the case in some real-life scenarios (see the notebook above) but that will become pretty more noticeable in combination with forthcoming, faster CPUs integrating more cores and wider vector units.

## Requisites

- Python >= 3.7
- NumPy >= 1.16.5, <1.23 (1.23 is not supported yet)
- Cython >= 0.22 (just for compiling the beast)
- C-Blosc >= 1.8.0 (optional, as the internal Blosc will be used by default)

Optional:

- numexpr >= 2.5.2
- dask >= 0.9.0
- pandas
- tables (pytables)

## Installing as wheel

There are wheels for Linux and Mac OS X that you can install with

```python
pip install bcolz-zipline
```

Then also install NumPy with

and test your installation with

```python
python -c 'import bcolz;bcolz.test()'
```

## Building

There are different ways to compile bcolz, depending on whether you want to link with an already installed Blosc library or not.

### Compiling with an installed Blosc library (recommended)

Python and Blosc-powered extensions have a difficult relationship when compiled using GCC, so this is why using an external C-Blosc library is recommended for maximum performance (for details, see
<https://github.com/Blosc/python-blosc/issues/110>).

Go to <https://github.com/Blosc/c-blosc/releases> and download and install the C-Blosc library. Then, you can tell bcolz where is the C-Blosc library in a couple of ways:

Using an environment variable:

``` {.sourceCode .console}
$ BLOSC_DIR=/usr/local     (or "set BLOSC_DIR=\blosc" on Win)
$ export BLOSC_DIR         (not needed on Win)
$ python setup.py build_ext --inplace
```

Using a flag:

``` {.sourceCode .console}
$ python setup.py build_ext --inplace --blosc=/usr/local
```

### Compiling without an installed Blosc library

bcolz also comes with the Blosc sources with it so, assuming that you have a C++ compiler installed, do:

``` {.sourceCode .console}
$ python setup.py build_ext --inplace
```

That\'s all. You can proceed with testing section now.

Note: The requirement for the C++ compiler is just for the Snappy dependency. The rest of the other components of Blosc are pure C
(including the LZ4 and Zlib libraries).

## Testing

After compiling, you can quickly check that the package is sane by running:

    $ PYTHONPATH=.   (or "set PYTHONPATH=." on Windows)
    $ export PYTHONPATH    (not needed on Windows)
    $ python -c "import bcolz; bcolz.test()"  # add `heavy=True` if desired

## Installing

Install it as a typical Python package:

    $ pip install -U .

Optionally Install the additional dependencies:

    $ pip install .[optional]pip 

## Documentation

You can find the online manual at:

<http://bcolz.blosc.org>

but of course, you can always access docstrings from the console (i.e.
`help(bcolz.ctable)`).

Also, you may want to look at the bench/ directory for some examples of use.

## Resources

Visit the main bcolz site repository at: <http://github.com/Blosc/bcolz>

Home of Blosc compressor: <http://blosc.org>

User\'s mail list: <http://groups.google.com/group/bcolz>
(<bcolz@googlegroups.com>)

An [introductory talk (20 min)](https://www.youtube.com/watch?v=-lKV4zC1gss) about bcolz at EuroPython

2014. [Slides here](http://blosc.org/docs/bcolz-EuroPython-2014.pdf).

## License

Please see `BCOLZ.txt` in `LICENSES/` directory.

## Share your experience

Let us know of any bugs, suggestions, gripes, kudos, etc. you may have.

**Enjoy Data!**


%package -n python3-bcolz-zipline
Summary:	Columnar and compressed data containers.
Provides:	python-bcolz-zipline
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
BuildRequires:	python3-cffi
BuildRequires:	gcc
BuildRequires:	gdb
%description -n python3-bcolz-zipline
# bcolz: columnar and compressed data containers

<p align="center">
    <a href="https://gitter.im/Blosc/bcolz?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge" target="_blank">
        <img alt="Gitter" src="https://badges.gitter.im/Blosc/bcolz.svg" />
    </a>
    <a href="https://pypi.org/project/bcolz-zipline/" target="_blank">
        <img alt="Version" src="https://img.shields.io/pypi/v/bcolz-zipline.svg?cacheSeconds=2592000" />
    </a>
    <a href="https://bcolz.readthedocs.io/en/latest/" target="_blank">
        <img alt="Documentation" src="https://img.shields.io/badge/documentation-yes-brightgreen.svg" />
    </a>
    <img alt="GitHub Workflow Status" src="https://img.shields.io/github/workflow/status/stefan-jansen/bcolz-zipline/Tests?label=tests"><a href='https://coveralls.io/github/stefan-jansen/bcolz-zipline?branch=main'><img src='https://coveralls.io/repos/github/stefan-jansen/bcolz-zipline/badge.svg?branch=main' alt='Coverage Status' /></a>
    <img alt="GitHub Workflow Status" src="https://img.shields.io/github/workflow/status/stefan-jansen/bcolz-zipline/Build%20Wheels?label=PyPI"><a href="#" target="_blank">
    <img alt="GitHub Workflow Status" src="https://img.shields.io/github/workflow/status/stefan-jansen/bcolz-zipline/Build%20conda%20distribution?label=Anaconda">
    <img alt="License: BSD" src="https://img.shields.io/badge/License-BSD-yellow.svg" />
    </a>
    <a href="https://twitter.com/ml4trading" target="_blank">
        <img alt="Twitter: @ml4t" src="https://img.shields.io/twitter/follow/ml4trading.svg?style=social" />
    </a>
    <a href="http://blosc.org" target="_blank">
        <img alt="Blosc" src="http://b.repl.ca/v1/Powered--By-Blosc-blue.png" />
    </a>
</p>


<p align="center">
<a href="https://www.amazon.com/Machine-Learning-Algorithmic-Trading-alternative/dp/1839217715?pf_rd_r=GZH2XZ35GB3BET09PCCA&pf_rd_p=c5b6893a-24f2-4a59-9d4b-aff5065c90ec&pd_rd_r=91a679c7-f069-4a6e-bdbb-a2b3f548f0c8&pd_rd_w=2B0Q0&pd_rd_wg=GMY5S&ref_=pd_gw_ci_mcx_mr_hp_d">
<img src="https://imgur.com/g8emkEZ.png" width="35%">
</a>
</p>

bcolz provides columnar, chunked data containers that can be compressed either in-memory and on-disk. Column storage allows for efficiently querying tables, as well as for cheap column addition and removal. It is based on [NumPy](http://www.numpy.org), and uses it as the standard data container to communicate with bcolz objects, but it also comes with support for import/export facilities to/from [HDF5/PyTables tables](http://www.pytables.org) and [pandas dataframes](http://pandas.pydata.org).

bcolz objects are compressed by default not only for reducing memory/disk storage, but also to improve I/O speed. The compression process is carried out internally by [Blosc](http://blosc.org), a high-performance, multithreaded meta-compressor that is optimized for binary data (although it works with text data just fine too).

bcolz can also use [numexpr](https://github.com/pydata/numexpr)
internally (it does that by default if it detects numexpr installed) or
[dask](https://github.com/dask/dask) so as to accelerate many vector and query operations (although it can use pure NumPy for doing so too). numexpr/dask can optimize the memory usage and use multithreading for doing the computations, so it is blazing fast. This, in combination with carray/ctable disk-based, compressed containers, can be used for performing out-of-core computations efficiently, but most importantly
*transparently*.

Just to whet your appetite, [here is an example](http://nbviewer.ipython.org/github/Blosc/movielens-bench/blob/master/querying-ep14.ipynb)
with real data, where bcolz is already fulfilling the promise of accelerating memory I/O by using compression.

## Rationale

By using compression, you can deal with more data using the same amount of memory, which is very good on itself. But in case you are wondering about the price to pay in terms of performance, you should know that nowadays memory access is the most common bottleneck in many computational scenarios, and that CPUs spend most of its time waiting for data. Hence, having data compressed in memory can reduce the stress of the memory subsystem as well.

Furthermore, columnar means that the tabular datasets are stored column-wise order, and this turns out to offer better opportunities to improve compression ratio. This is because data tends to expose more similarity in elements that sit in the same column rather than those in the same row, so compressors generally do a much better job when data is aligned in such column-wise order. In addition, when you have to deal with tables with a large number of columns and your operations only involve some
of them, a columnar-wise storage tends to be much more effective because minimizes the amount of data that travels to CPU caches.

So, the ultimate goal for bcolz is not only reducing the memory needs of large arrays/tables, but also making bcolz operations to go faster than using a traditional data container like those in NumPy or Pandas. That is actually already the case in some real-life scenarios (see the notebook above) but that will become pretty more noticeable in combination with forthcoming, faster CPUs integrating more cores and wider vector units.

## Requisites

- Python >= 3.7
- NumPy >= 1.16.5, <1.23 (1.23 is not supported yet)
- Cython >= 0.22 (just for compiling the beast)
- C-Blosc >= 1.8.0 (optional, as the internal Blosc will be used by default)

Optional:

- numexpr >= 2.5.2
- dask >= 0.9.0
- pandas
- tables (pytables)

## Installing as wheel

There are wheels for Linux and Mac OS X that you can install with

```python
pip install bcolz-zipline
```

Then also install NumPy with

and test your installation with

```python
python -c 'import bcolz;bcolz.test()'
```

## Building

There are different ways to compile bcolz, depending on whether you want to link with an already installed Blosc library or not.

### Compiling with an installed Blosc library (recommended)

Python and Blosc-powered extensions have a difficult relationship when compiled using GCC, so this is why using an external C-Blosc library is recommended for maximum performance (for details, see
<https://github.com/Blosc/python-blosc/issues/110>).

Go to <https://github.com/Blosc/c-blosc/releases> and download and install the C-Blosc library. Then, you can tell bcolz where is the C-Blosc library in a couple of ways:

Using an environment variable:

``` {.sourceCode .console}
$ BLOSC_DIR=/usr/local     (or "set BLOSC_DIR=\blosc" on Win)
$ export BLOSC_DIR         (not needed on Win)
$ python setup.py build_ext --inplace
```

Using a flag:

``` {.sourceCode .console}
$ python setup.py build_ext --inplace --blosc=/usr/local
```

### Compiling without an installed Blosc library

bcolz also comes with the Blosc sources with it so, assuming that you have a C++ compiler installed, do:

``` {.sourceCode .console}
$ python setup.py build_ext --inplace
```

That\'s all. You can proceed with testing section now.

Note: The requirement for the C++ compiler is just for the Snappy dependency. The rest of the other components of Blosc are pure C
(including the LZ4 and Zlib libraries).

## Testing

After compiling, you can quickly check that the package is sane by running:

    $ PYTHONPATH=.   (or "set PYTHONPATH=." on Windows)
    $ export PYTHONPATH    (not needed on Windows)
    $ python -c "import bcolz; bcolz.test()"  # add `heavy=True` if desired

## Installing

Install it as a typical Python package:

    $ pip install -U .

Optionally Install the additional dependencies:

    $ pip install .[optional]pip 

## Documentation

You can find the online manual at:

<http://bcolz.blosc.org>

but of course, you can always access docstrings from the console (i.e.
`help(bcolz.ctable)`).

Also, you may want to look at the bench/ directory for some examples of use.

## Resources

Visit the main bcolz site repository at: <http://github.com/Blosc/bcolz>

Home of Blosc compressor: <http://blosc.org>

User\'s mail list: <http://groups.google.com/group/bcolz>
(<bcolz@googlegroups.com>)

An [introductory talk (20 min)](https://www.youtube.com/watch?v=-lKV4zC1gss) about bcolz at EuroPython

2014. [Slides here](http://blosc.org/docs/bcolz-EuroPython-2014.pdf).

## License

Please see `BCOLZ.txt` in `LICENSES/` directory.

## Share your experience

Let us know of any bugs, suggestions, gripes, kudos, etc. you may have.

**Enjoy Data!**


%package help
Summary:	Development documents and examples for bcolz-zipline
Provides:	python3-bcolz-zipline-doc
%description help
# bcolz: columnar and compressed data containers

<p align="center">
    <a href="https://gitter.im/Blosc/bcolz?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge" target="_blank">
        <img alt="Gitter" src="https://badges.gitter.im/Blosc/bcolz.svg" />
    </a>
    <a href="https://pypi.org/project/bcolz-zipline/" target="_blank">
        <img alt="Version" src="https://img.shields.io/pypi/v/bcolz-zipline.svg?cacheSeconds=2592000" />
    </a>
    <a href="https://bcolz.readthedocs.io/en/latest/" target="_blank">
        <img alt="Documentation" src="https://img.shields.io/badge/documentation-yes-brightgreen.svg" />
    </a>
    <img alt="GitHub Workflow Status" src="https://img.shields.io/github/workflow/status/stefan-jansen/bcolz-zipline/Tests?label=tests"><a href='https://coveralls.io/github/stefan-jansen/bcolz-zipline?branch=main'><img src='https://coveralls.io/repos/github/stefan-jansen/bcolz-zipline/badge.svg?branch=main' alt='Coverage Status' /></a>
    <img alt="GitHub Workflow Status" src="https://img.shields.io/github/workflow/status/stefan-jansen/bcolz-zipline/Build%20Wheels?label=PyPI"><a href="#" target="_blank">
    <img alt="GitHub Workflow Status" src="https://img.shields.io/github/workflow/status/stefan-jansen/bcolz-zipline/Build%20conda%20distribution?label=Anaconda">
    <img alt="License: BSD" src="https://img.shields.io/badge/License-BSD-yellow.svg" />
    </a>
    <a href="https://twitter.com/ml4trading" target="_blank">
        <img alt="Twitter: @ml4t" src="https://img.shields.io/twitter/follow/ml4trading.svg?style=social" />
    </a>
    <a href="http://blosc.org" target="_blank">
        <img alt="Blosc" src="http://b.repl.ca/v1/Powered--By-Blosc-blue.png" />
    </a>
</p>


<p align="center">
<a href="https://www.amazon.com/Machine-Learning-Algorithmic-Trading-alternative/dp/1839217715?pf_rd_r=GZH2XZ35GB3BET09PCCA&pf_rd_p=c5b6893a-24f2-4a59-9d4b-aff5065c90ec&pd_rd_r=91a679c7-f069-4a6e-bdbb-a2b3f548f0c8&pd_rd_w=2B0Q0&pd_rd_wg=GMY5S&ref_=pd_gw_ci_mcx_mr_hp_d">
<img src="https://imgur.com/g8emkEZ.png" width="35%">
</a>
</p>

bcolz provides columnar, chunked data containers that can be compressed either in-memory and on-disk. Column storage allows for efficiently querying tables, as well as for cheap column addition and removal. It is based on [NumPy](http://www.numpy.org), and uses it as the standard data container to communicate with bcolz objects, but it also comes with support for import/export facilities to/from [HDF5/PyTables tables](http://www.pytables.org) and [pandas dataframes](http://pandas.pydata.org).

bcolz objects are compressed by default not only for reducing memory/disk storage, but also to improve I/O speed. The compression process is carried out internally by [Blosc](http://blosc.org), a high-performance, multithreaded meta-compressor that is optimized for binary data (although it works with text data just fine too).

bcolz can also use [numexpr](https://github.com/pydata/numexpr)
internally (it does that by default if it detects numexpr installed) or
[dask](https://github.com/dask/dask) so as to accelerate many vector and query operations (although it can use pure NumPy for doing so too). numexpr/dask can optimize the memory usage and use multithreading for doing the computations, so it is blazing fast. This, in combination with carray/ctable disk-based, compressed containers, can be used for performing out-of-core computations efficiently, but most importantly
*transparently*.

Just to whet your appetite, [here is an example](http://nbviewer.ipython.org/github/Blosc/movielens-bench/blob/master/querying-ep14.ipynb)
with real data, where bcolz is already fulfilling the promise of accelerating memory I/O by using compression.

## Rationale

By using compression, you can deal with more data using the same amount of memory, which is very good on itself. But in case you are wondering about the price to pay in terms of performance, you should know that nowadays memory access is the most common bottleneck in many computational scenarios, and that CPUs spend most of its time waiting for data. Hence, having data compressed in memory can reduce the stress of the memory subsystem as well.

Furthermore, columnar means that the tabular datasets are stored column-wise order, and this turns out to offer better opportunities to improve compression ratio. This is because data tends to expose more similarity in elements that sit in the same column rather than those in the same row, so compressors generally do a much better job when data is aligned in such column-wise order. In addition, when you have to deal with tables with a large number of columns and your operations only involve some
of them, a columnar-wise storage tends to be much more effective because minimizes the amount of data that travels to CPU caches.

So, the ultimate goal for bcolz is not only reducing the memory needs of large arrays/tables, but also making bcolz operations to go faster than using a traditional data container like those in NumPy or Pandas. That is actually already the case in some real-life scenarios (see the notebook above) but that will become pretty more noticeable in combination with forthcoming, faster CPUs integrating more cores and wider vector units.

## Requisites

- Python >= 3.7
- NumPy >= 1.16.5, <1.23 (1.23 is not supported yet)
- Cython >= 0.22 (just for compiling the beast)
- C-Blosc >= 1.8.0 (optional, as the internal Blosc will be used by default)

Optional:

- numexpr >= 2.5.2
- dask >= 0.9.0
- pandas
- tables (pytables)

## Installing as wheel

There are wheels for Linux and Mac OS X that you can install with

```python
pip install bcolz-zipline
```

Then also install NumPy with

and test your installation with

```python
python -c 'import bcolz;bcolz.test()'
```

## Building

There are different ways to compile bcolz, depending on whether you want to link with an already installed Blosc library or not.

### Compiling with an installed Blosc library (recommended)

Python and Blosc-powered extensions have a difficult relationship when compiled using GCC, so this is why using an external C-Blosc library is recommended for maximum performance (for details, see
<https://github.com/Blosc/python-blosc/issues/110>).

Go to <https://github.com/Blosc/c-blosc/releases> and download and install the C-Blosc library. Then, you can tell bcolz where is the C-Blosc library in a couple of ways:

Using an environment variable:

``` {.sourceCode .console}
$ BLOSC_DIR=/usr/local     (or "set BLOSC_DIR=\blosc" on Win)
$ export BLOSC_DIR         (not needed on Win)
$ python setup.py build_ext --inplace
```

Using a flag:

``` {.sourceCode .console}
$ python setup.py build_ext --inplace --blosc=/usr/local
```

### Compiling without an installed Blosc library

bcolz also comes with the Blosc sources with it so, assuming that you have a C++ compiler installed, do:

``` {.sourceCode .console}
$ python setup.py build_ext --inplace
```

That\'s all. You can proceed with testing section now.

Note: The requirement for the C++ compiler is just for the Snappy dependency. The rest of the other components of Blosc are pure C
(including the LZ4 and Zlib libraries).

## Testing

After compiling, you can quickly check that the package is sane by running:

    $ PYTHONPATH=.   (or "set PYTHONPATH=." on Windows)
    $ export PYTHONPATH    (not needed on Windows)
    $ python -c "import bcolz; bcolz.test()"  # add `heavy=True` if desired

## Installing

Install it as a typical Python package:

    $ pip install -U .

Optionally Install the additional dependencies:

    $ pip install .[optional]pip 

## Documentation

You can find the online manual at:

<http://bcolz.blosc.org>

but of course, you can always access docstrings from the console (i.e.
`help(bcolz.ctable)`).

Also, you may want to look at the bench/ directory for some examples of use.

## Resources

Visit the main bcolz site repository at: <http://github.com/Blosc/bcolz>

Home of Blosc compressor: <http://blosc.org>

User\'s mail list: <http://groups.google.com/group/bcolz>
(<bcolz@googlegroups.com>)

An [introductory talk (20 min)](https://www.youtube.com/watch?v=-lKV4zC1gss) about bcolz at EuroPython

2014. [Slides here](http://blosc.org/docs/bcolz-EuroPython-2014.pdf).

## License

Please see `BCOLZ.txt` in `LICENSES/` directory.

## Share your experience

Let us know of any bugs, suggestions, gripes, kudos, etc. you may have.

**Enjoy Data!**


%prep
%autosetup -n bcolz-zipline-1.2.6

%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-bcolz-zipline -f filelist.lst
%dir %{python3_sitearch}/*

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

%changelog
* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 1.2.6-1
- Package Spec generated