path: root/python-fastdownload.spec

%global _empty_manifest_terminate_build 0
Name:		python-fastdownload
Version:	0.0.7
Release:	1
Summary:	A general purpose data downloading library.
License:	Apache Software License 2.0
URL:		https://github.com/fastai/fastdownload/tree/master/
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/08/be/d2c2e8dc81aa88316ed27f1bd707440a83a7420c35e67c0b143fe81aeca9/fastdownload-0.0.7.tar.gz
BuildArch:	noarch

Requires:	python3-fastprogress
Requires:	python3-fastcore

%description
# fastdownload
> Easily download, verify, and extract archives


If you have datasets or other archives that you want to make available to your users, and ensure they always have the latest versions and that they are downloaded correctly, `fastdownload` can help.

## Install

Using pip:

    pip install fastdownload

...or using conda:

    conda install -c fastai fastdownload

## What's this about?

The situation where you might want to use `fastdownload` is where you have one or more URLs pointing at some archives you want to make available, and you want to ensure that your users download those archives correctly, have the latest version, and that it's as easy as possible for them to access the information in those archives.

Your user just calls a single method, `FastDownload.get`, passing the URL required, and the URL will be downloaded and extracted to the directories you choose. The path to the extracted file is returned. If that URL has already been downloaded, then the cached archive or contents will be used automatically. However, if that size or hash of the archive is different to what it should be, then the user will be informed, and a new version will be downloaded.

In the future, you may want to update one or more of your archives. When you do so, `fastdownload` will ensure your users have the latest version, by checking their downloaded archives against your updated file size and hash information.

For instance, `fastai` uses `fastdownload` to provide access to datasets for deep learning. `fastai` users can download and extract them with a single command, using the return value to access the files. The files are automatically placed in appropriate subdirectories of a `.fastai` folder in the user's homedir. If a dataset is updated, users are informed the next time they use the dataset, and the latest version is automatically downloaded and extracted for them.

## Usage: downloading files

When your users download an archive, `fastdownload` will automatically save it to a directory, check if the size and hash matches, and extract the contents. Minimal usage for downloading and extracting is:

```python
from fastdownload import FastDownload
d = FastDownload()
path = d.get('https://...')
```

After this, `path` will contain the path where the extracted files are located. By default, archives are saved to `{base}/archive`, and extracted to `{base}/data`. `{base}` defaults to `~/.fastdownload`. If there is more than one file or folder in the root of the downloaded archive, then a new folder is created in `data` for the contents.

Instead of `get`, use `download` to download the URL without extracting it, or `extract` to extract the URL without downloading it (assuming it's already been downloaded to the `archive` directory). All of these methods accept a `force` parameter which will download/extract the archive even if it's already present.

You can change any or all of the `base`, `archive`, and `data` paths by passing them to `FastDownload`:

```python
d = FastDownload(base='~/.mypath', archive='downloaded', data='extracted')
```

You can remove the cached archive file and/or the extracted contents with `rm`:

```python
d.rm('https://...')
```


## Usage: making archives available to download

`fastdownload` will add a file `download_checks.py` to your Python module which contains file sizes and hashes for your archives. The file is located in the same directory as a module you choose, e.g.:

```python
d = FastDownload(module=fastai.some_module)
```

Then use `update` to create or update the size and hash for a URL:

```python
d.update('https://...')
```

You will now find there is a file called `download_checks.py` in the same directory where `fastai.some_module` is located, which contains a Python dict with the URL, size, and hash for this file. If you've downloaded this file before to your `archive` path then it will be used, instead of downloading a new copy. Use `get(force=True)` first to download a new copy if even you have it in your archive.

## Config file

If there is a file called `config.ini` in your `base` directory, then keys `archive` and `data` will be used as the default values for `FastDownload`. The file should be in [configparser](https://docs.python.org/3/library/configparser.html) format. Here's a sample `config.ini`:

```
[DEFAULT]         
archive = downloaded
data = extracted
```

If there is no ini file present, one will be automatically created for for you using the details you pass to `FastDownload`.

You can add any additional key/value pairs to the config file that you want. When you call `FastDownload.get` pass `extract_key` to use a key other than `data` for choosing a location to extract to.




%package -n python3-fastdownload
Summary:	A general purpose data downloading library.
Provides:	python-fastdownload
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-fastdownload
# fastdownload
> Easily download, verify, and extract archives


If you have datasets or other archives that you want to make available to your users, and ensure they always have the latest versions and that they are downloaded correctly, `fastdownload` can help.

## Install

Using pip:

    pip install fastdownload

...or using conda:

    conda install -c fastai fastdownload

## What's this about?

The situation where you might want to use `fastdownload` is where you have one or more URLs pointing at some archives you want to make available, and you want to ensure that your users download those archives correctly, have the latest version, and that it's as easy as possible for them to access the information in those archives.

Your user just calls a single method, `FastDownload.get`, passing the URL required, and the URL will be downloaded and extracted to the directories you choose. The path to the extracted file is returned. If that URL has already been downloaded, then the cached archive or contents will be used automatically. However, if that size or hash of the archive is different to what it should be, then the user will be informed, and a new version will be downloaded.

In the future, you may want to update one or more of your archives. When you do so, `fastdownload` will ensure your users have the latest version, by checking their downloaded archives against your updated file size and hash information.

For instance, `fastai` uses `fastdownload` to provide access to datasets for deep learning. `fastai` users can download and extract them with a single command, using the return value to access the files. The files are automatically placed in appropriate subdirectories of a `.fastai` folder in the user's homedir. If a dataset is updated, users are informed the next time they use the dataset, and the latest version is automatically downloaded and extracted for them.

## Usage: downloading files

When your users download an archive, `fastdownload` will automatically save it to a directory, check if the size and hash matches, and extract the contents. Minimal usage for downloading and extracting is:

```python
from fastdownload import FastDownload
d = FastDownload()
path = d.get('https://...')
```

After this, `path` will contain the path where the extracted files are located. By default, archives are saved to `{base}/archive`, and extracted to `{base}/data`. `{base}` defaults to `~/.fastdownload`. If there is more than one file or folder in the root of the downloaded archive, then a new folder is created in `data` for the contents.

Instead of `get`, use `download` to download the URL without extracting it, or `extract` to extract the URL without downloading it (assuming it's already been downloaded to the `archive` directory). All of these methods accept a `force` parameter which will download/extract the archive even if it's already present.

You can change any or all of the `base`, `archive`, and `data` paths by passing them to `FastDownload`:

```python
d = FastDownload(base='~/.mypath', archive='downloaded', data='extracted')
```

You can remove the cached archive file and/or the extracted contents with `rm`:

```python
d.rm('https://...')
```


## Usage: making archives available to download

`fastdownload` will add a file `download_checks.py` to your Python module which contains file sizes and hashes for your archives. The file is located in the same directory as a module you choose, e.g.:

```python
d = FastDownload(module=fastai.some_module)
```

Then use `update` to create or update the size and hash for a URL:

```python
d.update('https://...')
```

You will now find there is a file called `download_checks.py` in the same directory where `fastai.some_module` is located, which contains a Python dict with the URL, size, and hash for this file. If you've downloaded this file before to your `archive` path then it will be used, instead of downloading a new copy. Use `get(force=True)` first to download a new copy if even you have it in your archive.

## Config file

If there is a file called `config.ini` in your `base` directory, then keys `archive` and `data` will be used as the default values for `FastDownload`. The file should be in [configparser](https://docs.python.org/3/library/configparser.html) format. Here's a sample `config.ini`:

```
[DEFAULT]         
archive = downloaded
data = extracted
```

If there is no ini file present, one will be automatically created for for you using the details you pass to `FastDownload`.

You can add any additional key/value pairs to the config file that you want. When you call `FastDownload.get` pass `extract_key` to use a key other than `data` for choosing a location to extract to.




%package help
Summary:	Development documents and examples for fastdownload
Provides:	python3-fastdownload-doc
%description help
# fastdownload
> Easily download, verify, and extract archives


If you have datasets or other archives that you want to make available to your users, and ensure they always have the latest versions and that they are downloaded correctly, `fastdownload` can help.

## Install

Using pip:

    pip install fastdownload

...or using conda:

    conda install -c fastai fastdownload

## What's this about?

The situation where you might want to use `fastdownload` is where you have one or more URLs pointing at some archives you want to make available, and you want to ensure that your users download those archives correctly, have the latest version, and that it's as easy as possible for them to access the information in those archives.

Your user just calls a single method, `FastDownload.get`, passing the URL required, and the URL will be downloaded and extracted to the directories you choose. The path to the extracted file is returned. If that URL has already been downloaded, then the cached archive or contents will be used automatically. However, if that size or hash of the archive is different to what it should be, then the user will be informed, and a new version will be downloaded.

In the future, you may want to update one or more of your archives. When you do so, `fastdownload` will ensure your users have the latest version, by checking their downloaded archives against your updated file size and hash information.

For instance, `fastai` uses `fastdownload` to provide access to datasets for deep learning. `fastai` users can download and extract them with a single command, using the return value to access the files. The files are automatically placed in appropriate subdirectories of a `.fastai` folder in the user's homedir. If a dataset is updated, users are informed the next time they use the dataset, and the latest version is automatically downloaded and extracted for them.

## Usage: downloading files

When your users download an archive, `fastdownload` will automatically save it to a directory, check if the size and hash matches, and extract the contents. Minimal usage for downloading and extracting is:

```python
from fastdownload import FastDownload
d = FastDownload()
path = d.get('https://...')
```

After this, `path` will contain the path where the extracted files are located. By default, archives are saved to `{base}/archive`, and extracted to `{base}/data`. `{base}` defaults to `~/.fastdownload`. If there is more than one file or folder in the root of the downloaded archive, then a new folder is created in `data` for the contents.

Instead of `get`, use `download` to download the URL without extracting it, or `extract` to extract the URL without downloading it (assuming it's already been downloaded to the `archive` directory). All of these methods accept a `force` parameter which will download/extract the archive even if it's already present.

You can change any or all of the `base`, `archive`, and `data` paths by passing them to `FastDownload`:

```python
d = FastDownload(base='~/.mypath', archive='downloaded', data='extracted')
```

You can remove the cached archive file and/or the extracted contents with `rm`:

```python
d.rm('https://...')
```


## Usage: making archives available to download

`fastdownload` will add a file `download_checks.py` to your Python module which contains file sizes and hashes for your archives. The file is located in the same directory as a module you choose, e.g.:

```python
d = FastDownload(module=fastai.some_module)
```

Then use `update` to create or update the size and hash for a URL:

```python
d.update('https://...')
```

You will now find there is a file called `download_checks.py` in the same directory where `fastai.some_module` is located, which contains a Python dict with the URL, size, and hash for this file. If you've downloaded this file before to your `archive` path then it will be used, instead of downloading a new copy. Use `get(force=True)` first to download a new copy if even you have it in your archive.

## Config file

If there is a file called `config.ini` in your `base` directory, then keys `archive` and `data` will be used as the default values for `FastDownload`. The file should be in [configparser](https://docs.python.org/3/library/configparser.html) format. Here's a sample `config.ini`:

```
[DEFAULT]         
archive = downloaded
data = extracted
```

If there is no ini file present, one will be automatically created for for you using the details you pass to `FastDownload`.

You can add any additional key/value pairs to the config file that you want. When you call `FastDownload.get` pass `extract_key` to use a key other than `data` for choosing a location to extract to.




%prep
%autosetup -n fastdownload-0.0.7

%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-fastdownload -f filelist.lst
%dir %{python3_sitelib}/*

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

%changelog
* Fri Apr 21 2023 Python_Bot <Python_Bot@openeuler.org> - 0.0.7-1
- Package Spec generated