%global _empty_manifest_terminate_build 0
Name:		python-AppMetrics
Version:	0.5.0
Release:	1
Summary:	Application metrics collector
License:	Apache 2.0
URL:		https://github.com/avalente/appmetrics
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/ec/67/36b09c8845d2a83f4746626a0ad90907d96e8e215bd0276cd1b87d5c53ad/AppMetrics-0.5.0.tar.gz
BuildArch:	noarch


%description
Install ``AppMetrics`` into your python environment::
    pip install appmetrics
or, if you don't use ``pip``, download and unpack the package an then::
    python setup.py install
Once you have installed ``AppMetrics`` you can access it by the ``metrics`` module::
    >>> from appmetrics import metrics
    >>> histogram = metrics.new_histogram("test")
    >>> histogram.notify(1.0)
    True
    >>> histogram.notify(2.0)
    True
    >>> histogram.notify(3.0)
    True
    >>> histogram.get()
    {'arithmetic_mean': 2.0, 'kind': 'histogram', 'skewness': 0.0, 'harmonic_mean': 1.6363636363636365, 'min': 1.0, 'standard_deviation': 1.0, 'median': 2.0, 'histogram': [(3.0, 3), (5.0, 0)], 'percentile': [(50, 2.0), (75, 2.0), (90, 3.0), (95, 3.0), (99, 3.0), (99.9, 3.0)], 'n': 3, 'max': 3.0, 'variance': 1.0, 'geometric_mean': 1.8171205928321397, 'kurtosis': -2.3333333333333335}
Basically you create a new metric by using one of the ``metrics.new_*`` functions. The metric will be stored into
an internal registry, so you can access it in different places in your application::
    >>> test_histogram = metrics.metric("test")
    >>> test_histogram.notify(4.0)
    True
The ``metrics`` registry is thread-safe, you can safely use it in multi-threaded web servers.
Using the ``with_histogram`` decorator we can time a function::
    >>> import time, random
    >>> @metrics.with_histogram("test")
    >>> my_worker()
    >>> my_worker()
    >>> my_worker()
and let's see the results::
    >>> metrics.get("test")
    {'arithmetic_mean': 0.41326093673706055, 'kind': 'histogram', 'skewness': 0.2739718270714368, 'harmonic_mean': 0.14326954591313346, 'min': 0.0613858699798584, 'standard_deviation': 0.4319169569113129, 'median': 0.2831099033355713, 'histogram': [(1.0613858699798584, 3), (2.0613858699798584, 0)], 'percentile': [(50, 0.2831099033355713), (75, 0.2831099033355713), (90, 0.895287036895752), (95, 0.895287036895752), (99, 0.895287036895752), (99.9, 0.895287036895752)], 'n': 3, 'max': 0.895287036895752, 'variance': 0.18655225766752892, 'geometric_mean': 0.24964828731906127, 'kurtosis': -2.3333333333333335}
It is also possible to time specific sections of the code by using the ``timer`` context manager::
    >>> import time, random
Let's print the metrics data on the screen every 5 seconds::
    >>> from appmetrics import reporter
    >>> def stdout_report(metrics):
    >>> reporter.register(stdout_report, reporter.fixed_interval_scheduler(5))
    '5680173c-0279-46ec-bd88-b318f8058ef4'
    >>> {'test': {'arithmetic_mean': 0.0, 'kind': 'histogram', 'skewness': 0.0, 'harmonic_mean': 0.0, 'min': 0, 'standard_deviation': 0.0, 'median': 0.0, 'histogram': [(0, 0)], 'percentile': [(50, 0.0), (75, 0.0), (90, 0.0), (95, 0.0), (99, 0.0), (99.9, 0.0)], 'n': 0, 'max': 0, 'variance': 0.0, 'geometric_mean': 0.0, 'kurtosis': 0.0}}
    >>> my_worker()
    >>> my_worker()
    >>> {'test': {'arithmetic_mean': 0.5028266906738281, 'kind': 'histogram', 'skewness': 0.0, 'harmonic_mean': 0.2534044030939462, 'min': 0.14868521690368652, 'standard_deviation': 0.50083167520453, 'median': 0.5028266906738281, 'histogram': [(1.1486852169036865, 2), (2.1486852169036865, 0)], 'percentile': [(50, 0.14868521690368652), (75, 0.8569681644439697), (90, 0.8569681644439697), (95, 0.8569681644439697), (99, 0.8569681644439697), (99.9, 0.8569681644439697)], 'n': 2, 'max': 0.8569681644439697, 'variance': 0.2508323668881758, 'geometric_mean': 0.35695727672917066, 'kurtosis': -2.75}}
    >>> reporter.remove('5680173c-0279-46ec-bd88-b318f8058ef4')
    <Timer(Thread-1, started daemon 4555313152)>
Decorators
**********
The ``metrics`` module also provides a couple of decorators: ``with_histogram`` and ``with_meter`` which are
an easy and fast way to use ``AppMetrics``: just decorate your functions/methods and you will have metrics
collected for them. You can decorate multiple functions with the same metric's name, as long as the decorator's
type and parameters are the same, or a ``DuplicateMetricError`` will be raised.
See the documentation for `Histograms`_ and `Meters`_ for more details.

%package -n python3-AppMetrics
Summary:	Application metrics collector
Provides:	python-AppMetrics
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-AppMetrics
Install ``AppMetrics`` into your python environment::
    pip install appmetrics
or, if you don't use ``pip``, download and unpack the package an then::
    python setup.py install
Once you have installed ``AppMetrics`` you can access it by the ``metrics`` module::
    >>> from appmetrics import metrics
    >>> histogram = metrics.new_histogram("test")
    >>> histogram.notify(1.0)
    True
    >>> histogram.notify(2.0)
    True
    >>> histogram.notify(3.0)
    True
    >>> histogram.get()
    {'arithmetic_mean': 2.0, 'kind': 'histogram', 'skewness': 0.0, 'harmonic_mean': 1.6363636363636365, 'min': 1.0, 'standard_deviation': 1.0, 'median': 2.0, 'histogram': [(3.0, 3), (5.0, 0)], 'percentile': [(50, 2.0), (75, 2.0), (90, 3.0), (95, 3.0), (99, 3.0), (99.9, 3.0)], 'n': 3, 'max': 3.0, 'variance': 1.0, 'geometric_mean': 1.8171205928321397, 'kurtosis': -2.3333333333333335}
Basically you create a new metric by using one of the ``metrics.new_*`` functions. The metric will be stored into
an internal registry, so you can access it in different places in your application::
    >>> test_histogram = metrics.metric("test")
    >>> test_histogram.notify(4.0)
    True
The ``metrics`` registry is thread-safe, you can safely use it in multi-threaded web servers.
Using the ``with_histogram`` decorator we can time a function::
    >>> import time, random
    >>> @metrics.with_histogram("test")
    >>> my_worker()
    >>> my_worker()
    >>> my_worker()
and let's see the results::
    >>> metrics.get("test")
    {'arithmetic_mean': 0.41326093673706055, 'kind': 'histogram', 'skewness': 0.2739718270714368, 'harmonic_mean': 0.14326954591313346, 'min': 0.0613858699798584, 'standard_deviation': 0.4319169569113129, 'median': 0.2831099033355713, 'histogram': [(1.0613858699798584, 3), (2.0613858699798584, 0)], 'percentile': [(50, 0.2831099033355713), (75, 0.2831099033355713), (90, 0.895287036895752), (95, 0.895287036895752), (99, 0.895287036895752), (99.9, 0.895287036895752)], 'n': 3, 'max': 0.895287036895752, 'variance': 0.18655225766752892, 'geometric_mean': 0.24964828731906127, 'kurtosis': -2.3333333333333335}
It is also possible to time specific sections of the code by using the ``timer`` context manager::
    >>> import time, random
Let's print the metrics data on the screen every 5 seconds::
    >>> from appmetrics import reporter
    >>> def stdout_report(metrics):
    >>> reporter.register(stdout_report, reporter.fixed_interval_scheduler(5))
    '5680173c-0279-46ec-bd88-b318f8058ef4'
    >>> {'test': {'arithmetic_mean': 0.0, 'kind': 'histogram', 'skewness': 0.0, 'harmonic_mean': 0.0, 'min': 0, 'standard_deviation': 0.0, 'median': 0.0, 'histogram': [(0, 0)], 'percentile': [(50, 0.0), (75, 0.0), (90, 0.0), (95, 0.0), (99, 0.0), (99.9, 0.0)], 'n': 0, 'max': 0, 'variance': 0.0, 'geometric_mean': 0.0, 'kurtosis': 0.0}}
    >>> my_worker()
    >>> my_worker()
    >>> {'test': {'arithmetic_mean': 0.5028266906738281, 'kind': 'histogram', 'skewness': 0.0, 'harmonic_mean': 0.2534044030939462, 'min': 0.14868521690368652, 'standard_deviation': 0.50083167520453, 'median': 0.5028266906738281, 'histogram': [(1.1486852169036865, 2), (2.1486852169036865, 0)], 'percentile': [(50, 0.14868521690368652), (75, 0.8569681644439697), (90, 0.8569681644439697), (95, 0.8569681644439697), (99, 0.8569681644439697), (99.9, 0.8569681644439697)], 'n': 2, 'max': 0.8569681644439697, 'variance': 0.2508323668881758, 'geometric_mean': 0.35695727672917066, 'kurtosis': -2.75}}
    >>> reporter.remove('5680173c-0279-46ec-bd88-b318f8058ef4')
    <Timer(Thread-1, started daemon 4555313152)>
Decorators
**********
The ``metrics`` module also provides a couple of decorators: ``with_histogram`` and ``with_meter`` which are
an easy and fast way to use ``AppMetrics``: just decorate your functions/methods and you will have metrics
collected for them. You can decorate multiple functions with the same metric's name, as long as the decorator's
type and parameters are the same, or a ``DuplicateMetricError`` will be raised.
See the documentation for `Histograms`_ and `Meters`_ for more details.

%package help
Summary:	Development documents and examples for AppMetrics
Provides:	python3-AppMetrics-doc
%description help
Install ``AppMetrics`` into your python environment::
    pip install appmetrics
or, if you don't use ``pip``, download and unpack the package an then::
    python setup.py install
Once you have installed ``AppMetrics`` you can access it by the ``metrics`` module::
    >>> from appmetrics import metrics
    >>> histogram = metrics.new_histogram("test")
    >>> histogram.notify(1.0)
    True
    >>> histogram.notify(2.0)
    True
    >>> histogram.notify(3.0)
    True
    >>> histogram.get()
    {'arithmetic_mean': 2.0, 'kind': 'histogram', 'skewness': 0.0, 'harmonic_mean': 1.6363636363636365, 'min': 1.0, 'standard_deviation': 1.0, 'median': 2.0, 'histogram': [(3.0, 3), (5.0, 0)], 'percentile': [(50, 2.0), (75, 2.0), (90, 3.0), (95, 3.0), (99, 3.0), (99.9, 3.0)], 'n': 3, 'max': 3.0, 'variance': 1.0, 'geometric_mean': 1.8171205928321397, 'kurtosis': -2.3333333333333335}
Basically you create a new metric by using one of the ``metrics.new_*`` functions. The metric will be stored into
an internal registry, so you can access it in different places in your application::
    >>> test_histogram = metrics.metric("test")
    >>> test_histogram.notify(4.0)
    True
The ``metrics`` registry is thread-safe, you can safely use it in multi-threaded web servers.
Using the ``with_histogram`` decorator we can time a function::
    >>> import time, random
    >>> @metrics.with_histogram("test")
    >>> my_worker()
    >>> my_worker()
    >>> my_worker()
and let's see the results::
    >>> metrics.get("test")
    {'arithmetic_mean': 0.41326093673706055, 'kind': 'histogram', 'skewness': 0.2739718270714368, 'harmonic_mean': 0.14326954591313346, 'min': 0.0613858699798584, 'standard_deviation': 0.4319169569113129, 'median': 0.2831099033355713, 'histogram': [(1.0613858699798584, 3), (2.0613858699798584, 0)], 'percentile': [(50, 0.2831099033355713), (75, 0.2831099033355713), (90, 0.895287036895752), (95, 0.895287036895752), (99, 0.895287036895752), (99.9, 0.895287036895752)], 'n': 3, 'max': 0.895287036895752, 'variance': 0.18655225766752892, 'geometric_mean': 0.24964828731906127, 'kurtosis': -2.3333333333333335}
It is also possible to time specific sections of the code by using the ``timer`` context manager::
    >>> import time, random
Let's print the metrics data on the screen every 5 seconds::
    >>> from appmetrics import reporter
    >>> def stdout_report(metrics):
    >>> reporter.register(stdout_report, reporter.fixed_interval_scheduler(5))
    '5680173c-0279-46ec-bd88-b318f8058ef4'
    >>> {'test': {'arithmetic_mean': 0.0, 'kind': 'histogram', 'skewness': 0.0, 'harmonic_mean': 0.0, 'min': 0, 'standard_deviation': 0.0, 'median': 0.0, 'histogram': [(0, 0)], 'percentile': [(50, 0.0), (75, 0.0), (90, 0.0), (95, 0.0), (99, 0.0), (99.9, 0.0)], 'n': 0, 'max': 0, 'variance': 0.0, 'geometric_mean': 0.0, 'kurtosis': 0.0}}
    >>> my_worker()
    >>> my_worker()
    >>> {'test': {'arithmetic_mean': 0.5028266906738281, 'kind': 'histogram', 'skewness': 0.0, 'harmonic_mean': 0.2534044030939462, 'min': 0.14868521690368652, 'standard_deviation': 0.50083167520453, 'median': 0.5028266906738281, 'histogram': [(1.1486852169036865, 2), (2.1486852169036865, 0)], 'percentile': [(50, 0.14868521690368652), (75, 0.8569681644439697), (90, 0.8569681644439697), (95, 0.8569681644439697), (99, 0.8569681644439697), (99.9, 0.8569681644439697)], 'n': 2, 'max': 0.8569681644439697, 'variance': 0.2508323668881758, 'geometric_mean': 0.35695727672917066, 'kurtosis': -2.75}}
    >>> reporter.remove('5680173c-0279-46ec-bd88-b318f8058ef4')
    <Timer(Thread-1, started daemon 4555313152)>
Decorators
**********
The ``metrics`` module also provides a couple of decorators: ``with_histogram`` and ``with_meter`` which are
an easy and fast way to use ``AppMetrics``: just decorate your functions/methods and you will have metrics
collected for them. You can decorate multiple functions with the same metric's name, as long as the decorator's
type and parameters are the same, or a ``DuplicateMetricError`` will be raised.
See the documentation for `Histograms`_ and `Meters`_ for more details.

%prep
%autosetup -n AppMetrics-0.5.0

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

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

%changelog
* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 0.5.0-1
- Package Spec generated