path: root/python-traces.spec

%global _empty_manifest_terminate_build 0
Name:		python-traces
Version:	0.6.0
Release:	1
Summary:	A library for unevenly-spaced time series analysis.
License:	MIT license
URL:		https://github.com/datascopeanalytics/traces
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/f4/51/f99afc6bf101cfe10cb5a5e1ca1de86a90029571150c692766769e01582d/traces-0.6.0.macosx-10.15-x86_64.tar.gz
BuildArch:	noarch

Requires:	python3-sortedcontainers
Requires:	python3-infinity
Requires:	python3-dateutil
Requires:	python3-cprofilev
Requires:	python3-pip
Requires:	python3-bumpversion
Requires:	python3-wheel
Requires:	python3-tox
Requires:	python3-flake8
Requires:	python3-coverage
Requires:	python3-cryptography
Requires:	python3-PyYAML
Requires:	python3-Sphinx
Requires:	python3-sphinxcontrib-napoleon
Requires:	python3-recommonmark
Requires:	python3-sphinx-autobuild
Requires:	python3-pandas
Requires:	python3-pycodestyle
Requires:	python3-coveralls
Requires:	python3-nose
Requires:	python3-pytest
Requires:	python3-pytest-mpl
Requires:	python3-numpy
Requires:	python3-scipy
Requires:	python3-pandas
Requires:	python3-matplotlib

%description
# traces

[![Version](https://img.shields.io/pypi/v/traces.svg?)](https://pypi.python.org/pypi/traces) [![PyVersions](https://img.shields.io/pypi/pyversions/traces.svg)](https://pypi.python.org/pypi/traces) [![CircleCI](https://circleci.com/gh/datascopeanalytics/traces/tree/master.svg?style=shield)](https://circleci.com/gh/datascopeanalytics/traces/tree/master) [![Documentation Status](https://readthedocs.org/projects/traces/badge/?version=master)](https://traces.readthedocs.io/en/master/?badge=master) [![Coverage Status](https://coveralls.io/repos/github/datascopeanalytics/traces/badge.svg?branch=master)](https://coveralls.io/github/datascopeanalytics/traces?branch=master)

A Python library for unevenly-spaced time series analysis.

## Why?

Taking measurements at irregular intervals is common, but most tools are
primarily designed for evenly-spaced measurements. Also, in the real
world, time series have missing observations or you may have multiple
series with different frequencies: it's can be useful to model these as
unevenly-spaced.

Traces was designed by the team at
[Datascope](https://datascopeanalytics.com/) based on several practical
applications in different domains, because it turns out [unevenly-spaced
data is actually pretty great, particularly for sensor data
analysis](https://datascopeanalytics.com/blog/unevenly-spaced-time-series/).

## Installation

To install traces, run this command in your terminal:

```bash
$ pip install traces
```

## Quickstart: using traces

To see a basic use of traces, let's look at these data from a light
switch, also known as _Big Data from the Internet of Things_.

![](docs/_static/img/trace.svg)

The main object in traces is a [TimeSeries](https://traces.readthedocs.io/en/master/api_reference.html#timeseries), which you
create just like a dictionary, adding the five measurements at 6:00am,
7:45:56am, etc.

```python
>>> time_series = traces.TimeSeries()
>>> time_series[datetime(2042, 2, 1,  6,  0,  0)] = 0 #  6:00:00am
>>> time_series[datetime(2042, 2, 1,  7, 45, 56)] = 1 #  7:45:56am
>>> time_series[datetime(2042, 2, 1,  8, 51, 42)] = 0 #  8:51:42am
>>> time_series[datetime(2042, 2, 1, 12,  3, 56)] = 1 # 12:03:56am
>>> time_series[datetime(2042, 2, 1, 12,  7, 13)] = 0 # 12:07:13am
```

What if you want to know if the light was on at 11am? Unlike a python
dictionary, you can look up the value at any time even if it's not one
of the measurement times.

```python
>>> time_series[datetime(2042, 2, 1, 11,  0, 0)] # 11:00am
0
```

The `distribution` function gives you the fraction of time that the
`TimeSeries` is in each state.

```python
>>> time_series.distribution(
>>>   start=datetime(2042, 2, 1,  6,  0,  0), # 6:00am
>>>   end=datetime(2042, 2, 1,  13,  0,  0)   # 1:00pm
>>> )
Histogram({0: 0.8355952380952381, 1: 0.16440476190476191})
```

The light was on about 16% of the time between 6am and 1pm.

### Adding more data...

Now let's get a little more complicated and look at the sensor readings
from forty lights in a house.

![](docs/_static/img/traces.svg)

How many lights are on throughout the day? The merge function takes the
forty individual `TimeSeries` and efficiently merges them into one
`TimeSeries` where the each value is a list of all lights.

```python
>>> trace_list = [... list of forty traces.TimeSeries ...]
>>> count = traces.TimeSeries.merge(trace_list, operation=sum)
```

We also applied a `sum` operation to the list of states to get the
`TimeSeries` of the number of lights that are on.

![](docs/_static/img/count.svg)

How many lights are on in the building on average during business hours,
from 8am to 6pm?

```python
>>> histogram = count.distribution(
>>>   start=datetime(2042, 2, 1,  8,  0,  0),   # 8:00am
>>>   end=datetime(2042, 2, 1,  12 + 6,  0,  0) # 6:00pm
>>> )
>>> histogram.median()
17
```

The `distribution` function returns a [Histogram](https://traces.readthedocs.io/en/master/api_reference.html#histogram) that
can be used to get summary metrics such as the mean or quantiles.

### It's flexible

The measurements points (keys) in a `TimeSeries` can be in any units as
long as they can be ordered. The values can be anything.

For example, you can use a `TimeSeries` to keep track the contents of a
grocery basket by the number of minutes within a shopping trip.

```python
>>> time_series = traces.TimeSeries()
>>> time_series[1.2] = {'broccoli'}
>>> time_series[1.7] = {'broccoli', 'apple'}
>>> time_series[2.2] = {'apple'}          # puts broccoli back
>>> time_series[3.5] = {'apple', 'beets'} # mmm, beets
```

To learn more, check the [examples](https://traces.readthedocs.io/en/master/examples.html) and the detailed [reference](https://traces.readthedocs.io/en/master/api_reference.html#).

## More info

## Contributing

Contributions are welcome and greatly appreciated! Please visit our [guidelines](https://github.com/datascopeanalytics/traces/blob/master/CONTRIBUTING.md)
for more info.




%package -n python3-traces
Summary:	A library for unevenly-spaced time series analysis.
Provides:	python-traces
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-traces
# traces

[![Version](https://img.shields.io/pypi/v/traces.svg?)](https://pypi.python.org/pypi/traces) [![PyVersions](https://img.shields.io/pypi/pyversions/traces.svg)](https://pypi.python.org/pypi/traces) [![CircleCI](https://circleci.com/gh/datascopeanalytics/traces/tree/master.svg?style=shield)](https://circleci.com/gh/datascopeanalytics/traces/tree/master) [![Documentation Status](https://readthedocs.org/projects/traces/badge/?version=master)](https://traces.readthedocs.io/en/master/?badge=master) [![Coverage Status](https://coveralls.io/repos/github/datascopeanalytics/traces/badge.svg?branch=master)](https://coveralls.io/github/datascopeanalytics/traces?branch=master)

A Python library for unevenly-spaced time series analysis.

## Why?

Taking measurements at irregular intervals is common, but most tools are
primarily designed for evenly-spaced measurements. Also, in the real
world, time series have missing observations or you may have multiple
series with different frequencies: it's can be useful to model these as
unevenly-spaced.

Traces was designed by the team at
[Datascope](https://datascopeanalytics.com/) based on several practical
applications in different domains, because it turns out [unevenly-spaced
data is actually pretty great, particularly for sensor data
analysis](https://datascopeanalytics.com/blog/unevenly-spaced-time-series/).

## Installation

To install traces, run this command in your terminal:

```bash
$ pip install traces
```

## Quickstart: using traces

To see a basic use of traces, let's look at these data from a light
switch, also known as _Big Data from the Internet of Things_.

![](docs/_static/img/trace.svg)

The main object in traces is a [TimeSeries](https://traces.readthedocs.io/en/master/api_reference.html#timeseries), which you
create just like a dictionary, adding the five measurements at 6:00am,
7:45:56am, etc.

```python
>>> time_series = traces.TimeSeries()
>>> time_series[datetime(2042, 2, 1,  6,  0,  0)] = 0 #  6:00:00am
>>> time_series[datetime(2042, 2, 1,  7, 45, 56)] = 1 #  7:45:56am
>>> time_series[datetime(2042, 2, 1,  8, 51, 42)] = 0 #  8:51:42am
>>> time_series[datetime(2042, 2, 1, 12,  3, 56)] = 1 # 12:03:56am
>>> time_series[datetime(2042, 2, 1, 12,  7, 13)] = 0 # 12:07:13am
```

What if you want to know if the light was on at 11am? Unlike a python
dictionary, you can look up the value at any time even if it's not one
of the measurement times.

```python
>>> time_series[datetime(2042, 2, 1, 11,  0, 0)] # 11:00am
0
```

The `distribution` function gives you the fraction of time that the
`TimeSeries` is in each state.

```python
>>> time_series.distribution(
>>>   start=datetime(2042, 2, 1,  6,  0,  0), # 6:00am
>>>   end=datetime(2042, 2, 1,  13,  0,  0)   # 1:00pm
>>> )
Histogram({0: 0.8355952380952381, 1: 0.16440476190476191})
```

The light was on about 16% of the time between 6am and 1pm.

### Adding more data...

Now let's get a little more complicated and look at the sensor readings
from forty lights in a house.

![](docs/_static/img/traces.svg)

How many lights are on throughout the day? The merge function takes the
forty individual `TimeSeries` and efficiently merges them into one
`TimeSeries` where the each value is a list of all lights.

```python
>>> trace_list = [... list of forty traces.TimeSeries ...]
>>> count = traces.TimeSeries.merge(trace_list, operation=sum)
```

We also applied a `sum` operation to the list of states to get the
`TimeSeries` of the number of lights that are on.

![](docs/_static/img/count.svg)

How many lights are on in the building on average during business hours,
from 8am to 6pm?

```python
>>> histogram = count.distribution(
>>>   start=datetime(2042, 2, 1,  8,  0,  0),   # 8:00am
>>>   end=datetime(2042, 2, 1,  12 + 6,  0,  0) # 6:00pm
>>> )
>>> histogram.median()
17
```

The `distribution` function returns a [Histogram](https://traces.readthedocs.io/en/master/api_reference.html#histogram) that
can be used to get summary metrics such as the mean or quantiles.

### It's flexible

The measurements points (keys) in a `TimeSeries` can be in any units as
long as they can be ordered. The values can be anything.

For example, you can use a `TimeSeries` to keep track the contents of a
grocery basket by the number of minutes within a shopping trip.

```python
>>> time_series = traces.TimeSeries()
>>> time_series[1.2] = {'broccoli'}
>>> time_series[1.7] = {'broccoli', 'apple'}
>>> time_series[2.2] = {'apple'}          # puts broccoli back
>>> time_series[3.5] = {'apple', 'beets'} # mmm, beets
```

To learn more, check the [examples](https://traces.readthedocs.io/en/master/examples.html) and the detailed [reference](https://traces.readthedocs.io/en/master/api_reference.html#).

## More info

## Contributing

Contributions are welcome and greatly appreciated! Please visit our [guidelines](https://github.com/datascopeanalytics/traces/blob/master/CONTRIBUTING.md)
for more info.




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

[![Version](https://img.shields.io/pypi/v/traces.svg?)](https://pypi.python.org/pypi/traces) [![PyVersions](https://img.shields.io/pypi/pyversions/traces.svg)](https://pypi.python.org/pypi/traces) [![CircleCI](https://circleci.com/gh/datascopeanalytics/traces/tree/master.svg?style=shield)](https://circleci.com/gh/datascopeanalytics/traces/tree/master) [![Documentation Status](https://readthedocs.org/projects/traces/badge/?version=master)](https://traces.readthedocs.io/en/master/?badge=master) [![Coverage Status](https://coveralls.io/repos/github/datascopeanalytics/traces/badge.svg?branch=master)](https://coveralls.io/github/datascopeanalytics/traces?branch=master)

A Python library for unevenly-spaced time series analysis.

## Why?

Taking measurements at irregular intervals is common, but most tools are
primarily designed for evenly-spaced measurements. Also, in the real
world, time series have missing observations or you may have multiple
series with different frequencies: it's can be useful to model these as
unevenly-spaced.

Traces was designed by the team at
[Datascope](https://datascopeanalytics.com/) based on several practical
applications in different domains, because it turns out [unevenly-spaced
data is actually pretty great, particularly for sensor data
analysis](https://datascopeanalytics.com/blog/unevenly-spaced-time-series/).

## Installation

To install traces, run this command in your terminal:

```bash
$ pip install traces
```

## Quickstart: using traces

To see a basic use of traces, let's look at these data from a light
switch, also known as _Big Data from the Internet of Things_.

![](docs/_static/img/trace.svg)

The main object in traces is a [TimeSeries](https://traces.readthedocs.io/en/master/api_reference.html#timeseries), which you
create just like a dictionary, adding the five measurements at 6:00am,
7:45:56am, etc.

```python
>>> time_series = traces.TimeSeries()
>>> time_series[datetime(2042, 2, 1,  6,  0,  0)] = 0 #  6:00:00am
>>> time_series[datetime(2042, 2, 1,  7, 45, 56)] = 1 #  7:45:56am
>>> time_series[datetime(2042, 2, 1,  8, 51, 42)] = 0 #  8:51:42am
>>> time_series[datetime(2042, 2, 1, 12,  3, 56)] = 1 # 12:03:56am
>>> time_series[datetime(2042, 2, 1, 12,  7, 13)] = 0 # 12:07:13am
```

What if you want to know if the light was on at 11am? Unlike a python
dictionary, you can look up the value at any time even if it's not one
of the measurement times.

```python
>>> time_series[datetime(2042, 2, 1, 11,  0, 0)] # 11:00am
0
```

The `distribution` function gives you the fraction of time that the
`TimeSeries` is in each state.

```python
>>> time_series.distribution(
>>>   start=datetime(2042, 2, 1,  6,  0,  0), # 6:00am
>>>   end=datetime(2042, 2, 1,  13,  0,  0)   # 1:00pm
>>> )
Histogram({0: 0.8355952380952381, 1: 0.16440476190476191})
```

The light was on about 16% of the time between 6am and 1pm.

### Adding more data...

Now let's get a little more complicated and look at the sensor readings
from forty lights in a house.

![](docs/_static/img/traces.svg)

How many lights are on throughout the day? The merge function takes the
forty individual `TimeSeries` and efficiently merges them into one
`TimeSeries` where the each value is a list of all lights.

```python
>>> trace_list = [... list of forty traces.TimeSeries ...]
>>> count = traces.TimeSeries.merge(trace_list, operation=sum)
```

We also applied a `sum` operation to the list of states to get the
`TimeSeries` of the number of lights that are on.

![](docs/_static/img/count.svg)

How many lights are on in the building on average during business hours,
from 8am to 6pm?

```python
>>> histogram = count.distribution(
>>>   start=datetime(2042, 2, 1,  8,  0,  0),   # 8:00am
>>>   end=datetime(2042, 2, 1,  12 + 6,  0,  0) # 6:00pm
>>> )
>>> histogram.median()
17
```

The `distribution` function returns a [Histogram](https://traces.readthedocs.io/en/master/api_reference.html#histogram) that
can be used to get summary metrics such as the mean or quantiles.

### It's flexible

The measurements points (keys) in a `TimeSeries` can be in any units as
long as they can be ordered. The values can be anything.

For example, you can use a `TimeSeries` to keep track the contents of a
grocery basket by the number of minutes within a shopping trip.

```python
>>> time_series = traces.TimeSeries()
>>> time_series[1.2] = {'broccoli'}
>>> time_series[1.7] = {'broccoli', 'apple'}
>>> time_series[2.2] = {'apple'}          # puts broccoli back
>>> time_series[3.5] = {'apple', 'beets'} # mmm, beets
```

To learn more, check the [examples](https://traces.readthedocs.io/en/master/examples.html) and the detailed [reference](https://traces.readthedocs.io/en/master/api_reference.html#).

## More info

## Contributing

Contributions are welcome and greatly appreciated! Please visit our [guidelines](https://github.com/datascopeanalytics/traces/blob/master/CONTRIBUTING.md)
for more info.




%prep
%autosetup -n traces-0.6.0

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

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

%changelog
* Tue Apr 25 2023 Python_Bot <Python_Bot@openeuler.org> - 0.6.0-1
- Package Spec generated