path: root/python-tdigest.spec

%global _empty_manifest_terminate_build 0
Name:		python-tdigest
Version:	0.5.2.2
Release:	1
Summary:	T-Digest data structure
License:	MIT
URL:		https://github.com/CamDavidsonPilon/tdigest
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/dd/34/7e2f78d1ed0af7d0039ab2cff45b6bf8512234b9f178bb21713084a1f2f0/tdigest-0.5.2.2.tar.gz
BuildArch:	noarch

Requires:	python3-accumulation-tree
Requires:	python3-pyudorandom
Requires:	python3-pytest
Requires:	python3-pytest-timeout
Requires:	python3-pytest-cov
Requires:	python3-numpy

%description
# tdigest
### Efficient percentile estimation of streaming or distributed data
[![PyPI version](https://badge.fury.io/py/tdigest.svg)](https://badge.fury.io/py/tdigest)
[![Build Status](https://travis-ci.org/CamDavidsonPilon/tdigest.svg?branch=master)](https://travis-ci.org/CamDavidsonPilon/tdigest)


This is a Python implementation of Ted Dunning's [t-digest](https://github.com/tdunning/t-digest) data structure. The t-digest data structure is designed around computing accurate estimates from either streaming data, or distributed data. These estimates are percentiles, quantiles, trimmed means, etc. Two t-digests can be added, making the data structure ideal for map-reduce settings, and can be serialized into much less than 10kB (instead of storing the entire list of data).

See a blog post about it here: [Percentile and Quantile Estimation of Big Data: The t-Digest](http://dataorigami.net/blogs/napkin-folding/19055451-percentile-and-quantile-estimation-of-big-data-the-t-digest)


### Installation
*tdigest* is compatible with both Python 2 and Python 3. 

```
pip install tdigest
```

### Usage

#### Update the digest sequentially

```
from tdigest import TDigest
from numpy.random import random

digest = TDigest()
for x in range(5000):
    digest.update(random())

print(digest.percentile(15))  # about 0.15, as 0.15 is the 15th percentile of the Uniform(0,1) distribution
```

#### Update the digest in batches

```
another_digest = TDigest()
another_digest.batch_update(random(5000))
print(another_digest.percentile(15))
```

#### Sum two digests to create a new digest

```
sum_digest = digest + another_digest 
sum_digest.percentile(30)  # about 0.3
```

#### To dict or serializing a digest with JSON

You can use the to_dict() method to turn a TDigest object into a standard Python dictionary.
```
digest = TDigest()
digest.update(1)
digest.update(2)
digest.update(3)
print(digest.to_dict())
```
Or you can get only a list of Centroids with `centroids_to_list()`.
```
digest.centroids_to_list()
```

Similarly, you can restore a Python dict of digest values with `update_from_dict()`. Centroids are merged with any existing ones in the digest.
For example, make a fresh digest and restore values from a python dictionary.
```
digest = TDigest()
digest.update_from_dict({'K': 25, 'delta': 0.01, 'centroids': [{'c': 1.0, 'm': 1.0}, {'c': 1.0, 'm': 2.0}, {'c': 1.0, 'm': 3.0}]})
```

K and delta values are optional, or you can provide only a list of centroids with `update_centroids_from_list()`.
```
digest = TDigest()
digest.update_centroids([{'c': 1.0, 'm': 1.0}, {'c': 1.0, 'm': 2.0}, {'c': 1.0, 'm': 3.0}])
```

If you want to serialize with other tools like JSON, you can first convert to_dict().
```
json.dumps(digest.to_dict())
```

Alternatively, make a custom encoder function to provide as default to the standard json module.
```
def encoder(digest_obj):
    return digest_obj.to_dict()
```
Then pass the encoder function as the default parameter.
```
json.dumps(digest, default=encoder)
```


### API 

`TDigest.`

 - `update(x, w=1)`: update the tdigest with value `x` and weight `w`.
 - `batch_update(x, w=1)`: update the tdigest with values in array `x` and weight `w`.
 - `compress()`: perform a compression on the underlying data structure that will shrink the memory footprint of it, without hurting accuracy. Good to perform after adding many values. 
 - `percentile(p)`: return the `p`th percentile. Example: `p=50` is the median.
 - `cdf(x)`: return the CDF the value `x` is at. 
 - `trimmed_mean(p1, p2)`: return the mean of data set without the values below and above the `p1` and `p2` percentile respectively. 
 - `to_dict()`: return a Python dictionary of the TDigest and internal Centroid values.
 - `update_from_dict(dict_values)`: update from serialized dictionary values into the TDigest object.
 - `centroids_to_list()`: return a Python list of the TDigest object's internal Centroid values.
 - `update_centroids_from_list(list_values)`: update Centroids from a python list.









%package -n python3-tdigest
Summary:	T-Digest data structure
Provides:	python-tdigest
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-tdigest
# tdigest
### Efficient percentile estimation of streaming or distributed data
[![PyPI version](https://badge.fury.io/py/tdigest.svg)](https://badge.fury.io/py/tdigest)
[![Build Status](https://travis-ci.org/CamDavidsonPilon/tdigest.svg?branch=master)](https://travis-ci.org/CamDavidsonPilon/tdigest)


This is a Python implementation of Ted Dunning's [t-digest](https://github.com/tdunning/t-digest) data structure. The t-digest data structure is designed around computing accurate estimates from either streaming data, or distributed data. These estimates are percentiles, quantiles, trimmed means, etc. Two t-digests can be added, making the data structure ideal for map-reduce settings, and can be serialized into much less than 10kB (instead of storing the entire list of data).

See a blog post about it here: [Percentile and Quantile Estimation of Big Data: The t-Digest](http://dataorigami.net/blogs/napkin-folding/19055451-percentile-and-quantile-estimation-of-big-data-the-t-digest)


### Installation
*tdigest* is compatible with both Python 2 and Python 3. 

```
pip install tdigest
```

### Usage

#### Update the digest sequentially

```
from tdigest import TDigest
from numpy.random import random

digest = TDigest()
for x in range(5000):
    digest.update(random())

print(digest.percentile(15))  # about 0.15, as 0.15 is the 15th percentile of the Uniform(0,1) distribution
```

#### Update the digest in batches

```
another_digest = TDigest()
another_digest.batch_update(random(5000))
print(another_digest.percentile(15))
```

#### Sum two digests to create a new digest

```
sum_digest = digest + another_digest 
sum_digest.percentile(30)  # about 0.3
```

#### To dict or serializing a digest with JSON

You can use the to_dict() method to turn a TDigest object into a standard Python dictionary.
```
digest = TDigest()
digest.update(1)
digest.update(2)
digest.update(3)
print(digest.to_dict())
```
Or you can get only a list of Centroids with `centroids_to_list()`.
```
digest.centroids_to_list()
```

Similarly, you can restore a Python dict of digest values with `update_from_dict()`. Centroids are merged with any existing ones in the digest.
For example, make a fresh digest and restore values from a python dictionary.
```
digest = TDigest()
digest.update_from_dict({'K': 25, 'delta': 0.01, 'centroids': [{'c': 1.0, 'm': 1.0}, {'c': 1.0, 'm': 2.0}, {'c': 1.0, 'm': 3.0}]})
```

K and delta values are optional, or you can provide only a list of centroids with `update_centroids_from_list()`.
```
digest = TDigest()
digest.update_centroids([{'c': 1.0, 'm': 1.0}, {'c': 1.0, 'm': 2.0}, {'c': 1.0, 'm': 3.0}])
```

If you want to serialize with other tools like JSON, you can first convert to_dict().
```
json.dumps(digest.to_dict())
```

Alternatively, make a custom encoder function to provide as default to the standard json module.
```
def encoder(digest_obj):
    return digest_obj.to_dict()
```
Then pass the encoder function as the default parameter.
```
json.dumps(digest, default=encoder)
```


### API 

`TDigest.`

 - `update(x, w=1)`: update the tdigest with value `x` and weight `w`.
 - `batch_update(x, w=1)`: update the tdigest with values in array `x` and weight `w`.
 - `compress()`: perform a compression on the underlying data structure that will shrink the memory footprint of it, without hurting accuracy. Good to perform after adding many values. 
 - `percentile(p)`: return the `p`th percentile. Example: `p=50` is the median.
 - `cdf(x)`: return the CDF the value `x` is at. 
 - `trimmed_mean(p1, p2)`: return the mean of data set without the values below and above the `p1` and `p2` percentile respectively. 
 - `to_dict()`: return a Python dictionary of the TDigest and internal Centroid values.
 - `update_from_dict(dict_values)`: update from serialized dictionary values into the TDigest object.
 - `centroids_to_list()`: return a Python list of the TDigest object's internal Centroid values.
 - `update_centroids_from_list(list_values)`: update Centroids from a python list.









%package help
Summary:	Development documents and examples for tdigest
Provides:	python3-tdigest-doc
%description help
# tdigest
### Efficient percentile estimation of streaming or distributed data
[![PyPI version](https://badge.fury.io/py/tdigest.svg)](https://badge.fury.io/py/tdigest)
[![Build Status](https://travis-ci.org/CamDavidsonPilon/tdigest.svg?branch=master)](https://travis-ci.org/CamDavidsonPilon/tdigest)


This is a Python implementation of Ted Dunning's [t-digest](https://github.com/tdunning/t-digest) data structure. The t-digest data structure is designed around computing accurate estimates from either streaming data, or distributed data. These estimates are percentiles, quantiles, trimmed means, etc. Two t-digests can be added, making the data structure ideal for map-reduce settings, and can be serialized into much less than 10kB (instead of storing the entire list of data).

See a blog post about it here: [Percentile and Quantile Estimation of Big Data: The t-Digest](http://dataorigami.net/blogs/napkin-folding/19055451-percentile-and-quantile-estimation-of-big-data-the-t-digest)


### Installation
*tdigest* is compatible with both Python 2 and Python 3. 

```
pip install tdigest
```

### Usage

#### Update the digest sequentially

```
from tdigest import TDigest
from numpy.random import random

digest = TDigest()
for x in range(5000):
    digest.update(random())

print(digest.percentile(15))  # about 0.15, as 0.15 is the 15th percentile of the Uniform(0,1) distribution
```

#### Update the digest in batches

```
another_digest = TDigest()
another_digest.batch_update(random(5000))
print(another_digest.percentile(15))
```

#### Sum two digests to create a new digest

```
sum_digest = digest + another_digest 
sum_digest.percentile(30)  # about 0.3
```

#### To dict or serializing a digest with JSON

You can use the to_dict() method to turn a TDigest object into a standard Python dictionary.
```
digest = TDigest()
digest.update(1)
digest.update(2)
digest.update(3)
print(digest.to_dict())
```
Or you can get only a list of Centroids with `centroids_to_list()`.
```
digest.centroids_to_list()
```

Similarly, you can restore a Python dict of digest values with `update_from_dict()`. Centroids are merged with any existing ones in the digest.
For example, make a fresh digest and restore values from a python dictionary.
```
digest = TDigest()
digest.update_from_dict({'K': 25, 'delta': 0.01, 'centroids': [{'c': 1.0, 'm': 1.0}, {'c': 1.0, 'm': 2.0}, {'c': 1.0, 'm': 3.0}]})
```

K and delta values are optional, or you can provide only a list of centroids with `update_centroids_from_list()`.
```
digest = TDigest()
digest.update_centroids([{'c': 1.0, 'm': 1.0}, {'c': 1.0, 'm': 2.0}, {'c': 1.0, 'm': 3.0}])
```

If you want to serialize with other tools like JSON, you can first convert to_dict().
```
json.dumps(digest.to_dict())
```

Alternatively, make a custom encoder function to provide as default to the standard json module.
```
def encoder(digest_obj):
    return digest_obj.to_dict()
```
Then pass the encoder function as the default parameter.
```
json.dumps(digest, default=encoder)
```


### API 

`TDigest.`

 - `update(x, w=1)`: update the tdigest with value `x` and weight `w`.
 - `batch_update(x, w=1)`: update the tdigest with values in array `x` and weight `w`.
 - `compress()`: perform a compression on the underlying data structure that will shrink the memory footprint of it, without hurting accuracy. Good to perform after adding many values. 
 - `percentile(p)`: return the `p`th percentile. Example: `p=50` is the median.
 - `cdf(x)`: return the CDF the value `x` is at. 
 - `trimmed_mean(p1, p2)`: return the mean of data set without the values below and above the `p1` and `p2` percentile respectively. 
 - `to_dict()`: return a Python dictionary of the TDigest and internal Centroid values.
 - `update_from_dict(dict_values)`: update from serialized dictionary values into the TDigest object.
 - `centroids_to_list()`: return a Python list of the TDigest object's internal Centroid values.
 - `update_centroids_from_list(list_values)`: update Centroids from a python list.









%prep
%autosetup -n tdigest-0.5.2.2

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

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

%changelog
* Fri Apr 21 2023 Python_Bot <Python_Bot@openeuler.org> - 0.5.2.2-1
- Package Spec generated