automatic import of python-tsfeatures

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+/tsfeatures-0.4.0.tar.gz
diff --git a/python-tsfeatures.spec b/python-tsfeatures.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-tsfeatures
+Version:	0.4.0
+Release:	1
+Summary:	Calculates various features from time series data.
+License:	MIT License
+URL:		https://github.com/Nixtla/tsfeatures
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/9a/c7/98f8aa7bf1e0078fb82a26ece9688c995bd7aeb5d46cdc6b7c294febe510/tsfeatures-0.4.0.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-antropy
+Requires:	python3-arch
+Requires:	python3-pandas
+Requires:	python3-scikit-learn
+Requires:	python3-statsmodels
+Requires:	python3-supersmoother
+
+%description
+[![Build](https://github.com/FedericoGarza/tsfeatures/workflows/Python%20package/badge.svg)](https://github.com/FedericoGarza/tsfeatures/tree/master)
+[![PyPI version fury.io](https://badge.fury.io/py/tsfeatures.svg)](https://pypi.python.org/pypi/tsfeatures/)
+[![Downloads](https://pepy.tech/badge/tsfeatures)](https://pepy.tech/project/tsfeatures)
+[![Python 3.6+](https://img.shields.io/badge/python-3.7+-blue.svg)](https://www.python.org/downloads/release/python-370+/)
+[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://github.com/FedericoGarza/tsfeatures/blob/master/LICENSE)
+
+# tsfeatures
+
+Calculates various features from time series data. Python implementation of the R package _[tsfeatures](https://github.com/robjhyndman/tsfeatures)_.
+
+# Installation
+
+You can install the *released* version of `tsfeatures` from the [Python package index](pypi.org) with:
+
+``` python
+pip install tsfeatures
+```
+
+# Usage
+
+The `tsfeatures` main function calculates by default the features used by Montero-Manso, Talagala, Hyndman and Athanasopoulos in [their implementation of the FFORMA model](https://htmlpreview.github.io/?https://github.com/robjhyndman/M4metalearning/blob/master/docs/M4_methodology.html#features).
+
+```python
+from tsfeatures import tsfeatures
+```
+
+This function receives a panel pandas df with columns `unique_id`, `ds`, `y` and optionally the frequency of the data.
+
+<img src=https://raw.githubusercontent.com/FedericoGarza/tsfeatures/master/.github/images/y_train.png width="152">
+
+```python
+tsfeatures(panel, freq=7)
+```
+
+By default (`freq=None`) the function will try to infer the frequency of each time series (using `infer_freq` from `pandas` on the `ds` column) and assign a seasonal period according to the built-in dictionary `FREQS`:
+
+```python
+FREQS = {'H': 24, 'D': 1,
+         'M': 12, 'Q': 4,
+         'W':1, 'Y': 1}
+```
+
+You can use your own dictionary using the `dict_freqs` argument:
+
+```python
+tsfeatures(panel, dict_freqs={'D': 7, 'W': 52})
+```
+
+## List of available features
+
+| Features |||
+|:--------|:------|:-------------|
+|acf_features|heterogeneity|series_length|
+|arch_stat|holt_parameters|sparsity|
+|count_entropy|hurst|stability|
+|crossing_points|hw_parameters|stl_features|
+|entropy|intervals|unitroot_kpss|
+|flat_spots|lumpiness|unitroot_pp|
+|frequency|nonlinearity||
+|guerrero|pacf_features||
+
+See the docs for a description of the features. To use a particular feature included in the package you need to import it:
+
+```python
+from tsfeatures import acf_features
+
+tsfeatures(panel, freq=7, features=[acf_features])
+```
+
+You can also define your own function and use it together with the included features:
+
+```python
+def number_zeros(x, freq):
+
+    number = (x == 0).sum()
+    return {'number_zeros': number}
+
+tsfeatures(panel, freq=7, features=[acf_features, number_zeros])
+```
+
+`tsfeatures` can handle functions that receives a numpy array `x` and a frequency `freq` (this parameter is needed even if you don't use it) and returns a dictionary with the feature name as a key and its value.
+
+## R implementation
+
+You can use this package to call `tsfeatures` from R inside python (you need to have installed R, the packages `forecast` and `tsfeatures`; also the python package `rpy2`):
+
+```python
+from tsfeatures.tsfeatures_r import tsfeatures_r
+
+tsfeatures_r(panel, freq=7, features=["acf_features"])
+```
+
+Observe that this function receives a list of strings instead of a list of functions.
+
+## Comparison with the R implementation (sum of absolute differences)
+
+### Non-seasonal data (100 Daily M4 time series)
+
+| feature         |   diff | feature         |   diff | feature         |   diff | feature         |   diff |
+|:----------------|-------:|:----------------|-------:|:----------------|-------:|:----------------|-------:|
+| e_acf10         |   0    | e_acf1         |   0    | diff2_acf1         |   0    | alpha         |   3.2    |
+| seasonal_period |   0    | spike         |   0    | diff1_acf10         |   0    | arch_acf         |   3.3    |
+| nperiods        |   0    | curvature         |   0    | x_acf1         |   0    | beta         |   4.04    |
+| linearity       |   0    | crossing_points         |   0    | nonlinearity         |   0    | garch_r2         |   4.74    |
+| hw_gamma        |   0    | lumpiness         |   0    | diff2x_pacf5         |   0    | hurst         |   5.45    |
+| hw_beta         |   0    | diff1x_pacf5         |   0    | unitroot_kpss         |   0    | garch_acf         |   5.53    |
+| hw_alpha        |   0    | diff1_acf10         |   0    | x_pacf5         |   0    | entropy         |   11.65    |
+| trend           |   0    | arch_lm         |   0    | x_acf10         |   0    |
+| flat_spots      |   0    | diff1_acf1         |   0    | unitroot_pp         |   0    |
+| series_length   |   0    | stability         |   0    | arch_r2         |   1.37    |
+
+To replicate this results use:
+
+``` console
+python -m tsfeatures.compare_with_r --results_directory /some/path
+                                    --dataset_name Daily --num_obs 100
+```
+
+### Sesonal data (100 Hourly M4 time series)
+
+| feature           |   diff | feature      | diff | feature   | diff    | feature    | diff    |
+|:------------------|-------:|:-------------|-----:|:----------|--------:|:-----------|--------:|
+| series_length     |   0    |seas_acf1     | 0    | trend | 2.28 | hurst | 26.02 |
+| flat_spots        |   0    |x_acf1|0| arch_r2 | 2.29 | hw_beta | 32.39 |
+| nperiods          |   0    |unitroot_kpss|0| alpha | 2.52 | trough | 35 |
+| crossing_points   |   0    |nonlinearity|0| beta | 3.67 | peak | 69 |
+| seasonal_period   |   0    |diff1_acf10|0| linearity | 3.97 |
+| lumpiness         |   0    |x_acf10|0| curvature | 4.8 |
+| stability         |   0    |seas_pacf|0| e_acf10 | 7.05 |
+| arch_lm           |   0    |unitroot_pp|0| garch_r2 | 7.32 |
+| diff2_acf1        |   0    |spike|0| hw_gamma | 7.32 |
+| diff2_acf10       |   0    |seasonal_strength|0.79| hw_alpha | 7.47 |
+| diff1_acf1        |   0    |e_acf1|1.67| garch_acf | 7.53 |
+| diff2x_pacf5      |   0    |arch_acf|2.18| entropy | 9.45 |
+
+To replicate this results use:
+
+``` console
+python -m tsfeatures.compare_with_r --results_directory /some/path \
+                                    --dataset_name Hourly --num_obs 100
+```
+
+# Authors
+
+* **Federico Garza** - [FedericoGarza](https://github.com/FedericoGarza)
+* **Kin Gutierrez** - [kdgutier](https://github.com/kdgutier)
+* **Cristian Challu** - [cristianchallu](https://github.com/cristianchallu)
+* **Jose Moralez** - [jose-moralez](https://github.com/jose-moralez)
+* **Ricardo Olivares** - [rolivaresar](https://github.com/rolivaresar)
+* **Max Mergenthaler** - [mergenthaler](https://github.com/mergenthaler)
+
+
+%package -n python3-tsfeatures
+Summary:	Calculates various features from time series data.
+Provides:	python-tsfeatures
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-tsfeatures
+[![Build](https://github.com/FedericoGarza/tsfeatures/workflows/Python%20package/badge.svg)](https://github.com/FedericoGarza/tsfeatures/tree/master)
+[![PyPI version fury.io](https://badge.fury.io/py/tsfeatures.svg)](https://pypi.python.org/pypi/tsfeatures/)
+[![Downloads](https://pepy.tech/badge/tsfeatures)](https://pepy.tech/project/tsfeatures)
+[![Python 3.6+](https://img.shields.io/badge/python-3.7+-blue.svg)](https://www.python.org/downloads/release/python-370+/)
+[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://github.com/FedericoGarza/tsfeatures/blob/master/LICENSE)
+
+# tsfeatures
+
+Calculates various features from time series data. Python implementation of the R package _[tsfeatures](https://github.com/robjhyndman/tsfeatures)_.
+
+# Installation
+
+You can install the *released* version of `tsfeatures` from the [Python package index](pypi.org) with:
+
+``` python
+pip install tsfeatures
+```
+
+# Usage
+
+The `tsfeatures` main function calculates by default the features used by Montero-Manso, Talagala, Hyndman and Athanasopoulos in [their implementation of the FFORMA model](https://htmlpreview.github.io/?https://github.com/robjhyndman/M4metalearning/blob/master/docs/M4_methodology.html#features).
+
+```python
+from tsfeatures import tsfeatures
+```
+
+This function receives a panel pandas df with columns `unique_id`, `ds`, `y` and optionally the frequency of the data.
+
+<img src=https://raw.githubusercontent.com/FedericoGarza/tsfeatures/master/.github/images/y_train.png width="152">
+
+```python
+tsfeatures(panel, freq=7)
+```
+
+By default (`freq=None`) the function will try to infer the frequency of each time series (using `infer_freq` from `pandas` on the `ds` column) and assign a seasonal period according to the built-in dictionary `FREQS`:
+
+```python
+FREQS = {'H': 24, 'D': 1,
+         'M': 12, 'Q': 4,
+         'W':1, 'Y': 1}
+```
+
+You can use your own dictionary using the `dict_freqs` argument:
+
+```python
+tsfeatures(panel, dict_freqs={'D': 7, 'W': 52})
+```
+
+## List of available features
+
+| Features |||
+|:--------|:------|:-------------|
+|acf_features|heterogeneity|series_length|
+|arch_stat|holt_parameters|sparsity|
+|count_entropy|hurst|stability|
+|crossing_points|hw_parameters|stl_features|
+|entropy|intervals|unitroot_kpss|
+|flat_spots|lumpiness|unitroot_pp|
+|frequency|nonlinearity||
+|guerrero|pacf_features||
+
+See the docs for a description of the features. To use a particular feature included in the package you need to import it:
+
+```python
+from tsfeatures import acf_features
+
+tsfeatures(panel, freq=7, features=[acf_features])
+```
+
+You can also define your own function and use it together with the included features:
+
+```python
+def number_zeros(x, freq):
+
+    number = (x == 0).sum()
+    return {'number_zeros': number}
+
+tsfeatures(panel, freq=7, features=[acf_features, number_zeros])
+```
+
+`tsfeatures` can handle functions that receives a numpy array `x` and a frequency `freq` (this parameter is needed even if you don't use it) and returns a dictionary with the feature name as a key and its value.
+
+## R implementation
+
+You can use this package to call `tsfeatures` from R inside python (you need to have installed R, the packages `forecast` and `tsfeatures`; also the python package `rpy2`):
+
+```python
+from tsfeatures.tsfeatures_r import tsfeatures_r
+
+tsfeatures_r(panel, freq=7, features=["acf_features"])
+```
+
+Observe that this function receives a list of strings instead of a list of functions.
+
+## Comparison with the R implementation (sum of absolute differences)
+
+### Non-seasonal data (100 Daily M4 time series)
+
+| feature         |   diff | feature         |   diff | feature         |   diff | feature         |   diff |
+|:----------------|-------:|:----------------|-------:|:----------------|-------:|:----------------|-------:|
+| e_acf10         |   0    | e_acf1         |   0    | diff2_acf1         |   0    | alpha         |   3.2    |
+| seasonal_period |   0    | spike         |   0    | diff1_acf10         |   0    | arch_acf         |   3.3    |
+| nperiods        |   0    | curvature         |   0    | x_acf1         |   0    | beta         |   4.04    |
+| linearity       |   0    | crossing_points         |   0    | nonlinearity         |   0    | garch_r2         |   4.74    |
+| hw_gamma        |   0    | lumpiness         |   0    | diff2x_pacf5         |   0    | hurst         |   5.45    |
+| hw_beta         |   0    | diff1x_pacf5         |   0    | unitroot_kpss         |   0    | garch_acf         |   5.53    |
+| hw_alpha        |   0    | diff1_acf10         |   0    | x_pacf5         |   0    | entropy         |   11.65    |
+| trend           |   0    | arch_lm         |   0    | x_acf10         |   0    |
+| flat_spots      |   0    | diff1_acf1         |   0    | unitroot_pp         |   0    |
+| series_length   |   0    | stability         |   0    | arch_r2         |   1.37    |
+
+To replicate this results use:
+
+``` console
+python -m tsfeatures.compare_with_r --results_directory /some/path
+                                    --dataset_name Daily --num_obs 100
+```
+
+### Sesonal data (100 Hourly M4 time series)
+
+| feature           |   diff | feature      | diff | feature   | diff    | feature    | diff    |
+|:------------------|-------:|:-------------|-----:|:----------|--------:|:-----------|--------:|
+| series_length     |   0    |seas_acf1     | 0    | trend | 2.28 | hurst | 26.02 |
+| flat_spots        |   0    |x_acf1|0| arch_r2 | 2.29 | hw_beta | 32.39 |
+| nperiods          |   0    |unitroot_kpss|0| alpha | 2.52 | trough | 35 |
+| crossing_points   |   0    |nonlinearity|0| beta | 3.67 | peak | 69 |
+| seasonal_period   |   0    |diff1_acf10|0| linearity | 3.97 |
+| lumpiness         |   0    |x_acf10|0| curvature | 4.8 |
+| stability         |   0    |seas_pacf|0| e_acf10 | 7.05 |
+| arch_lm           |   0    |unitroot_pp|0| garch_r2 | 7.32 |
+| diff2_acf1        |   0    |spike|0| hw_gamma | 7.32 |
+| diff2_acf10       |   0    |seasonal_strength|0.79| hw_alpha | 7.47 |
+| diff1_acf1        |   0    |e_acf1|1.67| garch_acf | 7.53 |
+| diff2x_pacf5      |   0    |arch_acf|2.18| entropy | 9.45 |
+
+To replicate this results use:
+
+``` console
+python -m tsfeatures.compare_with_r --results_directory /some/path \
+                                    --dataset_name Hourly --num_obs 100
+```
+
+# Authors
+
+* **Federico Garza** - [FedericoGarza](https://github.com/FedericoGarza)
+* **Kin Gutierrez** - [kdgutier](https://github.com/kdgutier)
+* **Cristian Challu** - [cristianchallu](https://github.com/cristianchallu)
+* **Jose Moralez** - [jose-moralez](https://github.com/jose-moralez)
+* **Ricardo Olivares** - [rolivaresar](https://github.com/rolivaresar)
+* **Max Mergenthaler** - [mergenthaler](https://github.com/mergenthaler)
+
+
+%package help
+Summary:	Development documents and examples for tsfeatures
+Provides:	python3-tsfeatures-doc
+%description help
+[![Build](https://github.com/FedericoGarza/tsfeatures/workflows/Python%20package/badge.svg)](https://github.com/FedericoGarza/tsfeatures/tree/master)
+[![PyPI version fury.io](https://badge.fury.io/py/tsfeatures.svg)](https://pypi.python.org/pypi/tsfeatures/)
+[![Downloads](https://pepy.tech/badge/tsfeatures)](https://pepy.tech/project/tsfeatures)
+[![Python 3.6+](https://img.shields.io/badge/python-3.7+-blue.svg)](https://www.python.org/downloads/release/python-370+/)
+[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://github.com/FedericoGarza/tsfeatures/blob/master/LICENSE)
+
+# tsfeatures
+
+Calculates various features from time series data. Python implementation of the R package _[tsfeatures](https://github.com/robjhyndman/tsfeatures)_.
+
+# Installation
+
+You can install the *released* version of `tsfeatures` from the [Python package index](pypi.org) with:
+
+``` python
+pip install tsfeatures
+```
+
+# Usage
+
+The `tsfeatures` main function calculates by default the features used by Montero-Manso, Talagala, Hyndman and Athanasopoulos in [their implementation of the FFORMA model](https://htmlpreview.github.io/?https://github.com/robjhyndman/M4metalearning/blob/master/docs/M4_methodology.html#features).
+
+```python
+from tsfeatures import tsfeatures
+```
+
+This function receives a panel pandas df with columns `unique_id`, `ds`, `y` and optionally the frequency of the data.
+
+<img src=https://raw.githubusercontent.com/FedericoGarza/tsfeatures/master/.github/images/y_train.png width="152">
+
+```python
+tsfeatures(panel, freq=7)
+```
+
+By default (`freq=None`) the function will try to infer the frequency of each time series (using `infer_freq` from `pandas` on the `ds` column) and assign a seasonal period according to the built-in dictionary `FREQS`:
+
+```python
+FREQS = {'H': 24, 'D': 1,
+         'M': 12, 'Q': 4,
+         'W':1, 'Y': 1}
+```
+
+You can use your own dictionary using the `dict_freqs` argument:
+
+```python
+tsfeatures(panel, dict_freqs={'D': 7, 'W': 52})
+```
+
+## List of available features
+
+| Features |||
+|:--------|:------|:-------------|
+|acf_features|heterogeneity|series_length|
+|arch_stat|holt_parameters|sparsity|
+|count_entropy|hurst|stability|
+|crossing_points|hw_parameters|stl_features|
+|entropy|intervals|unitroot_kpss|
+|flat_spots|lumpiness|unitroot_pp|
+|frequency|nonlinearity||
+|guerrero|pacf_features||
+
+See the docs for a description of the features. To use a particular feature included in the package you need to import it:
+
+```python
+from tsfeatures import acf_features
+
+tsfeatures(panel, freq=7, features=[acf_features])
+```
+
+You can also define your own function and use it together with the included features:
+
+```python
+def number_zeros(x, freq):
+
+    number = (x == 0).sum()
+    return {'number_zeros': number}
+
+tsfeatures(panel, freq=7, features=[acf_features, number_zeros])
+```
+
+`tsfeatures` can handle functions that receives a numpy array `x` and a frequency `freq` (this parameter is needed even if you don't use it) and returns a dictionary with the feature name as a key and its value.
+
+## R implementation
+
+You can use this package to call `tsfeatures` from R inside python (you need to have installed R, the packages `forecast` and `tsfeatures`; also the python package `rpy2`):
+
+```python
+from tsfeatures.tsfeatures_r import tsfeatures_r
+
+tsfeatures_r(panel, freq=7, features=["acf_features"])
+```
+
+Observe that this function receives a list of strings instead of a list of functions.
+
+## Comparison with the R implementation (sum of absolute differences)
+
+### Non-seasonal data (100 Daily M4 time series)
+
+| feature         |   diff | feature         |   diff | feature         |   diff | feature         |   diff |
+|:----------------|-------:|:----------------|-------:|:----------------|-------:|:----------------|-------:|
+| e_acf10         |   0    | e_acf1         |   0    | diff2_acf1         |   0    | alpha         |   3.2    |
+| seasonal_period |   0    | spike         |   0    | diff1_acf10         |   0    | arch_acf         |   3.3    |
+| nperiods        |   0    | curvature         |   0    | x_acf1         |   0    | beta         |   4.04    |
+| linearity       |   0    | crossing_points         |   0    | nonlinearity         |   0    | garch_r2         |   4.74    |
+| hw_gamma        |   0    | lumpiness         |   0    | diff2x_pacf5         |   0    | hurst         |   5.45    |
+| hw_beta         |   0    | diff1x_pacf5         |   0    | unitroot_kpss         |   0    | garch_acf         |   5.53    |
+| hw_alpha        |   0    | diff1_acf10         |   0    | x_pacf5         |   0    | entropy         |   11.65    |
+| trend           |   0    | arch_lm         |   0    | x_acf10         |   0    |
+| flat_spots      |   0    | diff1_acf1         |   0    | unitroot_pp         |   0    |
+| series_length   |   0    | stability         |   0    | arch_r2         |   1.37    |
+
+To replicate this results use:
+
+``` console
+python -m tsfeatures.compare_with_r --results_directory /some/path
+                                    --dataset_name Daily --num_obs 100
+```
+
+### Sesonal data (100 Hourly M4 time series)
+
+| feature           |   diff | feature      | diff | feature   | diff    | feature    | diff    |
+|:------------------|-------:|:-------------|-----:|:----------|--------:|:-----------|--------:|
+| series_length     |   0    |seas_acf1     | 0    | trend | 2.28 | hurst | 26.02 |
+| flat_spots        |   0    |x_acf1|0| arch_r2 | 2.29 | hw_beta | 32.39 |
+| nperiods          |   0    |unitroot_kpss|0| alpha | 2.52 | trough | 35 |
+| crossing_points   |   0    |nonlinearity|0| beta | 3.67 | peak | 69 |
+| seasonal_period   |   0    |diff1_acf10|0| linearity | 3.97 |
+| lumpiness         |   0    |x_acf10|0| curvature | 4.8 |
+| stability         |   0    |seas_pacf|0| e_acf10 | 7.05 |
+| arch_lm           |   0    |unitroot_pp|0| garch_r2 | 7.32 |
+| diff2_acf1        |   0    |spike|0| hw_gamma | 7.32 |
+| diff2_acf10       |   0    |seasonal_strength|0.79| hw_alpha | 7.47 |
+| diff1_acf1        |   0    |e_acf1|1.67| garch_acf | 7.53 |
+| diff2x_pacf5      |   0    |arch_acf|2.18| entropy | 9.45 |
+
+To replicate this results use:
+
+``` console
+python -m tsfeatures.compare_with_r --results_directory /some/path \
+                                    --dataset_name Hourly --num_obs 100
+```
+
+# Authors
+
+* **Federico Garza** - [FedericoGarza](https://github.com/FedericoGarza)
+* **Kin Gutierrez** - [kdgutier](https://github.com/kdgutier)
+* **Cristian Challu** - [cristianchallu](https://github.com/cristianchallu)
+* **Jose Moralez** - [jose-moralez](https://github.com/jose-moralez)
+* **Ricardo Olivares** - [rolivaresar](https://github.com/rolivaresar)
+* **Max Mergenthaler** - [mergenthaler](https://github.com/mergenthaler)
+
+
+%prep
+%autosetup -n tsfeatures-0.4.0
+
+%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-tsfeatures -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Wed May 31 2023 Python_Bot <Python_Bot@openeuler.org> - 0.4.0-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..64aaa7c
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+ea592217439d4cc18be9761e4b02453e  tsfeatures-0.4.0.tar.gz