automatic import of python-tsmoothie

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diff --git a/.gitignore b/.gitignore
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+/tsmoothie-1.0.4.tar.gz
diff --git a/python-tsmoothie.spec b/python-tsmoothie.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-tsmoothie
+Version:	1.0.4
+Release:	1
+Summary:	A python library for timeseries smoothing and outlier detection in a vectorized way.
+License:	MIT
+URL:		https://github.com/cerlymarco/tsmoothie
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/c2/e9/1e7e4df17b1b6d79bfb6f53dfe69866b99179973f0a325348c43c8159fb2/tsmoothie-1.0.4.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-numpy
+Requires:	python3-scipy
+Requires:	python3-simdkalman
+
+%description
+# tsmoothie
+
+A python library for time-series smoothing and outlier detection in a vectorized way.
+
+## Overview
+
+tsmoothie computes, in a fast and efficient way, the smoothing of single or multiple time-series. 
+
+The smoothing techniques available are:
+
+- Exponential Smoothing
+- Convolutional Smoothing with various window types (constant, hanning, hamming, bartlett, blackman)
+- Spectral Smoothing with Fourier Transform
+- Polynomial Smoothing 
+- Spline Smoothing of various kind (linear, cubic, natural cubic) 
+- Gaussian Smoothing 
+- Binner Smoothing 
+- LOWESS 
+- Seasonal Decompose Smoothing of various kind (convolution, lowess, natural cubic spline)
+- Kalman Smoothing with customizable components (level, trend, seasonality, long seasonality) 
+
+tsmoothie provides the calculation of intervals as result of the smoothing process. This can be useful to identify outliers and anomalies in time-series.
+
+In relation to the smoothing method used, the interval types available are:
+
+- sigma intervals
+- confidence intervals
+- predictions intervals
+- kalman intervals
+
+tsmoothie can carry out a sliding smoothing approach to simulate an online usage. This is possible splitting the time-series into equal sized pieces and smoothing them independently. As always, this functionality is implemented in a vectorized way through the **WindowWrapper** class.
+
+tsmoothie can operate time-series bootstrap through the **BootstrappingWrapper** class.
+
+The supported bootstrap algorithms are:
+
+- none overlapping block bootstrap
+- moving block bootstrap
+- circular block bootstrap
+- stationary bootstrap
+
+## Media
+
+Blog Posts:
+
+- [Time Series Smoothing for better Clustering](https://towardsdatascience.com/time-series-smoothing-for-better-clustering-121b98f308e8)
+- [Time Series Smoothing for better Forecasting](https://towardsdatascience.com/time-series-smoothing-for-better-forecasting-7fbf10428b2)
+- [Real-Time Time Series Anomaly Detection](https://towardsdatascience.com/real-time-time-series-anomaly-detection-981cf1e1ca13)
+- [Extreme Event Time Series Preprocessing](https://towardsdatascience.com/extreme-event-time-series-preprocessing-90aa59d5630c)
+- [Time Series Bootstrap in the age of Deep Learning](https://towardsdatascience.com/time-series-bootstrap-in-the-age-of-deep-learning-b98aa2aa32c4)
+
+## Installation
+
+```shell
+pip install tsmoothie
+```
+
+The module depends only on NumPy, SciPy and simdkalman. Python 3.6 or above is supported.
+
+## Usage: _smoothing_
+
+Below a couple of examples of how tsmoothie works. Full examples are available in the [notebooks folder](https://github.com/cerlymarco/tsmoothie/tree/master/notebooks).
+
+```python
+# import libraries
+import numpy as np
+import matplotlib.pyplot as plt
+from tsmoothie.utils_func import sim_randomwalk
+from tsmoothie.smoother import LowessSmoother
+
+# generate 3 randomwalks of lenght 200
+np.random.seed(123)
+data = sim_randomwalk(n_series=3, timesteps=200, 
+                      process_noise=10, measure_noise=30)
+
+# operate smoothing
+smoother = LowessSmoother(smooth_fraction=0.1, iterations=1)
+smoother.smooth(data)
+
+# generate intervals
+low, up = smoother.get_intervals('prediction_interval')
+
+# plot the smoothed timeseries with intervals
+plt.figure(figsize=(18,5))
+
+for i in range(3):
+
+    plt.subplot(1,3,i+1)
+    plt.plot(smoother.smooth_data[i], linewidth=3, color='blue')
+    plt.plot(smoother.data[i], '.k')
+    plt.title(f"timeseries {i+1}"); plt.xlabel('time')
+
+    plt.fill_between(range(len(smoother.data[i])), low[i], up[i], alpha=0.3)
+```
+
+![Randomwalk Smoothing](https://raw.githubusercontent.com/cerlymarco/tsmoothie/master/imgs/randomwalk_smoothing.png)
+
+```python
+# import libraries
+import numpy as np
+import matplotlib.pyplot as plt
+from tsmoothie.utils_func import sim_seasonal_data
+from tsmoothie.smoother import DecomposeSmoother
+
+# generate 3 periodic timeseries of lenght 300
+np.random.seed(123)
+data = sim_seasonal_data(n_series=3, timesteps=300, 
+                         freq=24, measure_noise=30)
+
+# operate smoothing
+smoother = DecomposeSmoother(smooth_type='lowess', periods=24,
+                             smooth_fraction=0.3)
+smoother.smooth(data)
+
+# generate intervals
+low, up = smoother.get_intervals('sigma_interval')
+
+# plot the smoothed timeseries with intervals
+plt.figure(figsize=(18,5))
+
+for i in range(3):
+
+    plt.subplot(1,3,i+1)
+    plt.plot(smoother.smooth_data[i], linewidth=3, color='blue')
+    plt.plot(smoother.data[i], '.k')
+    plt.title(f"timeseries {i+1}"); plt.xlabel('time')
+
+    plt.fill_between(range(len(smoother.data[i])), low[i], up[i], alpha=0.3)
+```
+
+![Sinusoidal Smoothing](https://raw.githubusercontent.com/cerlymarco/tsmoothie/master/imgs/sinusoidal_smoothing.png)
+
+## Usage: _bootstrap_
+
+```python
+# import libraries
+import numpy as np
+import matplotlib.pyplot as plt
+from tsmoothie.utils_func import sim_seasonal_data
+from tsmoothie.smoother import ConvolutionSmoother
+from tsmoothie.bootstrap import BootstrappingWrapper
+
+# generate a periodic timeseries of lenght 300
+np.random.seed(123)
+data = sim_seasonal_data(n_series=1, timesteps=300, 
+                         freq=24, measure_noise=15)
+
+# operate bootstrap
+bts = BootstrappingWrapper(ConvolutionSmoother(window_len=8, window_type='ones'), 
+                           bootstrap_type='mbb', block_length=24)
+bts_samples = bts.sample(data, n_samples=100)
+
+# plot the bootstrapped timeseries
+plt.figure(figsize=(13,5))
+plt.plot(bts_samples.T, alpha=0.3, c='orange')
+plt.plot(data[0], c='blue', linewidth=2)
+```
+
+![Sinusoidal Bootstrap](https://raw.githubusercontent.com/cerlymarco/tsmoothie/master/imgs/sinusoidal_bootstrap.png)
+
+## References
+
+- Polynomial, Spline, Gaussian and Binner smoothing are carried out building a regression on custom basis expansions. These implementations are based on the amazing intuitions of Matthew Drury available [here](https://github.com/madrury/basis-expansions/blob/master/examples/comparison-of-smoothing-methods.ipynb)
+- Time Series Modelling with Unobserved Components, Matteo M. Pelagatti
+- Bootstrap Methods in Time Series Analysis, Fanny Bergström, Stockholms universitet
+
+
+
+
+%package -n python3-tsmoothie
+Summary:	A python library for timeseries smoothing and outlier detection in a vectorized way.
+Provides:	python-tsmoothie
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-tsmoothie
+# tsmoothie
+
+A python library for time-series smoothing and outlier detection in a vectorized way.
+
+## Overview
+
+tsmoothie computes, in a fast and efficient way, the smoothing of single or multiple time-series. 
+
+The smoothing techniques available are:
+
+- Exponential Smoothing
+- Convolutional Smoothing with various window types (constant, hanning, hamming, bartlett, blackman)
+- Spectral Smoothing with Fourier Transform
+- Polynomial Smoothing 
+- Spline Smoothing of various kind (linear, cubic, natural cubic) 
+- Gaussian Smoothing 
+- Binner Smoothing 
+- LOWESS 
+- Seasonal Decompose Smoothing of various kind (convolution, lowess, natural cubic spline)
+- Kalman Smoothing with customizable components (level, trend, seasonality, long seasonality) 
+
+tsmoothie provides the calculation of intervals as result of the smoothing process. This can be useful to identify outliers and anomalies in time-series.
+
+In relation to the smoothing method used, the interval types available are:
+
+- sigma intervals
+- confidence intervals
+- predictions intervals
+- kalman intervals
+
+tsmoothie can carry out a sliding smoothing approach to simulate an online usage. This is possible splitting the time-series into equal sized pieces and smoothing them independently. As always, this functionality is implemented in a vectorized way through the **WindowWrapper** class.
+
+tsmoothie can operate time-series bootstrap through the **BootstrappingWrapper** class.
+
+The supported bootstrap algorithms are:
+
+- none overlapping block bootstrap
+- moving block bootstrap
+- circular block bootstrap
+- stationary bootstrap
+
+## Media
+
+Blog Posts:
+
+- [Time Series Smoothing for better Clustering](https://towardsdatascience.com/time-series-smoothing-for-better-clustering-121b98f308e8)
+- [Time Series Smoothing for better Forecasting](https://towardsdatascience.com/time-series-smoothing-for-better-forecasting-7fbf10428b2)
+- [Real-Time Time Series Anomaly Detection](https://towardsdatascience.com/real-time-time-series-anomaly-detection-981cf1e1ca13)
+- [Extreme Event Time Series Preprocessing](https://towardsdatascience.com/extreme-event-time-series-preprocessing-90aa59d5630c)
+- [Time Series Bootstrap in the age of Deep Learning](https://towardsdatascience.com/time-series-bootstrap-in-the-age-of-deep-learning-b98aa2aa32c4)
+
+## Installation
+
+```shell
+pip install tsmoothie
+```
+
+The module depends only on NumPy, SciPy and simdkalman. Python 3.6 or above is supported.
+
+## Usage: _smoothing_
+
+Below a couple of examples of how tsmoothie works. Full examples are available in the [notebooks folder](https://github.com/cerlymarco/tsmoothie/tree/master/notebooks).
+
+```python
+# import libraries
+import numpy as np
+import matplotlib.pyplot as plt
+from tsmoothie.utils_func import sim_randomwalk
+from tsmoothie.smoother import LowessSmoother
+
+# generate 3 randomwalks of lenght 200
+np.random.seed(123)
+data = sim_randomwalk(n_series=3, timesteps=200, 
+                      process_noise=10, measure_noise=30)
+
+# operate smoothing
+smoother = LowessSmoother(smooth_fraction=0.1, iterations=1)
+smoother.smooth(data)
+
+# generate intervals
+low, up = smoother.get_intervals('prediction_interval')
+
+# plot the smoothed timeseries with intervals
+plt.figure(figsize=(18,5))
+
+for i in range(3):
+
+    plt.subplot(1,3,i+1)
+    plt.plot(smoother.smooth_data[i], linewidth=3, color='blue')
+    plt.plot(smoother.data[i], '.k')
+    plt.title(f"timeseries {i+1}"); plt.xlabel('time')
+
+    plt.fill_between(range(len(smoother.data[i])), low[i], up[i], alpha=0.3)
+```
+
+![Randomwalk Smoothing](https://raw.githubusercontent.com/cerlymarco/tsmoothie/master/imgs/randomwalk_smoothing.png)
+
+```python
+# import libraries
+import numpy as np
+import matplotlib.pyplot as plt
+from tsmoothie.utils_func import sim_seasonal_data
+from tsmoothie.smoother import DecomposeSmoother
+
+# generate 3 periodic timeseries of lenght 300
+np.random.seed(123)
+data = sim_seasonal_data(n_series=3, timesteps=300, 
+                         freq=24, measure_noise=30)
+
+# operate smoothing
+smoother = DecomposeSmoother(smooth_type='lowess', periods=24,
+                             smooth_fraction=0.3)
+smoother.smooth(data)
+
+# generate intervals
+low, up = smoother.get_intervals('sigma_interval')
+
+# plot the smoothed timeseries with intervals
+plt.figure(figsize=(18,5))
+
+for i in range(3):
+
+    plt.subplot(1,3,i+1)
+    plt.plot(smoother.smooth_data[i], linewidth=3, color='blue')
+    plt.plot(smoother.data[i], '.k')
+    plt.title(f"timeseries {i+1}"); plt.xlabel('time')
+
+    plt.fill_between(range(len(smoother.data[i])), low[i], up[i], alpha=0.3)
+```
+
+![Sinusoidal Smoothing](https://raw.githubusercontent.com/cerlymarco/tsmoothie/master/imgs/sinusoidal_smoothing.png)
+
+## Usage: _bootstrap_
+
+```python
+# import libraries
+import numpy as np
+import matplotlib.pyplot as plt
+from tsmoothie.utils_func import sim_seasonal_data
+from tsmoothie.smoother import ConvolutionSmoother
+from tsmoothie.bootstrap import BootstrappingWrapper
+
+# generate a periodic timeseries of lenght 300
+np.random.seed(123)
+data = sim_seasonal_data(n_series=1, timesteps=300, 
+                         freq=24, measure_noise=15)
+
+# operate bootstrap
+bts = BootstrappingWrapper(ConvolutionSmoother(window_len=8, window_type='ones'), 
+                           bootstrap_type='mbb', block_length=24)
+bts_samples = bts.sample(data, n_samples=100)
+
+# plot the bootstrapped timeseries
+plt.figure(figsize=(13,5))
+plt.plot(bts_samples.T, alpha=0.3, c='orange')
+plt.plot(data[0], c='blue', linewidth=2)
+```
+
+![Sinusoidal Bootstrap](https://raw.githubusercontent.com/cerlymarco/tsmoothie/master/imgs/sinusoidal_bootstrap.png)
+
+## References
+
+- Polynomial, Spline, Gaussian and Binner smoothing are carried out building a regression on custom basis expansions. These implementations are based on the amazing intuitions of Matthew Drury available [here](https://github.com/madrury/basis-expansions/blob/master/examples/comparison-of-smoothing-methods.ipynb)
+- Time Series Modelling with Unobserved Components, Matteo M. Pelagatti
+- Bootstrap Methods in Time Series Analysis, Fanny Bergström, Stockholms universitet
+
+
+
+
+%package help
+Summary:	Development documents and examples for tsmoothie
+Provides:	python3-tsmoothie-doc
+%description help
+# tsmoothie
+
+A python library for time-series smoothing and outlier detection in a vectorized way.
+
+## Overview
+
+tsmoothie computes, in a fast and efficient way, the smoothing of single or multiple time-series. 
+
+The smoothing techniques available are:
+
+- Exponential Smoothing
+- Convolutional Smoothing with various window types (constant, hanning, hamming, bartlett, blackman)
+- Spectral Smoothing with Fourier Transform
+- Polynomial Smoothing 
+- Spline Smoothing of various kind (linear, cubic, natural cubic) 
+- Gaussian Smoothing 
+- Binner Smoothing 
+- LOWESS 
+- Seasonal Decompose Smoothing of various kind (convolution, lowess, natural cubic spline)
+- Kalman Smoothing with customizable components (level, trend, seasonality, long seasonality) 
+
+tsmoothie provides the calculation of intervals as result of the smoothing process. This can be useful to identify outliers and anomalies in time-series.
+
+In relation to the smoothing method used, the interval types available are:
+
+- sigma intervals
+- confidence intervals
+- predictions intervals
+- kalman intervals
+
+tsmoothie can carry out a sliding smoothing approach to simulate an online usage. This is possible splitting the time-series into equal sized pieces and smoothing them independently. As always, this functionality is implemented in a vectorized way through the **WindowWrapper** class.
+
+tsmoothie can operate time-series bootstrap through the **BootstrappingWrapper** class.
+
+The supported bootstrap algorithms are:
+
+- none overlapping block bootstrap
+- moving block bootstrap
+- circular block bootstrap
+- stationary bootstrap
+
+## Media
+
+Blog Posts:
+
+- [Time Series Smoothing for better Clustering](https://towardsdatascience.com/time-series-smoothing-for-better-clustering-121b98f308e8)
+- [Time Series Smoothing for better Forecasting](https://towardsdatascience.com/time-series-smoothing-for-better-forecasting-7fbf10428b2)
+- [Real-Time Time Series Anomaly Detection](https://towardsdatascience.com/real-time-time-series-anomaly-detection-981cf1e1ca13)
+- [Extreme Event Time Series Preprocessing](https://towardsdatascience.com/extreme-event-time-series-preprocessing-90aa59d5630c)
+- [Time Series Bootstrap in the age of Deep Learning](https://towardsdatascience.com/time-series-bootstrap-in-the-age-of-deep-learning-b98aa2aa32c4)
+
+## Installation
+
+```shell
+pip install tsmoothie
+```
+
+The module depends only on NumPy, SciPy and simdkalman. Python 3.6 or above is supported.
+
+## Usage: _smoothing_
+
+Below a couple of examples of how tsmoothie works. Full examples are available in the [notebooks folder](https://github.com/cerlymarco/tsmoothie/tree/master/notebooks).
+
+```python
+# import libraries
+import numpy as np
+import matplotlib.pyplot as plt
+from tsmoothie.utils_func import sim_randomwalk
+from tsmoothie.smoother import LowessSmoother
+
+# generate 3 randomwalks of lenght 200
+np.random.seed(123)
+data = sim_randomwalk(n_series=3, timesteps=200, 
+                      process_noise=10, measure_noise=30)
+
+# operate smoothing
+smoother = LowessSmoother(smooth_fraction=0.1, iterations=1)
+smoother.smooth(data)
+
+# generate intervals
+low, up = smoother.get_intervals('prediction_interval')
+
+# plot the smoothed timeseries with intervals
+plt.figure(figsize=(18,5))
+
+for i in range(3):
+
+    plt.subplot(1,3,i+1)
+    plt.plot(smoother.smooth_data[i], linewidth=3, color='blue')
+    plt.plot(smoother.data[i], '.k')
+    plt.title(f"timeseries {i+1}"); plt.xlabel('time')
+
+    plt.fill_between(range(len(smoother.data[i])), low[i], up[i], alpha=0.3)
+```
+
+![Randomwalk Smoothing](https://raw.githubusercontent.com/cerlymarco/tsmoothie/master/imgs/randomwalk_smoothing.png)
+
+```python
+# import libraries
+import numpy as np
+import matplotlib.pyplot as plt
+from tsmoothie.utils_func import sim_seasonal_data
+from tsmoothie.smoother import DecomposeSmoother
+
+# generate 3 periodic timeseries of lenght 300
+np.random.seed(123)
+data = sim_seasonal_data(n_series=3, timesteps=300, 
+                         freq=24, measure_noise=30)
+
+# operate smoothing
+smoother = DecomposeSmoother(smooth_type='lowess', periods=24,
+                             smooth_fraction=0.3)
+smoother.smooth(data)
+
+# generate intervals
+low, up = smoother.get_intervals('sigma_interval')
+
+# plot the smoothed timeseries with intervals
+plt.figure(figsize=(18,5))
+
+for i in range(3):
+
+    plt.subplot(1,3,i+1)
+    plt.plot(smoother.smooth_data[i], linewidth=3, color='blue')
+    plt.plot(smoother.data[i], '.k')
+    plt.title(f"timeseries {i+1}"); plt.xlabel('time')
+
+    plt.fill_between(range(len(smoother.data[i])), low[i], up[i], alpha=0.3)
+```
+
+![Sinusoidal Smoothing](https://raw.githubusercontent.com/cerlymarco/tsmoothie/master/imgs/sinusoidal_smoothing.png)
+
+## Usage: _bootstrap_
+
+```python
+# import libraries
+import numpy as np
+import matplotlib.pyplot as plt
+from tsmoothie.utils_func import sim_seasonal_data
+from tsmoothie.smoother import ConvolutionSmoother
+from tsmoothie.bootstrap import BootstrappingWrapper
+
+# generate a periodic timeseries of lenght 300
+np.random.seed(123)
+data = sim_seasonal_data(n_series=1, timesteps=300, 
+                         freq=24, measure_noise=15)
+
+# operate bootstrap
+bts = BootstrappingWrapper(ConvolutionSmoother(window_len=8, window_type='ones'), 
+                           bootstrap_type='mbb', block_length=24)
+bts_samples = bts.sample(data, n_samples=100)
+
+# plot the bootstrapped timeseries
+plt.figure(figsize=(13,5))
+plt.plot(bts_samples.T, alpha=0.3, c='orange')
+plt.plot(data[0], c='blue', linewidth=2)
+```
+
+![Sinusoidal Bootstrap](https://raw.githubusercontent.com/cerlymarco/tsmoothie/master/imgs/sinusoidal_bootstrap.png)
+
+## References
+
+- Polynomial, Spline, Gaussian and Binner smoothing are carried out building a regression on custom basis expansions. These implementations are based on the amazing intuitions of Matthew Drury available [here](https://github.com/madrury/basis-expansions/blob/master/examples/comparison-of-smoothing-methods.ipynb)
+- Time Series Modelling with Unobserved Components, Matteo M. Pelagatti
+- Bootstrap Methods in Time Series Analysis, Fanny Bergström, Stockholms universitet
+
+
+
+
+%prep
+%autosetup -n tsmoothie-1.0.4
+
+%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-tsmoothie -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Tue Apr 11 2023 Python_Bot <Python_Bot@openeuler.org> - 1.0.4-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..7d95122
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+b199562daadefaaba94412ad24a1b22b  tsmoothie-1.0.4.tar.gz