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+/pymannkendall-1.4.3.tar.gz
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+%global _empty_manifest_terminate_build 0
+Name:		python-pymannkendall
+Version:	1.4.3
+Release:	1
+Summary:	A python package for non-parametric Mann-Kendall family of trend tests.
+License:	MIT
+URL:		https://github.com/mmhs013/pymannkendall
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/7e/bb/c07714c8ba1e662de4482fa69d64f419f19cc3b9ea49f8903dc83235f7a3/pymannkendall-1.4.3.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-numpy
+Requires:	python3-scipy
+
+%description
+# pyMannKendall
+[![Build Status](https://travis-ci.com/mmhs013/pyMannKendall.svg?branch=master)](https://travis-ci.com/mmhs013/pyMannKendall)
+[![PyPI](https://img.shields.io/pypi/v/pymannkendall.svg)](https://pypi.org/project/pymannkendall/)
+[![PyPI - License](https://img.shields.io/pypi/l/pymannkendall.svg)](https://pypi.org/project/pymannkendall/)
+[![PyPI - Status](https://img.shields.io/pypi/status/pymannkendall.svg)](https://pypi.org/project/pymannkendall/)
+[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/pymannkendall.svg)](https://pypi.org/project/pymannkendall/)
+
+[![Downloads](https://pepy.tech/badge/pymannkendall)](https://pepy.tech/project/pymannkendall)
+[![Conda](https://img.shields.io/conda/dn/conda-forge/pymannkendall?label=conda-downloads)](https://anaconda.org/conda-forge/pymannkendall)
+
+[![Google Scholar](https://github.com/mmhs013/Citation_Parser/blob/main/images/gs_pymk_cite.svg?raw=true)](https://scholar.google.com/scholar?q=pyMannKendall%3A+a+python+package+for+non+parametric+Mann+Kendall+family+of+trend+tests.)
+[![Researchgate](https://github.com/mmhs013/Citation_Parser/blob/main/images/rg_pymk_cite.svg?raw=true)](https://www.researchgate.net/publication/334688255_pyMannKendall_a_python_package_for_non_parametric_Mann_Kendall_family_of_trend_tests)
+
+[![status](http://joss.theoj.org/papers/14903dbd55343be89105112e585d262a/status.svg)](http://joss.theoj.org/papers/14903dbd55343be89105112e585d262a)
+[![DOI](https://zenodo.org/badge/174495388.svg)](https://zenodo.org/badge/latestdoi/174495388)
+
+## What is the Mann-Kendall Test ?
+The Mann-Kendall Trend Test (sometimes called the MK test) is used to analyze time series data for consistently increasing or decreasing trends (monotonic trends). It is a non-parametric test, which means it works for all distributions (i.e. data doesn't have to meet the assumption of normality), but data should have no serial correlation. If the data has a serial correlation, it could affect in significant level (p-value). It could lead to misinterpretation. To overcome this problem, researchers proposed several modified Mann-Kendall tests (Hamed and Rao Modified MK Test, Yue and Wang Modified MK Test, Modified MK test using Pre-Whitening method, etc.). Seasonal Mann-Kendall test also developed to remove the effect of seasonality.
+
+Mann-Kendall Test is a powerful trend test, so several others modified Mann-Kendall tests like Multivariate MK Test, Regional MK Test, Correlated MK test, Partial MK Test, etc. were developed for the spacial condition. `pyMannkendal` is a pure Python implementation of non-parametric Mann-Kendall trend analysis, which bring together almost all types of Mann-Kendall Test. Currently, this package has 11 Mann-Kendall Tests and 2 sen's slope estimator function. Brief description of functions are below:
+
+1.	**Original Mann-Kendall test (*original_test*):** Original Mann-Kendall test is a nonparametric test, which does not consider serial correlation or seasonal effects.
+
+2.	**Hamed and Rao Modified MK Test (*hamed_rao_modification_test*):** This modified MK test proposed by *Hamed and Rao (1998)* to address serial autocorrelation issues. They suggested a variance correction approach to improve trend analysis. User can consider first n significant lag by insert lag number in this function. By default, it considered all significant lags.
+
+3.	**Yue and Wang Modified MK Test (*yue_wang_modification_test*):** This is also a variance correction method for considered serial autocorrelation proposed by *Yue, S., & Wang, C. Y. (2004)*. User can also set their desired significant n lags for the calculation.
+
+4.	**Modified MK test using Pre-Whitening method (*pre_whitening_modification_test*):** This test suggested by *Yue and Wang (2002)* to using Pre-Whitening the time series before the application of trend test.
+
+5.	**Modified MK test using Trend free Pre-Whitening method (*trend_free_pre_whitening_modification_test*):** This test also proposed by *Yue and Wang (2002)* to remove trend component and then Pre-Whitening the time series before application of trend test.
+
+6.	**Multivariate MK Test (*multivariate_test*):** This is an MK test for multiple parameters proposed by *Hirsch (1982)*. He used this method for seasonal mk test, where he considered every month as a parameter.
+
+7.	**Seasonal MK Test (*seasonal_test*):** For seasonal time series data, *Hirsch, R.M., Slack, J.R. and Smith, R.A. (1982)* proposed this test to calculate the seasonal trend.
+
+8.	**Regional MK Test (*regional_test*):** Based on*Hirsch (1982)* proposed seasonal mk test, *Helsel, D.R. and Frans, L.M., (2006)* suggest regional mk test to calculate the overall trend in a regional scale.
+
+9.	**Correlated Multivariate MK Test (*correlated_multivariate_test*):** This multivariate mk test proposed by *Hipel (1994)* where the parameters are correlated.
+
+10.	**Correlated Seasonal MK Test (*correlated_seasonal_test*):** This method proposed by *Hipel (1994)* used, when time series significantly correlated with the preceding one or more months/seasons.
+
+11.	**Partial MK Test (*partial_test*):** In a real event, many factors are affecting the main studied response parameter, which can bias the trend results. To overcome this problem, *Libiseller (2002)* proposed this partial mk test. It required two parameters as input, where, one is response parameter and other is an independent parameter.
+
+12.	**Theil-Sen's Slope Estimator (*sens_slope*):** This method proposed by *Theil (1950)* and *Sen (1968)* to estimate the magnitude of the monotonic trend. Intercept is calculate using *Conover, W.J. (1980)* method.
+
+13.	**Seasonal Theil-Sen's Slope Estimator (*seasonal_sens_slope*):** This method proposed by *Hipel (1994)* to estimate the magnitude of the monotonic trend, when data has seasonal effects. Intercept is calculate using *Conover, W.J. (1980)* method.
+
+## Function details:
+
+All Mann-Kendall test functions have almost similar input parameters. Those are:
+
+- **x**:   a vector (list, numpy array or pandas series) data
+- **alpha**: significance level (0.05 is the default)
+- **lag**: No. of First Significant Lags (Only available in hamed_rao_modification_test and yue_wang_modification_test)
+- **period**: seasonal cycle. For monthly data it is 12, weekly data it is 52 (Only available in seasonal tests)
+
+And all Mann-Kendall tests return a named tuple which contained:
+
+- **trend**: tells the trend (increasing, decreasing or no trend)
+- **h**: True (if trend is present) or False (if the trend is absence)
+- **p**: p-value of the significance test
+- **z**: normalized test statistics
+- **Tau**: Kendall Tau
+- **s**: Mann-Kendal's score
+- **var_s**: Variance S
+- **slope**: Theil-Sen estimator/slope
+- **intercept**: intercept of Kendall-Theil Robust Line, for seasonal test, full period cycle consider as unit time step
+
+sen's slope function required data vector. seasonal sen's slope also has optional input period, which by the default value is 12. Both sen's slope function return only slope value.
+
+## Dependencies
+
+For the installation of `pyMannKendall`, the following packages are required:
+- [numpy](https://www.numpy.org/)
+- [scipy](https://www.scipy.org/)
+
+## Installation
+
+You can install `pyMannKendall` using pip. For Linux users
+
+```python
+sudo pip install pymannkendall
+```
+
+or, for Windows user
+
+```python
+pip install pymannkendall
+```
+
+or, you can use conda
+```python
+conda install -c conda-forge pymannkendall
+```
+
+or you can clone the repo and install it:
+
+```bash
+git clone https://github.com/mmhs013/pymannkendall
+cd pymannkendall
+python setup.py install
+```
+
+## Tests
+
+`pyMannKendall` is automatically tested using `pytest` package on each commit [here](https://travis-ci.org/mmhs013/pyMannKendall/), but the tests can be manually run:
+
+```
+pytest -v
+```
+
+## Usage
+
+A quick example of `pyMannKendall` usage is given below. Several more examples are provided [here](https://github.com/mmhs013/pyMannKendall/blob/master/Examples/Example_pyMannKendall.ipynb).
+
+```python
+import numpy as np
+import pymannkendall as mk
+
+# Data generation for analysis
+data = np.random.rand(360,1)
+
+result = mk.original_test(data)
+print(result)
+```
+Output are like this:
+```python
+Mann_Kendall_Test(trend='no trend', h=False, p=0.9507221701045581, z=0.06179991635055463, Tau=0.0021974620860414733, s=142.0, var_s=5205500.0, slope=1.0353584906597959e-05, intercept=0.5232692553379981)
+```
+Whereas, the output is a named tuple, so you can call by name for specific result:
+```python
+print(result.slope)
+```
+or, you can directly unpack your results like this:
+```python
+trend, h, p, z, Tau, s, var_s, slope, intercept = mk.original_test(data)
+```
+
+## Citation
+
+[![Google Scholar](https://github.com/mmhs013/Citation_Parser/blob/main/images/gs_pymk_cite.svg?raw=true)](https://scholar.google.com/scholar?q=pyMannKendall%3A+a+python+package+for+non+parametric+Mann+Kendall+family+of+trend+tests.)
+[![Researchgate](https://github.com/mmhs013/Citation_Parser/blob/main/images/rg_pymk_cite.svg?raw=true)](https://www.researchgate.net/publication/334688255_pyMannKendall_a_python_package_for_non_parametric_Mann_Kendall_family_of_trend_tests)
+
+If you publish results for which you used `pyMannKendall`, please give credit by citing [Hussain et al., (2019)](https://doi.org/10.21105/joss.01556):
+
+> Hussain et al., (2019). pyMannKendall: a python package for non parametric Mann Kendall family of trend tests.. Journal of Open Source Software, 4(39), 1556, https://doi.org/10.21105/joss.01556
+
+
+```
+@article{Hussain2019pyMannKendall,
+	journal = {Journal of Open Source Software},
+	doi = {10.21105/joss.01556},
+	issn = {2475-9066},
+	number = {39},
+	publisher = {The Open Journal},
+	title = {pyMannKendall: a python package for non parametric Mann Kendall family of trend tests.},
+	url = {http://dx.doi.org/10.21105/joss.01556},
+	volume = {4},
+	author = {Hussain, Md. and Mahmud, Ishtiak},
+	pages = {1556},
+	date = {2019-07-25},
+	year = {2019},
+	month = {7},
+	day = {25},
+}
+```
+
+## Contributions
+
+`pyMannKendall` is a community project and welcomes contributions. Additional information can be found in the [contribution guidelines](https://github.com/mmhs013/pyMannKendall/blob/master/CONTRIBUTING.md).
+
+
+## Code of Conduct
+
+`pyMannKendall` wishes to maintain a positive community. Additional details can be found in the [Code of Conduct](https://github.com/mmhs013/pyMannKendall/blob/master/CODE_OF_CONDUCT.md).
+
+
+## References
+
+1. Bari, S. H., Rahman, M. T. U., Hoque, M. A., & Hussain, M. M. (2016). Analysis of seasonal and annual rainfall trends in the northern region of Bangladesh. *Atmospheric Research*, 176, 148-158. doi:[10.1016/j.atmosres.2016.02.008](https://doi.org/10.1016/j.atmosres.2016.02.008)
+
+2. Conover, W.J., (1980). Some methods based on ranks (Chapter 5), [Practical nonparametric statistics (2nd Ed.)](https://www.wiley.com/en-us/Practical+Nonparametric+Statistics%2C+3rd+Edition-p-9780471160687), *John Wiley and Sons*.
+
+3. Cox, D. R., & Stuart, A. (1955). Some quick sign tests for trend in location and dispersion. *Biometrika*, 42(1/2), 80-95. doi:[10.2307/2333424](https://doi.org/10.2307/2333424)
+
+4. Hamed, K. H., & Rao, A. R. (1998). A modified Mann-Kendall trend test for autocorrelated data. *Journal of hydrology*, 204(1-4), 182-196. doi:[10.1016/S0022-1694(97)00125-X](https://doi.org/10.1016/S0022-1694(97)00125-X)
+
+5. Helsel, D. R., & Frans, L. M. (2006). Regional Kendall test for trend. *Environmental science & technology*, 40(13), 4066-4073. doi:[10.1021/es051650b](https://doi.org/10.1021/es051650b)
+
+6. Hipel, K. W., & McLeod, A. I. (1994). Time series modelling of water resources and environmental systems (Vol. 45). Elsevier.
+
+7. Hirsch, R. M., Slack, J. R., & Smith, R. A. (1982). Techniques of trend analysis for monthly water quality data. *Water resources research*, 18(1), 107-121. doi:[10.1029/WR018i001p00107](https://doi.org/10.1029/WR018i001p00107)
+
+8. Jacquelin Dietz, E., (1987). A comparison of robust estimators in simple linear regression: A comparison of robust estimators. Communications in Statistics-Simulation and Computation, 16(4), pp.1209-1227. doi: [10.1080/03610918708812645](https://doi.org/10.1080/03610918708812645)
+
+9. Kendall, M. (1975). Rank correlation measures. *Charles Griffin*, London, 202, 15.
+
+10. Libiseller, C., & Grimvall, A. (2002). Performance of partial Mann-Kendall tests for trend detection in the presence of covariates. *Environmetrics: The official journal of the International Environmetrics Society*, 13(1), 71-84. doi:[10.1002/env.507](https://doi.org/1010.1002/env.507)
+
+11. Mann, H. B. (1945). Nonparametric tests against trend. *Econometrica: Journal of the Econometric Society*, 245-259. doi:[10.2307/1907187](https://doi.org/10.2307/1907187)
+
+12. Sen, P. K. (1968). Estimates of the regression coefficient based on Kendall's tau. *Journal of the American statistical association*, 63(324), 1379-1389. doi:[10.1080/01621459.1968.10480934](https://doi.org/10.1080/01621459.1968.10480934)
+
+13. Theil, H. (1950). A rank-invariant method of linear and polynominal regression analysis (parts 1-3). In *Ned. Akad. Wetensch. Proc. Ser. A* (Vol. 53, pp. 1397-1412).
+
+14. Yue, S., & Wang, C. (2004). The Mann-Kendall test modified by effective sample size to detect trend in serially correlated hydrological series. *Water resources management*, 18(3), 201-218. doi:[10.1023/B:WARM.0000043140.61082.60](https://doi.org/10.1023/B:WARM.0000043140.61082.60)
+
+15. Yue, S., & Wang, C. Y. (2002). Applicability of prewhitening to eliminate the influence of serial correlation on the Mann-Kendall test. *Water resources research*, 38(6), 4-1. doi:[10.1029/2001WR000861](https://doi.org/10.1029/2001WR000861)
+
+16. Yue, S., Pilon, P., Phinney, B., & Cavadias, G. (2002). The influence of autocorrelation on the ability to detect trend in hydrological series. *Hydrological processes*, 16(9), 1807-1829. doi:[10.1002/hyp.1095](https://doi.org/10.1002/hyp.1095)
+
+
+
+
+
+%package -n python3-pymannkendall
+Summary:	A python package for non-parametric Mann-Kendall family of trend tests.
+Provides:	python-pymannkendall
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-pymannkendall
+# pyMannKendall
+[![Build Status](https://travis-ci.com/mmhs013/pyMannKendall.svg?branch=master)](https://travis-ci.com/mmhs013/pyMannKendall)
+[![PyPI](https://img.shields.io/pypi/v/pymannkendall.svg)](https://pypi.org/project/pymannkendall/)
+[![PyPI - License](https://img.shields.io/pypi/l/pymannkendall.svg)](https://pypi.org/project/pymannkendall/)
+[![PyPI - Status](https://img.shields.io/pypi/status/pymannkendall.svg)](https://pypi.org/project/pymannkendall/)
+[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/pymannkendall.svg)](https://pypi.org/project/pymannkendall/)
+
+[![Downloads](https://pepy.tech/badge/pymannkendall)](https://pepy.tech/project/pymannkendall)
+[![Conda](https://img.shields.io/conda/dn/conda-forge/pymannkendall?label=conda-downloads)](https://anaconda.org/conda-forge/pymannkendall)
+
+[![Google Scholar](https://github.com/mmhs013/Citation_Parser/blob/main/images/gs_pymk_cite.svg?raw=true)](https://scholar.google.com/scholar?q=pyMannKendall%3A+a+python+package+for+non+parametric+Mann+Kendall+family+of+trend+tests.)
+[![Researchgate](https://github.com/mmhs013/Citation_Parser/blob/main/images/rg_pymk_cite.svg?raw=true)](https://www.researchgate.net/publication/334688255_pyMannKendall_a_python_package_for_non_parametric_Mann_Kendall_family_of_trend_tests)
+
+[![status](http://joss.theoj.org/papers/14903dbd55343be89105112e585d262a/status.svg)](http://joss.theoj.org/papers/14903dbd55343be89105112e585d262a)
+[![DOI](https://zenodo.org/badge/174495388.svg)](https://zenodo.org/badge/latestdoi/174495388)
+
+## What is the Mann-Kendall Test ?
+The Mann-Kendall Trend Test (sometimes called the MK test) is used to analyze time series data for consistently increasing or decreasing trends (monotonic trends). It is a non-parametric test, which means it works for all distributions (i.e. data doesn't have to meet the assumption of normality), but data should have no serial correlation. If the data has a serial correlation, it could affect in significant level (p-value). It could lead to misinterpretation. To overcome this problem, researchers proposed several modified Mann-Kendall tests (Hamed and Rao Modified MK Test, Yue and Wang Modified MK Test, Modified MK test using Pre-Whitening method, etc.). Seasonal Mann-Kendall test also developed to remove the effect of seasonality.
+
+Mann-Kendall Test is a powerful trend test, so several others modified Mann-Kendall tests like Multivariate MK Test, Regional MK Test, Correlated MK test, Partial MK Test, etc. were developed for the spacial condition. `pyMannkendal` is a pure Python implementation of non-parametric Mann-Kendall trend analysis, which bring together almost all types of Mann-Kendall Test. Currently, this package has 11 Mann-Kendall Tests and 2 sen's slope estimator function. Brief description of functions are below:
+
+1.	**Original Mann-Kendall test (*original_test*):** Original Mann-Kendall test is a nonparametric test, which does not consider serial correlation or seasonal effects.
+
+2.	**Hamed and Rao Modified MK Test (*hamed_rao_modification_test*):** This modified MK test proposed by *Hamed and Rao (1998)* to address serial autocorrelation issues. They suggested a variance correction approach to improve trend analysis. User can consider first n significant lag by insert lag number in this function. By default, it considered all significant lags.
+
+3.	**Yue and Wang Modified MK Test (*yue_wang_modification_test*):** This is also a variance correction method for considered serial autocorrelation proposed by *Yue, S., & Wang, C. Y. (2004)*. User can also set their desired significant n lags for the calculation.
+
+4.	**Modified MK test using Pre-Whitening method (*pre_whitening_modification_test*):** This test suggested by *Yue and Wang (2002)* to using Pre-Whitening the time series before the application of trend test.
+
+5.	**Modified MK test using Trend free Pre-Whitening method (*trend_free_pre_whitening_modification_test*):** This test also proposed by *Yue and Wang (2002)* to remove trend component and then Pre-Whitening the time series before application of trend test.
+
+6.	**Multivariate MK Test (*multivariate_test*):** This is an MK test for multiple parameters proposed by *Hirsch (1982)*. He used this method for seasonal mk test, where he considered every month as a parameter.
+
+7.	**Seasonal MK Test (*seasonal_test*):** For seasonal time series data, *Hirsch, R.M., Slack, J.R. and Smith, R.A. (1982)* proposed this test to calculate the seasonal trend.
+
+8.	**Regional MK Test (*regional_test*):** Based on*Hirsch (1982)* proposed seasonal mk test, *Helsel, D.R. and Frans, L.M., (2006)* suggest regional mk test to calculate the overall trend in a regional scale.
+
+9.	**Correlated Multivariate MK Test (*correlated_multivariate_test*):** This multivariate mk test proposed by *Hipel (1994)* where the parameters are correlated.
+
+10.	**Correlated Seasonal MK Test (*correlated_seasonal_test*):** This method proposed by *Hipel (1994)* used, when time series significantly correlated with the preceding one or more months/seasons.
+
+11.	**Partial MK Test (*partial_test*):** In a real event, many factors are affecting the main studied response parameter, which can bias the trend results. To overcome this problem, *Libiseller (2002)* proposed this partial mk test. It required two parameters as input, where, one is response parameter and other is an independent parameter.
+
+12.	**Theil-Sen's Slope Estimator (*sens_slope*):** This method proposed by *Theil (1950)* and *Sen (1968)* to estimate the magnitude of the monotonic trend. Intercept is calculate using *Conover, W.J. (1980)* method.
+
+13.	**Seasonal Theil-Sen's Slope Estimator (*seasonal_sens_slope*):** This method proposed by *Hipel (1994)* to estimate the magnitude of the monotonic trend, when data has seasonal effects. Intercept is calculate using *Conover, W.J. (1980)* method.
+
+## Function details:
+
+All Mann-Kendall test functions have almost similar input parameters. Those are:
+
+- **x**:   a vector (list, numpy array or pandas series) data
+- **alpha**: significance level (0.05 is the default)
+- **lag**: No. of First Significant Lags (Only available in hamed_rao_modification_test and yue_wang_modification_test)
+- **period**: seasonal cycle. For monthly data it is 12, weekly data it is 52 (Only available in seasonal tests)
+
+And all Mann-Kendall tests return a named tuple which contained:
+
+- **trend**: tells the trend (increasing, decreasing or no trend)
+- **h**: True (if trend is present) or False (if the trend is absence)
+- **p**: p-value of the significance test
+- **z**: normalized test statistics
+- **Tau**: Kendall Tau
+- **s**: Mann-Kendal's score
+- **var_s**: Variance S
+- **slope**: Theil-Sen estimator/slope
+- **intercept**: intercept of Kendall-Theil Robust Line, for seasonal test, full period cycle consider as unit time step
+
+sen's slope function required data vector. seasonal sen's slope also has optional input period, which by the default value is 12. Both sen's slope function return only slope value.
+
+## Dependencies
+
+For the installation of `pyMannKendall`, the following packages are required:
+- [numpy](https://www.numpy.org/)
+- [scipy](https://www.scipy.org/)
+
+## Installation
+
+You can install `pyMannKendall` using pip. For Linux users
+
+```python
+sudo pip install pymannkendall
+```
+
+or, for Windows user
+
+```python
+pip install pymannkendall
+```
+
+or, you can use conda
+```python
+conda install -c conda-forge pymannkendall
+```
+
+or you can clone the repo and install it:
+
+```bash
+git clone https://github.com/mmhs013/pymannkendall
+cd pymannkendall
+python setup.py install
+```
+
+## Tests
+
+`pyMannKendall` is automatically tested using `pytest` package on each commit [here](https://travis-ci.org/mmhs013/pyMannKendall/), but the tests can be manually run:
+
+```
+pytest -v
+```
+
+## Usage
+
+A quick example of `pyMannKendall` usage is given below. Several more examples are provided [here](https://github.com/mmhs013/pyMannKendall/blob/master/Examples/Example_pyMannKendall.ipynb).
+
+```python
+import numpy as np
+import pymannkendall as mk
+
+# Data generation for analysis
+data = np.random.rand(360,1)
+
+result = mk.original_test(data)
+print(result)
+```
+Output are like this:
+```python
+Mann_Kendall_Test(trend='no trend', h=False, p=0.9507221701045581, z=0.06179991635055463, Tau=0.0021974620860414733, s=142.0, var_s=5205500.0, slope=1.0353584906597959e-05, intercept=0.5232692553379981)
+```
+Whereas, the output is a named tuple, so you can call by name for specific result:
+```python
+print(result.slope)
+```
+or, you can directly unpack your results like this:
+```python
+trend, h, p, z, Tau, s, var_s, slope, intercept = mk.original_test(data)
+```
+
+## Citation
+
+[![Google Scholar](https://github.com/mmhs013/Citation_Parser/blob/main/images/gs_pymk_cite.svg?raw=true)](https://scholar.google.com/scholar?q=pyMannKendall%3A+a+python+package+for+non+parametric+Mann+Kendall+family+of+trend+tests.)
+[![Researchgate](https://github.com/mmhs013/Citation_Parser/blob/main/images/rg_pymk_cite.svg?raw=true)](https://www.researchgate.net/publication/334688255_pyMannKendall_a_python_package_for_non_parametric_Mann_Kendall_family_of_trend_tests)
+
+If you publish results for which you used `pyMannKendall`, please give credit by citing [Hussain et al., (2019)](https://doi.org/10.21105/joss.01556):
+
+> Hussain et al., (2019). pyMannKendall: a python package for non parametric Mann Kendall family of trend tests.. Journal of Open Source Software, 4(39), 1556, https://doi.org/10.21105/joss.01556
+
+
+```
+@article{Hussain2019pyMannKendall,
+	journal = {Journal of Open Source Software},
+	doi = {10.21105/joss.01556},
+	issn = {2475-9066},
+	number = {39},
+	publisher = {The Open Journal},
+	title = {pyMannKendall: a python package for non parametric Mann Kendall family of trend tests.},
+	url = {http://dx.doi.org/10.21105/joss.01556},
+	volume = {4},
+	author = {Hussain, Md. and Mahmud, Ishtiak},
+	pages = {1556},
+	date = {2019-07-25},
+	year = {2019},
+	month = {7},
+	day = {25},
+}
+```
+
+## Contributions
+
+`pyMannKendall` is a community project and welcomes contributions. Additional information can be found in the [contribution guidelines](https://github.com/mmhs013/pyMannKendall/blob/master/CONTRIBUTING.md).
+
+
+## Code of Conduct
+
+`pyMannKendall` wishes to maintain a positive community. Additional details can be found in the [Code of Conduct](https://github.com/mmhs013/pyMannKendall/blob/master/CODE_OF_CONDUCT.md).
+
+
+## References
+
+1. Bari, S. H., Rahman, M. T. U., Hoque, M. A., & Hussain, M. M. (2016). Analysis of seasonal and annual rainfall trends in the northern region of Bangladesh. *Atmospheric Research*, 176, 148-158. doi:[10.1016/j.atmosres.2016.02.008](https://doi.org/10.1016/j.atmosres.2016.02.008)
+
+2. Conover, W.J., (1980). Some methods based on ranks (Chapter 5), [Practical nonparametric statistics (2nd Ed.)](https://www.wiley.com/en-us/Practical+Nonparametric+Statistics%2C+3rd+Edition-p-9780471160687), *John Wiley and Sons*.
+
+3. Cox, D. R., & Stuart, A. (1955). Some quick sign tests for trend in location and dispersion. *Biometrika*, 42(1/2), 80-95. doi:[10.2307/2333424](https://doi.org/10.2307/2333424)
+
+4. Hamed, K. H., & Rao, A. R. (1998). A modified Mann-Kendall trend test for autocorrelated data. *Journal of hydrology*, 204(1-4), 182-196. doi:[10.1016/S0022-1694(97)00125-X](https://doi.org/10.1016/S0022-1694(97)00125-X)
+
+5. Helsel, D. R., & Frans, L. M. (2006). Regional Kendall test for trend. *Environmental science & technology*, 40(13), 4066-4073. doi:[10.1021/es051650b](https://doi.org/10.1021/es051650b)
+
+6. Hipel, K. W., & McLeod, A. I. (1994). Time series modelling of water resources and environmental systems (Vol. 45). Elsevier.
+
+7. Hirsch, R. M., Slack, J. R., & Smith, R. A. (1982). Techniques of trend analysis for monthly water quality data. *Water resources research*, 18(1), 107-121. doi:[10.1029/WR018i001p00107](https://doi.org/10.1029/WR018i001p00107)
+
+8. Jacquelin Dietz, E., (1987). A comparison of robust estimators in simple linear regression: A comparison of robust estimators. Communications in Statistics-Simulation and Computation, 16(4), pp.1209-1227. doi: [10.1080/03610918708812645](https://doi.org/10.1080/03610918708812645)
+
+9. Kendall, M. (1975). Rank correlation measures. *Charles Griffin*, London, 202, 15.
+
+10. Libiseller, C., & Grimvall, A. (2002). Performance of partial Mann-Kendall tests for trend detection in the presence of covariates. *Environmetrics: The official journal of the International Environmetrics Society*, 13(1), 71-84. doi:[10.1002/env.507](https://doi.org/1010.1002/env.507)
+
+11. Mann, H. B. (1945). Nonparametric tests against trend. *Econometrica: Journal of the Econometric Society*, 245-259. doi:[10.2307/1907187](https://doi.org/10.2307/1907187)
+
+12. Sen, P. K. (1968). Estimates of the regression coefficient based on Kendall's tau. *Journal of the American statistical association*, 63(324), 1379-1389. doi:[10.1080/01621459.1968.10480934](https://doi.org/10.1080/01621459.1968.10480934)
+
+13. Theil, H. (1950). A rank-invariant method of linear and polynominal regression analysis (parts 1-3). In *Ned. Akad. Wetensch. Proc. Ser. A* (Vol. 53, pp. 1397-1412).
+
+14. Yue, S., & Wang, C. (2004). The Mann-Kendall test modified by effective sample size to detect trend in serially correlated hydrological series. *Water resources management*, 18(3), 201-218. doi:[10.1023/B:WARM.0000043140.61082.60](https://doi.org/10.1023/B:WARM.0000043140.61082.60)
+
+15. Yue, S., & Wang, C. Y. (2002). Applicability of prewhitening to eliminate the influence of serial correlation on the Mann-Kendall test. *Water resources research*, 38(6), 4-1. doi:[10.1029/2001WR000861](https://doi.org/10.1029/2001WR000861)
+
+16. Yue, S., Pilon, P., Phinney, B., & Cavadias, G. (2002). The influence of autocorrelation on the ability to detect trend in hydrological series. *Hydrological processes*, 16(9), 1807-1829. doi:[10.1002/hyp.1095](https://doi.org/10.1002/hyp.1095)
+
+
+
+
+
+%package help
+Summary:	Development documents and examples for pymannkendall
+Provides:	python3-pymannkendall-doc
+%description help
+# pyMannKendall
+[![Build Status](https://travis-ci.com/mmhs013/pyMannKendall.svg?branch=master)](https://travis-ci.com/mmhs013/pyMannKendall)
+[![PyPI](https://img.shields.io/pypi/v/pymannkendall.svg)](https://pypi.org/project/pymannkendall/)
+[![PyPI - License](https://img.shields.io/pypi/l/pymannkendall.svg)](https://pypi.org/project/pymannkendall/)
+[![PyPI - Status](https://img.shields.io/pypi/status/pymannkendall.svg)](https://pypi.org/project/pymannkendall/)
+[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/pymannkendall.svg)](https://pypi.org/project/pymannkendall/)
+
+[![Downloads](https://pepy.tech/badge/pymannkendall)](https://pepy.tech/project/pymannkendall)
+[![Conda](https://img.shields.io/conda/dn/conda-forge/pymannkendall?label=conda-downloads)](https://anaconda.org/conda-forge/pymannkendall)
+
+[![Google Scholar](https://github.com/mmhs013/Citation_Parser/blob/main/images/gs_pymk_cite.svg?raw=true)](https://scholar.google.com/scholar?q=pyMannKendall%3A+a+python+package+for+non+parametric+Mann+Kendall+family+of+trend+tests.)
+[![Researchgate](https://github.com/mmhs013/Citation_Parser/blob/main/images/rg_pymk_cite.svg?raw=true)](https://www.researchgate.net/publication/334688255_pyMannKendall_a_python_package_for_non_parametric_Mann_Kendall_family_of_trend_tests)
+
+[![status](http://joss.theoj.org/papers/14903dbd55343be89105112e585d262a/status.svg)](http://joss.theoj.org/papers/14903dbd55343be89105112e585d262a)
+[![DOI](https://zenodo.org/badge/174495388.svg)](https://zenodo.org/badge/latestdoi/174495388)
+
+## What is the Mann-Kendall Test ?
+The Mann-Kendall Trend Test (sometimes called the MK test) is used to analyze time series data for consistently increasing or decreasing trends (monotonic trends). It is a non-parametric test, which means it works for all distributions (i.e. data doesn't have to meet the assumption of normality), but data should have no serial correlation. If the data has a serial correlation, it could affect in significant level (p-value). It could lead to misinterpretation. To overcome this problem, researchers proposed several modified Mann-Kendall tests (Hamed and Rao Modified MK Test, Yue and Wang Modified MK Test, Modified MK test using Pre-Whitening method, etc.). Seasonal Mann-Kendall test also developed to remove the effect of seasonality.
+
+Mann-Kendall Test is a powerful trend test, so several others modified Mann-Kendall tests like Multivariate MK Test, Regional MK Test, Correlated MK test, Partial MK Test, etc. were developed for the spacial condition. `pyMannkendal` is a pure Python implementation of non-parametric Mann-Kendall trend analysis, which bring together almost all types of Mann-Kendall Test. Currently, this package has 11 Mann-Kendall Tests and 2 sen's slope estimator function. Brief description of functions are below:
+
+1.	**Original Mann-Kendall test (*original_test*):** Original Mann-Kendall test is a nonparametric test, which does not consider serial correlation or seasonal effects.
+
+2.	**Hamed and Rao Modified MK Test (*hamed_rao_modification_test*):** This modified MK test proposed by *Hamed and Rao (1998)* to address serial autocorrelation issues. They suggested a variance correction approach to improve trend analysis. User can consider first n significant lag by insert lag number in this function. By default, it considered all significant lags.
+
+3.	**Yue and Wang Modified MK Test (*yue_wang_modification_test*):** This is also a variance correction method for considered serial autocorrelation proposed by *Yue, S., & Wang, C. Y. (2004)*. User can also set their desired significant n lags for the calculation.
+
+4.	**Modified MK test using Pre-Whitening method (*pre_whitening_modification_test*):** This test suggested by *Yue and Wang (2002)* to using Pre-Whitening the time series before the application of trend test.
+
+5.	**Modified MK test using Trend free Pre-Whitening method (*trend_free_pre_whitening_modification_test*):** This test also proposed by *Yue and Wang (2002)* to remove trend component and then Pre-Whitening the time series before application of trend test.
+
+6.	**Multivariate MK Test (*multivariate_test*):** This is an MK test for multiple parameters proposed by *Hirsch (1982)*. He used this method for seasonal mk test, where he considered every month as a parameter.
+
+7.	**Seasonal MK Test (*seasonal_test*):** For seasonal time series data, *Hirsch, R.M., Slack, J.R. and Smith, R.A. (1982)* proposed this test to calculate the seasonal trend.
+
+8.	**Regional MK Test (*regional_test*):** Based on*Hirsch (1982)* proposed seasonal mk test, *Helsel, D.R. and Frans, L.M., (2006)* suggest regional mk test to calculate the overall trend in a regional scale.
+
+9.	**Correlated Multivariate MK Test (*correlated_multivariate_test*):** This multivariate mk test proposed by *Hipel (1994)* where the parameters are correlated.
+
+10.	**Correlated Seasonal MK Test (*correlated_seasonal_test*):** This method proposed by *Hipel (1994)* used, when time series significantly correlated with the preceding one or more months/seasons.
+
+11.	**Partial MK Test (*partial_test*):** In a real event, many factors are affecting the main studied response parameter, which can bias the trend results. To overcome this problem, *Libiseller (2002)* proposed this partial mk test. It required two parameters as input, where, one is response parameter and other is an independent parameter.
+
+12.	**Theil-Sen's Slope Estimator (*sens_slope*):** This method proposed by *Theil (1950)* and *Sen (1968)* to estimate the magnitude of the monotonic trend. Intercept is calculate using *Conover, W.J. (1980)* method.
+
+13.	**Seasonal Theil-Sen's Slope Estimator (*seasonal_sens_slope*):** This method proposed by *Hipel (1994)* to estimate the magnitude of the monotonic trend, when data has seasonal effects. Intercept is calculate using *Conover, W.J. (1980)* method.
+
+## Function details:
+
+All Mann-Kendall test functions have almost similar input parameters. Those are:
+
+- **x**:   a vector (list, numpy array or pandas series) data
+- **alpha**: significance level (0.05 is the default)
+- **lag**: No. of First Significant Lags (Only available in hamed_rao_modification_test and yue_wang_modification_test)
+- **period**: seasonal cycle. For monthly data it is 12, weekly data it is 52 (Only available in seasonal tests)
+
+And all Mann-Kendall tests return a named tuple which contained:
+
+- **trend**: tells the trend (increasing, decreasing or no trend)
+- **h**: True (if trend is present) or False (if the trend is absence)
+- **p**: p-value of the significance test
+- **z**: normalized test statistics
+- **Tau**: Kendall Tau
+- **s**: Mann-Kendal's score
+- **var_s**: Variance S
+- **slope**: Theil-Sen estimator/slope
+- **intercept**: intercept of Kendall-Theil Robust Line, for seasonal test, full period cycle consider as unit time step
+
+sen's slope function required data vector. seasonal sen's slope also has optional input period, which by the default value is 12. Both sen's slope function return only slope value.
+
+## Dependencies
+
+For the installation of `pyMannKendall`, the following packages are required:
+- [numpy](https://www.numpy.org/)
+- [scipy](https://www.scipy.org/)
+
+## Installation
+
+You can install `pyMannKendall` using pip. For Linux users
+
+```python
+sudo pip install pymannkendall
+```
+
+or, for Windows user
+
+```python
+pip install pymannkendall
+```
+
+or, you can use conda
+```python
+conda install -c conda-forge pymannkendall
+```
+
+or you can clone the repo and install it:
+
+```bash
+git clone https://github.com/mmhs013/pymannkendall
+cd pymannkendall
+python setup.py install
+```
+
+## Tests
+
+`pyMannKendall` is automatically tested using `pytest` package on each commit [here](https://travis-ci.org/mmhs013/pyMannKendall/), but the tests can be manually run:
+
+```
+pytest -v
+```
+
+## Usage
+
+A quick example of `pyMannKendall` usage is given below. Several more examples are provided [here](https://github.com/mmhs013/pyMannKendall/blob/master/Examples/Example_pyMannKendall.ipynb).
+
+```python
+import numpy as np
+import pymannkendall as mk
+
+# Data generation for analysis
+data = np.random.rand(360,1)
+
+result = mk.original_test(data)
+print(result)
+```
+Output are like this:
+```python
+Mann_Kendall_Test(trend='no trend', h=False, p=0.9507221701045581, z=0.06179991635055463, Tau=0.0021974620860414733, s=142.0, var_s=5205500.0, slope=1.0353584906597959e-05, intercept=0.5232692553379981)
+```
+Whereas, the output is a named tuple, so you can call by name for specific result:
+```python
+print(result.slope)
+```
+or, you can directly unpack your results like this:
+```python
+trend, h, p, z, Tau, s, var_s, slope, intercept = mk.original_test(data)
+```
+
+## Citation
+
+[![Google Scholar](https://github.com/mmhs013/Citation_Parser/blob/main/images/gs_pymk_cite.svg?raw=true)](https://scholar.google.com/scholar?q=pyMannKendall%3A+a+python+package+for+non+parametric+Mann+Kendall+family+of+trend+tests.)
+[![Researchgate](https://github.com/mmhs013/Citation_Parser/blob/main/images/rg_pymk_cite.svg?raw=true)](https://www.researchgate.net/publication/334688255_pyMannKendall_a_python_package_for_non_parametric_Mann_Kendall_family_of_trend_tests)
+
+If you publish results for which you used `pyMannKendall`, please give credit by citing [Hussain et al., (2019)](https://doi.org/10.21105/joss.01556):
+
+> Hussain et al., (2019). pyMannKendall: a python package for non parametric Mann Kendall family of trend tests.. Journal of Open Source Software, 4(39), 1556, https://doi.org/10.21105/joss.01556
+
+
+```
+@article{Hussain2019pyMannKendall,
+	journal = {Journal of Open Source Software},
+	doi = {10.21105/joss.01556},
+	issn = {2475-9066},
+	number = {39},
+	publisher = {The Open Journal},
+	title = {pyMannKendall: a python package for non parametric Mann Kendall family of trend tests.},
+	url = {http://dx.doi.org/10.21105/joss.01556},
+	volume = {4},
+	author = {Hussain, Md. and Mahmud, Ishtiak},
+	pages = {1556},
+	date = {2019-07-25},
+	year = {2019},
+	month = {7},
+	day = {25},
+}
+```
+
+## Contributions
+
+`pyMannKendall` is a community project and welcomes contributions. Additional information can be found in the [contribution guidelines](https://github.com/mmhs013/pyMannKendall/blob/master/CONTRIBUTING.md).
+
+
+## Code of Conduct
+
+`pyMannKendall` wishes to maintain a positive community. Additional details can be found in the [Code of Conduct](https://github.com/mmhs013/pyMannKendall/blob/master/CODE_OF_CONDUCT.md).
+
+
+## References
+
+1. Bari, S. H., Rahman, M. T. U., Hoque, M. A., & Hussain, M. M. (2016). Analysis of seasonal and annual rainfall trends in the northern region of Bangladesh. *Atmospheric Research*, 176, 148-158. doi:[10.1016/j.atmosres.2016.02.008](https://doi.org/10.1016/j.atmosres.2016.02.008)
+
+2. Conover, W.J., (1980). Some methods based on ranks (Chapter 5), [Practical nonparametric statistics (2nd Ed.)](https://www.wiley.com/en-us/Practical+Nonparametric+Statistics%2C+3rd+Edition-p-9780471160687), *John Wiley and Sons*.
+
+3. Cox, D. R., & Stuart, A. (1955). Some quick sign tests for trend in location and dispersion. *Biometrika*, 42(1/2), 80-95. doi:[10.2307/2333424](https://doi.org/10.2307/2333424)
+
+4. Hamed, K. H., & Rao, A. R. (1998). A modified Mann-Kendall trend test for autocorrelated data. *Journal of hydrology*, 204(1-4), 182-196. doi:[10.1016/S0022-1694(97)00125-X](https://doi.org/10.1016/S0022-1694(97)00125-X)
+
+5. Helsel, D. R., & Frans, L. M. (2006). Regional Kendall test for trend. *Environmental science & technology*, 40(13), 4066-4073. doi:[10.1021/es051650b](https://doi.org/10.1021/es051650b)
+
+6. Hipel, K. W., & McLeod, A. I. (1994). Time series modelling of water resources and environmental systems (Vol. 45). Elsevier.
+
+7. Hirsch, R. M., Slack, J. R., & Smith, R. A. (1982). Techniques of trend analysis for monthly water quality data. *Water resources research*, 18(1), 107-121. doi:[10.1029/WR018i001p00107](https://doi.org/10.1029/WR018i001p00107)
+
+8. Jacquelin Dietz, E., (1987). A comparison of robust estimators in simple linear regression: A comparison of robust estimators. Communications in Statistics-Simulation and Computation, 16(4), pp.1209-1227. doi: [10.1080/03610918708812645](https://doi.org/10.1080/03610918708812645)
+
+9. Kendall, M. (1975). Rank correlation measures. *Charles Griffin*, London, 202, 15.
+
+10. Libiseller, C., & Grimvall, A. (2002). Performance of partial Mann-Kendall tests for trend detection in the presence of covariates. *Environmetrics: The official journal of the International Environmetrics Society*, 13(1), 71-84. doi:[10.1002/env.507](https://doi.org/1010.1002/env.507)
+
+11. Mann, H. B. (1945). Nonparametric tests against trend. *Econometrica: Journal of the Econometric Society*, 245-259. doi:[10.2307/1907187](https://doi.org/10.2307/1907187)
+
+12. Sen, P. K. (1968). Estimates of the regression coefficient based on Kendall's tau. *Journal of the American statistical association*, 63(324), 1379-1389. doi:[10.1080/01621459.1968.10480934](https://doi.org/10.1080/01621459.1968.10480934)
+
+13. Theil, H. (1950). A rank-invariant method of linear and polynominal regression analysis (parts 1-3). In *Ned. Akad. Wetensch. Proc. Ser. A* (Vol. 53, pp. 1397-1412).
+
+14. Yue, S., & Wang, C. (2004). The Mann-Kendall test modified by effective sample size to detect trend in serially correlated hydrological series. *Water resources management*, 18(3), 201-218. doi:[10.1023/B:WARM.0000043140.61082.60](https://doi.org/10.1023/B:WARM.0000043140.61082.60)
+
+15. Yue, S., & Wang, C. Y. (2002). Applicability of prewhitening to eliminate the influence of serial correlation on the Mann-Kendall test. *Water resources research*, 38(6), 4-1. doi:[10.1029/2001WR000861](https://doi.org/10.1029/2001WR000861)
+
+16. Yue, S., Pilon, P., Phinney, B., & Cavadias, G. (2002). The influence of autocorrelation on the ability to detect trend in hydrological series. *Hydrological processes*, 16(9), 1807-1829. doi:[10.1002/hyp.1095](https://doi.org/10.1002/hyp.1095)
+
+
+
+
+
+%prep
+%autosetup -n pymannkendall-1.4.3
+
+%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-pymannkendall -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Mon Apr 10 2023 Python_Bot <Python_Bot@openeuler.org> - 1.4.3-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..7f6b57f
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+55f3d5043bf7f94f0300443b50ffaabe  pymannkendall-1.4.3.tar.gz