automatic import of python-robustats

3 files changed, 395 insertions, 0 deletions

diff --git a/.gitignore b/.gitignore
index e69de29..f3d894e 100644
--- a/.gitignore
+++ b/.gitignore
@@ -0,0 +1 @@
+/robustats-0.1.7.tar.gz
diff --git a/python-robustats.spec b/python-robustats.spec
new file mode 100644
index 0000000..cf52456
--- /dev/null
+++ b/python-robustats.spec
@@ -0,0 +1,393 @@
+%global _empty_manifest_terminate_build 0
+Name:		python-robustats
+Version:	0.1.7
+Release:	1
+Summary:	Robustats is a Python library for high-performance computation of robust statistical estimators.
+License:	MIT
+URL:		https://github.com/FilippoBovo/robustats
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/10/e1/64507951c10912a423239c10b3842eea284951c083a1c12882cd3b147f84/robustats-0.1.7.tar.gz
+BuildArch:	noarch
+
+
+%description
+# Robustats
+
+Robustats is a Python library for high-performance computation of robust statistical estimators.
+
+The functions that compute the robust estimators are [implemented in C](c) for speed and [called by Python](robustats).
+
+Estimators implemented in the library:
+
+- **Weighted Median** (temporal complexity: `O(n)`) \[1, 2, 3\]
+- **Medcouple** (temporal complexity: `O(n * log(n))`) [4, 5, 6, 7]
+- **Mode** (temporal complexity: `O(n * log(n))`) [8]
+
+## How to Install
+
+This library requires Python 3.
+
+You can install the library using Pip.
+
+```shell
+pip install robustats
+```
+
+You can also install the library directly from GitHub using the following command.
+
+```shell
+pip install -e 'git+https://github.com/FilippoBovo/robustats.git#egg=robustats'
+```
+
+Otherwise, you may clone the repository, and install and test the Robustats package in the following way.
+
+```shell
+git clone https://github.com/FilippoBovo/robustats.git
+cd robustats
+pip install -e .
+python -m unittest
+```
+
+## How to Use
+
+This is an example of how to use the Robustats library in Python.
+
+```python
+import numpy as np
+import robustats
+
+
+# Weighted Median
+x = np.array([1.1, 5.3, 3.7, 2.1, 7.0, 9.9])
+weights = np.array([1.1, 0.4, 2.1, 3.5, 1.2, 0.8])
+
+weighted_median = robustats.weighted_median(x, weights)
+
+print("The weighted median is {}".format(weighted_median))
+# Output: The weighted median is 2.1
+
+
+# Medcouple
+x = np.array([0.2, 0.17, 0.08, 0.16, 0.88, 0.86, 0.09, 0.54, 0.27, 0.14])
+
+medcouple = robustats.medcouple(x)
+
+print("The medcouple is {}".format(medcouple))
+# Output: The medcouple is 0.7749999999999999
+
+
+# Mode
+x = np.array([1., 2., 2., 3., 3., 3., 4., 4., 5.])
+
+mode = robustats.mode(x)
+
+print("The mode is {}".format(mode))
+# Output: The mode is 3.0
+```
+
+## How to Contribute
+
+If you wish to contribute to this library, please follow the patterns and style of the rest of the code.
+
+Moreover, install the Git hooks.
+
+```shell
+git config core.hooksPath .githooks
+```
+
+
+
+Tips:
+
+- In C, use `malloc` to allocate memory to the heap, instead of creating arrays that allocate memory to the stack, as with large array we would incur in a [segmentation fault due to stack overflow](https://stackoverflow.com/a/1847886).
+- Avoid recursions where possible to limit the spatial complexity of the problem. In place of recursions, use loops.
+
+## References
+
+\[1\] [Cormen, Leiserson, Rivest, Stein - Introduction to Algorithms (3rd Edition)](https://books.google.co.uk/books?id=aefUBQAAQBAJ&lpg=PR5&ots=dN8rWuZQaW&dq=Cormen%2C%20Leiserson%2C%20Rivest%2C%20Stein%20-%20Introduction%20to%20Algorithms&lr&pg=PP1#v=onepage&q&f=false).
+
+\[2\] [Cormen - Introduction to Algorithms (3rd Edition) - Instructor's Manual](https://cdn.manesht.ir/19908/Introduction%20to%20Algorithms.pdf).
+
+\[3\] [Weighted median on Wikipedia](https://en.wikipedia.org/wiki/Weighted_median).
+
+\[4\] [G. Brys; M. Hubert; A. Struyf (November 2004). "A Robust Measure of Skewness". *Journal of Computational and Graphical Statistics*. **13** (4): 996–1017.](https://doi.org/10.1198%2F106186004X12632)
+
+\[5\] [Donald B. Johnson; Tetsuo Mizoguchi (May 1978). "Selecting The Kth Element In X + Y And X1 + X2 +...+ Xm". *SIAM Journal on Computing*. **7** (2): 147–153.](https://doi.org/10.1137%2F0207013)
+
+\[6\] [Medcouple implementation in Python by Jordi Gutiérrez Hermoso.](http://inversethought.com/hg/)
+
+\[7\] [Medcouple on Wikipedia.](https://en.wikipedia.org/wiki/Medcouple)
+
+\[8\] [David R. Bickel, Rudolf Frühwirth. "On a fast, robust estimator of the mode: Comparisons to other robust estimators with applications", *Computational Statistics & Data Analysis*, Volume 50, Issue 12, 2006, Pages 3500-3530, ISSN 0167-9473.](https://doi.org/10.1016/j.csda.2005.07.011)
+
+%package -n python3-robustats
+Summary:	Robustats is a Python library for high-performance computation of robust statistical estimators.
+Provides:	python-robustats
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-robustats
+# Robustats
+
+Robustats is a Python library for high-performance computation of robust statistical estimators.
+
+The functions that compute the robust estimators are [implemented in C](c) for speed and [called by Python](robustats).
+
+Estimators implemented in the library:
+
+- **Weighted Median** (temporal complexity: `O(n)`) \[1, 2, 3\]
+- **Medcouple** (temporal complexity: `O(n * log(n))`) [4, 5, 6, 7]
+- **Mode** (temporal complexity: `O(n * log(n))`) [8]
+
+## How to Install
+
+This library requires Python 3.
+
+You can install the library using Pip.
+
+```shell
+pip install robustats
+```
+
+You can also install the library directly from GitHub using the following command.
+
+```shell
+pip install -e 'git+https://github.com/FilippoBovo/robustats.git#egg=robustats'
+```
+
+Otherwise, you may clone the repository, and install and test the Robustats package in the following way.
+
+```shell
+git clone https://github.com/FilippoBovo/robustats.git
+cd robustats
+pip install -e .
+python -m unittest
+```
+
+## How to Use
+
+This is an example of how to use the Robustats library in Python.
+
+```python
+import numpy as np
+import robustats
+
+
+# Weighted Median
+x = np.array([1.1, 5.3, 3.7, 2.1, 7.0, 9.9])
+weights = np.array([1.1, 0.4, 2.1, 3.5, 1.2, 0.8])
+
+weighted_median = robustats.weighted_median(x, weights)
+
+print("The weighted median is {}".format(weighted_median))
+# Output: The weighted median is 2.1
+
+
+# Medcouple
+x = np.array([0.2, 0.17, 0.08, 0.16, 0.88, 0.86, 0.09, 0.54, 0.27, 0.14])
+
+medcouple = robustats.medcouple(x)
+
+print("The medcouple is {}".format(medcouple))
+# Output: The medcouple is 0.7749999999999999
+
+
+# Mode
+x = np.array([1., 2., 2., 3., 3., 3., 4., 4., 5.])
+
+mode = robustats.mode(x)
+
+print("The mode is {}".format(mode))
+# Output: The mode is 3.0
+```
+
+## How to Contribute
+
+If you wish to contribute to this library, please follow the patterns and style of the rest of the code.
+
+Moreover, install the Git hooks.
+
+```shell
+git config core.hooksPath .githooks
+```
+
+
+
+Tips:
+
+- In C, use `malloc` to allocate memory to the heap, instead of creating arrays that allocate memory to the stack, as with large array we would incur in a [segmentation fault due to stack overflow](https://stackoverflow.com/a/1847886).
+- Avoid recursions where possible to limit the spatial complexity of the problem. In place of recursions, use loops.
+
+## References
+
+\[1\] [Cormen, Leiserson, Rivest, Stein - Introduction to Algorithms (3rd Edition)](https://books.google.co.uk/books?id=aefUBQAAQBAJ&lpg=PR5&ots=dN8rWuZQaW&dq=Cormen%2C%20Leiserson%2C%20Rivest%2C%20Stein%20-%20Introduction%20to%20Algorithms&lr&pg=PP1#v=onepage&q&f=false).
+
+\[2\] [Cormen - Introduction to Algorithms (3rd Edition) - Instructor's Manual](https://cdn.manesht.ir/19908/Introduction%20to%20Algorithms.pdf).
+
+\[3\] [Weighted median on Wikipedia](https://en.wikipedia.org/wiki/Weighted_median).
+
+\[4\] [G. Brys; M. Hubert; A. Struyf (November 2004). "A Robust Measure of Skewness". *Journal of Computational and Graphical Statistics*. **13** (4): 996–1017.](https://doi.org/10.1198%2F106186004X12632)
+
+\[5\] [Donald B. Johnson; Tetsuo Mizoguchi (May 1978). "Selecting The Kth Element In X + Y And X1 + X2 +...+ Xm". *SIAM Journal on Computing*. **7** (2): 147–153.](https://doi.org/10.1137%2F0207013)
+
+\[6\] [Medcouple implementation in Python by Jordi Gutiérrez Hermoso.](http://inversethought.com/hg/)
+
+\[7\] [Medcouple on Wikipedia.](https://en.wikipedia.org/wiki/Medcouple)
+
+\[8\] [David R. Bickel, Rudolf Frühwirth. "On a fast, robust estimator of the mode: Comparisons to other robust estimators with applications", *Computational Statistics & Data Analysis*, Volume 50, Issue 12, 2006, Pages 3500-3530, ISSN 0167-9473.](https://doi.org/10.1016/j.csda.2005.07.011)
+
+%package help
+Summary:	Development documents and examples for robustats
+Provides:	python3-robustats-doc
+%description help
+# Robustats
+
+Robustats is a Python library for high-performance computation of robust statistical estimators.
+
+The functions that compute the robust estimators are [implemented in C](c) for speed and [called by Python](robustats).
+
+Estimators implemented in the library:
+
+- **Weighted Median** (temporal complexity: `O(n)`) \[1, 2, 3\]
+- **Medcouple** (temporal complexity: `O(n * log(n))`) [4, 5, 6, 7]
+- **Mode** (temporal complexity: `O(n * log(n))`) [8]
+
+## How to Install
+
+This library requires Python 3.
+
+You can install the library using Pip.
+
+```shell
+pip install robustats
+```
+
+You can also install the library directly from GitHub using the following command.
+
+```shell
+pip install -e 'git+https://github.com/FilippoBovo/robustats.git#egg=robustats'
+```
+
+Otherwise, you may clone the repository, and install and test the Robustats package in the following way.
+
+```shell
+git clone https://github.com/FilippoBovo/robustats.git
+cd robustats
+pip install -e .
+python -m unittest
+```
+
+## How to Use
+
+This is an example of how to use the Robustats library in Python.
+
+```python
+import numpy as np
+import robustats
+
+
+# Weighted Median
+x = np.array([1.1, 5.3, 3.7, 2.1, 7.0, 9.9])
+weights = np.array([1.1, 0.4, 2.1, 3.5, 1.2, 0.8])
+
+weighted_median = robustats.weighted_median(x, weights)
+
+print("The weighted median is {}".format(weighted_median))
+# Output: The weighted median is 2.1
+
+
+# Medcouple
+x = np.array([0.2, 0.17, 0.08, 0.16, 0.88, 0.86, 0.09, 0.54, 0.27, 0.14])
+
+medcouple = robustats.medcouple(x)
+
+print("The medcouple is {}".format(medcouple))
+# Output: The medcouple is 0.7749999999999999
+
+
+# Mode
+x = np.array([1., 2., 2., 3., 3., 3., 4., 4., 5.])
+
+mode = robustats.mode(x)
+
+print("The mode is {}".format(mode))
+# Output: The mode is 3.0
+```
+
+## How to Contribute
+
+If you wish to contribute to this library, please follow the patterns and style of the rest of the code.
+
+Moreover, install the Git hooks.
+
+```shell
+git config core.hooksPath .githooks
+```
+
+
+
+Tips:
+
+- In C, use `malloc` to allocate memory to the heap, instead of creating arrays that allocate memory to the stack, as with large array we would incur in a [segmentation fault due to stack overflow](https://stackoverflow.com/a/1847886).
+- Avoid recursions where possible to limit the spatial complexity of the problem. In place of recursions, use loops.
+
+## References
+
+\[1\] [Cormen, Leiserson, Rivest, Stein - Introduction to Algorithms (3rd Edition)](https://books.google.co.uk/books?id=aefUBQAAQBAJ&lpg=PR5&ots=dN8rWuZQaW&dq=Cormen%2C%20Leiserson%2C%20Rivest%2C%20Stein%20-%20Introduction%20to%20Algorithms&lr&pg=PP1#v=onepage&q&f=false).
+
+\[2\] [Cormen - Introduction to Algorithms (3rd Edition) - Instructor's Manual](https://cdn.manesht.ir/19908/Introduction%20to%20Algorithms.pdf).
+
+\[3\] [Weighted median on Wikipedia](https://en.wikipedia.org/wiki/Weighted_median).
+
+\[4\] [G. Brys; M. Hubert; A. Struyf (November 2004). "A Robust Measure of Skewness". *Journal of Computational and Graphical Statistics*. **13** (4): 996–1017.](https://doi.org/10.1198%2F106186004X12632)
+
+\[5\] [Donald B. Johnson; Tetsuo Mizoguchi (May 1978). "Selecting The Kth Element In X + Y And X1 + X2 +...+ Xm". *SIAM Journal on Computing*. **7** (2): 147–153.](https://doi.org/10.1137%2F0207013)
+
+\[6\] [Medcouple implementation in Python by Jordi Gutiérrez Hermoso.](http://inversethought.com/hg/)
+
+\[7\] [Medcouple on Wikipedia.](https://en.wikipedia.org/wiki/Medcouple)
+
+\[8\] [David R. Bickel, Rudolf Frühwirth. "On a fast, robust estimator of the mode: Comparisons to other robust estimators with applications", *Computational Statistics & Data Analysis*, Volume 50, Issue 12, 2006, Pages 3500-3530, ISSN 0167-9473.](https://doi.org/10.1016/j.csda.2005.07.011)
+
+%prep
+%autosetup -n robustats-0.1.7
+
+%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-robustats -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Wed Apr 12 2023 Python_Bot <Python_Bot@openeuler.org> - 0.1.7-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..5143945
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+5a9650d2ba3b56bc38a227f5fbdc896a  robustats-0.1.7.tar.gz