automatic import of python-pyxirr

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+/pyxirr-0.9.0.tar.gz
diff --git a/python-pyxirr.spec b/python-pyxirr.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-pyxirr
+Version:	0.9.0
+Release:	1
+Summary:	Rust-powered collection of financial functions for Python.
+License:	Unlicense
+URL:		https://github.com/Anexen/pyxirr
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/db/a7/6bc76d093c77a599b70b4e53009db1adc5e979711d103b5f03aef8043da7/pyxirr-0.9.0.tar.gz
+
+
+%description
+[![rust-lang.org](https://img.shields.io/badge/Made%20with-Rust-red)](https://www.rust-lang.org/)
+[![License](https://img.shields.io/github/license/Anexen/pyxirr.svg)](https://github.com/Anexen/pyxirr/blob/master/LICENSE)
+[![pypi](https://img.shields.io/pypi/v/pyxirr.svg)](https://pypi.org/project/pyxirr/)
+[![versions](https://img.shields.io/pypi/pyversions/pyxirr.svg)](https://pypi.org/project/pyxirr/)
+
+# PyXIRR
+
+Rust-powered collection of financial functions.
+
+PyXIRR stands for "Python XIRR" (for historical reasons), but contains many other financial functions such as IRR, FV, NPV, etc.
+
+Features:
+
+- correct
+- supports different day count conventions (e.g. ACT/360, 30E/360, etc.)
+- works with different input data types (iterators, numpy arrays, pandas DataFrames)
+- no external dependencies
+- type annotations
+- blazingly fast
+
+# Installation
+
+```
+pip install pyxirr
+```
+
+# Benchmarks
+
+Rust implementation has been tested against existing [xirr](https://pypi.org/project/xirr/) package
+(uses [scipy.optimize](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.newton.html) under the hood)
+and the [implementation from the Stack Overflow](https://stackoverflow.com/a/11503492) (pure python).
+
+![bench](https://raw.githubusercontent.com/Anexen/pyxirr/main/docs/static/bench.png)
+
+PyXIRR is ~10-20x faster in XIRR calculation than the other implementations.
+
+Powered by [github-action-benchmark](https://github.com/rhysd/github-action-benchmark) and [plotly.js](https://github.com/plotly/plotly.js).
+
+Live benchmarks are hosted on [Github Pages](https://anexen.github.io/pyxirr/bench).
+
+# Examples
+
+```python
+from datetime import date
+from pyxirr import xirr
+
+dates = [date(2020, 1, 1), date(2021, 1, 1), date(2022, 1, 1)]
+amounts = [-1000, 750, 500]
+
+# feed columnar data
+xirr(dates, amounts)
+# feed iterators
+xirr(iter(dates), (x / 2 for x in amounts))
+# feed an iterable of tuples
+xirr(zip(dates, amounts))
+# feed a dictionary
+xirr(dict(zip(dates, amounts)))
+# dates as strings
+xirr(['2020-01-01', '2021-01-01'], [-1000, 1200])
+```
+
+### Numpy and Pandas
+
+```python
+import numpy as np
+import pandas as pd
+
+# feed numpy array
+xirr(np.array([dates, amounts]))
+xirr(np.array(dates), np.array(amounts))
+
+# feed DataFrame (columns names doesn't matter; ordering matters)
+xirr(pd.DataFrame({"a": dates, "b": amounts}))
+
+# feed Series with DatetimeIndex
+xirr(pd.Series(amounts, index=pd.to_datetime(dates)))
+
+# bonus: apply xirr to a DataFrame with DatetimeIndex:
+df = pd.DataFrame(
+    index=pd.date_range("2021", "2022", freq="MS", closed="left"),
+    data={
+        "one": [-100] + [20] * 11,
+        "two": [-80] + [19] * 11,
+    },
+)
+df.apply(xirr)  # Series(index=["one", "two"], data=[5.09623547168478, 8.780801977141174])
+```
+
+### Day count conventions
+
+Check out the available options on the [docs/day-count-conventions](https://anexen.github.io/pyxirr/functions.html#day-count-conventions).
+
+```python
+from pyxirr import DayCount
+
+xirr(dates, amounts, day_count=DayCount.ACT_360)
+
+# parse day count from string
+xirr(dates, amounts, day_count="30E/360")
+```
+
+### Other financial functions
+
+```python
+import pyxirr
+
+# Future Value
+pyxirr.fv(0.05/12, 10*12, -100, -100)
+
+# Net Present Value
+pyxirr.npv(0, [-40_000, 5_000, 8_000, 12_000, 30_000])
+
+# IRR
+pyxirr.irr([-100, 39, 59, 55, 20])
+
+# ... and more! Check out the docs.
+```
+
+### Vectorization
+
+PyXIRR supports numpy-like vectorization.
+
+If all input is scalar, returns a scalar float. If any input is array_like,
+returns values for each input element. If multiple inputs are
+array_like, performs broadcasting and returns values for each element.
+
+```python
+import pyxirr
+
+# feed list
+pyxirr.fv([0.05/12, 0.06/12], 10*12, -100, -100)
+pyxirr.fv([0.05/12, 0.06/12], [10*12, 9*12], [-100, -200], -100)
+
+# feed numpy array
+import numpy as np
+rates = np.array([0.05, 0.06, 0.07])/12
+pyxirr.fv(rates, 10*12, -100, -100)
+
+# feed any iterable!
+pyxirr.fv(
+    np.linspace(0.01, 0.2, 10),
+    (x + 1 for x in range(10)),
+    range(-100, -1100, -100),
+    tuple(range(-100, -200, -10))
+)
+
+# 2d, 3d, 4d, and more!
+rates = [[[[[[0.01], [0.02]]]]]]
+pyxirr.fv(rates, 10*12, -100, -100)
+```
+
+# API reference
+
+See the [docs](https://anexen.github.io/pyxirr)
+
+# Roadmap
+
+- [x] Implement all functions from [numpy-financial](https://numpy.org/numpy-financial/latest/index.html)
+- [x] Improve docs, add more tests
+- [x] Type hints
+- [x] Vectorized versions of numpy-financial functions.
+- [ ] Compile library for rust/javascript/python
+
+# Development
+
+Running tests with pyo3 is a bit tricky. In short, you need to compile your tests without `extension-module` feature to avoid linking errors.
+See the following issues for the details: [#341](https://github.com/PyO3/pyo3/issues/341), [#771](https://github.com/PyO3/pyo3/issues/771).
+
+If you are using `pyenv`, make sure you have the shared library installed (check for `${PYENV_ROOT}/versions/<version>/lib/libpython3.so` file).
+
+```bash
+$ PYTHON_CONFIGURE_OPTS="--enable-shared" pyenv install <version>
+```
+
+Install dev-requirements
+
+```bash
+$ pip install -r dev-requirements.txt
+```
+
+### Building
+
+```bash
+$ maturin develop
+```
+
+### Testing
+
+```bash
+$ LD_LIBRARY_PATH=${PYENV_ROOT}/versions/3.10.8/lib cargo test --no-default-features --features tests
+```
+
+### Benchmarks
+
+```bash
+$ pip install -r bench-requirements.txt
+$ LD_LIBRARY_PATH=${PYENV_ROOT}/versions/3.10.8/lib cargo +nightly bench --no-default-features --features tests
+```
+
+# Building and distribution
+
+This library uses [maturin](https://github.com/PyO3/maturin) to build and distribute python wheels.
+
+```bash
+$ docker run --rm -v $(pwd):/io ghcr.io/pyo3/maturin build --release --manylinux 2010 --strip
+$ maturin upload target/wheels/pyxirr-${version}*
+```
+
+
+
+%package -n python3-pyxirr
+Summary:	Rust-powered collection of financial functions for Python.
+Provides:	python-pyxirr
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+BuildRequires:	python3-cffi
+BuildRequires:	gcc
+BuildRequires:	gdb
+%description -n python3-pyxirr
+[![rust-lang.org](https://img.shields.io/badge/Made%20with-Rust-red)](https://www.rust-lang.org/)
+[![License](https://img.shields.io/github/license/Anexen/pyxirr.svg)](https://github.com/Anexen/pyxirr/blob/master/LICENSE)
+[![pypi](https://img.shields.io/pypi/v/pyxirr.svg)](https://pypi.org/project/pyxirr/)
+[![versions](https://img.shields.io/pypi/pyversions/pyxirr.svg)](https://pypi.org/project/pyxirr/)
+
+# PyXIRR
+
+Rust-powered collection of financial functions.
+
+PyXIRR stands for "Python XIRR" (for historical reasons), but contains many other financial functions such as IRR, FV, NPV, etc.
+
+Features:
+
+- correct
+- supports different day count conventions (e.g. ACT/360, 30E/360, etc.)
+- works with different input data types (iterators, numpy arrays, pandas DataFrames)
+- no external dependencies
+- type annotations
+- blazingly fast
+
+# Installation
+
+```
+pip install pyxirr
+```
+
+# Benchmarks
+
+Rust implementation has been tested against existing [xirr](https://pypi.org/project/xirr/) package
+(uses [scipy.optimize](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.newton.html) under the hood)
+and the [implementation from the Stack Overflow](https://stackoverflow.com/a/11503492) (pure python).
+
+![bench](https://raw.githubusercontent.com/Anexen/pyxirr/main/docs/static/bench.png)
+
+PyXIRR is ~10-20x faster in XIRR calculation than the other implementations.
+
+Powered by [github-action-benchmark](https://github.com/rhysd/github-action-benchmark) and [plotly.js](https://github.com/plotly/plotly.js).
+
+Live benchmarks are hosted on [Github Pages](https://anexen.github.io/pyxirr/bench).
+
+# Examples
+
+```python
+from datetime import date
+from pyxirr import xirr
+
+dates = [date(2020, 1, 1), date(2021, 1, 1), date(2022, 1, 1)]
+amounts = [-1000, 750, 500]
+
+# feed columnar data
+xirr(dates, amounts)
+# feed iterators
+xirr(iter(dates), (x / 2 for x in amounts))
+# feed an iterable of tuples
+xirr(zip(dates, amounts))
+# feed a dictionary
+xirr(dict(zip(dates, amounts)))
+# dates as strings
+xirr(['2020-01-01', '2021-01-01'], [-1000, 1200])
+```
+
+### Numpy and Pandas
+
+```python
+import numpy as np
+import pandas as pd
+
+# feed numpy array
+xirr(np.array([dates, amounts]))
+xirr(np.array(dates), np.array(amounts))
+
+# feed DataFrame (columns names doesn't matter; ordering matters)
+xirr(pd.DataFrame({"a": dates, "b": amounts}))
+
+# feed Series with DatetimeIndex
+xirr(pd.Series(amounts, index=pd.to_datetime(dates)))
+
+# bonus: apply xirr to a DataFrame with DatetimeIndex:
+df = pd.DataFrame(
+    index=pd.date_range("2021", "2022", freq="MS", closed="left"),
+    data={
+        "one": [-100] + [20] * 11,
+        "two": [-80] + [19] * 11,
+    },
+)
+df.apply(xirr)  # Series(index=["one", "two"], data=[5.09623547168478, 8.780801977141174])
+```
+
+### Day count conventions
+
+Check out the available options on the [docs/day-count-conventions](https://anexen.github.io/pyxirr/functions.html#day-count-conventions).
+
+```python
+from pyxirr import DayCount
+
+xirr(dates, amounts, day_count=DayCount.ACT_360)
+
+# parse day count from string
+xirr(dates, amounts, day_count="30E/360")
+```
+
+### Other financial functions
+
+```python
+import pyxirr
+
+# Future Value
+pyxirr.fv(0.05/12, 10*12, -100, -100)
+
+# Net Present Value
+pyxirr.npv(0, [-40_000, 5_000, 8_000, 12_000, 30_000])
+
+# IRR
+pyxirr.irr([-100, 39, 59, 55, 20])
+
+# ... and more! Check out the docs.
+```
+
+### Vectorization
+
+PyXIRR supports numpy-like vectorization.
+
+If all input is scalar, returns a scalar float. If any input is array_like,
+returns values for each input element. If multiple inputs are
+array_like, performs broadcasting and returns values for each element.
+
+```python
+import pyxirr
+
+# feed list
+pyxirr.fv([0.05/12, 0.06/12], 10*12, -100, -100)
+pyxirr.fv([0.05/12, 0.06/12], [10*12, 9*12], [-100, -200], -100)
+
+# feed numpy array
+import numpy as np
+rates = np.array([0.05, 0.06, 0.07])/12
+pyxirr.fv(rates, 10*12, -100, -100)
+
+# feed any iterable!
+pyxirr.fv(
+    np.linspace(0.01, 0.2, 10),
+    (x + 1 for x in range(10)),
+    range(-100, -1100, -100),
+    tuple(range(-100, -200, -10))
+)
+
+# 2d, 3d, 4d, and more!
+rates = [[[[[[0.01], [0.02]]]]]]
+pyxirr.fv(rates, 10*12, -100, -100)
+```
+
+# API reference
+
+See the [docs](https://anexen.github.io/pyxirr)
+
+# Roadmap
+
+- [x] Implement all functions from [numpy-financial](https://numpy.org/numpy-financial/latest/index.html)
+- [x] Improve docs, add more tests
+- [x] Type hints
+- [x] Vectorized versions of numpy-financial functions.
+- [ ] Compile library for rust/javascript/python
+
+# Development
+
+Running tests with pyo3 is a bit tricky. In short, you need to compile your tests without `extension-module` feature to avoid linking errors.
+See the following issues for the details: [#341](https://github.com/PyO3/pyo3/issues/341), [#771](https://github.com/PyO3/pyo3/issues/771).
+
+If you are using `pyenv`, make sure you have the shared library installed (check for `${PYENV_ROOT}/versions/<version>/lib/libpython3.so` file).
+
+```bash
+$ PYTHON_CONFIGURE_OPTS="--enable-shared" pyenv install <version>
+```
+
+Install dev-requirements
+
+```bash
+$ pip install -r dev-requirements.txt
+```
+
+### Building
+
+```bash
+$ maturin develop
+```
+
+### Testing
+
+```bash
+$ LD_LIBRARY_PATH=${PYENV_ROOT}/versions/3.10.8/lib cargo test --no-default-features --features tests
+```
+
+### Benchmarks
+
+```bash
+$ pip install -r bench-requirements.txt
+$ LD_LIBRARY_PATH=${PYENV_ROOT}/versions/3.10.8/lib cargo +nightly bench --no-default-features --features tests
+```
+
+# Building and distribution
+
+This library uses [maturin](https://github.com/PyO3/maturin) to build and distribute python wheels.
+
+```bash
+$ docker run --rm -v $(pwd):/io ghcr.io/pyo3/maturin build --release --manylinux 2010 --strip
+$ maturin upload target/wheels/pyxirr-${version}*
+```
+
+
+
+%package help
+Summary:	Development documents and examples for pyxirr
+Provides:	python3-pyxirr-doc
+%description help
+[![rust-lang.org](https://img.shields.io/badge/Made%20with-Rust-red)](https://www.rust-lang.org/)
+[![License](https://img.shields.io/github/license/Anexen/pyxirr.svg)](https://github.com/Anexen/pyxirr/blob/master/LICENSE)
+[![pypi](https://img.shields.io/pypi/v/pyxirr.svg)](https://pypi.org/project/pyxirr/)
+[![versions](https://img.shields.io/pypi/pyversions/pyxirr.svg)](https://pypi.org/project/pyxirr/)
+
+# PyXIRR
+
+Rust-powered collection of financial functions.
+
+PyXIRR stands for "Python XIRR" (for historical reasons), but contains many other financial functions such as IRR, FV, NPV, etc.
+
+Features:
+
+- correct
+- supports different day count conventions (e.g. ACT/360, 30E/360, etc.)
+- works with different input data types (iterators, numpy arrays, pandas DataFrames)
+- no external dependencies
+- type annotations
+- blazingly fast
+
+# Installation
+
+```
+pip install pyxirr
+```
+
+# Benchmarks
+
+Rust implementation has been tested against existing [xirr](https://pypi.org/project/xirr/) package
+(uses [scipy.optimize](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.newton.html) under the hood)
+and the [implementation from the Stack Overflow](https://stackoverflow.com/a/11503492) (pure python).
+
+![bench](https://raw.githubusercontent.com/Anexen/pyxirr/main/docs/static/bench.png)
+
+PyXIRR is ~10-20x faster in XIRR calculation than the other implementations.
+
+Powered by [github-action-benchmark](https://github.com/rhysd/github-action-benchmark) and [plotly.js](https://github.com/plotly/plotly.js).
+
+Live benchmarks are hosted on [Github Pages](https://anexen.github.io/pyxirr/bench).
+
+# Examples
+
+```python
+from datetime import date
+from pyxirr import xirr
+
+dates = [date(2020, 1, 1), date(2021, 1, 1), date(2022, 1, 1)]
+amounts = [-1000, 750, 500]
+
+# feed columnar data
+xirr(dates, amounts)
+# feed iterators
+xirr(iter(dates), (x / 2 for x in amounts))
+# feed an iterable of tuples
+xirr(zip(dates, amounts))
+# feed a dictionary
+xirr(dict(zip(dates, amounts)))
+# dates as strings
+xirr(['2020-01-01', '2021-01-01'], [-1000, 1200])
+```
+
+### Numpy and Pandas
+
+```python
+import numpy as np
+import pandas as pd
+
+# feed numpy array
+xirr(np.array([dates, amounts]))
+xirr(np.array(dates), np.array(amounts))
+
+# feed DataFrame (columns names doesn't matter; ordering matters)
+xirr(pd.DataFrame({"a": dates, "b": amounts}))
+
+# feed Series with DatetimeIndex
+xirr(pd.Series(amounts, index=pd.to_datetime(dates)))
+
+# bonus: apply xirr to a DataFrame with DatetimeIndex:
+df = pd.DataFrame(
+    index=pd.date_range("2021", "2022", freq="MS", closed="left"),
+    data={
+        "one": [-100] + [20] * 11,
+        "two": [-80] + [19] * 11,
+    },
+)
+df.apply(xirr)  # Series(index=["one", "two"], data=[5.09623547168478, 8.780801977141174])
+```
+
+### Day count conventions
+
+Check out the available options on the [docs/day-count-conventions](https://anexen.github.io/pyxirr/functions.html#day-count-conventions).
+
+```python
+from pyxirr import DayCount
+
+xirr(dates, amounts, day_count=DayCount.ACT_360)
+
+# parse day count from string
+xirr(dates, amounts, day_count="30E/360")
+```
+
+### Other financial functions
+
+```python
+import pyxirr
+
+# Future Value
+pyxirr.fv(0.05/12, 10*12, -100, -100)
+
+# Net Present Value
+pyxirr.npv(0, [-40_000, 5_000, 8_000, 12_000, 30_000])
+
+# IRR
+pyxirr.irr([-100, 39, 59, 55, 20])
+
+# ... and more! Check out the docs.
+```
+
+### Vectorization
+
+PyXIRR supports numpy-like vectorization.
+
+If all input is scalar, returns a scalar float. If any input is array_like,
+returns values for each input element. If multiple inputs are
+array_like, performs broadcasting and returns values for each element.
+
+```python
+import pyxirr
+
+# feed list
+pyxirr.fv([0.05/12, 0.06/12], 10*12, -100, -100)
+pyxirr.fv([0.05/12, 0.06/12], [10*12, 9*12], [-100, -200], -100)
+
+# feed numpy array
+import numpy as np
+rates = np.array([0.05, 0.06, 0.07])/12
+pyxirr.fv(rates, 10*12, -100, -100)
+
+# feed any iterable!
+pyxirr.fv(
+    np.linspace(0.01, 0.2, 10),
+    (x + 1 for x in range(10)),
+    range(-100, -1100, -100),
+    tuple(range(-100, -200, -10))
+)
+
+# 2d, 3d, 4d, and more!
+rates = [[[[[[0.01], [0.02]]]]]]
+pyxirr.fv(rates, 10*12, -100, -100)
+```
+
+# API reference
+
+See the [docs](https://anexen.github.io/pyxirr)
+
+# Roadmap
+
+- [x] Implement all functions from [numpy-financial](https://numpy.org/numpy-financial/latest/index.html)
+- [x] Improve docs, add more tests
+- [x] Type hints
+- [x] Vectorized versions of numpy-financial functions.
+- [ ] Compile library for rust/javascript/python
+
+# Development
+
+Running tests with pyo3 is a bit tricky. In short, you need to compile your tests without `extension-module` feature to avoid linking errors.
+See the following issues for the details: [#341](https://github.com/PyO3/pyo3/issues/341), [#771](https://github.com/PyO3/pyo3/issues/771).
+
+If you are using `pyenv`, make sure you have the shared library installed (check for `${PYENV_ROOT}/versions/<version>/lib/libpython3.so` file).
+
+```bash
+$ PYTHON_CONFIGURE_OPTS="--enable-shared" pyenv install <version>
+```
+
+Install dev-requirements
+
+```bash
+$ pip install -r dev-requirements.txt
+```
+
+### Building
+
+```bash
+$ maturin develop
+```
+
+### Testing
+
+```bash
+$ LD_LIBRARY_PATH=${PYENV_ROOT}/versions/3.10.8/lib cargo test --no-default-features --features tests
+```
+
+### Benchmarks
+
+```bash
+$ pip install -r bench-requirements.txt
+$ LD_LIBRARY_PATH=${PYENV_ROOT}/versions/3.10.8/lib cargo +nightly bench --no-default-features --features tests
+```
+
+# Building and distribution
+
+This library uses [maturin](https://github.com/PyO3/maturin) to build and distribute python wheels.
+
+```bash
+$ docker run --rm -v $(pwd):/io ghcr.io/pyo3/maturin build --release --manylinux 2010 --strip
+$ maturin upload target/wheels/pyxirr-${version}*
+```
+
+
+
+%prep
+%autosetup -n pyxirr-0.9.0
+
+%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-pyxirr -f filelist.lst
+%dir %{python3_sitearch}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Tue Apr 11 2023 Python_Bot <Python_Bot@openeuler.org> - 0.9.0-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..5b4164e
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+4e008e732f169502c0eaf037939dbad6  pyxirr-0.9.0.tar.gz