From 6759a164106f4e845d121eac41308ab1171c6bc2 Mon Sep 17 00:00:00 2001 From: CoprDistGit Date: Mon, 29 May 2023 12:57:10 +0000 Subject: automatic import of python-ridgeplot --- python-ridgeplot.spec | 468 ++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 468 insertions(+) create mode 100644 python-ridgeplot.spec (limited to 'python-ridgeplot.spec') diff --git a/python-ridgeplot.spec b/python-ridgeplot.spec new file mode 100644 index 0000000..dbf4a23 --- /dev/null +++ b/python-ridgeplot.spec @@ -0,0 +1,468 @@ +%global _empty_manifest_terminate_build 0 +Name: python-ridgeplot +Version: 0.1.21 +Release: 1 +Summary: Beautiful ridgeline plots in python +License: MIT +URL: https://github.com/tpvasconcelos/ridgeplot +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/53/26/69baca59a24b98054a742575b24c064bca717a30134401bceecae4c14564/ridgeplot-0.1.21.tar.gz +BuildArch: noarch + +Requires: python3-numpy +Requires: python3-plotly +Requires: python3-statsmodels + +%description +

+ ridgeplot - beautiful ridgeline plots in Python +

+ +

+ ridgeplot: beautiful ridgeline plots in Python +

+ +

+ + + PyPI - Latest Release + PyPI - Python Versions + PyPI - Package Status + PyPI - License +
+ GitHub CI + Docs + codecov + CodeFactor + Codacy code quality +

+ + + +______________________________________________________________________ + +The `ridgeplot` python library aims at providing a simple API for plotting beautiful +[ridgeline plots](https://www.data-to-viz.com/graph/ridgeline.html) within the extensive +[Plotly](https://plotly.com/python/) interactive graphing environment. + +Bumper stickers: + +- Do one thing, and do it well! +- Use sensible defaults, but allow for extensive configuration! + +## How to get it? + +The source code is currently hosted on GitHub at: + +Install and update using [pip](https://pip.pypa.io/en/stable/quickstart/): + +```shell +pip install -U ridgeplot +``` + +### Dependencies + +- [plotly](https://plotly.com/) - the interactive graphing backend that powers `ridgeplot` +- [statsmodels](https://www.statsmodels.org/) - Used for Kernel Density Estimation (KDE) +- [numpy](https://numpy.org/) - Supporting library for multi-dimensional array manipulations + +## How to use it? + +The official docs can be found at: https://ridgeplot.readthedocs.io/en/stable/ + +### Sensible defaults + +```python +import numpy as np + +from ridgeplot import ridgeplot + +# Put your real samples here... +np.random.seed(0) +synthetic_samples = [np.random.normal(n / 1.2, size=600) for n in range(9, 0, -1)] + +# Call the `ridgeplot()` helper, packed with sensible defaults +fig = ridgeplot(samples=synthetic_samples) + +# The returned Plotly `Figure` is still fully customizable +fig.update_layout(height=500, width=800) + +# show us the work! +fig.show() +``` + +![ridgeline plot example using the ridgeplot Python library](docs/_static/img/example_simple.png) + +### Fully configurable + +In this example, we will be replicating the first ridgeline plot example in +[this _from Data to Viz_ post](https://www.data-to-viz.com/graph/ridgeline.html), which uses the +_probly_ dataset. You can find the _plobly_ dataset on multiple sources like in the +[bokeh](https://raw.githubusercontent.com/bokeh/bokeh/17a0b288052afac80ebcf0aa74e3915452fce3ca/src/bokeh/sampledata/_data/probly.csv) +python interactive visualization library. I'll be using the +[same source](https://raw.githubusercontent.com/zonination/perceptions/51207062aa173777264d3acce0131e1e2456d966/probly.csv) +used in the original post. + +```python +import numpy as np +from ridgeplot import ridgeplot +from ridgeplot.datasets import load_probly + +# Load the probly dataset +df = load_probly() + +# Let's grab only the subset of columns displayed in the example +column_names = [ + "Almost Certainly", "Very Good Chance", "We Believe", "Likely", + "About Even", "Little Chance", "Chances Are Slight", "Almost No Chance", +] # fmt: skip +df = df[column_names] + +# Not only does 'ridgeplot(...)' come configured with sensible defaults +# but is also fully configurable to your own style and preference! +fig = ridgeplot( + samples=df.values.T, + bandwidth=4, + kde_points=np.linspace(-12.5, 112.5, 400), + colorscale="viridis", + colormode="index", + coloralpha=0.6, + labels=column_names, + spacing=5 / 9, +) + +# Again, update the figure layout to your liking here +fig.update_layout( + title="What probability would you assign to the phrase “Highly likely”?", + height=650, + width=800, + plot_bgcolor="rgba(255, 255, 255, 0.0)", + xaxis_gridcolor="rgba(0, 0, 0, 0.1)", + yaxis_gridcolor="rgba(0, 0, 0, 0.1)", + yaxis_title="Assigned Probability (%)", +) +fig.show() +``` + +![ridgeline plot of the probly dataset using the ridgeplot Python library](docs/_static/img/example_probly.png) + + +%package -n python3-ridgeplot +Summary: Beautiful ridgeline plots in python +Provides: python-ridgeplot +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-ridgeplot +

+ ridgeplot - beautiful ridgeline plots in Python +

+ +

+ ridgeplot: beautiful ridgeline plots in Python +

+ +

+ + + PyPI - Latest Release + PyPI - Python Versions + PyPI - Package Status + PyPI - License +
+ GitHub CI + Docs + codecov + CodeFactor + Codacy code quality +

+ + + +______________________________________________________________________ + +The `ridgeplot` python library aims at providing a simple API for plotting beautiful +[ridgeline plots](https://www.data-to-viz.com/graph/ridgeline.html) within the extensive +[Plotly](https://plotly.com/python/) interactive graphing environment. + +Bumper stickers: + +- Do one thing, and do it well! +- Use sensible defaults, but allow for extensive configuration! + +## How to get it? + +The source code is currently hosted on GitHub at: + +Install and update using [pip](https://pip.pypa.io/en/stable/quickstart/): + +```shell +pip install -U ridgeplot +``` + +### Dependencies + +- [plotly](https://plotly.com/) - the interactive graphing backend that powers `ridgeplot` +- [statsmodels](https://www.statsmodels.org/) - Used for Kernel Density Estimation (KDE) +- [numpy](https://numpy.org/) - Supporting library for multi-dimensional array manipulations + +## How to use it? + +The official docs can be found at: https://ridgeplot.readthedocs.io/en/stable/ + +### Sensible defaults + +```python +import numpy as np + +from ridgeplot import ridgeplot + +# Put your real samples here... +np.random.seed(0) +synthetic_samples = [np.random.normal(n / 1.2, size=600) for n in range(9, 0, -1)] + +# Call the `ridgeplot()` helper, packed with sensible defaults +fig = ridgeplot(samples=synthetic_samples) + +# The returned Plotly `Figure` is still fully customizable +fig.update_layout(height=500, width=800) + +# show us the work! +fig.show() +``` + +![ridgeline plot example using the ridgeplot Python library](docs/_static/img/example_simple.png) + +### Fully configurable + +In this example, we will be replicating the first ridgeline plot example in +[this _from Data to Viz_ post](https://www.data-to-viz.com/graph/ridgeline.html), which uses the +_probly_ dataset. You can find the _plobly_ dataset on multiple sources like in the +[bokeh](https://raw.githubusercontent.com/bokeh/bokeh/17a0b288052afac80ebcf0aa74e3915452fce3ca/src/bokeh/sampledata/_data/probly.csv) +python interactive visualization library. I'll be using the +[same source](https://raw.githubusercontent.com/zonination/perceptions/51207062aa173777264d3acce0131e1e2456d966/probly.csv) +used in the original post. + +```python +import numpy as np +from ridgeplot import ridgeplot +from ridgeplot.datasets import load_probly + +# Load the probly dataset +df = load_probly() + +# Let's grab only the subset of columns displayed in the example +column_names = [ + "Almost Certainly", "Very Good Chance", "We Believe", "Likely", + "About Even", "Little Chance", "Chances Are Slight", "Almost No Chance", +] # fmt: skip +df = df[column_names] + +# Not only does 'ridgeplot(...)' come configured with sensible defaults +# but is also fully configurable to your own style and preference! +fig = ridgeplot( + samples=df.values.T, + bandwidth=4, + kde_points=np.linspace(-12.5, 112.5, 400), + colorscale="viridis", + colormode="index", + coloralpha=0.6, + labels=column_names, + spacing=5 / 9, +) + +# Again, update the figure layout to your liking here +fig.update_layout( + title="What probability would you assign to the phrase “Highly likely”?", + height=650, + width=800, + plot_bgcolor="rgba(255, 255, 255, 0.0)", + xaxis_gridcolor="rgba(0, 0, 0, 0.1)", + yaxis_gridcolor="rgba(0, 0, 0, 0.1)", + yaxis_title="Assigned Probability (%)", +) +fig.show() +``` + +![ridgeline plot of the probly dataset using the ridgeplot Python library](docs/_static/img/example_probly.png) + + +%package help +Summary: Development documents and examples for ridgeplot +Provides: python3-ridgeplot-doc +%description help +

+ ridgeplot - beautiful ridgeline plots in Python +

+ +

+ ridgeplot: beautiful ridgeline plots in Python +

+ +

+ + + PyPI - Latest Release + PyPI - Python Versions + PyPI - Package Status + PyPI - License +
+ GitHub CI + Docs + codecov + CodeFactor + Codacy code quality +

+ + + +______________________________________________________________________ + +The `ridgeplot` python library aims at providing a simple API for plotting beautiful +[ridgeline plots](https://www.data-to-viz.com/graph/ridgeline.html) within the extensive +[Plotly](https://plotly.com/python/) interactive graphing environment. + +Bumper stickers: + +- Do one thing, and do it well! +- Use sensible defaults, but allow for extensive configuration! + +## How to get it? + +The source code is currently hosted on GitHub at: + +Install and update using [pip](https://pip.pypa.io/en/stable/quickstart/): + +```shell +pip install -U ridgeplot +``` + +### Dependencies + +- [plotly](https://plotly.com/) - the interactive graphing backend that powers `ridgeplot` +- [statsmodels](https://www.statsmodels.org/) - Used for Kernel Density Estimation (KDE) +- [numpy](https://numpy.org/) - Supporting library for multi-dimensional array manipulations + +## How to use it? + +The official docs can be found at: https://ridgeplot.readthedocs.io/en/stable/ + +### Sensible defaults + +```python +import numpy as np + +from ridgeplot import ridgeplot + +# Put your real samples here... +np.random.seed(0) +synthetic_samples = [np.random.normal(n / 1.2, size=600) for n in range(9, 0, -1)] + +# Call the `ridgeplot()` helper, packed with sensible defaults +fig = ridgeplot(samples=synthetic_samples) + +# The returned Plotly `Figure` is still fully customizable +fig.update_layout(height=500, width=800) + +# show us the work! +fig.show() +``` + +![ridgeline plot example using the ridgeplot Python library](docs/_static/img/example_simple.png) + +### Fully configurable + +In this example, we will be replicating the first ridgeline plot example in +[this _from Data to Viz_ post](https://www.data-to-viz.com/graph/ridgeline.html), which uses the +_probly_ dataset. You can find the _plobly_ dataset on multiple sources like in the +[bokeh](https://raw.githubusercontent.com/bokeh/bokeh/17a0b288052afac80ebcf0aa74e3915452fce3ca/src/bokeh/sampledata/_data/probly.csv) +python interactive visualization library. I'll be using the +[same source](https://raw.githubusercontent.com/zonination/perceptions/51207062aa173777264d3acce0131e1e2456d966/probly.csv) +used in the original post. + +```python +import numpy as np +from ridgeplot import ridgeplot +from ridgeplot.datasets import load_probly + +# Load the probly dataset +df = load_probly() + +# Let's grab only the subset of columns displayed in the example +column_names = [ + "Almost Certainly", "Very Good Chance", "We Believe", "Likely", + "About Even", "Little Chance", "Chances Are Slight", "Almost No Chance", +] # fmt: skip +df = df[column_names] + +# Not only does 'ridgeplot(...)' come configured with sensible defaults +# but is also fully configurable to your own style and preference! +fig = ridgeplot( + samples=df.values.T, + bandwidth=4, + kde_points=np.linspace(-12.5, 112.5, 400), + colorscale="viridis", + colormode="index", + coloralpha=0.6, + labels=column_names, + spacing=5 / 9, +) + +# Again, update the figure layout to your liking here +fig.update_layout( + title="What probability would you assign to the phrase “Highly likely”?", + height=650, + width=800, + plot_bgcolor="rgba(255, 255, 255, 0.0)", + xaxis_gridcolor="rgba(0, 0, 0, 0.1)", + yaxis_gridcolor="rgba(0, 0, 0, 0.1)", + yaxis_title="Assigned Probability (%)", +) +fig.show() +``` + +![ridgeline plot of the probly dataset using the ridgeplot Python library](docs/_static/img/example_probly.png) + + +%prep +%autosetup -n ridgeplot-0.1.21 + +%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-ridgeplot -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Mon May 29 2023 Python_Bot - 0.1.21-1 +- Package Spec generated -- cgit v1.2.3