automatic import of python-chartopeneuler20.03

3 files changed, 680 insertions, 0 deletions

diff --git a/.gitignore b/.gitignore
index e69de29..7aacbe2 100644
--- a/.gitignore
+++ b/.gitignore
@@ -0,0 +1 @@
+/chart-0.2.3.tar.gz
diff --git a/python-chart.spec b/python-chart.spec
new file mode 100644
index 0000000..4173a0e
--- /dev/null
+++ b/python-chart.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-chart
+Version:	0.2.3
+Release:	1
+Summary:	chart
+License:	MIT
+URL:		https://github.com/maxhumber/chart
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/b3/e0/b10edf6b4ed5d4bc26b8d9b63d769b85efac89f186a733c73147561f1dd3/chart-0.2.3.tar.gz
+BuildArch:	noarch
+
+
+%description
+<h3 align="center">
+  <img src="https://raw.githubusercontent.com/maxhumber/chart/master/images/logo.png" width="400px" alt="chart">
+</h3>
+<p align="center">
+  <a href="https://opensource.org/licenses/MIT"><img alt="MIT" src="https://img.shields.io/github/license/maxhumber/chart.svg"></a>
+  <a href="https://travis-ci.org/maxhumber/chart"><img alt="Travis" src="https://img.shields.io/travis/maxhumber/chart.svg"></a>
+  <a href="https://pypi.python.org/pypi/chart"><img alt="PyPI" src="https://img.shields.io/pypi/v/chart.svg"></a>
+  <a href="https://pypi.python.org/pypi/chart"><img alt="Downloads" src="https://img.shields.io/pypi/dm/chart.svg"></a>
+</p>
+
+A zero-dependency python package that prints basic charts to a Jupyter output
+
+Charts supported:
+
+- Bar graphs
+- Scatter plots
+- Histograms
+- 🍑📊👏
+
+#### Examples
+
+Bar graphs can be drawn quickly with the `bar` function:
+
+```python
+from chart import bar
+
+x = [500, 200, 900, 400]
+y = ['marc', 'mummify', 'chart', 'sausagelink']
+
+bar(x, y)
+```
+
+```
+       marc: ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇             
+    mummify: ▇▇▇▇▇▇▇                       
+      chart: ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇
+sausagelink: ▇▇▇▇▇▇▇▇▇▇▇▇▇                              
+```
+
+And the `bar` function can accept columns from a `pd.DataFrame`:
+
+```python
+from chart import bar
+import pandas as pd
+
+df = pd.DataFrame({
+    'artist': ['Tame Impala', 'Childish Gambino', 'The Knocks'],
+    'listens': [8_456_831, 18_185_245, 2_556_448]
+})
+bar(df.listens, df.artist, width=20, label_width=11, mark='🔊')
+```
+
+```
+Tame Impala: 🔊🔊🔊🔊🔊🔊🔊🔊🔊           
+Childish Ga: 🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊
+ The Knocks: 🔊🔊🔊                                
+```
+
+Histograms are just as easy:
+
+```python
+from chart import histogram
+
+x = [1, 2, 4, 3, 3, 1, 7, 9, 9, 1, 3, 2, 1, 2]
+
+histogram(x)
+```
+
+```
+▇        
+▇        
+▇        
+▇        
+▇ ▇      
+▇ ▇      
+▇ ▇      
+▇ ▇     ▇
+▇ ▇     ▇
+▇ ▇   ▇ ▇
+```
+
+And they can accept objects created by `scipy`:
+
+```python
+from chart import histogram
+import scipy.stats as stats
+import numpy as np
+
+np.random.seed(14)
+n = stats.norm(loc=0, scale=10)
+
+histogram(n.rvs(100), bins=14, height=7, mark='🍑')
+```
+
+```
+            🍑              
+            🍑   🍑          
+            🍑 🍑 🍑          
+            🍑 🍑 🍑          
+        🍑   🍑 🍑 🍑          
+      🍑 🍑 🍑 🍑 🍑 🍑 🍑 🍑 🍑    
+      🍑 🍑 🍑 🍑 🍑 🍑 🍑 🍑 🍑   🍑
+```
+
+Scatter plots can be drawn with a simple `scatter` call:
+
+```python
+from chart import scatter
+
+x = range(0, 20)
+y = range(0, 20)
+
+scatter(x, y)
+```
+
+```python
+                                       •
+                                   • •  
+                                 •      
+                             • •        
+                         • •            
+                       •                
+                  •  •                  
+                •                       
+            • •                         
+        • •                             
+      •                                 
+  • •                                   
+•                                       
+```
+
+And at this point you gotta know it works with any `np.array`:
+
+```python
+from chart import scatter
+import numpy as np
+
+np.random.seed(1)
+N = 100
+x = np.random.normal(100, 50, size=N)
+y = x * -2 + 25 + np.random.normal(0, 25, size=N)
+
+scatter(x, y, width=20, height=9, mark='^')
+```
+
+```
+^^                  
+ ^                  
+    ^^^             
+    ^^^^^^^         
+       ^^^^^^       
+        ^^^^^^^     
+            ^^^^    
+             ^^^^^ ^
+                ^^ ^
+```
+
+In fact, all `chart` functions work with pandas, numpy, scipy and regular python objects.
+
+#### Preprocessors
+
+In order to create the simple outputs generated by `bar`, `histogram`, and `scatter` I had to create a couple of preprocessors, namely: `NumberBinarizer` and `RangeScaler`.
+
+I tried to adhere to the scikit-learn API in their construction. Although you won't need them to use `chart` here they are for your tinkering:
+
+```python
+from chart.preprocessing import NumberBinarizer
+
+nb = NumberBinarizer(bins=4)
+x = range(10)
+nb.fit(x)
+nb.transform(x)
+```
+
+```
+[0, 0, 0, 1, 1, 2, 2, 3, 3, 3]
+```
+
+```python
+from chart.preprocessing import RangeScaler
+
+rs = RangeScaler(out_range=(0, 10), round=False)
+x = range(50, 59)
+rs.fit_transform(x)
+```
+
+```
+[0.0, 1.25, 2.5, 3.75, 5.0, 6.25, 7.5, 8.75, 10.0]
+```
+
+#### Installation
+
+```python
+pip install chart
+```
+
+#### Contribute
+
+For feature requests or bug reports, please use [Github Issues](https://github.com/maxhumber/chart/issues)
+
+#### Inspiration
+
+I wanted a super-light-weight library that would allow me to quickly grok data. Matplotlib had too many dependencies, and Altair seemed overkill. Though I really like the idea of [termgraph](https://github.com/mkaz/termgraph), it didn't really fit well or integrate with my Jupyter workflow. Here's to `chart` 🥂 (still can't believe I got it on [PyPI](https://pypi.org/project/chart/))
+
+%package -n python3-chart
+Summary:	chart
+Provides:	python-chart
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-chart
+<h3 align="center">
+  <img src="https://raw.githubusercontent.com/maxhumber/chart/master/images/logo.png" width="400px" alt="chart">
+</h3>
+<p align="center">
+  <a href="https://opensource.org/licenses/MIT"><img alt="MIT" src="https://img.shields.io/github/license/maxhumber/chart.svg"></a>
+  <a href="https://travis-ci.org/maxhumber/chart"><img alt="Travis" src="https://img.shields.io/travis/maxhumber/chart.svg"></a>
+  <a href="https://pypi.python.org/pypi/chart"><img alt="PyPI" src="https://img.shields.io/pypi/v/chart.svg"></a>
+  <a href="https://pypi.python.org/pypi/chart"><img alt="Downloads" src="https://img.shields.io/pypi/dm/chart.svg"></a>
+</p>
+
+A zero-dependency python package that prints basic charts to a Jupyter output
+
+Charts supported:
+
+- Bar graphs
+- Scatter plots
+- Histograms
+- 🍑📊👏
+
+#### Examples
+
+Bar graphs can be drawn quickly with the `bar` function:
+
+```python
+from chart import bar
+
+x = [500, 200, 900, 400]
+y = ['marc', 'mummify', 'chart', 'sausagelink']
+
+bar(x, y)
+```
+
+```
+       marc: ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇             
+    mummify: ▇▇▇▇▇▇▇                       
+      chart: ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇
+sausagelink: ▇▇▇▇▇▇▇▇▇▇▇▇▇                              
+```
+
+And the `bar` function can accept columns from a `pd.DataFrame`:
+
+```python
+from chart import bar
+import pandas as pd
+
+df = pd.DataFrame({
+    'artist': ['Tame Impala', 'Childish Gambino', 'The Knocks'],
+    'listens': [8_456_831, 18_185_245, 2_556_448]
+})
+bar(df.listens, df.artist, width=20, label_width=11, mark='🔊')
+```
+
+```
+Tame Impala: 🔊🔊🔊🔊🔊🔊🔊🔊🔊           
+Childish Ga: 🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊
+ The Knocks: 🔊🔊🔊                                
+```
+
+Histograms are just as easy:
+
+```python
+from chart import histogram
+
+x = [1, 2, 4, 3, 3, 1, 7, 9, 9, 1, 3, 2, 1, 2]
+
+histogram(x)
+```
+
+```
+▇        
+▇        
+▇        
+▇        
+▇ ▇      
+▇ ▇      
+▇ ▇      
+▇ ▇     ▇
+▇ ▇     ▇
+▇ ▇   ▇ ▇
+```
+
+And they can accept objects created by `scipy`:
+
+```python
+from chart import histogram
+import scipy.stats as stats
+import numpy as np
+
+np.random.seed(14)
+n = stats.norm(loc=0, scale=10)
+
+histogram(n.rvs(100), bins=14, height=7, mark='🍑')
+```
+
+```
+            🍑              
+            🍑   🍑          
+            🍑 🍑 🍑          
+            🍑 🍑 🍑          
+        🍑   🍑 🍑 🍑          
+      🍑 🍑 🍑 🍑 🍑 🍑 🍑 🍑 🍑    
+      🍑 🍑 🍑 🍑 🍑 🍑 🍑 🍑 🍑   🍑
+```
+
+Scatter plots can be drawn with a simple `scatter` call:
+
+```python
+from chart import scatter
+
+x = range(0, 20)
+y = range(0, 20)
+
+scatter(x, y)
+```
+
+```python
+                                       •
+                                   • •  
+                                 •      
+                             • •        
+                         • •            
+                       •                
+                  •  •                  
+                •                       
+            • •                         
+        • •                             
+      •                                 
+  • •                                   
+•                                       
+```
+
+And at this point you gotta know it works with any `np.array`:
+
+```python
+from chart import scatter
+import numpy as np
+
+np.random.seed(1)
+N = 100
+x = np.random.normal(100, 50, size=N)
+y = x * -2 + 25 + np.random.normal(0, 25, size=N)
+
+scatter(x, y, width=20, height=9, mark='^')
+```
+
+```
+^^                  
+ ^                  
+    ^^^             
+    ^^^^^^^         
+       ^^^^^^       
+        ^^^^^^^     
+            ^^^^    
+             ^^^^^ ^
+                ^^ ^
+```
+
+In fact, all `chart` functions work with pandas, numpy, scipy and regular python objects.
+
+#### Preprocessors
+
+In order to create the simple outputs generated by `bar`, `histogram`, and `scatter` I had to create a couple of preprocessors, namely: `NumberBinarizer` and `RangeScaler`.
+
+I tried to adhere to the scikit-learn API in their construction. Although you won't need them to use `chart` here they are for your tinkering:
+
+```python
+from chart.preprocessing import NumberBinarizer
+
+nb = NumberBinarizer(bins=4)
+x = range(10)
+nb.fit(x)
+nb.transform(x)
+```
+
+```
+[0, 0, 0, 1, 1, 2, 2, 3, 3, 3]
+```
+
+```python
+from chart.preprocessing import RangeScaler
+
+rs = RangeScaler(out_range=(0, 10), round=False)
+x = range(50, 59)
+rs.fit_transform(x)
+```
+
+```
+[0.0, 1.25, 2.5, 3.75, 5.0, 6.25, 7.5, 8.75, 10.0]
+```
+
+#### Installation
+
+```python
+pip install chart
+```
+
+#### Contribute
+
+For feature requests or bug reports, please use [Github Issues](https://github.com/maxhumber/chart/issues)
+
+#### Inspiration
+
+I wanted a super-light-weight library that would allow me to quickly grok data. Matplotlib had too many dependencies, and Altair seemed overkill. Though I really like the idea of [termgraph](https://github.com/mkaz/termgraph), it didn't really fit well or integrate with my Jupyter workflow. Here's to `chart` 🥂 (still can't believe I got it on [PyPI](https://pypi.org/project/chart/))
+
+%package help
+Summary:	Development documents and examples for chart
+Provides:	python3-chart-doc
+%description help
+<h3 align="center">
+  <img src="https://raw.githubusercontent.com/maxhumber/chart/master/images/logo.png" width="400px" alt="chart">
+</h3>
+<p align="center">
+  <a href="https://opensource.org/licenses/MIT"><img alt="MIT" src="https://img.shields.io/github/license/maxhumber/chart.svg"></a>
+  <a href="https://travis-ci.org/maxhumber/chart"><img alt="Travis" src="https://img.shields.io/travis/maxhumber/chart.svg"></a>
+  <a href="https://pypi.python.org/pypi/chart"><img alt="PyPI" src="https://img.shields.io/pypi/v/chart.svg"></a>
+  <a href="https://pypi.python.org/pypi/chart"><img alt="Downloads" src="https://img.shields.io/pypi/dm/chart.svg"></a>
+</p>
+
+A zero-dependency python package that prints basic charts to a Jupyter output
+
+Charts supported:
+
+- Bar graphs
+- Scatter plots
+- Histograms
+- 🍑📊👏
+
+#### Examples
+
+Bar graphs can be drawn quickly with the `bar` function:
+
+```python
+from chart import bar
+
+x = [500, 200, 900, 400]
+y = ['marc', 'mummify', 'chart', 'sausagelink']
+
+bar(x, y)
+```
+
+```
+       marc: ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇             
+    mummify: ▇▇▇▇▇▇▇                       
+      chart: ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇
+sausagelink: ▇▇▇▇▇▇▇▇▇▇▇▇▇                              
+```
+
+And the `bar` function can accept columns from a `pd.DataFrame`:
+
+```python
+from chart import bar
+import pandas as pd
+
+df = pd.DataFrame({
+    'artist': ['Tame Impala', 'Childish Gambino', 'The Knocks'],
+    'listens': [8_456_831, 18_185_245, 2_556_448]
+})
+bar(df.listens, df.artist, width=20, label_width=11, mark='🔊')
+```
+
+```
+Tame Impala: 🔊🔊🔊🔊🔊🔊🔊🔊🔊           
+Childish Ga: 🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊🔊
+ The Knocks: 🔊🔊🔊                                
+```
+
+Histograms are just as easy:
+
+```python
+from chart import histogram
+
+x = [1, 2, 4, 3, 3, 1, 7, 9, 9, 1, 3, 2, 1, 2]
+
+histogram(x)
+```
+
+```
+▇        
+▇        
+▇        
+▇        
+▇ ▇      
+▇ ▇      
+▇ ▇      
+▇ ▇     ▇
+▇ ▇     ▇
+▇ ▇   ▇ ▇
+```
+
+And they can accept objects created by `scipy`:
+
+```python
+from chart import histogram
+import scipy.stats as stats
+import numpy as np
+
+np.random.seed(14)
+n = stats.norm(loc=0, scale=10)
+
+histogram(n.rvs(100), bins=14, height=7, mark='🍑')
+```
+
+```
+            🍑              
+            🍑   🍑          
+            🍑 🍑 🍑          
+            🍑 🍑 🍑          
+        🍑   🍑 🍑 🍑          
+      🍑 🍑 🍑 🍑 🍑 🍑 🍑 🍑 🍑    
+      🍑 🍑 🍑 🍑 🍑 🍑 🍑 🍑 🍑   🍑
+```
+
+Scatter plots can be drawn with a simple `scatter` call:
+
+```python
+from chart import scatter
+
+x = range(0, 20)
+y = range(0, 20)
+
+scatter(x, y)
+```
+
+```python
+                                       •
+                                   • •  
+                                 •      
+                             • •        
+                         • •            
+                       •                
+                  •  •                  
+                •                       
+            • •                         
+        • •                             
+      •                                 
+  • •                                   
+•                                       
+```
+
+And at this point you gotta know it works with any `np.array`:
+
+```python
+from chart import scatter
+import numpy as np
+
+np.random.seed(1)
+N = 100
+x = np.random.normal(100, 50, size=N)
+y = x * -2 + 25 + np.random.normal(0, 25, size=N)
+
+scatter(x, y, width=20, height=9, mark='^')
+```
+
+```
+^^                  
+ ^                  
+    ^^^             
+    ^^^^^^^         
+       ^^^^^^       
+        ^^^^^^^     
+            ^^^^    
+             ^^^^^ ^
+                ^^ ^
+```
+
+In fact, all `chart` functions work with pandas, numpy, scipy and regular python objects.
+
+#### Preprocessors
+
+In order to create the simple outputs generated by `bar`, `histogram`, and `scatter` I had to create a couple of preprocessors, namely: `NumberBinarizer` and `RangeScaler`.
+
+I tried to adhere to the scikit-learn API in their construction. Although you won't need them to use `chart` here they are for your tinkering:
+
+```python
+from chart.preprocessing import NumberBinarizer
+
+nb = NumberBinarizer(bins=4)
+x = range(10)
+nb.fit(x)
+nb.transform(x)
+```
+
+```
+[0, 0, 0, 1, 1, 2, 2, 3, 3, 3]
+```
+
+```python
+from chart.preprocessing import RangeScaler
+
+rs = RangeScaler(out_range=(0, 10), round=False)
+x = range(50, 59)
+rs.fit_transform(x)
+```
+
+```
+[0.0, 1.25, 2.5, 3.75, 5.0, 6.25, 7.5, 8.75, 10.0]
+```
+
+#### Installation
+
+```python
+pip install chart
+```
+
+#### Contribute
+
+For feature requests or bug reports, please use [Github Issues](https://github.com/maxhumber/chart/issues)
+
+#### Inspiration
+
+I wanted a super-light-weight library that would allow me to quickly grok data. Matplotlib had too many dependencies, and Altair seemed overkill. Though I really like the idea of [termgraph](https://github.com/mkaz/termgraph), it didn't really fit well or integrate with my Jupyter workflow. Here's to `chart` 🥂 (still can't believe I got it on [PyPI](https://pypi.org/project/chart/))
+
+%prep
+%autosetup -n chart-0.2.3
+
+%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-chart -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 0.2.3-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..8dd445e
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+8b0cfff12f565bf59bd92de4e7efd49d  chart-0.2.3.tar.gz