Metadata-Version: 2.1
Name: plotxel
Version: 0.0.5
Summary: A wordy but intuitive plotting library.
Home-page: https://github.com/danhitchcock/plotxel
Author: Daniel Hitchcock
Author-email: daniel.s.hitchcock@gmail.com
License: UNKNOWN
Description: # Plotxel
        
        *Control your plots down to the pixel!*  
        Ever have trouble moving a chart to the right? Moving your axis up? Getting rid of ticks? Then try out Plotxel!
        
        It's wordy, slow, and unnecessary 99% of the time. But that 1%, you'll be glad you have Plotxel.
        
        ## Installation
        
            pip3 install plotxel
            
        ## Example
        
        ![Example Image](https://github.com/danhitchcock/plotxel/wiki/example2.png)
        ```python
        from plotxel import Plotxel, Axis
        
        x = Plotxel((800, 500))  # our main drawing canvas in x, y
        
        # add some data as a series. The series name, the x data, and y data
        series1 = [i for i in range(10)]
        x.add_data('series1', series1, series1)
        x.add_data('series2', [1, 2, 3, 4, 5], [1, 2, 3, 4, 5])
        
        # left plot -- its name, type, and data it's linked to
        plot1 = x.add_drawable("plot1", "Scatter", "series1")
        plot1.title = 'Analysis of Goose Encounters'
        plot1.pos = [60, 50]
        
        # right plot and its position. Same data as plot1
        plot2 = x.add_drawable("plot2", "Scatter", "series1")
        # set a bunch of attributes at once!
        # since the default plot size is 300px, 360 will place 10 blank pixels between the graphs
        plot2.setattrs(
            pos=[450, 50],
            marker_shape='square',
            marker_fill_color=(255, 0, 0),
            title='Analysis of Goose Encounters (red)',
            line_width=0
        )
        
        # add some axes, and link them to our plots. It will copy the size, position, scale, and limits of whichever plot it is linked to
        ax1 = x.add_drawable("ax1", 'YAxis', link_to="plot1")
        ax1.axis_offset = 10
        ax1.title_offset = 25  # distance from the ticks. Will have an auto feature in the future!
        ax1.title = "Near Death Experiences With Geese"
        
        # all other axes, let's put them flush with the graph by changing the default
        # defaults are copied at the time the object is initialized, so this won't affect ax1
        Axis.defaults['axis_offset'] = -1
        ax1b = x.add_drawable('ax1b', 'XAxis', link_to='plot1')
        
        # you can keep setting attributes in bulk
        ax1r = x.add_drawable('ax1r', 'YAxis', link_to='plot1', title_offset=20)
        ax1r.setattrs(
            side='right',
            title_offset=20,
            title='Ax1 Right Title'
        )
        
        # or use the constructor!
        ax2 = x.add_drawable("ax2", 'YAxis', link_to="plot2", title_offset=20, side='right', axis_offset=10)
        
        ax3 = x.add_drawable("ax3", 'XAxis', link_to="plot2")
        ax3.setattrs(
            side='bottom',
            axis_offset=10,
            title="Number of Freaking Geese",
        )
        
        
        # I think I would prefer axes to be blue!
        Axis.defaults['color'] = (0, 0, 255)
        
        plot3_attrs = {
            'pos': (60, 300),
            'ylim': [0, 10],
            'title': 'Near Death Experiences With Geese'
        }
        plot3 = x.add_drawable('bar1', 'Bar', 'series2')
        # or unpack a dict
        plot3.setattrs(**plot3_attrs)
        
        ax4 = x.add_drawable('ax4', 'YAxis', link_to="bar1", title='Near Death Experiences With Geese', title_offset=25)
        
        # coming soon, Jupyter magic!
        x.show()
        
        # or for SVG
        # svg_html = x.draw()
        
        # or for image  in BytesIO / save to filename
        # x.render(filename='example2.png')
        
        #x.anti_aliasing=False
        # quick test! another test
        #x.show()
        ```
            
        
        
            
        This program is being developed based on my own needs, and unfortunately I don't do a lot of plotting today, therefore I don't need a lot of features.
        
        In any case, I'll be prioritizing features, up next is bar charts and histograms! 
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
