A wordy but intuitive plotting library.

# 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

from plotxel import Plotxel, Axis

x = Plotxel()  # 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('series2', [1, 2, 3, 4, 5, 10], [5, 2, 1, 4, 3, 10])
x.add_data('series3', [10, 5, 4, 3, 2, 1], [5, 2, 1, 4, 3, 10])

# left plot -- its name, type, and data it's linked to
plot1 = x.add_drawable("plot1", "Scatter", ["series1", 'series2', 'series3'])
plot1.title = 'Analysis of Goose Encounters'
plot1.pos = [60, 50]
plot1.title_offset = 23
plot1.marker_opacity = {.5}  # this must be a set so it can iterate through data. Will make this more intuitive

# right plot and its position. Same data as plot1
# set a bunch of attributes at once!
plot2.setattrs(
ylim=[-1, 10],
xlim=[-1, 10],
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.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

# you can keep setting attributes in bulk
ax1r.setattrs(
side='right',
title_offset=20,
title='Ax1 Right Title'
)

ax1t.setattrs(
side='top',
title=''
)

# or use the constructor!

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)

# let's add some bar chart data. Since it's a vertical bar chart, we will pull Y data
# the labels aren't implemented quite yet
x.add_data('bar_data', ['Sunday', 'Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday'], [1, 9, 4, 5, 3, 6, 2])
x.add_data('bar_data2', ['Sunday', 'Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday'], [1, 7, 4, 3, 4, 5, 1])
x.add_data('bar_data3', ['Sunday', 'Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday'], [-3, 14, 2, 1, 2, 7, 9])

plot3 = x.add_drawable('bar1', 'Bar', ['bar_data', 'bar_data2', 'bar_data3'])
# or unpack a dict
plot3_attrs = {
'pos': (150, 300),
'dim': (500, 150),
'ylim': [-5, 15],
'group_spacing': 30,
'bar_spacing': 0,
'title': 'Safely Navigating Geese'
}
plot3.setattrs(**plot3_attrs)

# coming soon, Jupyter magic!
# x.anti_aliasing=False
x.show()

# or for SVG
# svg_html = x.draw()

# or for image  in BytesIO / save to filename
# x.render(filename='example2.png')


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!

## Project details

Uploaded Source
Uploaded Python 3