A tool for visualize keypress data from ZipChord's debug log files.

visualize_zipchord_debug.py

import re
import matplotlib.pyplot as plt
from matplotlib.widgets import Slider

# open the chords file and read the lines
print('Scroll through the diagram using the "Pos" slider.')
print('Click on the chart to draw a vertical line (useful for seeing when keypresses overlap.')
file = open(input('Enter the path to the debug.txt file: '), 'r')

lines = file.readlines()
file.close()
# if the line starts with a number with one or more digits followed by a tab, keep it
lines = [line for line in lines if re.match(r'\d+\t', line)]
# ignore lines with the string "*Interrupt*" in them
lines = [line for line in lines if '*Interrupt*' not in line]


# the format of the lines is <number><tab>~<keypress><newline> or
# <number><tab>~<keypress> Up<newline>
# create list of [keypress, [down_time, up_time]]
key_presses = []
# iterate through the lines
for line in lines:
    # split the line into the number and the key info
    number, keyinfo = line.split('\t')
    # split the key info into the keypress and is_up
    if len(keyinfo.split(' ')) == 1:
        keypress = keyinfo[1:-1] # remove the ~ and the newline
        is_up = False
    else:
        keypress, is_up = keyinfo.split(' ')
        keypress = keypress[1:] # remove the ~
        is_up = True
    if not is_up:
        key_presses.append([keypress, [int(number), None]])
    else:
        for (i, key_press) in enumerate(key_presses):
            if key_press[0] == keypress and key_press[1][1] is None:
                key_presses[i][1][1] = int(number)
                break

# create a chart showing a timeline of keypresses
fig, ax = plt.subplots()
for key_press in key_presses:
    ax.plot(key_press[1], [key_press[0], key_press[0]], linewidth=5, solid_capstyle='round')
# large y axis labels
ax.tick_params(axis='y', which='major')
# x and y axis labels
ax.set_xlabel('Time (ms)')
ax.set_ylabel('Keypress')
# set xlim to first second
ax.set_xlim(key_presses[0][1][0]-10, key_presses[0][1][0]+990)

# make an interactive, scrollable chart
# Choose the Slider color
slider_color = 'White'

# Set the axis and slider position in the plot
axis_position = plt.axes([0.2, 0.0, 0.65, 0.03],
                         facecolor=slider_color)
slider_position = Slider(axis_position,
                         'Pos', key_presses[0][1][0], key_presses[-1][1][1],)


# update() function to change the graph when the
# slider is in use
def update(val):
    pos = slider_position.val
    ax.axis([pos, pos + 1000, ax.get_ylim()[0], ax.get_ylim()[1]])
    fig.canvas.draw_idle()


# update function called using on_changed() function
slider_position.on_changed(update)

# create a vertical line at the mouse position when the mouse is clicked
def onMouseClick(event):
    if event.inaxes == ax:
        ax.axvline(x=event.xdata, color='r', linewidth=1)
        fig.canvas.draw_idle()

# connect the mouse move event to the function
fig.canvas.mpl_connect('button_press_event', onMouseClick)

plt.show()