tcurve 0.1.2

A sleek tool that displays visually appealing progress bars and draws metric based curves in real-time.

TCurve

A sleek progress bar tool
For better experience of printing on the terminal.

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🚀 Spotlight

📈 Fascinating Dashboard

It shows visually appealing progress bars and draws metric based curves in real-time.

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⚡ Quick Start

You can simply take it as a wrapper or help you plot curves.

import tcurve as tc
from time import sleep

# a simple wrapper
for i in tc.Dash(range(10)):
    time.sleep(0.5)

# wrap a generator
for i in tc.Dash(enumerate(range(30))):
    time.sleep(0.3)

# with keyword arguments
for i,n in tc.Dash(enumerate(range(30)), format={'number': [lambda x:x[1], tc.CUSTOM]}, epoch=2, mpe=30, stage='COUNT', interv=1, wipe=False):
    time.sleep(0.3)

# in a complicated manner
tcd = tc.Dash(format={'Acc': ['.1f', tc.PERCENT]})
unit_acc = [0.012, 0.045, 0.134, 0.189, 0.234, 0.278, 0.345, 0.378, 0.456, 0.423, 0.51, 0.599, 0.623, 0.62, 0.7] # create a fake array for this tutorial
fake_acc = unit_acc + unit_acc[::-1] + unit_acc + unit_acc[::-1] + unit_acc + unit_acc[::-1]
for i, a in enumerate(fake_acc):
    time.sleep(0.1)
    tcd({'Accuracy': a}, 0, i, len(fake_acc))

The genre macros are listed below.

• RAW: display as it is
• PERCENT: shown as xx.yy% for example
• INVIZ: do not display but log to files
• IMAGE: to visualize the image using characters. usually collocated with lambda *x:1 (or lambda *x:0) to display on terminal (or not)
• CUSTOM: define your own function to process the content. the function is supposed to be like

def process_fn(value, epoch, mile, mpe, stage):
  # value: the loss/accuracy/generated image or anything else you get in this step
  # epoch: the current epoch
  # mile: the current step/iter within this epoch
  # mpe: how many steps/iters an epoch would go through
  # stage: the current stage, which is input by users
  
  # ----- do something here ----- #
  
  # return a string to display

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🔥 Dive Deeper

Let's take the last example in previous part to show how could you use more arguments for delicate control.

# below is the common setting
unit_acc = [0.012, 0.045, 0.134, 0.189, 0.234, 0.278, 0.345, 0.378, 0.456, 0.423, 0.51, 0.599, 0.623, 0.62, 0.7] # create a fake array for this tutorial
flat_acc = [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]
fake_acc = 10 * (unit_acc + unit_acc[::-1] + flat_acc)

We only take this for-loop to demonstrate. First, you can use is_global=True to plot the entire curve.

tcd = tc.Dash(format={'Acc': ['.1f', tc.PERCENT]})
for i, a in enumerate(fake_acc):
    time.sleep(0.1)
    tcd({'Accuracy': a}, 0, i, len(fake_acc), is_global=True)

Let is_global=False to view the recent changes of this curve.

tcd = tc.Dash(format={'Acc': ['.1f', tc.PERCENT]})
for i, a in enumerate(fake_acc):
    time.sleep(0.1)
    tcd({'Accuracy': a}, 0, i, len(fake_acc), is_global=False)

Make is_elastic=True to dynamically stretch or squeeze the vertical axis so that the fluctuation of curves is prominent enough to be observed.

tcd = tc.Dash(format={'Acc': ['.1f', tc.PERCENT]})
for i, a in enumerate(fake_acc):
    time.sleep(0.1)
    tcd({'Accuracy': a}, 0, i, len(fake_acc), is_elastic=True)

When you have several variables, set in_loop and last_for to switch among them.

e.g in_loop=(0, 1), last_for=5 means dashboard is going to show curves of var[0] and var[1] in turns. The displayed curve changes every 5 steps.

tcd = tc.Dash(format={'Acc': ['.1f', tc.PERCENT], 'Level': ['.d', tc.RAW]})
for i, a in enumerate(fake_acc):
    time.sleep(0.1)
    tcd({'Accuracy': a, 'Level': i*i}, 0, i, len(fake_acc), in_loop=(0, 1), last_for=5)

What's more, you can even draw the gray image on the terminal.

tcd = tc.Dash(format={'I': [lambda *x:1, tc.IMAGE]})
for i, img in enumerate(images):
    time.sleep(0.1)
    # read images here
    tcd({'I': img}, 0, i, len(images))

Try to squint at the right side. :D

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📦 Installation

Install tcurve via pip

pip install tcurve

Install the following libs to access full functions.

• numpy
• pandas
• matplotlib
• seaborn

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❤️ Support

If you find TCurve helpful, please give it a ⭐ on GitHub! ▶️ https://github.com/SeriaQ/TCurve