tcurve 0.1.2
A sleek tool that displays visually appealing progress bars and draws metric based curves in real-time.

<p align="center">
  <img src="https://s41.ax1x.com/2026/01/18/pZywpa8.png" alt="tcurve-icon.png" width = "150" height = "100" />
</p>
<h1 align="center">TCurve</h1>

<p align="center">
  <b> A sleek progress bar tool </b><br>
  For better experience of printing on the terminal.
</p>


<p align="center">
  <img src="https://img.shields.io/pypi/v/tcurve?color=blue&label=Version" alt="Version">
  <img src="https://img.shields.io/github/stars/SeriaQ/TCurve?style=social" alt="GitHub Repo Stars">
  <img src="https://img.shields.io/badge/Made%20with-Python-blue" alt="Python">
  <img src="https://img.shields.io/badge/License-MIT-green" alt="MIT License">
</p>


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## 🚀 Spotlight

📈 **Fascinating Dashboard**

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

[![THAIh9.gif](https://s4.ax1x.com/2022/01/03/THAIh9.gif)](https://imgtu.com/i/THAIh9)



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## ⚡ Quick Start

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

```python
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

```python
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.

```python
# 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.

```python
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.

```python
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.

```python
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.

```python
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.

```python
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

<p align="center">
  <img src="https://ooo.0x0.ooo/2025/03/20/OSgJ3l.jpg" alt="donut-photo.jpg" width = "280" height = "280" />
  <img src="https://ooo.0x0.ooo/2025/03/20/OSgRig.png" alt="donut-chars.png" width = "280" height = "280" />
</p>




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## 📦 Installation

Install tcurve via pip

```sh
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