DEV Community: Sepand Haghighi

Introducing MyCoffee - Brew Your Perfect Cup, Right from the Terminal!

Sepand Haghighi — Thu, 09 Oct 2025 21:55:53 +0000

If you’re a coffee enthusiast who also loves the command line, I’ve got something special for you.
Meet MyCoffee, a simple yet precise command-line tool designed to help you brew coffee like a pro.

What Is MyCoffee?

MyCoffee helps you calculate the perfect ratio, temperature, grind size, and other parameters for various brewing methods, all without leaving your terminal. Whether you’re using a French press, pour-over, Aeropress, or espresso machine, MyCoffee ensures your brew stays balanced and delicious every time.

Why I Built It

As a developer and coffee lover, I often found myself switching between ratio charts, calculators, and apps just to figure out how much coffee and water I needed for a specific brew.
I thought, Why not bring it all to the terminal?
After all, it’s where we spend most of our time anyway!

Features

🔢 Customizable parameters for different brewing methods

⚡ Instant calculations - no web app or GUI needed

🧮 Precision control - tweak strength and volume easily

💾 Lightweight & open-source - no dependencies beyond Python itself

💬 Friendly CLI interface - clean, minimal, and easy to use

Example

> mycoffee --method=v60 --mode="ratio" --coffee=18 --water=300
 __  __  _  _   ___  _____  ____  ____  ____  ____
(  \/  )( \/ ) / __)(  _  )( ___)( ___)( ___)( ___)
 )    (  \  / ( (__  )(_)(  )__)  )__)  )__)  )__)
(_/\/\_) (__)  \___)(_____)(__)  (__)  (____)(____)



Date: 2025-09-10 17:56

Mode: Water & Coffee --> Ratio

Method: `v60`

Cups: 1

Coffee:

    - Cup:   18 g
    - Total: 18 g

Water:

    - Cup: 300 g
    - Total: 300 g

Ratio: 3/50 (0.06)

Strength: Medium

Grind: 550 um (Medium-Fine)

Temperature: 91 C

Message: V60 method

Installation

You can install MyCoffee directly via pip:

pip install mycoffee

Then just type mycoffee in your terminal and start brewing smarter.

Open Source & Contributions

MyCoffee is fully open-source; contributions, ideas, and feedback are always welcome!
You can check out the repository here:
👉 GitHub Repository Link

MyTimer: A Geeky Timer for Terminal Enthusiasts

Sepand Haghighi — Thu, 05 Oct 2023 11:46:01 +0000

Github Repo

Overview

MyTimer is a Python project that aims to provide a simple yet efficient timer for terminal users, particularly targeting the geek community. This project allows users to set timers directly from their command line interface, making it convenient for those who spend a significant amount of time working in the terminal! The main objective of MyTimer is to offer a minimalistic and distraction-free timer experience. It provides a clean and straightforward interface, ensuring that users can focus solely on tracking time without any unnecessary clutter or distractions.

Installation

Source Code

Download Version 0.9 or Latest Source
pip install .

PyPI

Check Python Packaging User Guide
pip install mytimer==0.9

Demo

Usage

⚠️ You can use mytimer or python -m mytimer to run this program

Version

mytimer --version

Basic

⚠️ Press Ctrl + C to exit

mytimer

Time Limit

mytimer --minute=7 --second=30

mytimer --hour=2 --minute=20

Timer Mode

⚠️ The default mode is count-up

mytimer --minute=7 --second=30 --countdown

mytimer --minute=7 --second=30 --countup

Alarm

⚠️ This mode may not be supported on all systems

mytimer --minute=7 --second=30 --alarm

Alarm Repeat

⚠️ The default value is 1

mytimer --minute=7 --second=30 --alarm --alarm-repeat=5

Tone

⚠️ The default tone is 1

mytimer --minute=7 --second=30 --alarm --tone=2

Tones List

Face

mytimer --minute=7 --second=30 --face=3

Faces List

Program

mytimer --program=black-tea

Programs List

Message

mytimer --minute=7 --second=30 --message="Test message"

Show your support

Give a ⭐️ if this project helped you!

ASCII Art In Python

Sepand Haghighi — Tue, 20 Nov 2018 09:34:05 +0000

ASCII art is also known as "computer text art". It involves the smart placement of typed special characters or letters to make a visual shape that is spread over multiple lines of text.

Art is a Python lib for text converting to ASCII ART fancy. ;-)

Open Hub
PyPI Counter
Github Stars
Font Counter	235
1-Line-Art Counter	250

1-Line Art

>>> from art import *
>>> art_1=art("coffee") # return art as str in normal mode
>>> print(art_1)
c[_]
>>> art_2=art("woman",number=2) # return multiple art as str
>>> print(art_2)
▓⚗_⚗▓ ▓⚗_⚗▓ 
>>> art_3=art("love_you",number=1,text="test") # 2-part art
>>> print(art_3)
»-(¯`·.·´¯)->test<-(¯`·.·´¯)-« 
>>> art("random") # random 1-line art mode
'(っ◕‿◕)っ '
>>> art("rand")   # random 1-line art mode
't(-_-t) '
>>> art(22,number=1,text="") # raise artError
Traceback (most recent call last):
        ...
art.art.artError: artname shoud have str type


>>> aprint("butterfly") # print art
Ƹ̵̡Ӝ̵̨̄Ʒ 
>>> aprint("happy") # print art
 ۜ\(סּںסּَ` )/ۜ
>>> aprint("love_you",number=1,text="test")  # 2-part art
»-(¯`·.·´¯)->test<-(¯`·.·´¯)-« 
>>> aprint("random") # random 1-line art mode
'(っ◕‿◕)っ '
>>> aprint("rand")   # random 1-line art mode
't(-_-t) '
>>> aprint("woman",number="22",text="") # raise artError
Traceback (most recent call last):
        ...
art.art.artError: number should have int type

2-ASCII Text

>>> Art=text2art("art") # Return ascii text (default font) and default chr_ignore=True 
>>> print(Art)
              _   
  __ _  _ __ | |_ 
 / _` || '__|| __|
| (_| || |   | |_ 
 \__,_||_|    \__|


>>> Art=text2art("art",font='block',chr_ignore=True) # Return ascii text with block font
>>> print(Art)


 .----------------.  .----------------.  .----------------.
| .--------------. || .--------------. || .--------------. |
| |      __      | || |  _______     | || |  _________   | |
| |     /  \     | || | |_   __ \    | || | |  _   _  |  | |
| |    / /\ \    | || |   | |__) |   | || | |_/ | | \_|  | |
| |   / ____ \   | || |   |  __ /    | || |     | |      | |
| | _/ /    \ \_ | || |  _| |  \ \_  | || |    _| |_     | |
| ||____|  |____|| || | |____| |___| | || |   |_____|    | |
| |              | || |              | || |              | |
| '--------------' || '--------------' || '--------------' |
 '----------------'  '----------------'  '----------------'

>>> Art=text2art("test","random") # random font mode
>>> print(Art)
 |       | 
~|~/~/(~~|~
 | \/__) | 

>>> Art=text2art("test","rand") # random font mode
>>> print(Art)
___ ____ ____ ___ 
 |  |___ [__   |  
 |  |___ ___]  | 

>>> text2art("seسسس",font=DEFAULT_FONT,chr_ignore=False) # raise artError in exception
Traceback (most recent call last):
        ...
art.art.artError: س is invalid

>>> tprint("art") # print ascii text (default font) 
              _   
  __ _  _ __ | |_ 
 / _` || '__|| __|
| (_| || |   | |_ 
 \__,_||_|    \__|


>>> tprint("art",font="block",chr_ignore=True) # print ascii text (block font)

 .----------------.  .----------------.  .----------------.
| .--------------. || .--------------. || .--------------. |
| |      __      | || |  _______     | || |  _________   | |
| |     /  \     | || | |_   __ \    | || | |  _   _  |  | |
| |    / /\ \    | || |   | |__) |   | || | |_/ | | \_|  | |
| |   / ____ \   | || |   |  __ /    | || |     | |      | |
| | _/ /    \ \_ | || |  _| |  \ \_  | || |    _| |_     | |
| ||____|  |____|| || | |____| |___| | || |   |_____|    | |
| |              | || |              | || |              | |
| '--------------' || '--------------' || '--------------' |
 '----------------'  '----------------'  '----------------'

>>> tprint('testسس')  # chr_ignore flag ==True (Default)
 _               _   
| |_   ___  ___ | |_ 
| __| / _ \/ __|| __|
| |_ |  __/\__ \| |_ 
 \__| \___||___/ \__|

>>> tprint("test","random") # random font mode
 |       | 
~|~/~/(~~|~
 | \/__) | 

>>> tprint("test","rand") # random font mode
___ ____ ____ ___ 
 |  |___ [__   |  
 |  |___ ___]  |  


>>> tprint('testسس',chr_ignore=False) # raise artError in exception 
Traceback (most recent call last):
       ...
art.art.artError: س is invalid
>>> tprint('''Lorem  # Multi-line print
ipsum 
dolor''', font="cybermedium")
_    ____ ____ ____ _  _    
|    |  | |__/ |___ |\/|    
|___ |__| |  \ |___ |  |    

_ ___  ____ _  _ _  _    
| |__] [__  |  | |\/|    
| |    ___] |__| |  |    

___  ____ _    ____ ____ 
|  \ |  | |    |  | |__/ 
|__/ |__| |___ |__| |  \

Github Repo
Webpage

PyCM : Full analysis of confusion matrix

Sepand Haghighi — Sat, 17 Nov 2018 15:15:11 +0000

PyCM is a multi-class confusion matrix library written in Python that supports both input data vectors and direct matrix, and a proper tool for post-classification model evaluation that supports most classes and overall statistics parameters. PyCM is the swiss-army knife of confusion matrices, targeted mainly at data scientists that need a broad array of metrics for predictive models and an accurate evaluation of large variety of classifiers.

From Vector

>>> from pycm import *
>>> y_actu = [2, 0, 2, 2, 0, 1, 1, 2, 2, 0, 1, 2] # or y_actu = numpy.array([2, 0, 2, 2, 0, 1, 1, 2, 2, 0, 1, 2])
>>> y_pred = [0, 0, 2, 1, 0, 2, 1, 0, 2, 0, 2, 2] # or y_pred = numpy.array([0, 0, 2, 1, 0, 2, 1, 0, 2, 0, 2, 2])
>>> cm = ConfusionMatrix(actual_vector=y_actu, predict_vector=y_pred) # Create CM From Data
>>> cm.classes
[0, 1, 2]
>>> cm.table
{0: {0: 3, 1: 0, 2: 0}, 1: {0: 0, 1: 1, 2: 2}, 2: {0: 2, 1: 1, 2: 3}}
>>> print(cm)
Predict          0    1    2    
Actual
0                3    0    0    
1                0    1    2    
2                2    1    3    




Overall Statistics : 

95% CI                                                           (0.30439,0.86228)
AUNP                                                             0.66667
AUNU                                                             0.69444
Bennett_S                                                        0.375
CBA                                                              0.47778
Chi-Squared                                                      6.6
Chi-Squared DF                                                   4
Conditional Entropy                                              0.95915
Cramer_V                                                         0.5244
Cross Entropy                                                    1.59352
Gwet_AC1                                                         0.38931
Hamming Loss                                                     0.41667
Joint Entropy                                                    2.45915
KL Divergence                                                    0.09352
Kappa                                                            0.35484
Kappa 95% CI                                                     (-0.07708,0.78675)
Kappa No Prevalence                                              0.16667
Kappa Standard Error                                             0.22036
Kappa Unbiased                                                   0.34426
Lambda A                                                         0.16667
Lambda B                                                         0.42857
Mutual Information                                               0.52421
NIR                                                              0.5
Overall_ACC                                                      0.58333
Overall_CEN                                                      0.46381
Overall_J                                                        (1.225,0.40833)
Overall_MCC                                                      0.36667
Overall_MCEN                                                     0.51894
Overall_RACC                                                     0.35417
Overall_RACCU                                                    0.36458
P-Value                                                          0.38721
PPV_Macro                                                        0.56667
PPV_Micro                                                        0.58333
Phi-Squared                                                      0.55
RR                                                               4.0
Reference Entropy                                                1.5
Response Entropy                                                 1.48336
Scott_PI                                                         0.34426
Standard Error                                                   0.14232
Strength_Of_Agreement(Altman)                                    Fair
Strength_Of_Agreement(Cicchetti)                                 Poor
Strength_Of_Agreement(Fleiss)                                    Poor
Strength_Of_Agreement(Landis and Koch)                           Fair
TPR_Macro                                                        0.61111
TPR_Micro                                                        0.58333
Zero-one Loss                                                    5

Class Statistics :

Classes                                                          0                       1                       2                       
ACC(Accuracy)                                                    0.83333                 0.75                    0.58333                 
AUC(Area under the roc curve)                                    0.88889                 0.61111                 0.58333                 
BM(Informedness or bookmaker informedness)                       0.77778                 0.22222                 0.16667                 
CEN(Confusion entropy)                                           0.25                    0.49658                 0.60442                 
DOR(Diagnostic odds ratio)                                       None                    4.0                     2.0                     
ERR(Error rate)                                                  0.16667                 0.25                    0.41667                 
F0.5(F0.5 score)                                                 0.65217                 0.45455                 0.57692                 
F1(F1 score - harmonic mean of precision and sensitivity)        0.75                    0.4                     0.54545                 
F2(F2 score)                                                     0.88235                 0.35714                 0.51724                 
FDR(False discovery rate)                                        0.4                     0.5                     0.4                     
FN(False negative/miss/type 2 error)                             0                       2                       3                       
FNR(Miss rate or false negative rate)                            0.0                     0.66667                 0.5                     
FOR(False omission rate)                                         0.0                     0.2                     0.42857                 
FP(False positive/type 1 error/false alarm)                      2                       1                       2                       
FPR(Fall-out or false positive rate)                             0.22222                 0.11111                 0.33333                 
G(G-measure geometric mean of precision and sensitivity)         0.7746                  0.40825                 0.54772                 
IS(Information score)                                            1.26303                 1.0                     0.26303                 
J(Jaccard index)                                                 0.6                     0.25                    0.375                   
LR+(Positive likelihood ratio)                                   4.5                     3.0                     1.5                     
LR-(Negative likelihood ratio)                                   0.0                     0.75                    0.75                    
MCC(Matthews correlation coefficient)                            0.68313                 0.2582                  0.16903                 
MCEN(Modified confusion entropy)                                 0.26439                 0.5                     0.6875                  
MK(Markedness)                                                   0.6                     0.3                     0.17143                 
N(Condition negative)                                            9                       9                       6                       
NPV(Negative predictive value)                                   1.0                     0.8                     0.57143                 
P(Condition positive or support)                                 3                       3                       6                       
POP(Population)                                                  12                      12                      12                      
PPV(Precision or positive predictive value)                      0.6                     0.5                     0.6                     
PRE(Prevalence)                                                  0.25                    0.25                    0.5                     
RACC(Random accuracy)                                            0.10417                 0.04167                 0.20833                 
RACCU(Random accuracy unbiased)                                  0.11111                 0.0434                  0.21007                 
TN(True negative/correct rejection)                              7                       8                       4                       
TNR(Specificity or true negative rate)                           0.77778                 0.88889                 0.66667                 
TON(Test outcome negative)                                       7                       10                      7                       
TOP(Test outcome positive)                                       5                       2                       5                       
TP(True positive/hit)                                            3                       1                       3                       
TPR(Sensitivity, recall, hit rate, or true positive rate)        1.0                     0.33333                 0.5                     
dInd(Distance index)                                             0.22222                 0.67586                 0.60093                 
sInd(Similarity index)                                           0.84287                 0.52209                 0.57508                 

>>> cm.matrix()
Predict          0        1        2        
Actual
0                3        0        0        
1                0        1        2        
2                2        1        3        

>>> cm.normalized_matrix()
Predict          0              1              2              
Actual
0                1.0            0.0            0.0            
1                0.0            0.33333        0.66667        
2                0.33333        0.16667        0.5            

>>> cm.matrix(one_vs_all=True,class_name=0)   # One-Vs-All, new in version 1.4
Predict          0    ~    
Actual
0                3    0    
~                2    7

Direct CM

>>> from pycm import *
>>> cm2 = ConfusionMatrix(matrix={"Class1": {"Class1": 1, "Class2":2}, "Class2": {"Class1": 0, "Class2": 5}}) # Create CM Directly
>>> cm2
pycm.ConfusionMatrix(classes: ['Class1', 'Class2'])
>>> print(cm2)
Predict          Class1    Class2    
Actual
Class1           1         2         
Class2           0         5         




Overall Statistics : 

95% CI                                                           (0.44994,1.05006)
AUNP                                                             0.66667
AUNU                                                             0.66667
Bennett_S                                                        0.5
CBA                                                              0.52381
Chi-Squared                                                      1.90476
Chi-Squared DF                                                   1
Conditional Entropy                                              0.34436
Cramer_V                                                         0.48795
Cross Entropy                                                    1.2454
Gwet_AC1                                                         0.6
Hamming Loss                                                     0.25
Joint Entropy                                                    1.29879
KL Divergence                                                    0.29097
Kappa                                                            0.38462
Kappa 95% CI                                                     (-0.354,1.12323)
Kappa No Prevalence                                              0.5
Kappa Standard Error                                             0.37684
Kappa Unbiased                                                   0.33333
Lambda A                                                         0.33333
Lambda B                                                         0.0
Mutual Information                                               0.1992
NIR                                                              0.625
Overall_ACC                                                      0.75
Overall_CEN                                                      0.44812
Overall_J                                                        (1.04762,0.52381)
Overall_MCC                                                      0.48795
Overall_MCEN                                                     0.29904
Overall_RACC                                                     0.59375
Overall_RACCU                                                    0.625
P-Value                                                          0.36974
PPV_Macro                                                        0.85714
PPV_Micro                                                        0.75
Phi-Squared                                                      0.2381
RR                                                               4.0
Reference Entropy                                                0.95443
Response Entropy                                                 0.54356
Scott_PI                                                         0.33333
Standard Error                                                   0.15309
Strength_Of_Agreement(Altman)                                    Fair
Strength_Of_Agreement(Cicchetti)                                 Poor
Strength_Of_Agreement(Fleiss)                                    Poor
Strength_Of_Agreement(Landis and Koch)                           Fair
TPR_Macro                                                        0.66667
TPR_Micro                                                        0.75
Zero-one Loss                                                    2

Class Statistics :

Classes                                                          Class1                  Class2                  
ACC(Accuracy)                                                    0.75                    0.75                    
AUC(Area under the roc curve)                                    0.66667                 0.66667                 
BM(Informedness or bookmaker informedness)                       0.33333                 0.33333                 
CEN(Confusion entropy)                                           0.5                     0.43083                 
DOR(Diagnostic odds ratio)                                       None                    None                    
ERR(Error rate)                                                  0.25                    0.25                    
F0.5(F0.5 score)                                                 0.71429                 0.75758                 
F1(F1 score - harmonic mean of precision and sensitivity)        0.5                     0.83333                 
F2(F2 score)                                                     0.38462                 0.92593                 
FDR(False discovery rate)                                        0.0                     0.28571                 
FN(False negative/miss/type 2 error)                             2                       0                       
FNR(Miss rate or false negative rate)                            0.66667                 0.0                     
FOR(False omission rate)                                         0.28571                 0.0                     
FP(False positive/type 1 error/false alarm)                      0                       2                       
FPR(Fall-out or false positive rate)                             0.0                     0.66667                 
G(G-measure geometric mean of precision and sensitivity)         0.57735                 0.84515                 
IS(Information score)                                            1.41504                 0.19265                 
J(Jaccard index)                                                 0.33333                 0.71429                 
LR+(Positive likelihood ratio)                                   None                    1.5                     
LR-(Negative likelihood ratio)                                   0.66667                 0.0                     
MCC(Matthews correlation coefficient)                            0.48795                 0.48795                 
MCEN(Modified confusion entropy)                                 0.38998                 0.51639                 
MK(Markedness)                                                   0.71429                 0.71429                 
N(Condition negative)                                            5                       3                       
NPV(Negative predictive value)                                   0.71429                 1.0                     
P(Condition positive or support)                                 3                       5                       
POP(Population)                                                  8                       8                       
PPV(Precision or positive predictive value)                      1.0                     0.71429                 
PRE(Prevalence)                                                  0.375                   0.625                   
RACC(Random accuracy)                                            0.04688                 0.54688                 
RACCU(Random accuracy unbiased)                                  0.0625                  0.5625                  
TN(True negative/correct rejection)                              5                       1                       
TNR(Specificity or true negative rate)                           1.0                     0.33333                 
TON(Test outcome negative)                                       7                       1                       
TOP(Test outcome positive)                                       1                       7                       
TP(True positive/hit)                                            1                       5                       
TPR(Sensitivity, recall, hit rate, or true positive rate)        0.33333                 1.0                     
dInd(Distance index)                                             0.66667                 0.66667                 
sInd(Similarity index)                                           0.5286                  0.5286                  

>>> cm3 = ConfusionMatrix(matrix={"Class1": {"Class1": 1, "Class2":0}, "Class2": {"Class1": 2, "Class2": 5}},transpose=True) # Transpose Matrix      
>>> cm3.matrix()
Predict          Class1    Class2    
Actual
Class1           1         2         
Class2           0         5

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