shivs-per-lib 1.2.5

A Python toolkit for engineering, DSP, embedded systems, machine learning, and data analysis.

shivs_per_lib

A Python utility library for Engineering, Embedded Systems, Signal Processing, Control Systems, Machine Learning, and Data Analysis.

Developed by Shivsamb Harkare, Instrumentation & Control Engineering, COEP Technological University.

shivs_per_lib was created to provide a collection of practical engineering and development utilities frequently used in academic projects, industrial applications, research work, embedded systems development, and machine learning workflows.

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Installation

pip install shivs_per_lib

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Quick Start

import shivs_per_lib as sp

print(
    sp.binary_to_decimal("1010")
)

print(
    sp.adc_to_voltage(
        adc_value=2048,
        bits=12,
        vref=3.3
    )
)

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Function Reference

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Digital Electronics

q_mc()

Performs Boolean expression minimization using the Quine-McCluskey Algorithm.

Applications

• Logic minimization
• Digital circuit design
• FPGA development
• Switching theory

Example

sp.q_mc()

The function requests:

• Minterms
• Don't-care terms
• Number of variables

and generates a simplified Boolean expression.

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binary_to_gray(binary)

Converts Binary Code into Gray Code.

Parameters

Parameter	Type
binary	str

Example

sp.binary_to_gray("1010")

Returns

1111

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binary_to_decimal(binary)

Converts a binary number to decimal.

Parameters

Parameter	Type
binary	str

Example

sp.binary_to_decimal("1010")

Returns

10

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decimal_to_binary(number)

Converts a decimal number to binary.

Parameters

Parameter	Type
number	int

Example

sp.decimal_to_binary(10)

Returns

1010

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Signal Processing

fft_analysis(signal, fs)

Performs Fast Fourier Transform analysis.

Parameters

Parameter	Description
signal	Input signal
fs	Sampling frequency

Returns

frequency, magnitude

Applications

• Audio analysis
• Vibration monitoring
• Biomedical signals
• IMU signal processing

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butter_lowpass(signal, cutoff, fs, order = 4)

Applies a Butterworth Low Pass Filter.

Parameters

Parameter	Description
signal	Input signal
cutoff	Cutoff frequency
fs	Sampling frequency
order	Filter order

Returns

filtered_signal

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butter_highpass(signal, cutoff, fs, order = 4)

Applies a Butterworth High Pass Filter.

Returns

filtered_signal

Applications

• Drift removal
• Baseline correction
• Sensor preprocessing

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moving_average(signal, window = 5)

Smooths noisy data using Moving Average Filtering.

Parameters

Parameter	Description
signal	Input signal
window	Window size

Returns

smoothed_signal

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rms(signal)

Calculates Root Mean Square value.

Returns

float

Applications

• Signal energy estimation
• Power calculations
• Condition monitoring

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Control Systems

pid_control(setpoint, measured, kp, ki, kd, integral, prev_error, dt)

Performs PID Controller calculations.

Parameters

Parameter	Description
setpoint	Desired value
measured	Current value
kp	Proportional gain
ki	Integral gain
kd	Derivative gain
integral	Previous integral value
prev_error	Previous error
dt	Sampling interval

Returns

output, integral, error

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first_order_response(K, tau, time)

Computes first-order system response.

Parameters

Parameter	Description
K	System gain
tau	Time constant
time	Time vector

Returns

response

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percent_overshoot(peak, final)

Calculates percentage overshoot.

Example

sp.percent_overshoot(
    peak=120,
    final=100
)

Returns

20

---

steady_state_error(reference, output)

Calculates steady-state error.

Returns

error

---

Embedded Systems

adc_to_voltage(adc_value, bits = 12, vref = 3.3)

Converts ADC count into voltage.

Parameters

Parameter	Description
adc_value	ADC reading
bits	ADC resolution
vref	Reference voltage

Returns

float

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voltage_divider(vin, r1, r2)

Calculates voltage divider output.

Returns

output_voltage

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ohms_law(v = None, i = None, r = None)

Solves Ohm's Law.

Provide any two parameters and calculate the third.

Example

sp.ohms_law(
    v=12,
    r=4
)

Returns

3

---

pwm_duty_cycle(on_time, period)

Calculates PWM Duty Cycle percentage.

Returns

float

---

timer_frequency(clock_frequency, prescaler)

Calculates timer frequency.

Applications

• AVR
• PIC
• STM32
• Arduino

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uart_tx_time(bytes_count, baudrate)

Calculates UART transmission time.

Returns

seconds

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battery_runtime(capacity_mAh, load_current_mA)

Estimates battery runtime.

Returns

hours

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crc8(data)

Computes CRC-8 checksum.

Applications

• Embedded communication
• Error detection
• Packet verification

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debounce(samples)

Performs digital switch debouncing.

Applications

• Push buttons
• Mechanical switches
• Industrial inputs

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byte_to_hex(value)

Converts a byte to hexadecimal representation.

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hex_to_binary(hex_value)

Converts hexadecimal to binary.

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swap_endian_16(value)

Performs 16-bit endian conversion.

Example

0x1234

becomes

0x3412

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Machine Learning

normalize(data)

Performs Z-score normalization.

Returns

normalized_data

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minmax_normalize(data)

Performs Min-Max normalization.

Returns

normalized_data

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sliding_window(data, window_size, step)

Creates overlapping windows from sequential data.

Parameters

Parameter	Description
data	Input sequence
window_size	Window length
step	Step size

Applications

• Time series analysis
• Sensor analytics

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quaternion_magnitude(w, x, y, z)

Calculates quaternion magnitude.

Returns

float

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train_test_split(data, test_size = 0.2)

Randomly splits dataset into training and testing portions.

Returns

train_data, test_data

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accuracy_score(y_true, y_pred)

Computes classification accuracy.

Returns

float

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mae(y_true, y_pred)

Computes Mean Absolute Error.

Returns

float

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confusion_matrix(y_true, y_pred)

Computes confusion matrix statistics.

Returns

{
    "TP": int,
    "TN": int,
    "FP": int,
    "FN": int
}

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Exploratory Data Analysis

missing_percentage(data)

Calculates percentage of missing values.

Returns

float

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outliers_iqr(data)

Detects outliers using Interquartile Range.

Returns

list

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eda_plot(df, plot_type, col1, col2 = None)

Creates common EDA visualizations.

Supported Plot Types

• hist
• box
• scatter
• line
• bar

Example

sp.eda_plot(
    df,
    plot_type="scatter",
    col1="Height",
    col2="Weight"
)

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data_overview(df)

Provides dataset summary.

Returns

{
    "rows": int,
    "columns": int,
    "missing_values": int,
    "duplicates": int
}

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quick_report(df)

Prints a quick exploratory data analysis report.

Displays:

• Dataset size
• Missing values
• Duplicates
• Column names

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Software Development Utilities

password_strength(password)

Evaluates password strength.

Returns

0-4

Higher score indicates stronger password.

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validate_email(email)

Validates email format.

Returns

True / False

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timer

Function execution time decorator.

Example

@sp.timer
def train_model():
    pass

Output:

train_model took 0.1234 seconds

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Author

Shivsamb Harkare

B.Tech Instrumentation & Control Engineering, COEP Technological University

Areas of Interest:

• Embedded Systems
• Signal Processing
• Control Systems
• Artificial Intelligence
• Machine Learning
• Automation

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License

MIT License Copyright (c) 2025 Shivsamb Harkare