125 lines
4.9 KiB
Python
125 lines
4.9 KiB
Python
import sys
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import csv
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import numpy as np
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import sounddevice as sd
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import os
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# OffiTracker, the tracker that no one asked for but I made it anyways :3
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# This has started off as a silly little joke program, I never thought it would turn into such a complex little beast of a python project.
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# (c) 2024 mueller_minki, Feel free to modify or share.
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stop_signal = False
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noise_data_cache = {} # Cache to store loaded noise data
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def load_noise_data(noise_type, sample_rate):
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amplitude_factor = 0.5 # Adjust the amplitude factor as needed
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noise_file_path = os.path.join('drums', f'drum{noise_type}.txt')
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try:
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with open(noise_file_path, 'r') as file:
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noise_data = np.array(eval(file.readline()))
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return amplitude_factor * noise_data
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except Exception as e:
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print(f"Error loading noise data from {noise_file_path}: {e}")
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return None
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def load_all_noise_data():
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global noise_data_cache
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for i in range(1, 6):
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noise_data_cache[i] = load_noise_data(i, 44100)
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def generate_noise(noise_type):
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return noise_data_cache.get(noise_type, None)
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def play_square_waves(output_stream, frequencies, effects, duration, amplitude=1, noise_amplitude=0, sample_rate=44100):
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global stop_signal
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if stop_signal:
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output_stream.stop()
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else:
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num_waves = len(frequencies)
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t = np.linspace(0, duration / 1000, int(sample_rate * duration / 1000), endpoint=False)
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# Generate and sum square waves for each frequency with corresponding effects
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waves = [amplitude * (effect / 100) * np.sign(np.sin(2 * np.pi * freq * t)) for freq, effect in zip(frequencies, effects)]
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# Add optional noise channel based on the noise column values
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if noise_amplitude > 0:
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noise_type = int(noise_amplitude)
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noise = generate_noise(noise_type)
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if noise is not None:
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# Pad the noise with zeros to match the duration of the other waves
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noise = np.concatenate((noise, np.zeros(len(t) - len(noise))))
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waves.append(noise)
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combined_wave = np.sum(waves, axis=0)
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combined_wave = combined_wave.astype(np.float32)
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output_stream.write(combined_wave)
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def play_csv_file(file_path, start_row=None, stop_row=None):
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global stop_signal
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global noise_data_cache
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if 'playback_row_index' in locals():
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global playback_row_index
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# Load all noise data into the cache
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load_all_noise_data()
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with open(file_path, 'r') as csv_file:
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csv_reader = csv.DictReader(csv_file)
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header = csv_reader.fieldnames
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num_columns = len(header)
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num_pairs = (num_columns - 1) // 2
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total_rows = sum(1 for _ in csv_reader) # Count the total number of rows
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# Reset the file pointer to the beginning
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csv_file.seek(0)
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next(csv_reader) # Skip the header
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with sd.OutputStream(channels=1) as output_stream:
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for idx, row in enumerate(csv_reader):
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if start_row is not None and idx < start_row:
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continue
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if stop_row is not None and idx > stop_row:
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break
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frequencies = [float(row[f'Frequency{i}']) for i in range(1, num_pairs + 1)]
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effects = [float(row[f'Effect{i}']) for i in range(1, num_pairs + 1)]
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duration = float(row['Duration'])
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# Check if 'Noise' column exists in the CSV file
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noise_amplitude = float(row.get('Noise', 0))
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# Update row info
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if 'playback_row_index' in globals():
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playback_row_index = idx
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else:
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print(f"\rRow {idx + 1} of {total_rows}", end='', flush=True)
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if stop_signal == False:
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play_square_waves(output_stream, frequencies, effects, duration, noise_amplitude=noise_amplitude)
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if __name__ == "__main__":
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print(' ')
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print(' Mueller\'s Software Domain proudly presents:')
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print('________ _____ _____._____________ __ ')
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print('\\_____ \\_/ ____\\/ ____\\__\\__ ___/___________ ____ | | __ ___________ ')
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print(' / | \\ __\\ | __\\| | | | \\_ __ \\__ \\ _/ ___\\| |/ // __ \\_ __ \\')
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print('/ | \\ | | | | | | | | | \\// __ \\\\ \\___| <\\ ___/| | \\/')
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print('\\_______ /__| |__| |__| |____| |__| (____ /\\___ >__|_ \\\\___ >__| ')
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print(' \\/ \\/ \\/ \\/ \\/ ')
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print(' Version 1.4')
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if len(sys.argv) > 1:
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csv_file_path = sys.argv[1]
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else:
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csv_file_path = input("Choose a CSV file: ")
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# These should not be set in player mode
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start_row = None
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stop_row = None
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play_csv_file(csv_file_path, start_row=start_row, stop_row=stop_row)
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print("\nPlayback complete.")
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