Added power-spectral-density curve (#170)

Added power-spectral-density graph. It shows how the power of a signal
is distributed across different frequency components. This helps in
understanding the frequency content of the signal, identifying dominant
frequencies, and analyzing harmonic structures.



https://github.com/user-attachments/assets/8f646ecd-d307-4cef-a141-612860c575f0

@dinxsh @Soumya-Kushwaha Please review and merge my PR.

Home.py

@@ -17,6 +17,7 @@
     [sg.Button("Spectrogram", **button_style, pad=(10, 10))],
     [sg.Button("Intensity vs Frequency and Time", **button_style, pad=(10, 10))],
     [sg.Button("Real-Time VU Meter", **button_style, pad=(10, 10))],  # New button for Triangle Wave
+    [sg.Button("Power spectral density curve", **button_style, pad=(10, 10))],  
 ]
 
 # Layout for the main landing page
@@ -49,6 +50,7 @@ def handle_event(event, process):
         "Spectrogram": "Spectrogram.py",
         "Intensity vs Frequency and Time": "Intensity-vs-Frequency-and-time.py",
         "Real-Time VU Meter": "Real-Time VU Meter.py",  # Added button for "Triangle Wave"
+        "Power spectral density curve": "Power-spectral-density.py",
     }
 
     if event in script_mapping:

Power-spectral-density.py

@@ -0,0 +1,186 @@
+import PySimpleGUI as sg
+import pyaudio
+import numpy as np
+from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
+import soundfile as sf
+import matplotlib.pyplot as plt
+import subprocess
+import traceback
+from scipy.signal import welch
+
+# VARS CONSTS:
+
+_VARS = {
+    "window": False,
+    "stream": False,
+    "audioData": np.array([]),
+    "audioBuffer": np.array([]),
+    "current_visualizer_process": None,
+}
+
+# PySimpleGUI INIT:
+AppFont = "Helvetica"
+sg.theme("DarkBlue3")
+
+menu_layout = [
+    ['Run Visualizers', ['Amplitude-Frequency-Visualizer', 'Waveform', 'Spectrogram', 'Power-Spectral-Density']],
+]
+
+layout = [
+    [sg.Menu(menu_layout)],
+    [
+        sg.Graph(
+            canvas_size=(600, 600),
+            graph_bottom_left=(-2, -2),
+            graph_top_right=(102, 102),
+            background_color="#809AB6",
+            key="graph",
+            tooltip="Power Spectral Density graph"
+        )
+    ],
+    [sg.Text("Progress:", text_color='white', font=('Helvetica', 15, 'bold')), sg.ProgressBar(4000, orientation="h", size=(20, 20), key="-PROG-")],
+    [
+        sg.Button("Listen", font=AppFont, tooltip="Start listening"),
+        sg.Button("Pause", font=AppFont, disabled=True, tooltip="Pause listening"),
+        sg.Button("Resume", font=AppFont, disabled=True, tooltip="Resume listening"),
+        sg.Button("Stop", font=AppFont, disabled=True, tooltip="Stop listening"),
+        sg.Button("Save", font=AppFont, disabled=True, tooltip="Save the plot"),
+        sg.Button("Exit", font=AppFont, tooltip="Exit the application"),
+    ],
+]
+
+_VARS["window"] = sg.Window("Mic to power spectral density plot", layout, finalize=True)
+graph = _VARS["window"]["graph"]
+
+# INIT vars:
+CHUNK = 1024  # Samples: 1024,  512, 256, 128
+RATE = 44100  # Equivalent to Human Hearing at 40 kHz
+INTERVAL = 1  # Sampling Interval in Seconds -> Interval to listen
+TIMEOUT = 10  # In ms for the event loop
+pAud = pyaudio.PyAudio()
+
+# FUNCTIONS:
+
+def draw_figure(canvas, figure):
+    figure_canvas_agg = FigureCanvasTkAgg(figure, canvas)
+    figure_canvas_agg.draw()
+    figure_canvas_agg.get_tk_widget().pack(side="top", fill="both", expand=1)
+    return figure_canvas_agg
+
+def stop():
+    if _VARS["stream"]:
+        _VARS["stream"].stop_stream()
+        _VARS["stream"].close()
+        _VARS["stream"] = None
+        _VARS["window"]["-PROG-"].update(0)
+        _VARS["window"]["Stop"].Update(disabled=True)
+        _VARS["window"]["Listen"].Update(disabled=False)
+
+def pause():
+    if _VARS["stream"] and _VARS["stream"].is_active():
+        _VARS["stream"].stop_stream()
+        _VARS["window"]["Pause"].Update(disabled=True)
+        _VARS["window"]["Resume"].Update(disabled=False)
+
+def resume():
+    if _VARS["stream"] and not _VARS["stream"].is_active():
+        _VARS["stream"].start_stream()
+        _VARS["window"]["Pause"].Update(disabled=False)
+        _VARS["window"]["Resume"].Update(disabled=True)
+
+def save():
+    # Ask the user for a directory to save the image file
+    folder = sg.popup_get_folder('Please select a directory to save the files')
+    if folder:
+        # Save the figure as an image file
+        fig.savefig(f'{folder}/psd_output.png')
+        sg.popup('Success', f'Image saved as {folder}/psd_output.png')
+        # Save the recorded audio data to a file
+        sf.write(f'{folder}/psd_output.wav', _VARS["audioBuffer"], RATE)
+        sg.popup('Success', f'Audio saved as {folder}/psd_output.wav')
+
+def callback(in_data, frame_count, time_info, status):
+    try:
+        _VARS["audioData"] = np.frombuffer(in_data, dtype=np.int16)
+        _VARS["audioBuffer"] = np.append(_VARS["audioBuffer"], _VARS["audioData"])
+    except Exception as e:
+        print("Error in callback:", e)
+        traceback.print_exc()
+    return (in_data, pyaudio.paContinue)
+
+def listen():
+    try:
+        _VARS["window"]["Stop"].Update(disabled=False)
+        _VARS["window"]["Listen"].Update(disabled=True)
+        _VARS["stream"] = pAud.open(
+            format=pyaudio.paInt16,
+            channels=1,
+            rate=RATE,
+            input=True,
+            frames_per_buffer=CHUNK,
+            stream_callback=callback,
+        )
+        _VARS["stream"].start_stream()
+    except Exception as e:
+        sg.popup_error(f"Error: {e}")
+
+def close_current_visualizer():
+    if _VARS["current_visualizer_process"] and _VARS["current_visualizer_process"].poll() is None:
+        _VARS["current_visualizer_process"].kill()
+
+# INIT:
+fig, ax = plt.subplots()  # create a figure and an axis object
+fig_agg = draw_figure(graph.TKCanvas, fig)  # draw the figure on the graph
+
+# MAIN LOOP
+while True:
+    event, values = _VARS["window"].read(timeout=TIMEOUT)
+    if event == "Exit" or event == sg.WIN_CLOSED:
+        stop()
+        pAud.terminate()
+        break
+    if event == "Listen":
+        listen()
+        _VARS["window"]["Save"].Update(disabled=False)
+    if event == "Pause":
+        pause()
+    if event == "Resume":
+        resume()
+    if event == "Stop":
+        stop()
+    if event == "Save":
+        save()
+    if event == 'Amplitude-Frequency-Visualizer':
+        close_current_visualizer()
+        _VARS["current_visualizer_process"] = subprocess.Popen(['python', 'Amplitude-Frequency-Visualizer.py'])
+        _VARS["window"].close()
+        break 
+    if event == 'Waveform':
+        close_current_visualizer()
+        _VARS["current_visualizer_process"] = subprocess.Popen(['python', 'Waveform.py'])
+        _VARS["window"].close()
+        break 
+    if event == 'Spectrogram':
+        close_current_visualizer()
+        _VARS["current_visualizer_process"] = subprocess.Popen(['python', 'Spectrogram.py'])
+        _VARS["window"].close()
+        break 
+    if event == 'Power-Spectral-Density':
+        close_current_visualizer()
+        _VARS["current_visualizer_process"] = subprocess.Popen(['python', 'Power-Spectral-Density.py'])
+        _VARS["window"].close()
+        break    
+
+    elif _VARS["audioData"].size != 0:
+        try:
+            _VARS["window"]["-PROG-"].update(np.amax(_VARS["audioData"]))
+            f, Pxx = welch(_VARS["audioData"], RATE, nperseg=CHUNK, scaling='density')
+            ax.clear()
+            ax.semilogy(f, Pxx)
+            ax.set_title("Power Spectral Density")
+            ax.set_ylabel("Power/Frequency [dB/Hz]")
+            ax.set_xlabel("Frequency [Hz]")
+            fig_agg.draw()
+        except Exception as e:
+            print("Error during plotting:", e)
+            traceback.print_exc()