fixed audio rendering

src/project.py

@@ -0,0 +1,3 @@
+class Project:
+    def __init__(self):
+        pass

src/ui/app.py

@@ -9,7 +9,7 @@ from ui.widgets.project_settings import ProjectSettings
 class AppUI(App):    
     def __init__(self):
         super().__init__()
-        self.zoom_level = 0.1
+        self.zoom_level = 0.05
     
     def compose(self) -> ComposeResult:
         yield Sidebar()

src/ui/widgets/chunk_types/audio.py

@@ -1,10 +1,10 @@
 import librosa
 import pyloudnorm as pyln
+import numpy as np
 import math
 import random
 
 from textual.containers import Vertical
-from textual.widgets import Sparkline
 from textual_plot import HiResMode, PlotWidget
 
 from ui.widgets.chunk_types.chunk import Chunk
@@ -14,11 +14,14 @@ from ui.widgets.chunk_types.chunk import Chunk
 class AudioChunk(Chunk):
     DEFAULT_CSS = """
     AudioChunk {
-        align: left middle;
+        PlotWidget {
-        Sparkline {
+            height: 1fr;
-            margin: 1;
-        }
 
+            .plot--axis {
+                color: transparent;
+            }
+            
+        }
     }
     """
 
@@ -28,7 +31,6 @@ class AudioChunk(Chunk):
         self.file_path = file_path
         self.audio, self.sample_rate = librosa.load(self.file_path, sr=None, mono=False)
         
-        
         self.num_channels = None
         if len(self.audio.shape) == 1:
             self.num_samples = self.audio.shape[0]
@@ -36,7 +38,6 @@ class AudioChunk(Chunk):
         else:
             self.num_samples = self.audio.shape[1]
             self.num_channels = self.audio.shape[0]
-        self.notify(str(self.num_samples))
         
         self.meter = pyln.Meter(self.sample_rate)
         self.loudness_values = []
@@ -49,6 +50,50 @@ class AudioChunk(Chunk):
             plot.margin_left = 0
             plot.margin_bottom = 0
             
+            num_values = len(list(range(0, int(self.num_samples), int(self.sample_rate * 0.05))))
+            x = range(num_values)
+
+            y = []
+            if self.num_channels == 1:
+                # get rms
+                rms = librosa.feature.rms(y=self.audio)[0]
+                
+                # split into N chunks
+                rms = np.array([
+                    np.mean(chunk) if len(chunk) > 0 else 0.0
+                    for chunk in np.array_split(rms, num_values) 
+                ])
+                
+                # get the decibel values from that
+                y = list(librosa.amplitude_to_db(rms, ref=np.min))
+                
+            else:
+                # get rms
+                rms = librosa.feature.rms(y=self.audio[int(plot.id[-1])])[0]
+                
+                # split into N chunks
+                rms = np.array([
+                    np.mean(chunk) if len(chunk) > 0 else 0.0
+                    for chunk in np.array_split(rms, num_values)
+                ])
+                
+                # get the decibel values from that
+                y = librosa.amplitude_to_db(rms, ref=np.min)
+
+                
+            plot.bar(
+                    x,
+                    y,
+                    1.0,
+                    bar_style=self.app.theme_variables["primary"],
+                    hires_mode=HiResMode.HALFBLOCK
+                )
+                
+                
+            plot.set_xticks([])
+            plot.set_yticks([])
+            plot.set_ylimits(ymin=0)
+        
     def compose(self) -> ComposeResult:
         
         
@@ -57,19 +102,23 @@ class AudioChunk(Chunk):
             # loop over each channel in the audio and display it seperately
             for channel in range(self.num_channels):
                 
-                samples = []
+                """samples = []
                 
                 for sample in range(0, self.num_samples, int(self.sample_rate*0.1)):
-                    samples.append(self.audio[channel, sample])
+                    samples.append(self.audio[channel, sample])"""
                     
-                yield Sparkline(data=samples)
+                yield PlotWidget(allow_pan_and_zoom=False, id=f"channel-{channel}")
+                    
+                #yield Sparkline(data=samples)
                 
                 
         else:
             # just display the one channel
-            samples = []
+            """samples = []
             
             for sample in range(0, self.num_samples, int(self.sample_rate*0.1)):
-                samples.append(self.audio[sample])
+                samples.append(self.audio[sample])"""
                 
-            yield Sparkline(data=samples)
+            yield PlotWidget(allow_pan_and_zoom=False)
+
+            #yield Sparkline(data=samples)