initial commit

.gitignore

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+__pycache__/

.python-version

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+3.13

README.md

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 ---
 title: OneDtracing
-emoji: 😻
+emoji: ♫
 colorFrom: green
 colorTo: purple
 sdk: gradio

data/1.npy

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9696de747529f706de056dffa26815d315e60244e34c4d728a80088266e9a3f2
+size 1000128

data/2.npy

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+version https://git-lfs.github.com/spec/v1
+oid sha256:82ea583f00318e7adc98609d2e62c1b2a7c566ff597cd5b70000de9df7dba991
+size 1000128

data/3.npy

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e9b0daf8bf094903e2a597637a6311521a0d10ceca5d40392b40f7630a89534b
+size 1000128

data/4.npy

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6fe0e49907576e2c6ace1b6bc41686c5ed0872bfb501af4c3865b79cb5396d85
+size 1000128

data/5.npy

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8ed56ccb36582570febe9f038a223be382cd45810911733c888fe088a35a3114
+size 1000128

dataset.py

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+import numpy as np
+import os
+
+def generate_npy_files(num_files=5, samples_per_file=1_000_000):
+    """Generates .npy files containing 1D arrays of random floats."""
+    
+    print(f"Starting generation of {num_files} files...")
+    
+    for i in range(1, num_files + 1):
+        filename = f"{i}.npy"
+        
+        # Generate 1M samples of random data (float64 by default)
+        data = np.random.randint(low=-128, high=128, size=samples_per_file, dtype=np.int8)
+        if not os.path.exists("data"):
+            os.mkdir("data")
+            # Save to disk with specified folder
+        np.save(f"data/{filename}", data)
+    
+        file_size_mb = os.path.getsize(f"data/{filename}") / (1024 * 1024)
+        print(f"Created {filename} | Shape: {data.shape} | Size: {file_size_mb:.2f} MB")
+
+    print("\nSuccess: All files generated in the current directory.")
+
+if __name__ == "__main__":
+    generate_npy_files()

main.py

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+from pathlib import Path
+import gradio as gr
+import numpy as np
+import plotly.graph_objects as go
+
+POINT_BUDGET = 1500
+DATA_DIR = Path(__file__).parent / "data"
+
+def load_data_files():
+    data_arrays = []
+    for i in range(5):
+        file_path = DATA_DIR / f"{i}.npy"
+        if file_path.exists():
+            data_arrays.append(np.load(file_path, mmap_mode="r").reshape(-1))
+    return data_arrays
+
+DATA_SOURCES = load_data_files()
+TOTAL_SAMPLES = len(DATA_SOURCES[0]) if DATA_SOURCES else 1_000_000
+
+
+def create_figure(start_pt, window_size):
+
+    end_pt = min(start_pt + window_size, TOTAL_SAMPLES)
+    view_size = end_pt - start_pt
+
+    dynamic_stride = max(1, view_size // POINT_BUDGET)
+    indices = np.arange(start_pt, end_pt, dynamic_stride)
+
+    fig = go.Figure()
+    colors = ['#00E5FF', '#FF3D00', '#76FF03', '#D500F9', '#FFC400']
+
+    for i, data in enumerate(DATA_SOURCES):
+        fig.add_trace(go.Scattergl(
+            x=indices,
+            y=data[indices],
+            mode="lines",
+            name=f"Trace {i}",
+            line=dict(width=1, color=colors[i]),
+            hoverinfo="skip"
+        ))
+
+    fig.update_layout(
+        template="plotly_dark",
+        title=f"Stride: {dynamic_stride}x | Window: {start_pt:,} → {end_pt:,}",
+        height=600
+    )
+
+    return fig
+
+
+with gr.Blocks() as demo:
+
+    gr.Markdown("### 🔬 ASCAD Trace Viewer (Stable HF Version)")
+
+    start_slider = gr.Slider(
+        minimum=0,
+        maximum=TOTAL_SAMPLES - 1000,
+        step=100,
+        value=0,
+        label="Start Index"
+    )
+
+    window_slider = gr.Slider(
+        minimum=1000,
+        maximum=50000,
+        step=1000,
+        value=20000,
+        label="Window Size"
+    )
+
+    trace_plot = gr.Plot()
+
+    start_slider.change(
+        create_figure,
+        inputs=[start_slider, window_slider],
+        outputs=trace_plot
+    )
+
+    window_slider.change(
+        create_figure,
+        inputs=[start_slider, window_slider],
+        outputs=trace_plot
+    )
+
+    # Initial load
+    demo.load(
+        create_figure,
+        inputs=[start_slider, window_slider],
+        outputs=trace_plot
+    )
+
+
+if __name__ == "__main__":
+    demo.launch()

pyproject.toml

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+[project]
+name = "onedtracing"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.13"
+dependencies = [
+    "dash>=4.0.0",
+    "gradio>=6.8.0",
+    "numpy>=2.4.2",
+    "plotly>=6.6.0",
+]