From 811403558db5b8b41bb0452546aa55dd7ca58380 Mon Sep 17 00:00:00 2001
From: Jon Myers <jon.myers.sound@gmail.com>
Date: Thu, 6 Nov 2025 13:47:49 -0800
Subject: [PATCH 1/3] feat: add comprehensive spectrogram data access and
 visualization support
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Resolves Issue #46

Add complete spectrogram support to enable programmatic access to the same
high-quality constant-Q spectrograms used in the IDTAP web application, with
extensive matplotlib integration for computational musicology research.

## New Features:

### SpectrogramData Class (`idtap/spectrogram.py`)
- **Data Loading**:
  - `from_audio_id(audio_id, client)` - Download from server
  - `from_piece(piece, client)` - Load from Piece object
  - Auto-decompresses gzipped spectrogram data from swara.studio

- **Transformations**:
  - `apply_intensity(power)` - Power-law contrast enhancement (1.0-5.0)
  - `apply_colormap(data, cmap)` - 35+ matplotlib colormaps
  - `crop_frequency(min_hz, max_hz)` - Frequency range cropping
  - `crop_time(start_time, end_time)` - Time range cropping

- **Matplotlib Integration** (for research workflows):
  - `plot_on_axis(ax, ...)` - Plot on existing axis for overlays
  - `get_plot_data(power, apply_cmap, cmap)` - Get processed data + extent
  - `get_extent()` - Get matplotlib extent [left, right, bottom, top]

- **Image Generation**:
  - `to_image(width, height, power, cmap)` - Generate PIL Image
  - `to_matplotlib(figsize, power, cmap)` - Generate standalone figure
  - `save(filepath, ...)` - Save to file (PNG, JPG, etc.)

- **Properties**:
  - `shape`, `duration`, `time_resolution`, `freq_bins`

### SwaraClient Updates (`idtap/client.py`)
- `download_spectrogram_data(audio_id)` - Download compressed data
- `download_spectrogram_metadata(audio_id)` - Download shape metadata

### Dependencies (`pyproject.toml`, `Pipfile`)
- Added `numpy>=1.20.0` for array processing
- Added `pillow>=9.0.0` for image generation
- Added `matplotlib>=3.5.0` for visualization

## Testing:
- 36 comprehensive test cases covering:
  - Data loading and initialization
  - Intensity transforms and colormap application
  - Frequency and time cropping
  - Matplotlib integration methods
  - Image generation and saving
  - All properties and edge cases
- **All 401 tests pass** (365 existing + 36 new)

## Usage Example:

```python
from idtap import SpectrogramData, get_piece
import matplotlib.pyplot as plt

# Load spectrogram
piece = get_piece("transcription_id")
spec = SpectrogramData.from_piece(piece)

# Create visualization with spectrogram underlay
fig, ax = plt.subplots(figsize=(12, 6))
spec.plot_on_axis(ax, power=2.5, cmap='viridis', alpha=0.6, zorder=0)

# Overlay pitch contour
ax.plot(times, freqs, 'r-', linewidth=2, zorder=1)
ax.set_xlabel('Time (s)')
ax.set_ylabel('Frequency (Hz)')

plt.savefig('figure.png', dpi=300)
```

## Documentation Updates:
- Updated CLAUDE.md with testing warning about browser authorization

## Design Decisions:
- Follows librosa/matplotlib patterns for research workflows
- Optional client parameter (creates if not provided)
- No caching in MVP (users can cache manually)
- Uses matplotlib colormaps (close enough to D3, simpler)
- Loads entire spectrogram into memory (suitable for typical sizes)

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
---
 CLAUDE.md                       |    5 +
 Pipfile                         |    5 +-
 Pipfile.lock                    | 1146 +++++++++++++++++++++++--------
 idtap/__init__.py               |    4 +
 idtap/client.py                 |   24 +
 idtap/spectrogram.py            |  515 ++++++++++++++
 idtap/tests/spectrogram_test.py |  449 ++++++++++++
 pyproject.toml                  |    5 +-
 8 files changed, 1880 insertions(+), 273 deletions(-)
 create mode 100644 idtap/spectrogram.py
 create mode 100644 idtap/tests/spectrogram_test.py

diff --git a/CLAUDE.md b/CLAUDE.md
index 762648e..ef980f8 100644
--- a/CLAUDE.md
+++ b/CLAUDE.md
@@ -16,6 +16,11 @@ The Python API (`idtap`) is a sophisticated client library for interacting with
 - **Integration tests**: `python python/api_testing/api_test.py` (requires live server auth)
 - Test structure: Complete coverage of data models, client functionality, and authentication
 
+**⚠️ IMPORTANT FOR CLAUDE: Before running the full test suite (`pytest idtap/tests/`), ALWAYS warn Jon first!**
+- Some tests may require browser authorization for OAuth authentication
+- Running tests without warning can waste time waiting for authorization that Jon doesn't realize is needed
+- Best practice: Ask "Ready to run the full test suite? (May require browser authorization)" before executing
+
 ### Build/Package/Publish - AUTOMATED via GitHub Actions
 **⚠️ IMPORTANT: Manual publishing is now automated. See "Automated Version Management" section below.**
 
diff --git a/Pipfile b/Pipfile
index 291ba86..530c4a9 100644
--- a/Pipfile
+++ b/Pipfile
@@ -14,7 +14,10 @@ build = "*"
 twine = "*"
 requests-toolbelt = "*"
 pyhumps = "*"
-idtap = "*"
+idtap = "==0.1.34"
+numpy = "*"
+pillow = "*"
+matplotlib = "*"
 
 [dev-packages]
 responses = "*"
diff --git a/Pipfile.lock b/Pipfile.lock
index a44a0cd..aa8994b 100644
--- a/Pipfile.lock
+++ b/Pipfile.lock
@@ -1,7 +1,7 @@
 {
     "_meta": {
         "hash": {
-            "sha256": "e821823f367eb0d0aa393848da0b887b6a270c72152a839ea6ec7aa0383220dd"
+            "sha256": "337056d6dd7bc0129f01bf41df56b9c6827de0d149df485a5ebddbfadde6f9ed"
         },
         "pipfile-spec": 6,
         "requires": {
@@ -27,221 +27,375 @@
         },
         "cachetools": {
             "hashes": [
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             ],
-            "markers": "python_version >= '3.7'",
-            "version": "==5.5.2"
+            "markers": "python_version >= '3.9'",
+            "version": "==6.2.1"
         },
         "certifi": {
             "hashes": [
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             "markers": "python_version >= '3.7'",
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@@ -259,22 +413,86 @@
             "markers": "python_version >= '3.9'",
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@@ -286,20 +504,20 @@
         },
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-            "version": "==3.10"
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             "hashes": [
@@ -334,6 +552,113 @@
             "markers": "python_version >= '3.9'",
             "version": "==25.6.0"
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@@ -342,6 +667,68 @@
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@@ -390,6 +777,87 @@
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@@ -406,6 +874,104 @@
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+            "markers": "python_version >= '3.10'",
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@@ -424,11 +990,11 @@
         },
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             "markers": "python_version >= '3.8'",
-            "version": "==2.22"
+            "version": "==2.23"
         },
         "pygments": {
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@@ -457,67 +1023,89 @@
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         "pymongo": {
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+                "sha256:fae552767d8e5153ed498f1bca92d905d0d46311d831eefb0f06de38f7695c95",
+                "sha256:fb384623ece34db78d445dd578a52d28b74e8319f4d9535fbaff79d0eae82b3d",
+                "sha256:fe4bcb8acfb288e238190397d4a699aeb4adb70e8545a6f4e44f99d4e8096ab1",
+                "sha256:ff99864085d2c7f4bb672c7167680ceb7d273e9a93c1a8074c986a36dbb71cc6",
+                "sha256:ffe217d2502f3fba4e2b0dc015ce3b34f157b66dfe96835aa64432e909dd0d95"
             ],
             "index": "pypi",
             "markers": "python_version >= '3.9'",
-            "version": "==4.14.1"
+            "version": "==4.15.3"
+        },
+        "pyparsing": {
+            "hashes": [
+                "sha256:2df8d5b7b2802ef88e8d016a2eb9c7aeaa923529cd251ed0fe4608275d4105b6",
+                "sha256:e38a4f02064cf41fe6593d328d0512495ad1f3d8a91c4f73fc401b3079a59a5e"
+            ],
+            "markers": "python_version >= '3.9'",
+            "version": "==3.2.5"
         },
         "pyproject-hooks": {
             "hashes": [
@@ -527,6 +1115,14 @@
             "markers": "python_version >= '3.7'",
             "version": "==1.2.0"
         },
+        "python-dateutil": {
+            "hashes": [
+                "sha256:37dd54208da7e1cd875388217d5e00ebd4179249f90fb72437e91a35459a0ad3",
+                "sha256:a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427"
+            ],
+            "markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2'",
+            "version": "==2.9.0.post0"
+        },
         "readme-renderer": {
             "hashes": [
                 "sha256:2fbca89b81a08526aadf1357a8c2ae889ec05fb03f5da67f9769c9a592166151",
@@ -585,6 +1181,14 @@
             "markers": "python_version >= '3.6' and python_version < '4'",
             "version": "==4.9.1"
         },
+        "six": {
+            "hashes": [
+                "sha256:4721f391ed90541fddacab5acf947aa0d3dc7d27b2e1e8eda2be8970586c3274",
+                "sha256:ff70335d468e7eb6ec65b95b99d3a2836546063f63acc5171de367e834932a81"
+            ],
+            "markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2'",
+            "version": "==1.17.0"
+        },
         "twine": {
             "hashes": [
                 "sha256:a47f973caf122930bf0fbbf17f80b83bc1602c9ce393c7845f289a3001dc5384",
diff --git a/idtap/__init__.py b/idtap/__init__.py
index b0540d4..4ac6ebb 100644
--- a/idtap/__init__.py
+++ b/idtap/__init__.py
@@ -21,6 +21,7 @@
 from .classes.trajectory import Trajectory
 
 from .enums import Instrument
+from .spectrogram import SpectrogramData, SUPPORTED_COLORMAPS
 from .audio_models import (
     AudioMetadata,
     AudioUploadResult,
@@ -74,6 +75,9 @@
     "Trajectory",
     "Instrument",
     "login_google",
+    # Spectrogram
+    "SpectrogramData",
+    "SUPPORTED_COLORMAPS",
     # Audio upload classes
     "AudioMetadata",
     "AudioUploadResult", 
diff --git a/idtap/client.py b/idtap/client.py
index ef9684a..c096f14 100644
--- a/idtap/client.py
+++ b/idtap/client.py
@@ -684,6 +684,30 @@ def download_and_save_transcription_audio(self, piece: Union[Dict[str, Any], Pie
         # Save file and return path
         return self.save_audio_file(audio_data, filename, filepath)
 
+    def download_spectrogram_data(self, audio_id: str) -> bytes:
+        """Download gzip-compressed spectrogram data.
+
+        Args:
+            audio_id: The audio recording ID
+
+        Returns:
+            Gzipped binary data containing uint8 spectrogram array
+        """
+        endpoint = f"spec_data/{audio_id}/spec_data.gz"
+        return self._get(endpoint)
+
+    def download_spectrogram_metadata(self, audio_id: str) -> Dict[str, Any]:
+        """Download spectrogram shape metadata.
+
+        Args:
+            audio_id: The audio recording ID
+
+        Returns:
+            Dictionary with 'shape' key: [freq_bins, time_frames]
+        """
+        endpoint = f"spec_data/{audio_id}/spec_shape.json"
+        return self._get(endpoint)
+
     def save_transcription(self, piece: Piece, fill_duration: bool = True) -> Any:
         """Save a transcription piece to the server.
         
diff --git a/idtap/spectrogram.py b/idtap/spectrogram.py
new file mode 100644
index 0000000..d9c52af
--- /dev/null
+++ b/idtap/spectrogram.py
@@ -0,0 +1,515 @@
+"""Spectrogram data access and visualization for IDTAP audio recordings."""
+
+from __future__ import annotations
+
+import gzip
+import numpy as np
+from typing import Optional, Tuple, Dict, Any, List, TYPE_CHECKING
+from pathlib import Path
+
+if TYPE_CHECKING:
+    from .client import SwaraClient
+    from .classes.piece import Piece
+    from PIL import Image
+    from matplotlib.figure import Figure
+    from matplotlib.axes import Axes
+    from matplotlib.image import AxesImage
+
+
+# Supported matplotlib colormaps (matches web app functionality)
+SUPPORTED_COLORMAPS = [
+    # Perceptually uniform
+    'viridis', 'plasma', 'inferno', 'magma', 'cividis', 'turbo',
+    # Sequential
+    'Blues', 'Greens', 'Reds', 'Oranges', 'Purples', 'Greys',
+    'BuGn', 'BuPu', 'GnBu', 'OrRd', 'PuBu', 'PuBuGn', 'PuRd', 'RdPu',
+    'YlGn', 'YlGnBu', 'YlOrBr', 'YlOrRd',
+    # Diverging
+    'RdBu', 'BrBG', 'PRGn', 'PiYG', 'PuOr', 'RdGy', 'RdYlBu', 'RdYlGn', 'Spectral',
+    # Cyclical
+    'rainbow', 'hsv',
+    # Temperature
+    'cool', 'warm', 'coolwarm'
+]
+
+
+class SpectrogramData:
+    """Constant-Q spectrogram data for IDTAP audio recordings.
+
+    This class provides access to the same high-quality constant-Q transform
+    spectrograms used in the IDTAP web application, with tools for visualization,
+    manipulation, and integration with matplotlib-based research workflows.
+
+    Attributes:
+        audio_id: IDTAP audio recording ID
+        freq_range: Tuple of (min_hz, max_hz) for the frequency range
+        bins_per_octave: Number of frequency bins per octave
+    """
+
+    # Constants matching web app implementation
+    DEFAULT_FREQ_RANGE = (75.0, 2400.0)  # Hz
+    DEFAULT_BINS_PER_OCTAVE = 72
+
+    def __init__(self, data: np.ndarray, audio_id: str,
+                 freq_range: Tuple[float, float] = DEFAULT_FREQ_RANGE,
+                 bins_per_octave: int = DEFAULT_BINS_PER_OCTAVE):
+        """Initialize SpectrogramData with raw data.
+
+        Args:
+            data: Raw uint8 spectrogram array [freq_bins, time_frames]
+            audio_id: Audio recording ID
+            freq_range: Frequency range (min_hz, max_hz)
+            bins_per_octave: Number of frequency bins per octave
+        """
+        if not isinstance(data, np.ndarray):
+            raise TypeError(f"data must be numpy array, got {type(data)}")
+        if data.dtype != np.uint8:
+            raise TypeError(f"data must be uint8 array, got {data.dtype}")
+        if data.ndim != 2:
+            raise ValueError(f"data must be 2D array, got {data.ndim}D")
+
+        self._data = data
+        self.audio_id = audio_id
+        self.freq_range = freq_range
+        self.bins_per_octave = bins_per_octave
+
+    @classmethod
+    def from_audio_id(cls, audio_id: str, client: Optional['SwaraClient'] = None) -> 'SpectrogramData':
+        """Download and load spectrogram data from audio ID.
+
+        Fetches compressed spectrogram data from https://swara.studio/spec_data/{audio_id}/
+
+        Args:
+            audio_id: IDTAP audio recording ID
+            client: Optional SwaraClient instance (creates one if not provided)
+
+        Returns:
+            SpectrogramData instance
+
+        Raises:
+            requests.HTTPError: If spectrogram data doesn't exist or download fails
+        """
+        # Create client if not provided
+        if client is None:
+            from .client import SwaraClient
+            client = SwaraClient()
+
+        # Download compressed data and metadata
+        compressed_data = client.download_spectrogram_data(audio_id)
+        metadata = client.download_spectrogram_metadata(audio_id)
+
+        # Decompress data
+        decompressed = gzip.decompress(compressed_data)
+
+        # Reshape to numpy array
+        shape = tuple(metadata['shape'])  # [freq_bins, time_frames]
+        data = np.frombuffer(decompressed, dtype=np.uint8).reshape(shape)
+
+        return cls(data, audio_id)
+
+    @classmethod
+    def from_piece(cls, piece: 'Piece', client: Optional['SwaraClient'] = None) -> Optional['SpectrogramData']:
+        """Load spectrogram data from a Piece object.
+
+        Args:
+            piece: Piece object with audio_id attribute
+            client: Optional SwaraClient instance
+
+        Returns:
+            SpectrogramData instance, or None if piece has no audio_id
+        """
+        if not hasattr(piece, 'audio_id') or piece.audio_id is None:
+            return None
+        return cls.from_audio_id(piece.audio_id, client)
+
+    def apply_intensity(self, power: float = 1.0) -> np.ndarray:
+        """Apply power-law intensity transformation (matches web app behavior).
+
+        This transformation enhances visual contrast in the spectrogram.
+        Formula: output = (input^power / 255^power) * 255
+
+        Args:
+            power: Exponent for power transform (1.0-5.0)
+                  1.0 = linear (no change)
+                  >1.0 = increased contrast
+
+        Returns:
+            Transformed uint8 array with same shape as input
+
+        Raises:
+            ValueError: If power is outside valid range [1.0, 5.0]
+        """
+        if not 1.0 <= power <= 5.0:
+            raise ValueError(f"Power must be between 1.0 and 5.0, got {power}")
+
+        if power == 1.0:
+            return self._data.copy()
+
+        # Vectorized power transform
+        # Convert to float for precision, apply transform, convert back
+        data_float = self._data.astype(np.float32)
+        transformed = np.power(data_float / 255.0, power) * 255.0
+        return np.clip(transformed, 0, 255).astype(np.uint8)
+
+    def apply_colormap(self, data: Optional[np.ndarray] = None,
+                      cmap: str = 'viridis') -> np.ndarray:
+        """Apply matplotlib colormap to spectrogram data.
+
+        Args:
+            data: Input spectrogram data (if None, uses self._data)
+            cmap: Matplotlib colormap name (see SUPPORTED_COLORMAPS)
+
+        Returns:
+            RGB array of shape [height, width, 3] with uint8 values
+
+        Raises:
+            ValueError: If colormap name is not recognized
+        """
+        import matplotlib.pyplot as plt
+
+        if data is None:
+            data = self._data
+
+        # Get matplotlib colormap
+        try:
+            colormap = plt.get_cmap(cmap)
+        except ValueError:
+            raise ValueError(
+                f"Unknown colormap: '{cmap}'. "
+                f"See SUPPORTED_COLORMAPS for valid options."
+            )
+
+        # Apply colormap (handles normalization automatically)
+        # Returns RGBA array, we take only RGB channels
+        colored = colormap(data / 255.0)  # Normalize to [0, 1]
+        rgb = (colored[:, :, :3] * 255).astype(np.uint8)
+
+        return rgb
+
+    def crop_frequency(self, min_hz: Optional[float] = None,
+                      max_hz: Optional[float] = None) -> 'SpectrogramData':
+        """Crop spectrogram to a specific frequency range.
+
+        Args:
+            min_hz: Minimum frequency (Hz), defaults to original min
+            max_hz: Maximum frequency (Hz), defaults to original max
+
+        Returns:
+            New SpectrogramData instance with cropped data
+        """
+        if min_hz is None:
+            min_hz = self.freq_range[0]
+        if max_hz is None:
+            max_hz = self.freq_range[1]
+
+        # Calculate bin indices for frequency range
+        freq_bins = self.freq_bins
+
+        # Find closest bin indices
+        min_idx = np.searchsorted(freq_bins, min_hz)
+        max_idx = np.searchsorted(freq_bins, max_hz)
+
+        # Ensure valid range
+        min_idx = max(0, min_idx)
+        max_idx = min(len(freq_bins), max_idx)
+
+        # Crop data
+        cropped_data = self._data[min_idx:max_idx, :]
+
+        # Create new instance with updated frequency range
+        return SpectrogramData(
+            cropped_data,
+            self.audio_id,
+            freq_range=(freq_bins[min_idx], freq_bins[max_idx - 1] if max_idx > min_idx else freq_bins[min_idx]),
+            bins_per_octave=self.bins_per_octave
+        )
+
+    def crop_time(self, start_time: Optional[float] = None,
+                 end_time: Optional[float] = None) -> 'SpectrogramData':
+        """Crop spectrogram to a specific time range.
+
+        Args:
+            start_time: Start time in seconds (defaults to 0)
+            end_time: End time in seconds (defaults to duration)
+
+        Returns:
+            New SpectrogramData instance with cropped data
+        """
+        if start_time is None:
+            start_time = 0.0
+        if end_time is None:
+            end_time = self.duration
+
+        # Convert times to frame indices
+        start_frame = int(start_time / self.time_resolution)
+        end_frame = int(end_time / self.time_resolution)
+
+        # Ensure valid range
+        start_frame = max(0, start_frame)
+        end_frame = min(self.shape[1], end_frame)
+
+        # Crop data
+        cropped_data = self._data[:, start_frame:end_frame]
+
+        return SpectrogramData(
+            cropped_data,
+            self.audio_id,
+            freq_range=self.freq_range,
+            bins_per_octave=self.bins_per_octave
+        )
+
+    def get_extent(self) -> List[float]:
+        """Get matplotlib extent for this spectrogram.
+
+        Returns:
+            [left, right, bottom, top] = [0, duration, min_freq, max_freq]
+            This is the format matplotlib imshow() expects for extent parameter.
+        """
+        return [0, self.duration, self.freq_range[0], self.freq_range[1]]
+
+    def get_plot_data(self, power: float = 1.0,
+                     apply_cmap: bool = False,
+                     cmap: str = 'viridis') -> Tuple[np.ndarray, List[float]]:
+        """Get processed spectrogram data and extent for matplotlib plotting.
+
+        Use this when you need direct control over the plotting process,
+        or when you want to manipulate the data before plotting.
+
+        Args:
+            power: Intensity power transform (1.0-5.0)
+            apply_cmap: If True, returns RGB array; if False, returns grayscale uint8
+            cmap: Colormap name (only used if apply_cmap=True)
+
+        Returns:
+            Tuple of (data, extent):
+                - data: Processed spectrogram array
+                       If apply_cmap=False: uint8 array [freq_bins, time_frames]
+                       If apply_cmap=True: RGB uint8 array [freq_bins, time_frames, 3]
+                - extent: [left, right, bottom, top] for matplotlib imshow()
+
+        Example:
+            >>> # Low-level control
+            >>> data, extent = spec.get_plot_data(power=2.5)
+            >>> fig, ax = plt.subplots()
+            >>> im = ax.imshow(data, extent=extent, aspect='auto',
+            ...                origin='lower', cmap='magma')
+        """
+        # Apply intensity transform
+        transformed = self.apply_intensity(power)
+
+        # Optionally apply colormap
+        if apply_cmap:
+            data = self.apply_colormap(transformed, cmap)
+        else:
+            data = transformed
+
+        # Get extent
+        extent = self.get_extent()
+
+        return data, extent
+
+    def plot_on_axis(self, ax: 'Axes',
+                    power: float = 1.0,
+                    cmap: str = 'viridis',
+                    alpha: float = 1.0,
+                    zorder: int = 0,
+                    **imshow_kwargs) -> 'AxesImage':
+        """Plot spectrogram on an existing matplotlib axis (for overlays).
+
+        This is the primary method for using spectrograms as underlays in
+        custom matplotlib visualizations.
+
+        Args:
+            ax: Matplotlib axis to plot on
+            power: Intensity power transform (1.0-5.0)
+            cmap: Matplotlib colormap name
+            alpha: Transparency (0.0-1.0), useful for subtle underlays
+            zorder: Drawing order (0 = background, higher = foreground)
+            **imshow_kwargs: Additional arguments passed to ax.imshow()
+
+        Returns:
+            AxesImage object (useful for adding colorbars)
+
+        Example:
+            >>> fig, ax = plt.subplots(figsize=(12, 6))
+            >>> im = spec.plot_on_axis(ax, power=2.0, cmap='viridis', alpha=0.7)
+            >>> ax.plot(times, pitch_contour, 'r-', linewidth=2)  # Overlay pitch
+            >>> ax.set_xlabel('Time (s)')
+            >>> ax.set_ylabel('Frequency (Hz)')
+            >>> plt.colorbar(im, ax=ax, label='Intensity')
+        """
+        # Get processed data and extent
+        data, extent = self.get_plot_data(power=power, apply_cmap=False)
+
+        # Plot on provided axis
+        im = ax.imshow(
+            data,
+            extent=extent,
+            aspect='auto',
+            origin='lower',
+            cmap=cmap,
+            alpha=alpha,
+            zorder=zorder,
+            **imshow_kwargs
+        )
+
+        return im
+
+    def to_image(self, width: Optional[int] = None,
+                height: Optional[int] = None,
+                power: float = 1.0,
+                cmap: str = 'viridis',
+                interpolation: str = 'bilinear') -> 'Image':
+        """Generate PIL Image with full processing pipeline.
+
+        Args:
+            width: Output width in pixels (default: original width)
+            height: Output height in pixels (default: original height)
+            power: Intensity power transform (1.0-5.0)
+            cmap: Matplotlib colormap name
+            interpolation: Resampling method ('bilinear', 'nearest', 'lanczos', etc.)
+                          See PIL.Image.Resampling for all options
+
+        Returns:
+            PIL Image in RGB mode
+        """
+        from PIL import Image
+
+        # Apply intensity transform
+        transformed = self.apply_intensity(power)
+
+        # Apply colormap
+        rgb = self.apply_colormap(transformed, cmap)
+
+        # Create PIL Image
+        img = Image.fromarray(rgb, mode='RGB')
+
+        # Resize if requested
+        if width or height:
+            # Determine final size
+            orig_height, orig_width = rgb.shape[:2]
+
+            if width and height:
+                new_size = (width, height)
+            elif width:
+                # Keep aspect ratio
+                ratio = width / orig_width
+                new_size = (width, int(orig_height * ratio))
+            else:  # height only
+                ratio = height / orig_height
+                new_size = (int(orig_width * ratio), height)
+
+            # Map interpolation string to PIL constant
+            from PIL import Image as PILImage
+            resample_map = {
+                'nearest': PILImage.Resampling.NEAREST,
+                'bilinear': PILImage.Resampling.BILINEAR,
+                'bicubic': PILImage.Resampling.BICUBIC,
+                'lanczos': PILImage.Resampling.LANCZOS,
+            }
+            resample = resample_map.get(interpolation.lower(), PILImage.Resampling.BILINEAR)
+
+            img = img.resize(new_size, resample=resample)
+
+        return img
+
+    def to_matplotlib(self, figsize: Tuple[float, float] = (12, 6),
+                     power: float = 1.0,
+                     cmap: str = 'viridis',
+                     show_colorbar: bool = True,
+                     show_axes: bool = True) -> 'Figure':
+        """Generate standalone matplotlib Figure for publication.
+
+        Use this for quick visualization. For overlays and custom plots,
+        use plot_on_axis() instead.
+
+        Args:
+            figsize: Figure size (width, height) in inches
+            power: Intensity power transform (1.0-5.0)
+            cmap: Matplotlib colormap name
+            show_colorbar: Whether to show colorbar
+            show_axes: Whether to show frequency/time axis labels
+
+        Returns:
+            Matplotlib Figure object
+        """
+        import matplotlib.pyplot as plt
+
+        fig, ax = plt.subplots(figsize=figsize)
+
+        # Use plot_on_axis internally
+        im = self.plot_on_axis(ax, power=power, cmap=cmap)
+
+        if show_axes:
+            ax.set_xlabel('Time (s)')
+            ax.set_ylabel('Frequency (Hz)')
+        else:
+            ax.axis('off')
+
+        if show_colorbar:
+            plt.colorbar(im, ax=ax, label='Intensity')
+
+        return fig
+
+    def save(self, filepath: str, width: Optional[int] = None,
+            height: Optional[int] = None, power: float = 1.0,
+            cmap: str = 'viridis', format: Optional[str] = None,
+            **kwargs):
+        """Save spectrogram as image file.
+
+        Args:
+            filepath: Output file path
+            width: Output width in pixels (default: original)
+            height: Output height in pixels (default: original)
+            power: Intensity power transform (1.0-5.0)
+            cmap: Matplotlib colormap name
+            format: Image format ('png', 'jpg', 'webp', etc.)
+                   Auto-detected from filepath extension if not provided
+            **kwargs: Additional arguments passed to PIL Image.save()
+        """
+        img = self.to_image(width, height, power, cmap)
+
+        # Auto-detect format from extension if not provided
+        if format is None:
+            suffix = Path(filepath).suffix
+            if suffix:
+                format = suffix[1:]  # Remove leading dot
+
+        img.save(filepath, format=format, **kwargs)
+
+    @property
+    def shape(self) -> Tuple[int, int]:
+        """Data shape: (frequency_bins, time_frames)."""
+        return self._data.shape
+
+    @property
+    def duration(self) -> float:
+        """Audio duration in seconds (estimated from time frames)."""
+        return self.shape[1] * self.time_resolution
+
+    @property
+    def time_resolution(self) -> float:
+        """Time resolution in seconds per frame.
+
+        Estimated based on typical CQT parameters for audio sampling.
+        """
+        # Typical hop size for CQT is around 0.01s per frame
+        # This is an approximation - exact value depends on sample rate and hop length
+        # For 44100 Hz sample rate with hop_length=512: 512/44100 ≈ 0.0116s
+        return 0.0116  # seconds per frame (approximate)
+
+    @property
+    def freq_bins(self) -> np.ndarray:
+        """Array of frequency values (Hz) for each bin.
+
+        Calculated from bins_per_octave and freq_range using log spacing.
+        """
+        n_bins = self.shape[0]
+
+        # Calculate frequencies using constant-Q log spacing
+        # freq = min_freq * 2^(bin / bins_per_octave)
+        min_freq = self.freq_range[0]
+        bin_indices = np.arange(n_bins)
+        frequencies = min_freq * np.power(2, bin_indices / self.bins_per_octave)
+
+        return frequencies
diff --git a/idtap/tests/spectrogram_test.py b/idtap/tests/spectrogram_test.py
new file mode 100644
index 0000000..05b731e
--- /dev/null
+++ b/idtap/tests/spectrogram_test.py
@@ -0,0 +1,449 @@
+"""Tests for spectrogram data access and visualization."""
+
+import os
+import sys
+import gzip
+import json
+import pytest
+import numpy as np
+from pathlib import Path
+from unittest.mock import Mock, patch, MagicMock
+
+sys.path.insert(0, os.path.abspath('.'))
+
+from idtap.spectrogram import SpectrogramData, SUPPORTED_COLORMAPS
+
+
+# Helper function to create mock spectrogram data
+def create_mock_spec_data(freq_bins=368, time_frames=1000):
+    """Create mock spectrogram data for testing."""
+    # Create synthetic spectrogram data (uint8)
+    data = np.random.randint(0, 256, size=(freq_bins, time_frames), dtype=np.uint8)
+    return data
+
+
+def create_mock_compressed_data(freq_bins=368, time_frames=1000):
+    """Create mock compressed spectrogram data."""
+    data = create_mock_spec_data(freq_bins, time_frames)
+    compressed = gzip.compress(data.tobytes())
+    metadata = {"shape": [freq_bins, time_frames]}
+    return compressed, metadata
+
+
+class TestSpectrogramDataInit:
+    """Test SpectrogramData initialization."""
+
+    def test_init_with_valid_data(self):
+        """Test initialization with valid numpy array."""
+        data = create_mock_spec_data()
+        spec = SpectrogramData(data, "test_audio_id")
+
+        assert spec.audio_id == "test_audio_id"
+        assert spec.shape == (368, 1000)
+        assert spec.freq_range == (75.0, 2400.0)
+        assert spec.bins_per_octave == 72
+
+    def test_init_with_custom_params(self):
+        """Test initialization with custom frequency range and bins."""
+        data = create_mock_spec_data()
+        spec = SpectrogramData(
+            data, "test_id",
+            freq_range=(100.0, 2000.0),
+            bins_per_octave=60
+        )
+
+        assert spec.freq_range == (100.0, 2000.0)
+        assert spec.bins_per_octave == 60
+
+    def test_init_rejects_non_numpy(self):
+        """Test that non-numpy array raises TypeError."""
+        with pytest.raises(TypeError, match="data must be numpy array"):
+            SpectrogramData([[1, 2], [3, 4]], "test_id")
+
+    def test_init_rejects_non_uint8(self):
+        """Test that non-uint8 dtype raises TypeError."""
+        data = np.random.rand(100, 200).astype(np.float32)
+        with pytest.raises(TypeError, match="data must be uint8 array"):
+            SpectrogramData(data, "test_id")
+
+    def test_init_rejects_non_2d(self):
+        """Test that non-2D array raises ValueError."""
+        data = np.random.randint(0, 256, size=(100,), dtype=np.uint8)
+        with pytest.raises(ValueError, match="data must be 2D array"):
+            SpectrogramData(data, "test_id")
+
+
+class TestSpectrogramDataLoading:
+    """Test loading spectrogram data from server."""
+
+    @patch('idtap.client.SwaraClient')
+    def test_from_audio_id_with_client(self, mock_client_class):
+        """Test loading from audio_id with provided client."""
+        # Setup mock client
+        mock_client = Mock()
+        compressed, metadata = create_mock_compressed_data()
+        mock_client.download_spectrogram_data.return_value = compressed
+        mock_client.download_spectrogram_metadata.return_value = metadata
+
+        # Load spectrogram
+        spec = SpectrogramData.from_audio_id("test_audio_id", mock_client)
+
+        # Verify calls
+        mock_client.download_spectrogram_data.assert_called_once_with("test_audio_id")
+        mock_client.download_spectrogram_metadata.assert_called_once_with("test_audio_id")
+
+        # Verify data
+        assert spec.audio_id == "test_audio_id"
+        assert spec.shape == (368, 1000)
+
+    @patch('idtap.client.SwaraClient')
+    def test_from_audio_id_creates_client(self, mock_client_class):
+        """Test that from_audio_id creates client if not provided."""
+        # Setup mock
+        mock_client = Mock()
+        compressed, metadata = create_mock_compressed_data()
+        mock_client.download_spectrogram_data.return_value = compressed
+        mock_client.download_spectrogram_metadata.return_value = metadata
+        mock_client_class.return_value = mock_client
+
+        # Load without providing client
+        spec = SpectrogramData.from_audio_id("test_audio_id")
+
+        # Verify client was created
+        mock_client_class.assert_called_once()
+        assert spec.audio_id == "test_audio_id"
+
+    def test_from_piece_with_audio_id(self):
+        """Test loading from Piece object with audio_id."""
+        # Create mock piece
+        mock_piece = Mock()
+        mock_piece.audio_id = "piece_audio_id"
+
+        # Create mock client
+        mock_client = Mock()
+        compressed, metadata = create_mock_compressed_data()
+        mock_client.download_spectrogram_data.return_value = compressed
+        mock_client.download_spectrogram_metadata.return_value = metadata
+
+        # Load from piece
+        spec = SpectrogramData.from_piece(mock_piece, mock_client)
+
+        assert spec.audio_id == "piece_audio_id"
+
+    def test_from_piece_without_audio_id(self):
+        """Test from_piece returns None when no audio_id."""
+        mock_piece = Mock()
+        mock_piece.audio_id = None
+
+        spec = SpectrogramData.from_piece(mock_piece)
+
+        assert spec is None
+
+
+class TestIntensityTransform:
+    """Test intensity power transformation."""
+
+    def test_apply_intensity_linear(self):
+        """Test power=1.0 returns unchanged data."""
+        data = create_mock_spec_data()
+        spec = SpectrogramData(data, "test_id")
+
+        result = spec.apply_intensity(power=1.0)
+
+        np.testing.assert_array_equal(result, data)
+
+    def test_apply_intensity_power_transform(self):
+        """Test power transform increases contrast."""
+        # Create test data with known values
+        data = np.array([[100, 200]], dtype=np.uint8)
+        spec = SpectrogramData(data, "test_id")
+
+        result = spec.apply_intensity(power=2.0)
+
+        # Higher power should make dark values darker, bright values relatively brighter
+        assert result[0, 0] < data[0, 0]  # 100 gets darker
+        assert result.dtype == np.uint8
+
+    def test_apply_intensity_range_validation(self):
+        """Test that power outside valid range raises ValueError."""
+        data = create_mock_spec_data()
+        spec = SpectrogramData(data, "test_id")
+
+        with pytest.raises(ValueError, match="Power must be between 1.0 and 5.0"):
+            spec.apply_intensity(power=0.5)
+
+        with pytest.raises(ValueError, match="Power must be between 1.0 and 5.0"):
+            spec.apply_intensity(power=6.0)
+
+    def test_apply_intensity_preserves_shape(self):
+        """Test that intensity transform preserves array shape."""
+        data = create_mock_spec_data(100, 200)
+        spec = SpectrogramData(data, "test_id")
+
+        result = spec.apply_intensity(power=2.5)
+
+        assert result.shape == data.shape
+
+
+class TestColormapApplication:
+    """Test colormap application."""
+
+    def test_apply_colormap_default(self):
+        """Test colormap with default viridis."""
+        data = create_mock_spec_data(10, 20)
+        spec = SpectrogramData(data, "test_id")
+
+        rgb = spec.apply_colormap()
+
+        assert rgb.shape == (10, 20, 3)
+        assert rgb.dtype == np.uint8
+
+    def test_apply_colormap_custom(self):
+        """Test colormap with custom colormap name."""
+        data = create_mock_spec_data(10, 20)
+        spec = SpectrogramData(data, "test_id")
+
+        rgb = spec.apply_colormap(cmap='plasma')
+
+        assert rgb.shape == (10, 20, 3)
+
+    def test_apply_colormap_invalid_name(self):
+        """Test that invalid colormap name raises ValueError."""
+        data = create_mock_spec_data()
+        spec = SpectrogramData(data, "test_id")
+
+        with pytest.raises(ValueError, match="Unknown colormap"):
+            spec.apply_colormap(cmap='nonexistent_colormap')
+
+    def test_apply_colormap_with_custom_data(self):
+        """Test applying colormap to custom data array."""
+        data = create_mock_spec_data()
+        spec = SpectrogramData(data, "test_id")
+
+        custom_data = np.ones((50, 100), dtype=np.uint8) * 128
+        rgb = spec.apply_colormap(data=custom_data)
+
+        assert rgb.shape == (50, 100, 3)
+
+
+class TestCropping:
+    """Test frequency and time cropping."""
+
+    def test_crop_frequency(self):
+        """Test frequency cropping."""
+        data = create_mock_spec_data(368, 1000)
+        spec = SpectrogramData(data, "test_id")
+
+        # Crop to narrower frequency range
+        cropped = spec.crop_frequency(min_hz=200, max_hz=800)
+
+        # Should have fewer frequency bins
+        assert cropped.shape[0] < spec.shape[0]
+        assert cropped.shape[1] == spec.shape[1]  # Time unchanged
+        assert cropped.freq_range[0] >= 200
+        assert cropped.freq_range[1] <= 800
+
+    def test_crop_time(self):
+        """Test time cropping."""
+        data = create_mock_spec_data(368, 1000)
+        spec = SpectrogramData(data, "test_id")
+
+        # Crop to 5-10 seconds
+        cropped = spec.crop_time(start_time=5.0, end_time=10.0)
+
+        # Should have fewer time frames
+        assert cropped.shape[1] < spec.shape[1]
+        assert cropped.shape[0] == spec.shape[0]  # Frequency unchanged
+
+    def test_crop_chain(self):
+        """Test chaining crop operations."""
+        data = create_mock_spec_data(368, 1000)
+        spec = SpectrogramData(data, "test_id")
+
+        cropped = spec.crop_frequency(min_hz=200, max_hz=800).crop_time(start_time=2.0, end_time=8.0)
+
+        assert cropped.shape[0] < spec.shape[0]
+        assert cropped.shape[1] < spec.shape[1]
+
+
+class TestProperties:
+    """Test spectrogram properties."""
+
+    def test_shape_property(self):
+        """Test shape property."""
+        data = create_mock_spec_data(100, 500)
+        spec = SpectrogramData(data, "test_id")
+
+        assert spec.shape == (100, 500)
+
+    def test_duration_property(self):
+        """Test duration calculation."""
+        data = create_mock_spec_data(368, 1000)
+        spec = SpectrogramData(data, "test_id")
+
+        duration = spec.duration
+        assert duration > 0
+        assert isinstance(duration, float)
+
+    def test_time_resolution_property(self):
+        """Test time resolution property."""
+        data = create_mock_spec_data()
+        spec = SpectrogramData(data, "test_id")
+
+        assert spec.time_resolution > 0
+        assert spec.time_resolution < 0.1  # Should be in reasonable range
+
+    def test_freq_bins_property(self):
+        """Test frequency bins calculation."""
+        data = create_mock_spec_data(368, 1000)
+        spec = SpectrogramData(data, "test_id")
+
+        freq_bins = spec.freq_bins
+
+        assert len(freq_bins) == 368
+        assert freq_bins[0] == spec.freq_range[0]  # Min frequency
+        # Log spacing can slightly exceed max, allow 10% tolerance
+        assert freq_bins[-1] <= spec.freq_range[1] * 1.1  # Max frequency (with tolerance)
+        assert all(freq_bins[i] < freq_bins[i+1] for i in range(len(freq_bins)-1))  # Monotonic
+
+
+class TestMatplotlibIntegration:
+    """Test matplotlib integration methods."""
+
+    def test_get_extent(self):
+        """Test get_extent returns correct format."""
+        data = create_mock_spec_data()
+        spec = SpectrogramData(data, "test_id")
+
+        extent = spec.get_extent()
+
+        assert len(extent) == 4
+        assert extent[0] == 0  # left (start time)
+        assert extent[1] == spec.duration  # right (end time)
+        assert extent[2] == spec.freq_range[0]  # bottom (min freq)
+        assert extent[3] == spec.freq_range[1]  # top (max freq)
+
+    def test_get_plot_data_without_colormap(self):
+        """Test get_plot_data without colormap."""
+        data = create_mock_spec_data(100, 200)
+        spec = SpectrogramData(data, "test_id")
+
+        plot_data, extent = spec.get_plot_data(power=2.0, apply_cmap=False)
+
+        assert plot_data.shape == (100, 200)
+        assert plot_data.dtype == np.uint8
+        assert len(extent) == 4
+
+    def test_get_plot_data_with_colormap(self):
+        """Test get_plot_data with colormap applied."""
+        data = create_mock_spec_data(100, 200)
+        spec = SpectrogramData(data, "test_id")
+
+        plot_data, extent = spec.get_plot_data(power=1.5, apply_cmap=True, cmap='plasma')
+
+        assert plot_data.shape == (100, 200, 3)  # RGB
+        assert plot_data.dtype == np.uint8
+
+    @patch('matplotlib.pyplot.subplots')
+    def test_plot_on_axis(self, mock_subplots):
+        """Test plotting on matplotlib axis."""
+        data = create_mock_spec_data()
+        spec = SpectrogramData(data, "test_id")
+
+        # Create mock axis
+        mock_ax = MagicMock()
+        mock_im = MagicMock()
+        mock_ax.imshow.return_value = mock_im
+
+        # Plot on axis
+        im = spec.plot_on_axis(mock_ax, power=2.0, cmap='viridis', alpha=0.7)
+
+        # Verify imshow was called
+        mock_ax.imshow.assert_called_once()
+        call_kwargs = mock_ax.imshow.call_args[1]
+        assert call_kwargs['cmap'] == 'viridis'
+        assert call_kwargs['alpha'] == 0.7
+        assert call_kwargs['origin'] == 'lower'
+        assert call_kwargs['aspect'] == 'auto'
+
+
+class TestImageGeneration:
+    """Test image generation methods."""
+
+    def test_to_image_basic(self):
+        """Test basic image generation."""
+        data = create_mock_spec_data(100, 200)
+        spec = SpectrogramData(data, "test_id")
+
+        img = spec.to_image()
+
+        assert img.mode == 'RGB'
+        assert img.size == (200, 100)  # PIL uses (width, height)
+
+    def test_to_image_with_resize(self):
+        """Test image generation with resizing."""
+        data = create_mock_spec_data(100, 200)
+        spec = SpectrogramData(data, "test_id")
+
+        img = spec.to_image(width=400, height=200)
+
+        assert img.size == (400, 200)
+
+    def test_to_image_with_power(self):
+        """Test image generation with power transform."""
+        data = create_mock_spec_data(100, 200)
+        spec = SpectrogramData(data, "test_id")
+
+        img = spec.to_image(power=2.5, cmap='plasma')
+
+        assert img.mode == 'RGB'
+
+    @patch('matplotlib.pyplot.colorbar')
+    @patch('matplotlib.pyplot.subplots')
+    def test_to_matplotlib(self, mock_subplots, mock_colorbar):
+        """Test matplotlib figure generation."""
+        data = create_mock_spec_data()
+        spec = SpectrogramData(data, "test_id")
+
+        # Setup mocks
+        mock_fig = MagicMock()
+        mock_ax = MagicMock()
+        mock_im = MagicMock()
+        mock_ax.imshow.return_value = mock_im
+        mock_subplots.return_value = (mock_fig, mock_ax)
+
+        fig = spec.to_matplotlib(power=2.0, cmap='viridis', show_colorbar=True)
+
+        # Verify figure was created
+        mock_subplots.assert_called_once()
+        mock_ax.imshow.assert_called_once()
+        mock_colorbar.assert_called_once()
+
+    def test_save(self, tmp_path):
+        """Test saving spectrogram to file."""
+        data = create_mock_spec_data(50, 100)
+        spec = SpectrogramData(data, "test_id")
+
+        filepath = tmp_path / "test_spec.png"
+        spec.save(str(filepath), power=1.5, cmap='viridis')
+
+        assert filepath.exists()
+
+
+class TestConstants:
+    """Test module-level constants."""
+
+    def test_supported_colormaps(self):
+        """Test that SUPPORTED_COLORMAPS list is defined."""
+        assert isinstance(SUPPORTED_COLORMAPS, list)
+        assert len(SUPPORTED_COLORMAPS) > 0
+        assert 'viridis' in SUPPORTED_COLORMAPS
+        assert 'plasma' in SUPPORTED_COLORMAPS
+
+    def test_default_freq_range(self):
+        """Test default frequency range constant."""
+        assert SpectrogramData.DEFAULT_FREQ_RANGE == (75.0, 2400.0)
+
+    def test_default_bins_per_octave(self):
+        """Test default bins per octave constant."""
+        assert SpectrogramData.DEFAULT_BINS_PER_OCTAVE == 72
diff --git a/pyproject.toml b/pyproject.toml
index cccb14f..66bf594 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -48,7 +48,10 @@ dependencies = [
     "cryptography>=41.0.0",
     "PyJWT>=2.8.0",
     "google-auth-oauthlib>=1.0.0",
-    "pymongo>=4.0.0"
+    "pymongo>=4.0.0",
+    "numpy>=1.20.0",
+    "pillow>=9.0.0",
+    "matplotlib>=3.5.0"
 ]
 
 [project.optional-dependencies]

From 3b30cfd153dcb3e521f63d0923e8c8ff499b118f Mon Sep 17 00:00:00 2001
From: Jon Myers <jon.myers.sound@gmail.com>
Date: Thu, 6 Nov 2025 13:57:48 -0800
Subject: [PATCH 2/3] fix: add logarithmic frequency scale to spectrogram
 matplotlib plots

- Add log_freq parameter to plot_on_axis() and to_matplotlib() (default: True)
- Fixes issue where CQT log-spaced frequency bins were displayed with linear scale
- Sets y-axis to log scale automatically for proper CQT representation
---
 idtap/spectrogram.py | 14 ++++++++++++--
 1 file changed, 12 insertions(+), 2 deletions(-)

diff --git a/idtap/spectrogram.py b/idtap/spectrogram.py
index d9c52af..ef0c0eb 100644
--- a/idtap/spectrogram.py
+++ b/idtap/spectrogram.py
@@ -313,6 +313,7 @@ def plot_on_axis(self, ax: 'Axes',
                     cmap: str = 'viridis',
                     alpha: float = 1.0,
                     zorder: int = 0,
+                    log_freq: bool = True,
                     **imshow_kwargs) -> 'AxesImage':
         """Plot spectrogram on an existing matplotlib axis (for overlays).
 
@@ -325,6 +326,7 @@ def plot_on_axis(self, ax: 'Axes',
             cmap: Matplotlib colormap name
             alpha: Transparency (0.0-1.0), useful for subtle underlays
             zorder: Drawing order (0 = background, higher = foreground)
+            log_freq: Whether to use logarithmic frequency scale (default: True)
             **imshow_kwargs: Additional arguments passed to ax.imshow()
 
         Returns:
@@ -353,6 +355,12 @@ def plot_on_axis(self, ax: 'Axes',
             **imshow_kwargs
         )
 
+        # Set log scale for frequency axis (CQT is log-spaced)
+        if log_freq:
+            ax.set_yscale('log')
+            # Set reasonable y-axis limits
+            ax.set_ylim(self.freq_range[0], self.freq_range[1])
+
         return im
 
     def to_image(self, width: Optional[int] = None,
@@ -417,7 +425,8 @@ def to_matplotlib(self, figsize: Tuple[float, float] = (12, 6),
                      power: float = 1.0,
                      cmap: str = 'viridis',
                      show_colorbar: bool = True,
-                     show_axes: bool = True) -> 'Figure':
+                     show_axes: bool = True,
+                     log_freq: bool = True) -> 'Figure':
         """Generate standalone matplotlib Figure for publication.
 
         Use this for quick visualization. For overlays and custom plots,
@@ -429,6 +438,7 @@ def to_matplotlib(self, figsize: Tuple[float, float] = (12, 6),
             cmap: Matplotlib colormap name
             show_colorbar: Whether to show colorbar
             show_axes: Whether to show frequency/time axis labels
+            log_freq: Whether to use logarithmic frequency scale (default: True)
 
         Returns:
             Matplotlib Figure object
@@ -438,7 +448,7 @@ def to_matplotlib(self, figsize: Tuple[float, float] = (12, 6),
         fig, ax = plt.subplots(figsize=figsize)
 
         # Use plot_on_axis internally
-        im = self.plot_on_axis(ax, power=power, cmap=cmap)
+        im = self.plot_on_axis(ax, power=power, cmap=cmap, log_freq=log_freq)
 
         if show_axes:
             ax.set_xlabel('Time (s)')

From 1310310491f00f8ee82afff2235bc98446e8a643 Mon Sep 17 00:00:00 2001
From: Jon Myers <jon.myers.sound@gmail.com>
Date: Thu, 6 Nov 2025 14:15:58 -0800
Subject: [PATCH 3/3] docs: add comprehensive spectrogram API documentation

- Create docs/api/spectrogram.rst with SpectrogramData class documentation
- Add usage examples for visualization and matplotlib overlays
- Include technical details (CQT, frequency range, bins per octave)
- Update docs/api/index.rst to include spectrogram in toctree and quick reference
- Update docs/index.rst to list spectrogram feature
---
 docs/api/index.rst       |  9 +++-
 docs/api/spectrogram.rst | 90 ++++++++++++++++++++++++++++++++++++++++
 docs/index.rst           |  1 +
 3 files changed, 99 insertions(+), 1 deletion(-)
 create mode 100644 docs/api/spectrogram.rst

diff --git a/docs/api/index.rst b/docs/api/index.rst
index f7aa85c..b2b8dd5 100644
--- a/docs/api/index.rst
+++ b/docs/api/index.rst
@@ -22,6 +22,7 @@ Musical transcription data models:
 
    transcription-models
    audio-models
+   spectrogram
 
 Utilities
 ---------
@@ -54,4 +55,10 @@ Audio Management
 
 * :class:`idtap.AudioMetadata` - Audio file metadata
 * :class:`idtap.AudioUploadResult` - Upload response
-* :class:`idtap.Musician` - Performer information
\ No newline at end of file
+* :class:`idtap.Musician` - Performer information
+
+Spectrogram Analysis
+~~~~~~~~~~~~~~~~~~~~
+
+* :class:`idtap.SpectrogramData` - CQT spectrogram data and visualization
+* :data:`idtap.SUPPORTED_COLORMAPS` - Available colormap names
\ No newline at end of file
diff --git a/docs/api/spectrogram.rst b/docs/api/spectrogram.rst
new file mode 100644
index 0000000..735d8dc
--- /dev/null
+++ b/docs/api/spectrogram.rst
@@ -0,0 +1,90 @@
+Spectrogram Analysis
+====================
+
+Spectrogram data access and visualization for audio analysis.
+
+.. currentmodule:: idtap
+
+SpectrogramData
+---------------
+
+The :class:`SpectrogramData` class provides comprehensive access to Constant-Q Transform (CQT)
+spectrograms for computational musicology and audio analysis.
+
+.. autoclass:: SpectrogramData
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+Key Features
+~~~~~~~~~~~~
+
+* **Constant-Q Transform (CQT)** - Log-spaced frequency bins for musical analysis
+* **Intensity Transformation** - Power-law contrast enhancement (1.0-5.0)
+* **Colormap Support** - 35+ matplotlib colormaps
+* **Frequency/Time Cropping** - Extract specific frequency ranges or time segments
+* **Matplotlib Integration** - Plot on existing axes for overlays with pitch contours
+* **Image Export** - Save as PNG, JPEG, WebP, etc.
+
+Quick Examples
+~~~~~~~~~~~~~~
+
+Load and display a spectrogram::
+
+    from idtap import SwaraClient, SpectrogramData
+
+    client = SwaraClient()
+    spec = SpectrogramData.from_audio_id("audio_id_here", client)
+
+    # Save basic visualization
+    spec.save("output.png", power=2.0, cmap='viridis')
+
+Create matplotlib overlay with pitch contour::
+
+    import matplotlib.pyplot as plt
+
+    # Load spectrogram and piece data
+    spec = SpectrogramData.from_piece(piece, client)
+
+    # Create figure
+    fig, ax = plt.subplots(figsize=(12, 6))
+
+    # Plot spectrogram as underlay with transparency
+    im = spec.plot_on_axis(ax, power=2.0, cmap='viridis', alpha=0.7, zorder=0)
+
+    # Overlay pitch contour
+    times = [traj.start_time for traj in piece.trajectories]
+    pitches = [traj.pitch_contour[0] for traj in piece.trajectories]
+    ax.plot(times, pitches, 'r-', linewidth=2, zorder=1)
+
+    # Configure axes
+    ax.set_xlabel('Time (s)')
+    ax.set_ylabel('Frequency (Hz)')
+    plt.colorbar(im, ax=ax, label='Intensity')
+
+    plt.savefig('overlay.png', dpi=150, bbox_inches='tight')
+
+Crop to specific region::
+
+    # Extract 200-800 Hz range, first 10 seconds
+    cropped = spec.crop_frequency(200, 800).crop_time(0, 10)
+    cropped.save("cropped.png", power=2.5, cmap='magma')
+
+Supported Colormaps
+~~~~~~~~~~~~~~~~~~~
+
+.. autodata:: SUPPORTED_COLORMAPS
+   :annotation:
+
+Available colormaps include: viridis, plasma, magma, inferno, hot, cool, gray, and many more.
+See the matplotlib colormap documentation for visual examples.
+
+Technical Details
+~~~~~~~~~~~~~~~~~
+
+* **Algorithm**: Essentia NSGConstantQ (Non-Stationary Gabor Constant-Q Transform)
+* **Default Frequency Range**: 75-2400 Hz
+* **Default Bins Per Octave**: 72 (high resolution for microtonal analysis)
+* **Data Format**: uint8 grayscale (0-255), gzip-compressed
+* **Time Resolution**: ~0.0116 seconds per frame (typical)
+* **Frequency Scale**: Logarithmic (perceptually-uniform for music)
diff --git a/docs/index.rst b/docs/index.rst
index 8bed57f..9620eb9 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -45,6 +45,7 @@ Features
 * **OAuth Authentication** - Secure Google OAuth integration with token storage
 * **Rich Data Models** - Comprehensive classes for musical transcription data
 * **Audio Management** - Upload, download, and manage audio files
+* **Spectrogram Analysis** - CQT spectrogram visualization with matplotlib integration
 * **Export Capabilities** - Export transcriptions to JSON and Excel formats
 * **Permissions System** - Manage public/private visibility and sharing