Fix merge conflicts and port all imports from utils to ria_toolkit_oss

Resolves unresolved merge conflict markers left in committed files across
the annotations, view, data, and CLI packages. Updates all remaining
imports from the old utils.* namespace to ria_toolkit_oss.datatypes,
ria_toolkit_oss.io, and ria_toolkit_oss.view equivalents.

src/ria_toolkit_oss/annotations/__init__.py

@@ -1,4 +1,3 @@
-<<<<<<< HEAD
 
 """
 The annotations package contains tools and utilities for creating, managing, and processing annotations.
@@ -54,9 +53,3 @@ from .parallel_signal_separator import (
 from .qualify_slice import qualify_slice_from_annotations
 from .signal_isolation import isolate_signal
 from .threshold_qualifier import threshold_qualifier
-=======
-from .cusum_annotator import annotate_with_cusum
-from .energy_detector import detect_signals_energy
-from .parallel_signal_separator import split_recording_annotations
-from .threshold_qualifier import threshold_qualifier
->>>>>>> 2bb2d9d5a780dbc17172135a5a1f10eba14b1af4

src/ria_toolkit_oss/annotations/annotation_transforms.py

@@ -1,8 +1,4 @@
-<<<<<<< HEAD
-from utils.data.annotation import Annotation
-=======
 from ria_toolkit_oss.datatypes.annotation import Annotation
->>>>>>> 2bb2d9d5a780dbc17172135a5a1f10eba14b1af4
 
 # TODO figure out how to transfer labels in the merge case
 

src/ria_toolkit_oss/annotations/cusum_annotator.py

@@ -3,11 +3,7 @@ from typing import Optional
 
 import numpy as np
 
-<<<<<<< HEAD
-from utils.data import Annotation, Recording
-=======
 from ria_toolkit_oss.datatypes import Annotation, Recording
->>>>>>> 2bb2d9d5a780dbc17172135a5a1f10eba14b1af4
 
 
 def annotate_with_cusum(
@@ -28,11 +24,7 @@ def annotate_with_cusum(
     changes between a low and high amplitude.
 
     :param recording: A ``Recording`` object to annotate.
-<<<<<<< HEAD
-    :type recording: ``utils.data.Recording``
-=======
     :type recording: ``ria_toolkit_oss.datatypes.Recording``
->>>>>>> 2bb2d9d5a780dbc17172135a5a1f10eba14b1af4
     :param label: Label for the detected segments.
     :type label: str
     :param window_size: The length (in samples) of the moving average window.

src/ria_toolkit_oss/annotations/energy_detector.py

@@ -11,11 +11,7 @@ from typing import Tuple
 import numpy as np
 from scipy.signal import filtfilt
 
-<<<<<<< HEAD
-from utils.data import Annotation, Recording
-=======
 from ria_toolkit_oss.datatypes import Annotation, Recording
->>>>>>> 2bb2d9d5a780dbc17172135a5a1f10eba14b1af4
 
 
 def detect_signals_energy(
@@ -77,13 +73,8 @@ def detect_signals_energy(
 
     **Example**::
 
-<<<<<<< HEAD
-        >>> from utils.io import load_recording
-        >>> from utils.annotations import detect_signals_energy
-=======
         >>> from ria.io import load_recording
         >>> from ria_toolkit_oss.annotations import detect_signals_energy
->>>>>>> 2bb2d9d5a780dbc17172135a5a1f10eba14b1af4
         >>> recording = load_recording("capture.sigmf")
 
         >>> # Detect with NBW frequency bounds (default, best for real signals)
@@ -239,7 +230,7 @@ def calculate_nominal_bandwidth(
 
     **Example**::
 
-        >>> from utils.annotations import calculate_nominal_bandwidth
+        >>> from ria_toolkit_oss.annotations import calculate_nominal_bandwidth
         >>> nbw, center = calculate_nominal_bandwidth(signal, sampling_rate=10e6)
         >>> print(f"NBW: {nbw/1e6:.3f} MHz, Center: {center/1e6:.3f} MHz")
     """
@@ -356,11 +347,7 @@ def annotate_with_obw(
 
     **Example**::
 
-<<<<<<< HEAD
-        >>> from utils.annotations import annotate_with_obw
-=======
         >>> from ria_toolkit_oss.annotations import annotate_with_obw
->>>>>>> 2bb2d9d5a780dbc17172135a5a1f10eba14b1af4
         >>> annotated = annotate_with_obw(recording, label="signal_obw")
     """
     signal = recording.data[0]

src/ria_toolkit_oss/annotations/parallel_signal_separator.py

@@ -38,11 +38,7 @@ sub-annotations.
 Example:
     Two WiFi channels captured simultaneously:
 
-<<<<<<< HEAD
-    >>> from utils.annotations import find_spectral_components
-=======
     >>> from ria_toolkit_oss.annotations import find_spectral_components
->>>>>>> 2bb2d9d5a780dbc17172135a5a1f10eba14b1af4
     >>> # Detect the two distinct channels (returns relative frequencies)
     >>> components = find_spectral_components(signal, sampling_rate=20e6)
     >>> print(f"Found {len(components)} components")
@@ -59,11 +55,7 @@ import numpy as np
 from scipy import ndimage
 from scipy import signal as scipy_signal
 
-<<<<<<< HEAD
-from utils.data import Annotation, Recording
-=======
 from ria_toolkit_oss.datatypes import Annotation, Recording
->>>>>>> 2bb2d9d5a780dbc17172135a5a1f10eba14b1af4
 
 
 def find_spectral_components(
@@ -119,13 +111,8 @@ def find_spectral_components(
 
     **Example**::
 
-<<<<<<< HEAD
-        >>> from utils.io import load_recording
-        >>> from utils.annotations import find_spectral_components
-=======
         >>> from ria.io import load_recording
         >>> from ria_toolkit_oss.annotations import find_spectral_components
->>>>>>> 2bb2d9d5a780dbc17172135a5a1f10eba14b1af4
         >>> recording = load_recording("capture.sigmf")
         >>> segment = recording.data[0][start:end]
         >>> # Components in relative (baseband) frequency
@@ -254,13 +241,8 @@ def split_annotation_by_components(
 
     **Example**::
 
-<<<<<<< HEAD
-        >>> from utils.io import load_recording
-        >>> from utils.annotations import split_annotation_by_components
-=======
         >>> from ria.io import load_recording
         >>> from ria_toolkit_oss.annotations import split_annotation_by_components
->>>>>>> 2bb2d9d5a780dbc17172135a5a1f10eba14b1af4
         >>> recording = load_recording("capture.sigmf")
         >>> # Original annotation spans multiple channels
         >>> original = recording.annotations[0]
@@ -387,13 +369,8 @@ def split_recording_annotations(
 
     **Example**::
 
-<<<<<<< HEAD
-        >>> from utils.io import load_recording
-        >>> from utils.annotations import split_recording_annotations
-=======
         >>> from ria.io import load_recording
         >>> from ria_toolkit_oss.annotations import split_recording_annotations
->>>>>>> 2bb2d9d5a780dbc17172135a5a1f10eba14b1af4
         >>> recording = load_recording("capture.sigmf")
         >>> # Split all annotations
         >>> split_rec = split_recording_annotations(recording)

src/ria_toolkit_oss/annotations/qualify_slice.py

@@ -1,10 +1,6 @@
 import numpy as np
 
-<<<<<<< HEAD
-from utils.data import Recording
-=======
 from ria_toolkit_oss.datatypes import Recording
->>>>>>> 2bb2d9d5a780dbc17172135a5a1f10eba14b1af4
 
 
 def qualify_slice_from_annotations(recording: Recording, slice_length: int):

src/ria_toolkit_oss/annotations/signal_isolation.py

@@ -1,13 +1,8 @@
 import numpy as np
 from scipy.signal import butter, lfilter
 
-<<<<<<< HEAD
-from utils.data.annotation import Annotation
-from utils.data.recording import Recording
-=======
 from ria_toolkit_oss.datatypes.annotation import Annotation
 from ria_toolkit_oss.datatypes.recording import Recording
->>>>>>> 2bb2d9d5a780dbc17172135a5a1f10eba14b1af4
 
 
 def isolate_signal(recording: Recording, annotation: Annotation) -> Recording:

src/ria_toolkit_oss/annotations/threshold_qualifier.py

@@ -46,29 +46,17 @@ from typing import Optional
 
 import numpy as np
 
-<<<<<<< HEAD
-from utils.data import Annotation, Recording
-
-
-def _find_ranges(indices, window_size):
-=======
 from ria_toolkit_oss.datatypes import Annotation, Recording
 
 
 def _find_ranges(indices, max_gap):
->>>>>>> 2bb2d9d5a780dbc17172135a5a1f10eba14b1af4
     """
     Groups individual indices into continuous temporal ranges.
 
     Args:
         indices: Array of indices where the signal exceeded a threshold.
-<<<<<<< HEAD
-        window_size: Maximum gap allowed between indices to consider them part
-                     of the same range.
-=======
         max_gap: Maximum gap allowed between indices to consider them part
                  of the same range.
->>>>>>> 2bb2d9d5a780dbc17172135a5a1f10eba14b1af4
 
     Returns:
         A list of (start, stop) tuples representing detected signal segments.
@@ -77,30 +65,6 @@ def _find_ranges(indices, max_gap):
     if len(indices) == 0:
         return []
 
-<<<<<<< HEAD
-    ranges = []
-
-    start = indices[0]
-    in_range = False
-
-    for i in range(1, len(indices)):
-        # If the gap between current and previous index is within window_size,
-        # keep the range alive.
-        if indices[i] - indices[i - 1] <= window_size:
-            if not in_range:
-                # Start a new range
-                start = indices[i - 1]
-                in_range = True
-        else:
-            # Gap is too large; close the current range if one was active.
-            if in_range:
-                ranges.append((start, indices[i - 1]))
-                in_range = False
-
-    # Ensure the final segment is captured if the loop ends while in_range.
-    if in_range:
-        ranges.append((start, indices[-1]))
-=======
     start = indices[0]
     prev = indices[0]
     ranges = []
@@ -112,19 +76,10 @@ def _find_ranges(indices, max_gap):
         prev = indices[i]
 
     ranges.append((start, prev))
->>>>>>> 2bb2d9d5a780dbc17172135a5a1f10eba14b1af4
 
     return ranges
 
 
-<<<<<<< HEAD
-def threshold_qualifier(
-    recording: Recording,
-    threshold: float,
-    window_size: Optional[int] = 1024,
-    label: Optional[str] = None,
-    annotation_type: Optional[str] = "standalone",
-=======
 def _expand_and_filter_ranges(
     smoothed_power: np.ndarray,
     initial_ranges: list[tuple[int, int]],
@@ -231,7 +186,6 @@ def threshold_qualifier(
     label: Optional[str] = None,
     annotation_type: Optional[str] = "standalone",
     channel: int = 0,
->>>>>>> 2bb2d9d5a780dbc17172135a5a1f10eba14b1af4
 ) -> Recording:
     """
     Annotate a recording with bounding boxes for regions above a threshold.
@@ -249,27 +203,15 @@ def threshold_qualifier(
     Args:
         recording: The Recording object containing IQ or real signal data.
         threshold: Sensitivity multiplier (0.0 to 1.0) applied to max power.
-<<<<<<< HEAD
-        window_size: Size of the smoothing filter and max gap for merging hits.
-        label: Custom string label for annotations.
-        annotation_type: Metadata string for the 'type' field in the annotation.
-=======
         window_size: Size of the smoothing filter in samples. Defaults to 1ms worth of samples.
         label: Custom string label for annotations.
         annotation_type: Metadata string for the 'type' field in the annotation.
         channel: Index of the channel to annotate. Defaults to 0.
->>>>>>> 2bb2d9d5a780dbc17172135a5a1f10eba14b1af4
 
     Returns:
         A new Recording object populated with detected Annotations.
     """
     # Extract signal and metadata
-<<<<<<< HEAD
-    sample_data = recording.data[0]
-    sample_rate = recording.metadata["sample_rate"]
-    center_frequency = recording.metadata.get("center_frequency", 0)
-
-=======
     sample_data = recording.data[channel]
     sample_rate = recording.metadata["sample_rate"]
     center_frequency = recording.metadata.get("center_frequency", 0)
@@ -277,69 +219,11 @@ def threshold_qualifier(
     if window_size is None:
         window_size = max(64, int(sample_rate * 0.001))
 
->>>>>>> 2bb2d9d5a780dbc17172135a5a1f10eba14b1af4
     # --- 1. SIGNAL CONDITIONING ---
     # Convert to power (Magnitude squared)
     power_data = np.abs(sample_data) ** 2
     smoothing_window = np.ones(window_size) / window_size
     smoothed_power = np.convolve(power_data, smoothing_window, mode="same")
-<<<<<<< HEAD
-
-    # Define thresholds based on the global peak of the smoothed signal
-    max_power = np.max(smoothed_power)
-    trigger_val = threshold * max_power  # High threshold to trigger detection
-    boundary_val = (threshold / 2) * max_power  # Low threshold to define signal edges
-
-    # --- 2. INITIAL DETECTION ---
-    # Identify indices that strictly exceed the high trigger
-    indices = np.where(smoothed_power > trigger_val)[0]
-    initial_ranges = _find_ranges(indices=indices, window_size=window_size)
-
-    annotations = []
-
-    threshold_base = min(sample_rate, len(sample_data))
-
-    for start, stop in initial_ranges:
-        if (stop - start) < (threshold_base * 0.01):
-            continue
-
-        # --- 3. HYSTERESIS (Boundary Expansion) ---
-        # Search backward from 'start' until power drops below the low boundary_val
-        true_start = start
-        while true_start > 0 and smoothed_power[true_start] > boundary_val:
-            true_start -= 1
-
-        # Search forward from 'stop' until power drops below the low boundary_val
-        true_stop = stop
-        while true_stop < len(smoothed_power) - 1 and smoothed_power[true_stop] > boundary_val:
-            true_stop += 1
-
-        # --- 4. SPECTRAL ANALYSIS (Frequency Detection) ---
-        signal_segment = sample_data[true_start:true_stop]
-        if len(signal_segment) > 0:
-            fft_data = np.abs(np.fft.fftshift(np.fft.fft(signal_segment)))
-            fft_freqs = np.fft.fftshift(np.fft.fftfreq(len(signal_segment), 1 / sample_rate))
-
-            # Determine frequency bounds where spectral energy is > 15% of segment peak
-            spectral_thresh = np.max(fft_data) * 0.15
-            sig_indices = np.where(fft_data > spectral_thresh)[0]
-
-            # Ensure the signal has some spectral width before annotating
-            if len(sig_indices) < 5:
-                continue
-
-            if len(sig_indices) > 0:
-                f_min, f_max = fft_freqs[sig_indices[0]], fft_freqs[sig_indices[-1]]
-            else:
-                # Default to middle half of bandwidth if no clear peaks found
-                f_min, f_max = -sample_rate / 4, sample_rate / 4
-        else:
-            f_min, f_max = -sample_rate / 4, sample_rate / 4
-
-        # --- 5. ANNOTATION GENERATION ---
-        if label is None:
-            label = f"{int(threshold*100)}%"
-=======
     group_gap_samples = _estimate_group_gap(sample_rate)
 
     # Define thresholds using peak relative to baseline.
@@ -442,7 +326,6 @@ def threshold_qualifier(
 
         # --- 5. ANNOTATION GENERATION ---
         ann_label = label if label is not None else f"{int(threshold*100)}%"
->>>>>>> 2bb2d9d5a780dbc17172135a5a1f10eba14b1af4
 
         # Pack metadata for the UI/Downstream processing
         comment_data = {
@@ -459,11 +342,7 @@ def threshold_qualifier(
             sample_count=true_stop - true_start,
             freq_lower_edge=center_frequency + f_min,
             freq_upper_edge=center_frequency + f_max,
-<<<<<<< HEAD
-            label=label,
-=======
             label=ann_label,
->>>>>>> 2bb2d9d5a780dbc17172135a5a1f10eba14b1af4
             comment=json.dumps(comment_data),
             detail={"generator": "hysteresis_qualifier"},
         )

src/ria_toolkit_oss/data/recording.py

@@ -12,7 +12,7 @@ from typing import Any, Iterator, Optional
 import numpy as np
 from numpy.typing import ArrayLike
 
-from utils.data.annotation import Annotation
+from ria_toolkit_oss.datatypes.annotation import Annotation
 
 PROTECTED_KEYS = ["rec_id", "timestamp"]
 
@@ -66,7 +66,7 @@ class Recording:
     **Examples:**
 
     >>> import numpy
-    >>> from utils.data import Recording, Annotation
+    >>> from ria_toolkit_oss.datatypes import Recording, Annotation
 
     >>> # Create an array of complex samples, just 1s in this case.
     >>> samples = numpy.ones(10000, dtype=numpy.complex64)
@@ -305,7 +305,7 @@ class Recording:
         Create a recording and add metadata:
 
         >>> import numpy
-        >>> from utils.data import Recording
+        >>> from ria_toolkit_oss.datatypes import Recording
         >>>
         >>> samples = numpy.ones(10000, dtype=numpy.complex64)
         >>> metadata = {
@@ -360,7 +360,7 @@ class Recording:
         Create a recording and update metadata:
 
         >>> import numpy
-        >>> from utils.data import Recording
+        >>> from ria_toolkit_oss.datatypes import Recording
 
         >>> samples = numpy.ones(10000, dtype=numpy.complex64)
         >>> metadata = {
@@ -414,7 +414,7 @@ class Recording:
         Create a recording and add metadata:
 
         >>> import numpy
-        >>> from utils.data import Recording
+        >>> from ria_toolkit_oss.datatypes import Recording
 
         >>> samples = numpy.ones(10000, dtype=numpy.complex64)
         >>> metadata = {
@@ -455,7 +455,7 @@ class Recording:
         Create a recording and view it as a plot in a .png image:
 
         >>> import numpy
-        >>> from utils.data import Recording
+        >>> from ria_toolkit_oss.datatypes import Recording
 
         >>> samples = numpy.ones(10000, dtype=numpy.complex64)
         >>> metadata = {
@@ -466,14 +466,14 @@ class Recording:
         >>> recording = Recording(data=samples, metadata=metadata)
         >>> recording.view()
         """
-        from utils.view import view_sig
+        from ria_toolkit_oss.view import view_sig
 
         view_sig(recording=self, output_path=output_path, **kwargs)
 
     def simple_view(self, **kwargs) -> None:
         """Create a plot of various signal visualizations as a PNG or SVG image.
 
-        :param kwargs: Keyword arguments passed on to utils.view.view_signal_simple.create_plots.
+        :param kwargs: Keyword arguments passed on to ria_toolkit_oss.view.view_signal_simple.create_plots.
         :type: dict of keyword arguments
 
         **Examples:**
@@ -481,7 +481,7 @@ class Recording:
         Create a recording and view it as a plot in a .png image:
 
         >>> import numpy
-        >>> from utils.data import Recording
+        >>> from ria_toolkit_oss.datatypes import Recording
 
         >>> samples = numpy.ones(10000, dtype=numpy.complex64)
         >>> metadata = {
@@ -492,7 +492,7 @@ class Recording:
         >>> recording = Recording(data=samples, metadata=metadata)
         >>> recording.simple_view()
         """
-        from utils.view.view_signal_simple import view_simple_sig
+        from ria_toolkit_oss.view.view_signal_simple import view_simple_sig
 
         view_simple_sig(recording=self, **kwargs)
 
@@ -530,7 +530,7 @@ class Recording:
         >>> recording = Recording(data=samples, metadata=metadata)
         >>> recording.view()
         """
-        from utils.io.recording import to_sigmf
+        from ria_toolkit_oss.io.recording import to_sigmf
 
         to_sigmf(filename=filename, path=path, recording=self, overwrite=overwrite)
 
@@ -565,7 +565,7 @@ class Recording:
         >>> recording = Recording(data=samples, metadata=metadata)
         >>> recording.to_npy()
         """
-        from utils.io.recording import to_npy
+        from ria_toolkit_oss.io.recording import to_npy
 
         to_npy(recording=self, filename=filename, path=path, overwrite=overwrite)
 
@@ -611,7 +611,7 @@ class Recording:
         >>> recording = Recording(data=samples, metadata=metadata)
         >>> recording.to_wav()
         """
-        from utils.io.recording import to_wav
+        from ria_toolkit_oss.io.recording import to_wav
 
         return to_wav(
             recording=self,
@@ -661,7 +661,7 @@ class Recording:
         >>> recording = Recording(data=samples, metadata=metadata)
         >>> recording.to_blue()
         """
-        from utils.io.recording import to_blue
+        from ria_toolkit_oss.io.recording import to_blue
 
         return to_blue(recording=self, filename=filename, path=path, data_format=data_format, overwrite=overwrite)
 

src/ria_toolkit_oss/signal/block_generator/frequency_translation/upconversion.py

@@ -1,6 +1,6 @@
 import numpy as np
-from utils.signal.block_generator.block import Block
+from ria_toolkit_oss.signal.block_generator.block import Block
-from utils.signal.block_generator.data_types import DataType
+from ria_toolkit_oss.signal.block_generator.data_types import DataType
 
 
 class FrequencyUpConversion(Block):

src/ria_toolkit_oss/view/recording.py

@@ -11,7 +11,7 @@ def spectrogram(rec: Recording, thumbnail: bool = False) -> Figure:
     """Create a spectrogram for the recording.
 
     :param rec: Signal to plot.
-    :type rec: utils.data.Recording
+    :type rec: ria_toolkit_oss.datatypes.Recording
     :param thumbnail: Whether to return a small thumbnail version or full plot.
     :type thumbnail: bool
 
@@ -95,7 +95,7 @@ def iq_time_series(rec: Recording) -> Figure:
     """Create a time series plot of the real and imaginary parts of signal.
 
     :param rec: Signal to plot.
-    :type rec: utils.data.Recording
+    :type rec: ria_toolkit_oss.datatypes.Recording
 
     :return: Time series plot as a Plotly figure.
     """
@@ -125,7 +125,7 @@ def frequency_spectrum(rec: Recording) -> Figure:
     """Create a frequency spectrum plot from the recording.
 
     :param rec: Input signal to plot.
-    :type rec: utils.data.Recording
+    :type rec: ria_toolkit_oss.datatypes.Recording
 
     :return: Frequency spectrum as a Plotly figure.
     """
@@ -160,7 +160,7 @@ def constellation(rec: Recording) -> Figure:
     """Create a constellation plot from the recording.
 
     :param rec: Input signal to plot.
-    :type rec: utils.data.Recording
+    :type rec: ria_toolkit_oss.datatypes.Recording
 
     :return: Constellation as a Plotly figure.
     """

src/ria_toolkit_oss/view/view_signal.py

@@ -13,8 +13,8 @@ from scipy.fft import fft, fftshift
 from scipy.signal import spectrogram
 from scipy.signal.windows import hann
 
-from utils.data.recording import Recording
+from ria_toolkit_oss.datatypes.recording import Recording
-from utils.view.tools import COLORS, decimate, extract_metadata_fields, set_path
+from ria_toolkit_oss.view.tools import COLORS, decimate, extract_metadata_fields, set_path
 
 
 def get_fft_size(plot_length):
@@ -58,17 +58,6 @@ def view_annotations(
     sample_rate, center_frequency, _ = extract_metadata_fields(recording.metadata)
     annotations = recording.annotations
 
-<<<<<<< HEAD
-    # 2. Setup Color Mapping (No more hardcoded yellow fallback!)
-    # available_colors = [
-    #     COLORS.get("magenta", "magenta"),
-    #     COLORS.get("accent", "cyan"),
-    #     COLORS.get("light", "white"),
-    #     "lime",
-    # ]
-
-    palette = ["#FF00FF", "#00FF00", "#00FFFF", "#FFFF00", "#FF8000"]
-=======
     # 2. Setup Color Mapping
     available_colors = [
         COLORS.get("magenta", "magenta"),
@@ -78,7 +67,6 @@ def view_annotations(
     ]
 
     palette = ["#2196F3", "#9C27B0", "#64B5F6", "#7B1FA2", "#5C6BC0", "#CE93D8", "#1565C0", "#7C4DFF"]
->>>>>>> 2bb2d9d5a780dbc17172135a5a1f10eba14b1af4
     unique_labels = sorted(list(set(ann.label for ann in annotations if ann.label)))
     label_to_color = {label: palette[i % len(palette)] for i, label in enumerate(unique_labels)}
 
@@ -87,11 +75,6 @@ def view_annotations(
         complex_signal, NFFT=256, Fs=sample_rate, Fc=center_frequency, noverlap=128, cmap="twilight"
     )
 
-<<<<<<< HEAD
-    # 4. Draw Annotations
-    for annotation in annotations:
-        # --- DEFINING VARIABLES FIRST ---
-=======
     # 4. Draw Annotations (highest threshold % first so lower % renders on top)
     def _threshold_sort_key(ann):
         try:
@@ -100,21 +83,13 @@ def view_annotations(
             return 0
 
     for annotation in sorted(annotations, key=_threshold_sort_key, reverse=True):
->>>>>>> 2bb2d9d5a780dbc17172135a5a1f10eba14b1af4
         t_start = annotation.sample_start / sample_rate
         t_width = annotation.sample_count / sample_rate
         f_start = annotation.freq_lower_edge
         f_height = annotation.freq_upper_edge - annotation.freq_lower_edge
 
-<<<<<<< HEAD
-        # Look up the color for this specific label
         ann_color = label_to_color.get(annotation.label, "gray")
 
-        # Draw the Rectangle
-=======
-        ann_color = label_to_color.get(annotation.label, "gray")
-
->>>>>>> 2bb2d9d5a780dbc17172135a5a1f10eba14b1af4
         rect = plt.Rectangle(
             (t_start, f_start), t_width, f_height, linewidth=1.5, edgecolor=ann_color, facecolor="none", alpha=0.8
         )
@@ -130,11 +105,7 @@ def view_annotations(
     ax.set_title(title, fontsize=title_fontsize, pad=20)
     ax.set_xlabel("Time (s)", fontsize=12)
     ax.set_ylabel("Frequency (MHz)", fontsize=12)
-<<<<<<< HEAD
-    ax.grid(alpha=0.1)  # Add faint grid
-=======
     ax.grid(alpha=0.1)
->>>>>>> 2bb2d9d5a780dbc17172135a5a1f10eba14b1af4
 
     output_path, _ = set_path(output_path=output_path)
     plt.savefig(output_path, dpi=dpi, bbox_inches="tight")

src/ria_toolkit_oss/view/view_signal_simple.py

@@ -12,8 +12,8 @@ import numpy as np
 from scipy.fft import fft, fftshift
 from scipy.signal.windows import hann
 
-from utils.data.recording import Recording
+from ria_toolkit_oss.datatypes.recording import Recording
-from utils.view.tools import COLORS, decimate, extract_metadata_fields, set_path
+from ria_toolkit_oss.view.tools import COLORS, decimate, extract_metadata_fields, set_path
 
 
 def _add_annotations(annotations, compact_mode, show_labels, sample_rate_hz, center_freq_hz, ax2):

src/ria_toolkit_oss_cli/ria_toolkit_oss/annotate.py

@@ -11,13 +11,8 @@ from ria_toolkit_oss.annotations import (
     split_recording_annotations,
     threshold_qualifier,
 )
-<<<<<<< HEAD
-from ria_toolkit_oss.data import Annotation
-from ria_toolkit_oss.data.recording import Recording
-=======
 from ria_toolkit_oss.datatypes import Annotation
 from ria_toolkit_oss.datatypes.recording import Recording
->>>>>>> 2bb2d9d5a780dbc17172135a5a1f10eba14b1af4
 from ria_toolkit_oss.io import load_recording, to_blue, to_npy, to_sigmf, to_wav
 from ria_toolkit_oss_cli.ria_toolkit_oss.common import format_frequency, format_sample_count
 
@@ -55,15 +50,6 @@ def detect_input_format(filepath):
 
 def determine_output_path(input_path, output_path, fmt, quiet, overwrite):
     input_path = Path(input_path)
-<<<<<<< HEAD
-
-    if output_path:
-        target = Path(output_path)
-        final_path = target
-    else:
-        annotated_name = f"{input_path.stem}_annotated"
-        target = input_path.with_name(f"{annotated_name}{input_path.suffix}")
-=======
     input_is_annotated = input_path.stem.endswith("_annotated")
 
     if output_path:
@@ -73,7 +59,6 @@ def determine_output_path(input_path, output_path, fmt, quiet, overwrite):
         target = input_path
     else:
         target = input_path.with_name(f"{input_path.stem}_annotated{input_path.suffix}")
->>>>>>> 2bb2d9d5a780dbc17172135a5a1f10eba14b1af4
 
     if fmt == "sigmf":
         final_path = normalize_sigmf_path(target)
@@ -84,15 +69,10 @@ def determine_output_path(input_path, output_path, fmt, quiet, overwrite):
         if not quiet:
             click.echo(f"Saving to: {final_path}")
 
-<<<<<<< HEAD
-    if final_path.exists() and not overwrite and final_path != input_path:
-        click.echo(f"Error: {final_path} already exists. Use --overwrite to replace it.", err=True)
-=======
     # Always allow writing to _annotated files; guard against overwriting originals
     target_is_annotated = final_path.stem.endswith("_annotated")
     if final_path.exists() and not target_is_annotated and final_path != input_path:
         click.echo(f"Error: {final_path} is not an annotated file and cannot be overwritten.", err=True)
->>>>>>> 2bb2d9d5a780dbc17172135a5a1f10eba14b1af4
         return None
 
     return final_path
@@ -250,13 +230,8 @@ def list(input, verbose):
 
     \b
     Examples:
-<<<<<<< HEAD
-      utils annotate list recording.sigmf-data
-      utils annotate list signal.npy --verbose
-=======
       ria annotate list recording.sigmf-data
       ria annotate list signal.npy --verbose
->>>>>>> 2bb2d9d5a780dbc17172135a5a1f10eba14b1af4
     """
     try:
         recording = load_recording(input)
@@ -324,13 +299,8 @@ def add(input, start, count, label, freq_lower, freq_upper, comment, annotation_
 
     \b
     Examples:
-<<<<<<< HEAD
-      utils annotate add file.npy --start 1000 --count 500 --label wifi
-      utils annotate add signal.sigmf-data --start 0 --count 1000 --label burst --comment "Strong signal"
-=======
       ria annotate add file.npy --start 1000 --count 500 --label wifi
       ria annotate add signal.sigmf-data --start 0 --count 1000 --label burst --comment "Strong signal"
->>>>>>> 2bb2d9d5a780dbc17172135a5a1f10eba14b1af4
     """
     try:
         recording = load_recording(input)
@@ -412,21 +382,12 @@ def add(input, start, count, label, freq_lower, freq_upper, comment, annotation_
 def remove(input, index, output, overwrite, quiet):
     """Remove annotation by index.
 
-<<<<<<< HEAD
-    Use 'utils annotate list' to see annotation indices.
-
-    \b
-    Examples:
-      utils annotate remove signal.sigmf-data 2
-      utils annotate remove file.npy 0
-=======
     Use 'ria annotate list' to see annotation indices.
 
     \b
     Examples:
       ria annotate remove signal.sigmf-data 2
       ria annotate remove file.npy 0
->>>>>>> 2bb2d9d5a780dbc17172135a5a1f10eba14b1af4
     """
     try:
         recording = load_recording(input)
@@ -475,13 +436,8 @@ def clear(input, output, overwrite, force, quiet):
 
     \b
     Examples:
-<<<<<<< HEAD
-      utils annotate clear signal.sigmf-data
-      utils annotate clear file.npy --force
-=======
       ria annotate clear signal.sigmf-data
       ria annotate clear file.npy --force
->>>>>>> 2bb2d9d5a780dbc17172135a5a1f10eba14b1af4
     """
     try:
         recording = load_recording(input)
@@ -576,17 +532,10 @@ def energy(
 
     \b
     Examples:
-<<<<<<< HEAD
-      utils annotate energy capture.sigmf-data --label burst
-      utils annotate energy signal.npy --threshold 1.5 --min-distance 10000
-      utils annotate energy signal.sigmf-data --freq-method obw
-      utils annotate energy signal.sigmf-data --freq-method full-detected
-=======
       ria annotate energy capture.sigmf-data --label burst
       ria annotate energy signal.npy --threshold 1.5 --min-distance 10000
       ria annotate energy signal.sigmf-data --freq-method obw
       ria annotate energy signal.sigmf-data --freq-method full-detected
->>>>>>> 2bb2d9d5a780dbc17172135a5a1f10eba14b1af4
 
     """
     try:
@@ -662,13 +611,8 @@ def cusum(input, label, min_duration, window_size, tolerance, annotation_type, o
 
     \b
     Examples:
-<<<<<<< HEAD
-      utils annotate cusum signal.sigmf-data --min-duration 5.0
-      utils annotate cusum data.npy --min-duration 10.0 --label state
-=======
       ria annotate cusum signal.sigmf-data --min-duration 5.0
       ria annotate cusum data.npy --min-duration 10.0 --label state
->>>>>>> 2bb2d9d5a780dbc17172135a5a1f10eba14b1af4
     """
     try:
         recording = load_recording(input)
@@ -714,11 +658,7 @@ def cusum(input, label, min_duration, window_size, tolerance, annotation_type, o
 @click.argument("input", type=click.Path(exists=True))
 @click.option("--threshold", type=float, required=True, help="Threshold (0.0-1.0, fraction of max magnitude)")
 @click.option("--label", type=str, default=None, help="Annotation label")
-<<<<<<< HEAD
-@click.option("--window-size", type=int, default=1024, help="Smoothing window size")
-=======
 @click.option("--window-size", type=int, default=None, help="Smoothing window size in samples (default: 1ms at recording sample rate)")
->>>>>>> 2bb2d9d5a780dbc17172135a5a1f10eba14b1af4
 @click.option(
     "--type",
     "annotation_type",
@@ -726,18 +666,11 @@ def cusum(input, label, min_duration, window_size, tolerance, annotation_type, o
     default="standalone",
     help="Annotation type",
 )
-<<<<<<< HEAD
-@click.option("--output", "-o", type=click.Path(), help="Output file path")
-@click.option("--overwrite", is_flag=True, help="Overwrite input file (non-SigMF only)")
-@click.option("--quiet", is_flag=True, help="Quiet mode")
-def threshold(input, threshold, label, window_size, annotation_type, output, overwrite, quiet):
-=======
 @click.option("--channel", type=int, default=0, help="Channel index to annotate (default: 0)")
 @click.option("--output", "-o", type=click.Path(), help="Output file path")
 @click.option("--overwrite", is_flag=True, help="Overwrite input file (non-SigMF only)")
 @click.option("--quiet", is_flag=True, help="Quiet mode")
 def threshold(input, threshold, label, window_size, annotation_type, channel, output, overwrite, quiet):
->>>>>>> 2bb2d9d5a780dbc17172135a5a1f10eba14b1af4
     """Auto-detect signals using threshold method.
 
     Detects samples above a percentage of maximum magnitude. Best for simple
@@ -745,13 +678,8 @@ def threshold(input, threshold, label, window_size, annotation_type, channel, ou
 
     \b
     Examples:
-<<<<<<< HEAD
-      utils annotate threshold signal.sigmf-data --threshold 0.7 --label wifi
-      utils annotate threshold data.npy --threshold 0.5 --window-size 2048
-=======
       ria annotate threshold signal.sigmf-data --threshold 0.7 --label wifi
       ria annotate threshold data.npy --threshold 0.5 --window-size 2048
->>>>>>> 2bb2d9d5a780dbc17172135a5a1f10eba14b1af4
     """
     if not (0.0 <= threshold <= 1.0):
         raise click.ClickException(f"--threshold must be between 0.0 and 1.0, got {threshold}")
@@ -766,12 +694,8 @@ def threshold(input, threshold, label, window_size, annotation_type, channel, ou
     if not quiet:
         click.echo("\nDetecting signals using threshold qualifier...")
         click.echo(f"  Threshold: {threshold * 100:.1f}% of max magnitude")
-<<<<<<< HEAD
-        click.echo(f"  Window size: {window_size} samples")
-=======
         click.echo(f"  Window size: {'auto (1ms)' if window_size is None else f'{window_size} samples'}")
         click.echo(f"  Channel: {channel}")
->>>>>>> 2bb2d9d5a780dbc17172135a5a1f10eba14b1af4
 
     try:
         initial_count = len(recording.annotations)
@@ -781,10 +705,7 @@ def threshold(input, threshold, label, window_size, annotation_type, channel, ou
             window_size=window_size,
             label=label,
             annotation_type=annotation_type,
-<<<<<<< HEAD
-=======
             channel=channel,
->>>>>>> 2bb2d9d5a780dbc17172135a5a1f10eba14b1af4
         )
         added = len(recording.annotations) - initial_count
 
@@ -833,17 +754,10 @@ def separate(input, indices, nfft, noise_threshold_db, min_component_bw, output,
 
     \b
     Examples:
-<<<<<<< HEAD
-      utils annotate separate capture.sigmf-data
-      utils annotate separate signal.npy --indices 0,1,2
-      utils annotate separate data.sigmf-data --noise-threshold-db -70
-      utils annotate separate signal.npy --min-component-bw 100000
-=======
       ria annotate separate capture.sigmf-data
       ria annotate separate signal.npy --indices 0,1,2
       ria annotate separate data.sigmf-data --noise-threshold-db -70
       ria annotate separate signal.npy --min-component-bw 100000
->>>>>>> 2bb2d9d5a780dbc17172135a5a1f10eba14b1af4
 
     """
     try:

src/ria_toolkit_oss_cli/ria_toolkit_oss/commands.py

@@ -2,10 +2,8 @@
 """
 This module contains all the CLI bindings for the ria package.
 """
-<<<<<<< HEAD
-=======
 
->>>>>>> 2bb2d9d5a780dbc17172135a5a1f10eba14b1af4
+
 from .annotate import annotate
 from .capture import capture
 from .combine import combine