perda.analyzer.analyzer#

class perda.analyzer.analyzer.Analyzer(filepath, ts_offset=0, parsing_errors_limit=100, verbose=1, preprocessing=None)[source]#

Bases: object

Primary class for loading and analyzing car log data.

After loading, all variables live in analyzer.data (a SingleRunData), which supports dictionary-like access returning DataInstance objects.

Access by name or variable ID#

>>> di = aly.data["pcm.wheelSpeeds.frontRight"]   # by cpp_name
>>> di = aly.data[42]                             # by variable ID

Check membership#

>>> "pcm.wheelSpeeds.frontRight" in aly.data       # True / False

Read raw arrays#

>>> di.timestamp_np   # NDArray[int64] — timestamps in the log's native unit
>>> di.value_np       # NDArray[float64] — sample values

Arithmetic between variables#

>>> avg_speed = (aly.data["pcm.wheelSpeeds.frontRight"] + aly.data["pcm.wheelSpeeds.frontLeft"]) / 2.0

Trim to a time window (timestamps in the log’s native unit)#

>>> di_trimmed = aly.data["pcm.wheelSpeeds.frontRight"].trim(ts_start=10_000, ts_end=30_000)

Find variables when you don’t know the exact name#

>>> results = aly.search("front wheel speed")   # prints + returns list[SearchResult]
>>> di = aly.data[results[0].cpp_name]

Enumerate all variables with summary stats#

>>> summaries = aly.variable_summary()          # list[VariableSummary], sorted by name
>>> [v.cpp_name for v in summaries]
analyze_frequency(var, expected_frequency_hz=None, gap_threshold_multiplier=2.0, font_config=FontConfig(large=20, medium=14, small=10), layout_config=LayoutConfig(width=1200, height=700, margin={'l': 70, 'r': 50, 't': 90, 'b': 70}, plot_bgcolor='white', title_x=0.5, title_xanchor='center', title_yanchor='top', grid_width_per_col=400, grid_height_per_row=350, grid_horizontal_spacing=0.08, grid_vertical_spacing=0.12, max_display_resolution=50), plot_config=ScatterHistogramPlotConfig(color_scatter='blue', color_line='crimson', color_histogram='blue', histogram_bins=80))[source]#

Analyse the sampling frequency of a variable and return a diagnostic figure.

Prints a summary to stdout and returns a Plotly figure with two subplots: instantaneous frequency over time and an inter-sample interval histogram.

Parameters:

  • var (Union[str, int]) – Variable name or ID to look up in the parsed data.

  • expected_frequency_hz (float | None, optional) – Nominal expected sampling frequency in Hz for error and gap diagnostics. Default is None.

  • gap_threshold_multiplier (float, optional) – Intervals exceeding this multiple of the expected (or median) interval are flagged as gaps. Default is 2.0.

  • font_config (FontConfig, optional) – Font sizes for plot elements.

  • layout_config (LayoutConfig, optional) – Plot dimensions and margins.

  • plot_config (ScatterHistogramPlotConfig)

Returns:

Plotly figure with frequency diagnostics.

Return type:

go.Figure

Examples

>>> fig = aly.analyze_frequency("ams.stack.thermistors.temperature[38]", expected_frequency_hz=100)
>>> fig.show()
diff(server_data, timestamp_tolerance_s=0.002, diff_rtol=0.001, diff_atol=0.001, diff_plot_config=DiffPlotConfig(bucket_size_s=1.0, color_base_extra='blue', color_incom_extra='darkorange', color_value_mismatch='crimson', color_total='gray'), layout_config=LayoutConfig(width=1200, height=700, margin={'l': 70, 'r': 50, 't': 90, 'b': 70}, plot_bgcolor='white', title_x=0.5, title_xanchor='center', title_yanchor='top', grid_width_per_col=400, grid_height_per_row=350, grid_horizontal_spacing=0.08, grid_vertical_spacing=0.12, max_display_resolution=50), font_config=FontConfig(large=20, medium=14, small=10))[source]#

Compute the differences between the current data (assumed to be from RPI) and server data.

Parameters:

  • server_data (SingleRunData) – The server data to compare against.

  • timestamp_tolerance_s (float, optional) – Timestamp tolerance in seconds used to match points between streams. Defaults to 0.002 (2 ms).

  • diff_rtol (float, optional) – Relative tolerance for value comparison (numpy.isclose).

  • diff_atol (float, optional) – Absolute tolerance for value comparison (numpy.isclose).

  • diff_plot_config (DiffPlotConfig, optional)

  • layout_config (LayoutConfig, optional)

  • font_config (FontConfig, optional)

Return type:

Figure

Examples

>>> fig = aly.diff(server_data)
>>> fig.show()
get_accel_times()[source]#

Intelligently detect and extract segments of the log where an acceleration run occurs, then compute acceleration times.

Returns:

List of acceleration segment results.

Return type:

list[AccelSegmentResult]

Examples

>>> results = aly.get_accel_times()
>>> for r in results:
...     print(r)
plot(var_1, var_2=None, ts_start=None, ts_end=None, title=None, y_label_1=None, y_label_2=None, show_legend=True, font_config=FontConfig(large=20, medium=14, small=10), layout_config=LayoutConfig(width=1200, height=700, margin={'l': 70, 'r': 50, 't': 90, 'b': 70}, plot_bgcolor='white', title_x=0.5, title_xanchor='center', title_yanchor='top', grid_width_per_col=400, grid_height_per_row=350, grid_horizontal_spacing=0.08, grid_vertical_spacing=0.12, max_display_resolution=50), vline_config=VLineConfig(color='gray', width=2, dash='dash', opacity=0.7))[source]#

Display variables from the parsed data on an interactive Plotly plot.

Concat boundaries (if any) are automatically shown as vertical lines.

Parameters:

  • var_1 (Union[str, int, DataInstance, List[Union[str, int, DataInstance]]]) – Variable(s) to plot on the left y-axis. Can be variable name(s), variable ID(s), or DataInstance(s)

  • var_2 (Union[str, int, DataInstance, List[Union[str, int, DataInstance]]] | None, optional) – Optional variable(s) to plot on the right y-axis. Can be variable name(s), variable ID(s), or DataInstance(s).

  • ts_start (float | None, optional) – Start of the time window in seconds. Data points before this time are excluded. Default is None (no lower bound).

  • ts_end (float | None, optional) – End of the time window in seconds. Data points after this time are excluded. Default is None (no upper bound).

  • title (str | None, optional)

  • y_label_1 (str | None, optional) – Label for left y-axis (or only y-axis if no right input).

  • y_label_2 (str | None, optional) – Label for right y-axis.

  • show_legend (bool, optional) – Whether to show plot legends. Default is True

  • font_config (FontConfig, optional) – Font configuration for plot elements.

  • layout_config (LayoutConfig, optional) – Layout configuration for plot dimensions.

  • vline_config (VLineConfig, optional) – Visual configuration for concat boundary lines.

Return type:

Figure

Examples

>>> fig = aly.plot("pcm.wheelSpeeds.frontRight")
>>> fig = aly.plot(["pcm.wheelSpeeds.frontRight", "pcm.wheelSpeeds.frontLeft"], title="Front Wheel Speeds")
>>> fig = aly.plot("pcm.moc.motor.requestedTorque", "pcm.wheelSpeeds.frontRight", ts_start=10.0, ts_end=30.0)
>>> # Plot a derived DataInstance (e.g. average of two signals)
>>> avg_speed = (aly.data["pcm.wheelSpeeds.frontRight"] + aly.data["pcm.wheelSpeeds.frontLeft"]) / 2.0
>>> fig = aly.plot(avg_speed)
>>> fig.show()
search(query, top_n=10)[source]#

Natural language search for available variables in the parsed data.

Prints matching results to stdout and returns them for programmatic use.

Parameters:

  • query (str) – Free-text search query (e.g. “front wheel speed”).

  • top_n (int) – Maximum number of results to return and display (default 10).

Returns:

Top matches in descending relevance order (at most top_n entries). Each entry has rank, score, var_id, cpp_name, and descript.

Return type:

list[SearchResult]

Examples

>>> results = aly.search("front wheel speed")
>>> results = aly.search("front wheel speed", top_n=5)
>>> names = [r.cpp_name for r in results]
subplots(rows, title=None, row_y_labels=None, ts_start=None, ts_end=None, show_legend=True, layout_config=LayoutConfig(width=1200, height=700, margin={'l': 70, 'r': 50, 't': 90, 'b': 70}, plot_bgcolor='white', title_x=0.5, title_xanchor='center', title_yanchor='top', grid_width_per_col=400, grid_height_per_row=350, grid_horizontal_spacing=0.08, grid_vertical_spacing=0.12, max_display_resolution=50), font_config=FontConfig(large=20, medium=14, small=10))[source]#

Plot multiple variables as stacked subplots on a shared time axis.

Each entry in rows becomes one subplot row. Pass a list of variables for a row to overlay multiple signals on the same panel, or a single variable for a dedicated panel.

Parameters:

  • rows (List[str | int | DataInstance | List[str | int | DataInstance]]) – One entry per subplot row (top to bottom). Each entry may be a single variable (name, ID, or DataInstance) or a list of variables to overlay on that row.

  • title (str | None, optional) – Figure-level title. Default is None.

  • row_y_labels (List[str | None] | None, optional) – Y-axis label for each row. None entries fall back to the DataInstance labels. Must match the length of rows when provided. Default is None.

  • ts_start (float | None, optional) – Start of the time window in seconds. Data before this time is excluded from all rows. Default is None (no lower bound).

  • ts_end (float | None, optional) – End of the time window in seconds. Data after this time is excluded from all rows. Default is None (no upper bound).

  • show_legend (bool, optional) – Whether to show the figure legend. Default is True.

  • layout_config (LayoutConfig, optional) – Figure dimensions and spacing.

  • font_config (FontConfig, optional) – Font sizes for plot elements.

Return type:

go.Figure

Examples

>>> fig = aly.subplots(["pcm.wheelSpeeds.frontRight", "pcm.moc.motor.requestedTorque"])
>>> fig = aly.subplots(
...     rows=[
...         ["pcm.wheelSpeeds.frontRight", "pcm.wheelSpeeds.frontLeft"],
...         "pcm.moc.motor.requestedTorque",
...     ],
...     title="Run Overview",
...     row_y_labels=["Wheel Speed (mph)", "Torque (Nm)"],
...     ts_start=5.0,
...     ts_end=30.0,
... )
>>> fig.show()
Parameters:

  • filepath (str)

  • ts_offset (int)

  • parsing_errors_limit (int)

  • verbose (int)

  • preprocessing (list[Callable[[SingleRunData], SingleRunData]] | None)