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288 lines (267 loc) · 8.96 KB
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from __future__ import annotations
from typing import Dict, Iterable
import re
import numpy as np
import pandas as pd
import plotly.graph_objects as go
from plotly.subplots import make_subplots
from .export_contract import TIME_COLUMN, normalize_frame_for_contract
TIME_EXACT = {
TIME_COLUMN, "time", "time_sec", "timestamp", "ts",
"header.stamp.sec", "header.stamp.nanosec",
"stamp.sec", "stamp.nanosec",
"sec", "nanosec"
}
TIME_PATTERNS = [
r"(^|\.|\_)time($|\.|\_)",
r"(^|\.|\_)timestamp($|\.|\_)",
r"(^|\.|\_)stamp($|\.|\_)",
r"header\.stamp\.(sec|nanosec)$",
r"\.stamp\.(sec|nanosec)$",
r"(^|\.)(sec|nanosec)$",
]
def _is_time_like(col: str) -> bool:
c = col.lower()
if c in TIME_EXACT:
return True
return any(re.search(p, c) for p in TIME_PATTERNS)
def _y_columns(df: pd.DataFrame):
cols = []
for c in df.columns:
if c == TIME_COLUMN:
continue
if _is_time_like(c):
continue
if pd.api.types.is_numeric_dtype(df[c]):
cols.append(c)
return cols
def _row_kind_title(topic: str, kind: str) -> str:
if kind == "base":
return topic
if kind == "d1":
return f"{topic} - first derivative"
return f"{topic} - second derivative"
def _build_topic_row_specs(topics: list[str], derivative_topics: set[str]) -> list[tuple[str, str]]:
specs: list[tuple[str, str]] = []
for topic in topics:
specs.append((topic, "base"))
if topic in derivative_topics:
specs.append((topic, "d1"))
specs.append((topic, "d2"))
return specs
def _plot_topic_base_traces(
fig: go.Figure,
row: int,
topic: str,
x: np.ndarray,
df: pd.DataFrame,
*,
include_topic_prefix: bool,
):
for c in _y_columns(df):
y = pd.to_numeric(df[c], errors="coerce").to_numpy(dtype=float)
trace_name = f"{topic}:{c}" if include_topic_prefix else c
fig.add_trace(
go.Scatter(x=x, y=y, mode="lines", name=trace_name),
row=row,
col=1,
)
def _plot_topic_derivative_traces(
fig: go.Figure,
row: int,
topic: str,
x: np.ndarray,
df: pd.DataFrame,
order: int,
*,
include_topic_prefix: bool,
smooth: bool = False,
smooth_window: int = 5,
):
suffix = "d1" if order == 1 else "d2"
for c in _y_columns(df):
y = pd.to_numeric(df[c], errors="coerce").to_numpy(dtype=float)
d1, d2 = _first_second_derivative(x, y)
dy = d1 if order == 1 else d2
if smooth:
dy = _smooth_derivative(dy, window=smooth_window)
trace_name = f"{topic}:{c} {suffix}" if include_topic_prefix else f"{c} {suffix}"
if smooth:
trace_name = f"{trace_name} (smoothed)"
if np.isfinite(dy).any():
fig.add_trace(
go.Scatter(x=x, y=dy, mode="lines", name=trace_name),
row=row,
col=1,
)
def build_combined_figure(
topic_frames: Dict[str, pd.DataFrame],
derivative_topics: Iterable[str] | None = None,
derivative_smoothing_topics: Iterable[str] | None = None,
derivative_smoothing_window: int = 5,
) -> go.Figure:
topics = list(topic_frames.keys())
derivative_set = set(derivative_topics or [])
smooth_set = set(derivative_smoothing_topics or [])
row_specs = _build_topic_row_specs(topics, derivative_set)
if not row_specs:
row_specs = [("no_data", "base")]
fig = make_subplots(
rows=len(row_specs),
cols=1,
subplot_titles=[_row_kind_title(topic, kind) for topic, kind in row_specs],
vertical_spacing=0.04,
)
for row, (topic, kind) in enumerate(row_specs, start=1):
if topic not in topic_frames:
continue
df = normalize_frame_for_contract(topic_frames[topic].copy())
if TIME_COLUMN not in df.columns:
continue
x = pd.to_numeric(df[TIME_COLUMN], errors="coerce").to_numpy(dtype=float)
if kind == "base":
_plot_topic_base_traces(fig, row, topic, x, df, include_topic_prefix=True)
fig.update_yaxes(title_text="value", row=row, col=1)
elif kind == "d1":
_plot_topic_derivative_traces(
fig,
row,
topic,
x,
df,
order=1,
include_topic_prefix=True,
smooth=(topic in smooth_set),
smooth_window=derivative_smoothing_window,
)
fig.update_yaxes(title_text="first derivative", row=row, col=1)
else:
_plot_topic_derivative_traces(
fig,
row,
topic,
x,
df,
order=2,
include_topic_prefix=True,
smooth=(topic in smooth_set),
smooth_window=derivative_smoothing_window,
)
fig.update_yaxes(title_text="second derivative", row=row, col=1)
fig.update_xaxes(title_text="time (s from bag start)", row=row, col=1)
fig.update_layout(
template="plotly_white",
title="ROS2 signals vs normalized time",
height=max(500, 280 * len(row_specs)),
legend=dict(itemsizing="constant"),
)
return fig
def build_per_topic_figure(
topic: str,
df: pd.DataFrame,
include_derivatives: bool = False,
smooth_derivatives: bool = False,
smooth_window: int = 5,
) -> go.Figure:
df = normalize_frame_for_contract(df.copy())
if TIME_COLUMN not in df.columns:
return go.Figure()
x = pd.to_numeric(df[TIME_COLUMN], errors="coerce").to_numpy(dtype=float)
if not include_derivatives:
fig = go.Figure()
_plot_topic_base_traces(fig, 1, topic, x, df, include_topic_prefix=False)
fig.update_layout(
template="plotly_white",
title=f"{topic} signals vs normalized time",
xaxis_title="time (s from bag start)",
yaxis_title="value",
)
return fig
fig = make_subplots(
rows=3,
cols=1,
subplot_titles=[
topic,
f"{topic} - first derivative",
f"{topic} - second derivative",
],
vertical_spacing=0.08,
)
_plot_topic_base_traces(fig, 1, topic, x, df, include_topic_prefix=False)
_plot_topic_derivative_traces(
fig,
2,
topic,
x,
df,
order=1,
include_topic_prefix=False,
smooth=smooth_derivatives,
smooth_window=smooth_window,
)
_plot_topic_derivative_traces(
fig,
3,
topic,
x,
df,
order=2,
include_topic_prefix=False,
smooth=smooth_derivatives,
smooth_window=smooth_window,
)
fig.update_xaxes(title_text="time (s from bag start)", row=1, col=1)
fig.update_xaxes(title_text="time (s from bag start)", row=2, col=1)
fig.update_xaxes(title_text="time (s from bag start)", row=3, col=1)
fig.update_yaxes(title_text="value", row=1, col=1)
fig.update_yaxes(title_text="first derivative", row=2, col=1)
fig.update_yaxes(title_text="second derivative", row=3, col=1)
fig.update_layout(
template="plotly_white",
title=f"{topic} signals vs normalized time",
height=980,
legend=dict(itemsizing="constant"),
)
return fig
def _first_second_derivative(x: np.ndarray, y: np.ndarray):
d1 = np.full_like(y, np.nan, dtype=float)
d2 = np.full_like(y, np.nan, dtype=float)
mask = np.isfinite(x) & np.isfinite(y)
if mask.sum() < 2:
return d1, d2
xv = x[mask]
yv = y[mask]
d1v = np.gradient(yv, xv)
d1[mask] = d1v
if mask.sum() >= 3:
d2[mask] = np.gradient(d1v, xv)
return d1, d2
def _smooth_derivative(values: np.ndarray, window: int = 5) -> np.ndarray:
series = pd.Series(values, dtype=float)
smooth = series.rolling(window=window, min_periods=1, center=True).mean()
return smooth.to_numpy(dtype=float)
def build_topic_derivatives_figure(topic: str, df: pd.DataFrame) -> go.Figure:
full = build_per_topic_figure(topic, df, include_derivatives=True)
fig = make_subplots(
rows=2,
cols=1,
subplot_titles=[f"{topic} - first derivative", f"{topic} - second derivative"],
vertical_spacing=0.10,
)
for trace in full.data:
name = str(trace.name or "")
if " d1" in name:
fig.add_trace(trace, row=1, col=1)
elif " d2" in name:
fig.add_trace(trace, row=2, col=1)
fig.update_xaxes(title_text="time (s from bag start)", row=1, col=1)
fig.update_xaxes(title_text="time (s from bag start)", row=2, col=1)
fig.update_yaxes(title_text="first derivative", row=1, col=1)
fig.update_yaxes(title_text="second derivative", row=2, col=1)
fig.update_layout(
template="plotly_white",
title=f"{topic} first and second derivatives",
height=760,
legend=dict(itemsizing="constant"),
)
return fig
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