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576 | def plot_dynamic(df: pd.DataFrame, time_col: str, **kwargs) -> go.Figure:
"""
Creates an animated 50% Highest Density Region (HDR) plot in the Pleasantness-Presence space,
based on time-varying survey data.
This function computes a kernel density estimation (KDE) for each time step defined in
`time_col`, builds the corresponding 50% HDR contour, and assembles all frames into an
interactive Plotly animation. If a grouping column is provided, separate HDR contours are
generated per category with a stable colour map across frames.
Plot appearance, animation behaviour, KDE resolution, colours, axis limits, annotations,
and output options can be customised through `**kwargs`.
Parameters
----------
df : pd.DataFrame
A DataFrame containing the input data. It must include at least the columns
`'pleasantness_score'`, `'presence_score'`, and the time variable specified in `time_col`.
time_col : str
Name of the column defining the temporal sequence for animation frames.
**kwargs : dict, optional**
Additional keyword arguments to override default plotting parameters, including:
- `group_by_col` : str, column used to generate category-specific HDR contours.
- `xlim`, `ylim` : tuple of float, axis limits for Pleasantness and Presence.
- `eval_n` : int, resolution of the KDE evaluation grid.
- `palette` : list or dict, custom colour palette for categories.
- `frame_order` : list, custom ordering of animation frames.
- `labels` : dict, annotation labels to display in the plot.
- `write_html` : str, path to export the animation as an HTML file.
- `auto_open` : bool, whether to open the exported HTML automatically.
- `show` : bool, display the animated figure in the notebook or interface.
Returns
-------
fig : plotly.graph_objects.Figure
A fully configured Plotly figure containing HDR contours, animation frames,
slider controls, and play/pause buttons.
Examples
--------
>>> from smellscapy.plotting import plot_dynamic
>>> fig = plot_dynamic(df, time_col="How long have you been in your office without leaving?", group_by_col="Smell source")
>>> fig.show()
"""
# ------------------------------------------------------------------
# Parameters
# ------------------------------------------------------------------
params = ut.get_default_dynamic_plot_params()
params = ut.update_params(params, **kwargs)
x_col = "pleasantness_score"
y_col = "presence_score"
group_col = params.get("group_by_col", None)
xlim = tuple(params["xlim"])
ylim = tuple(params["ylim"])
eval_n = int(params["eval_n"])
frame_order = params.get("frame_order", None)
# ------------------------------------------------------------------
# Global categories and global colour map
# ------------------------------------------------------------------
if group_col and group_col in df.columns:
all_cats = df[group_col].dropna()
global_categories: List[str] = sorted(all_cats.unique(), key=str)
else:
global_categories = []
def _build_global_color_map(order: List[str]) -> Dict[str, str]:
palette = params.get("palette", None)
# palette as dict {cat: colour}
if isinstance(palette, dict):
return {cat: palette.get(cat, "#0033FF") for cat in order}
# palette as list or default
default = [
"#636EFA", "#EF553B", "#336759", "#00FF6E", "#F78F40",
"#00D9FF", "#C67F93", "#B6E880", "#EE00EE", "#6C634D"
]
if isinstance(palette, list) and len(palette) >= len(order):
seq = palette
else:
seq = default
return {cat: seq[i % len(seq)] for i, cat in enumerate(order)}
if global_categories:
global_color_map = _build_global_color_map(global_categories)
else:
global_color_map = {}
# ------------------------------------------------------------------
# KDE grid
# ------------------------------------------------------------------
xi = np.linspace(xlim[0], xlim[1], eval_n)
yi = np.linspace(ylim[0], ylim[1], eval_n)
XX, YY = np.meshgrid(xi, yi, indexing="xy")
# ------------------------------------------------------------------
# Helper: KDE / HDR
# ------------------------------------------------------------------
def _compute_hdr_field(x: np.ndarray,
y: np.ndarray,
hdr_prob: float = 0.5) -> tuple[np.ndarray, float]:
"""
Return (Z_norm, thr) where Z_norm in [0,1],
and thr is the HDR threshold in the same [0,1] scale.
"""
if x.size == 0 or y.size == 0:
return np.full_like(XX, np.nan, dtype=float), np.nan
Z = ut.kde_on_grid(x, y, XX, YY)
if Z is None:
return np.full_like(XX, np.nan, dtype=float), np.nan
if isinstance(Z, tuple):
Z = Z[0]
Z = np.asarray(Z, dtype=float)
finite_mask = np.isfinite(Z)
if not finite_mask.any():
return np.full_like(XX, np.nan, dtype=float), np.nan
lvl = ut._hdr_level(Z, hdr_prob)
if not np.isfinite(lvl):
return np.full_like(XX, np.nan, dtype=float), np.nan
z_finite = Z[finite_mask]
z_min = float(z_finite.min())
z_max = float(z_finite.max())
if not np.isfinite(z_min) or not np.isfinite(z_max) or z_max == z_min:
return np.full_like(XX, np.nan, dtype=float), np.nan
denom = z_max - z_min
Z_norm = (Z - z_min) / denom
thr = (lvl - z_min) / denom
return Z_norm, thr
# ------------------------------------------------------------------
# Trace for ONE category in ONE frame (grouped case)
# ------------------------------------------------------------------
def _trace_for_category(subdf: pd.DataFrame,
cat: str) -> go.Contour:
"""
Always returns a trace for category 'cat'.
If there are no data or HDR is not defined, the trace is transparent.
"""
if group_col is None or group_col not in subdf.columns:
return go.Contour(
x=xi,
y=yi,
z=np.full_like(XX, np.nan, dtype=float),
showscale=False,
colorscale=[
[0.0, "rgba(0,0,0,0)"],
[1.0, "rgba(0,0,0,0)"],
],
showlegend=False,
opacity=0.0,
name=str(cat),
legendgroup=str(cat),
)
mask = (subdf[group_col] == cat).values
x = subdf.loc[mask, x_col].to_numpy()
y = subdf.loc[mask, y_col].to_numpy()
Z_norm, thr = _compute_hdr_field(x, y, hdr_prob=0.5)
# No HDR → transparent trace, keeps structure for animation
if not np.isfinite(thr):
return go.Contour(
x=xi,
y=yi,
z=np.full_like(XX, np.nan, dtype=float),
showscale=False,
colorscale=[
[0.0, "rgba(0,0,0,0)"],
[1.0, "rgba(0,0,0,0)"],
],
showlegend=False,
opacity=0.0,
name=str(cat),
legendgroup=str(cat),
)
color = global_color_map.get(cat, "#0033FF")
return go.Contour(
x=xi,
y=yi,
z=Z_norm,
showscale=False,
colorscale=[
[0.0, "rgba(0,0,0,0)"],
[max(thr - 1e-6, 0.0), "rgba(0,0,0,0)"],
[thr, color],
[1.0, color],
],
showlegend=False, # legend is manged with "fake" scatters
opacity=0.35,
contours=dict(
start=thr,
end=1.0,
size=1.0 - thr,
coloring="fill",
showlines=False,
),
line=dict(width=0),
name=str(cat),
legendgroup=str(cat),
)
# ------------------------------------------------------------------
# Ungrouped case
# ------------------------------------------------------------------
def _make_traces_ungrouped(subdf: pd.DataFrame, show_legend: bool = True):
traces: List[go.BaseTraceType] = []
x = subdf[x_col].to_numpy()
y = subdf[y_col].to_numpy()
Z_norm, thr = _compute_hdr_field(x, y, hdr_prob=0.5)
if not np.isfinite(thr):
return []
traces.append(
go.Contour(
x=xi,
y=yi,
z=Z_norm,
showscale=False,
colorscale=[
[0.0, "rgba(0,0,0,0)"],
[max(thr - 1e-6, 0.0), "rgba(0,0,0,0)"],
[thr, "rgba(255,192,203,1)"],
[1.0, "rgba(255,192,203,1)"],
],
showlegend=show_legend,
opacity=0.4,
contours=dict(
start=thr,
end=1.0,
size=1.0 - thr,
coloring="fill",
showlines=False,
),
line=dict(width=0),
name="HDR 50%",
)
)
return traces
# ------------------------------------------------------------------
# Animation: frames and initial figure
# ------------------------------------------------------------------
values = df[time_col]
ordered_values = ut.order_values_for_frames(df[time_col], order_override=frame_order)
frames: List[go.Frame] = []
if group_col and group_col in df.columns and global_categories:
# ---- grouped case: same traces structure in every frame
for val in ordered_values:
sub = df[values == val]
frame_traces = []
for cat in global_categories:
frame_traces.append(_trace_for_category(sub, cat))
frames.append(go.Frame(name=str(val), data=frame_traces))
# initial data (first frame)
initial_sub = df[values == ordered_values[0]]
initial_traces = []
for cat in global_categories:
initial_traces.append(_trace_for_category(initial_sub, cat))
fig = go.Figure(data=initial_traces, frames=frames)
else:
# ---- ungrouped case
for val in ordered_values:
sub = df[values == val]
traces = _make_traces_ungrouped(sub, show_legend=False)
frames.append(go.Frame(name=str(val), data=traces))
initial_sub = df[values == ordered_values[0]]
fig = go.Figure(
data=_make_traces_ungrouped(initial_sub, show_legend=True),
frames=frames,
)
# ------------------------------------------------------------------
# Stable legend: one fake Scatter per category (grouped only)
# ------------------------------------------------------------------
if group_col and group_col in df.columns and global_categories:
for cat in global_categories:
fig.add_trace(
go.Scatter(
x=[None],
y=[None],
mode="markers",
marker=dict(
size=10,
color=global_color_map.get(cat, "#0033FF"),
),
name=str(cat),
legendgroup=str(cat),
showlegend=True,
hoverinfo="skip",
)
)
# ------------------------------------------------------------------
# Slider steps
# ------------------------------------------------------------------
steps = []
for v in ordered_values:
steps.append(
{
"method": "animate",
"label": str(v),
"args": [
[str(v)],
{
"mode": "immediate",
"transition": {"duration": 0},
"frame": {"duration": 0, "redraw": True},
},
],
}
)
# ------------------------------------------------------------------
# Layout
# ------------------------------------------------------------------
major_step = 0.25
minor_step = 0.05
xmin, xmax = xlim
ymin, ymax = ylim
xticks = np.arange(xmin, xmax + 1e-4, minor_step)
yticks = np.arange(ymin, ymax + 1e-4, minor_step)
def _tick_labels(vals: np.ndarray) -> List[str]:
labels = []
for v in vals:
if abs(v % major_step) < 1e-8:
labels.append(f"{v:.2f}")
else:
labels.append("")
return labels
xticktext = _tick_labels(xticks)
yticktext = _tick_labels(yticks)
fig.update_layout(
xaxis=dict(
range=[xmin, xmax],
constrain="domain",
tickvals=xticks,
ticktext=xticktext,
tickfont=dict(size=12),
tickcolor="black",
tickwidth=2,
ticklen=8,
minor=dict(
tick0=0,
dtick=minor_step,
ticklen=0,
tickwidth=1,
tickcolor="rgba(0,0,0,0.4)",
),
),
yaxis=dict(
range=[ymin, ymax],
scaleanchor="x",
scaleratio=1,
tickvals=yticks,
ticktext=yticktext,
tickfont=dict(size=12),
tickcolor="black",
tickwidth=2,
ticklen=8,
minor=dict(
tick0=0,
dtick=minor_step,
ticklen=0,
tickwidth=1,
tickcolor="rgba(0,0,0,0.4)"
),
),
xaxis_title="Pleasantness",
yaxis_title="Presence",
paper_bgcolor="white",
plot_bgcolor="white",
title="Simple Density (HDR 50%)",
updatemenus=[
{
"type": "buttons",
"showactive": False,
"buttons": [
{
"label": "▶ Play",
"method": "animate",
"args": [
None,
{
"fromcurrent": True,
"frame": {
"duration": 500,
"redraw": True,
},
"transition": {"duration": 200},
},
],
},
{
"label": "⏸ Pause",
"method": "animate",
"args": [
[None],
{
"mode": "immediate",
"frame": {
"duration": 0,
"redraw": False,
},
},
],
},
],
"x": 0.02,
"y": 0.5,
"xanchor": "left",
"yanchor": "top",
}
],
sliders=[
{
"active": 0,
"x": 0.02,
"y": -0.06,
"xanchor": "left",
"yanchor": "top",
"len": 0.96,
"pad": {"t": 5, "b": 0},
"steps": steps,
}
],
)
# ------------------------------------------------------------------
# Layout
# ------------------------------------------------------------------
for yy in [0]:
fig.add_hline(
y=yy,
line_color="grey",
line_width=0.5,
line_dash="solid",
)
for xx in [0]:
fig.add_vline(
x=xx,
line_color="grey",
line_width=0.5,
line_dash="solid",
)
for yy in [1, -1]:
fig.add_hline(
y=yy,
line_color="black",
line_width=1,
line_dash="solid",
)
for xx in [1, -1]:
fig.add_vline(
x=xx,
line_color="black",
line_width=1,
line_dash="solid",
)
for yy in [-0.75, -0.5, -0.25, 0.75, 0.5, 0.25]:
fig.add_hline(
y=yy,
line_color="grey",
line_width=0.1,
line_dash="solid",
)
for xx in [-0.75, -0.5, -0.25, 0.75, 0.5, 0.25]:
fig.add_vline(
x=xx,
line_color="grey",
line_width=0.1,
line_dash="solid",
)
x_vals = np.linspace(xmin, xmax, 200)
fig.add_trace(
go.Scatter(
x=x_vals,
y=x_vals,
mode="lines",
line=dict(
color="grey",
width=0.5,
dash="dot",
),
showlegend=False,
hoverinfo="skip",
)
)
fig.add_trace(
go.Scatter(
x=x_vals,
y=-x_vals,
mode="lines",
line=dict(
color="grey",
width=0.5,
dash="dot",
),
showlegend=False,
hoverinfo="skip",
)
)
for lbl in params.get("labels", {}).values():
fig.add_annotation(
x=lbl["pos"][0],
y=lbl["pos"][1],
text=lbl["text"],
showarrow=False,
xanchor="center",
yanchor="middle",
font=dict(
family="Arial",
size=10,
color="rgba(0,0,0,0.7)",
),
)
# ------------------------------------------------------------------
# Save/show
# ------------------------------------------------------------------
if params.get("write_html"):
fig.write_html(params["write_html"], auto_open=params.get("auto_open", False))
if params.get("show"):
try:
import plotly.io as pio # noqa: F401
except Exception:
pass
fig.show()
return fig
|