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import plotly.graph_objects as go
import plotly.io as pio
import numpy as np
import os
import uuid
"""
Interactive line chart example (Baseline / Improved / Target) with a live slider.
Context: research-style training curves for multiple datasets (CIFAR-10, CIFAR-100, ImageNet-1K).
The slider "Augmentation α" blends the Improved curve between the Baseline (α=0)
and an augmented counterpart (α=1) via a simple mixing equation.
Export remains responsive, with no zoom and no mode bar.
"""
# Grid (x) and parameterization
N = 240
x = np.linspace(0, 1, N)
# Logistic helper for smooth learning curves
def logistic(xv: np.ndarray, ymin: float, ymax: float, k: float, x0: float) -> np.ndarray:
return ymin + (ymax - ymin) / (1.0 + np.exp(-k * (xv - x0)))
# Plausible dataset params (baseline vs augmented) + a constant target line
datasets_params = [
{
"name": "CIFAR-10",
"base": {"ymin": 0.10, "ymax": 0.90, "k": 10.0, "x0": 0.55},
"aug": {"ymin": 0.15, "ymax": 0.96, "k": 12.0, "x0": 0.40},
"target": 0.97,
},
{
"name": "CIFAR-100",
"base": {"ymin": 0.05, "ymax": 0.70, "k": 9.5, "x0": 0.60},
"aug": {"ymin": 0.08, "ymax": 0.80, "k": 11.0, "x0": 0.45},
"target": 0.85,
},
{
"name": "ImageNet-1K",
"base": {"ymin": 0.02, "ymax": 0.68, "k": 8.5, "x0": 0.65},
"aug": {"ymin": 0.04, "ymax": 0.75, "k": 9.5, "x0": 0.50},
"target": 0.82,
},
]
# Initial dataset index and alpha
alpha0 = 0.7
ds0 = datasets_params[0]
base0 = logistic(x, **ds0["base"])
aug0 = logistic(x, **ds0["aug"])
target0 = np.full_like(x, ds0["target"], dtype=float)
# Traces: Baseline (fixed), Improved (blended by α), Target (constant goal)
blend = lambda l, e, a: (1 - a) * l + a * e
y1 = base0
y2 = blend(base0, aug0, alpha0)
y3 = target0
color_base = "#64748b" # slate-500
color_improved = "#F981D4" # pink
color_target = "#4b5563" # gray-600 (dash)
fig = go.Figure()
fig.add_trace(
go.Scatter(
x=x,
y=y1,
name="Baseline",
mode="lines",
line=dict(color=color_base, width=2, shape="spline", smoothing=0.6),
hovertemplate="<b>%{fullData.name}</b><br>x=%{x:.2f}<br>y=%{y:.3f}<extra></extra>",
showlegend=True,
)
)
fig.add_trace(
go.Scatter(
x=x,
y=y2,
name="Improved",
mode="lines",
line=dict(color=color_improved, width=2, shape="spline", smoothing=0.6),
hovertemplate="<b>%{fullData.name}</b><br>x=%{x:.2f}<br>y=%{y:.3f}<extra></extra>",
showlegend=True,
)
)
fig.add_trace(
go.Scatter(
x=x,
y=y3,
name="Target",
mode="lines",
line=dict(color=color_target, width=2, dash="dash"),
hovertemplate="<b>%{fullData.name}</b><br>x=%{x:.2f}<br>y=%{y:.3f}<extra></extra>",
showlegend=True,
)
)
fig.update_layout(
autosize=True,
paper_bgcolor="rgba(0,0,0,0)",
plot_bgcolor="rgba(0,0,0,0)",
margin=dict(l=40, r=28, t=20, b=40),
hovermode="x unified",
legend=dict(
orientation="v",
x=1,
y=0,
xanchor="right",
yanchor="bottom",
bgcolor="rgba(255,255,255,0)",
borderwidth=0,
),
hoverlabel=dict(
bgcolor="white",
font=dict(color="#111827", size=12),
bordercolor="rgba(0,0,0,0.15)",
align="left",
namelength=-1,
),
xaxis=dict(
showgrid=False,
zeroline=False,
showline=True,
linecolor="rgba(0,0,0,0.25)",
linewidth=1,
ticks="outside",
ticklen=6,
tickcolor="rgba(0,0,0,0.25)",
tickfont=dict(size=12, color="rgba(0,0,0,0.55)"),
title=None,
automargin=True,
fixedrange=True,
),
yaxis=dict(
showgrid=False,
zeroline=False,
showline=True,
linecolor="rgba(0,0,0,0.25)",
linewidth=1,
ticks="outside",
ticklen=6,
tickcolor="rgba(0,0,0,0.25)",
tickfont=dict(size=12, color="rgba(0,0,0,0.55)"),
title=None,
tickformat=".2f",
rangemode="tozero",
automargin=True,
fixedrange=True,
),
)
# Write the fragment next to this file into src/fragments/line.html (robust path)
output_path = os.path.join(os.path.dirname(__file__), "fragments", "line.html")
os.makedirs(os.path.dirname(output_path), exist_ok=True)
# Inject a small post-render script to round the hover box corners
post_script = """
(function(){
function attach(gd){
function round(){
try {
var root = gd && gd.parentNode ? gd.parentNode : document;
var rects = root.querySelectorAll('.hoverlayer .hovertext rect');
rects.forEach(function(r){ r.setAttribute('rx', 8); r.setAttribute('ry', 8); });
} catch(e) {}
}
if (gd && gd.on) {
gd.on('plotly_hover', round);
gd.on('plotly_unhover', round);
gd.on('plotly_relayout', round);
}
setTimeout(round, 0);
}
var plots = document.querySelectorAll('.js-plotly-plot');
plots.forEach(attach);
})();
"""
html_plot = pio.to_html(
fig,
include_plotlyjs=False,
full_html=False,
post_script=post_script,
config={
"displayModeBar": False,
"responsive": True,
"scrollZoom": False,
"doubleClick": False,
"modeBarButtonsToRemove": [
"zoom2d", "pan2d", "select2d", "lasso2d",
"zoomIn2d", "zoomOut2d", "autoScale2d", "resetScale2d",
"toggleSpikelines"
],
},
)
# Build a self-contained fragment with a live slider (no mouseup required)
uid = uuid.uuid4().hex[:8]
slider_id = f"line-ex-alpha-{uid}"
container_id = f"line-ex-container-{uid}"
slider_tpl = '''
<div id="__CID__">
__PLOT__
<div class="plotly_controls" style="margin-top:12px; display:flex; gap:16px; align-items:center;">
<label style="font-size:12px;color:rgba(0,0,0,.65); display:flex; align-items:center; gap:6px; white-space:nowrap; padding:6px 10px;">
Dataset
<select id="__DSID__" style="font-size:12px; padding:2px 6px;">
<option value="0">CIFAR-10</option>
<option value="1">CIFAR-100</option>
<option value="2">ImageNet-1K</option>
</select>
</label>
<label style="font-size:12px;color:rgba(0,0,0,.65);display:flex;align-items:center;gap:10px; flex:1; padding:6px 10px;">
Augmentation α
<input id="__SID__" type="range" min="0" max="1" step="0.01" value="__A0__" style="flex:1;">
<span class="alpha-value">__A0__</span>
</label>
</div>
</div>
<script>
(function(){
var container = document.getElementById('__CID__');
if(!container) return;
var gd = container.querySelector('.js-plotly-plot');
var slider = document.getElementById('__SID__');
var dsSelect = document.getElementById('__DSID__');
var valueEl = container.querySelector('.alpha-value');
var N = __N__;
var xs = Array.from({length: N}, function(_,i){ return i/(N-1); });
function logistic(x, ymin, ymax, k, x0){ return ymin + (ymax - ymin) / (1 + Math.exp(-k*(x - x0))); }
function blend(l,e,a){ return (1-a)*l + a*e; }
var datasets = [
{ name:'CIFAR-10', base:{ymin:0.10,ymax:0.90,k:10.0,x0:0.55}, aug:{ymin:0.15,ymax:0.96,k:12.0,x0:0.40}, target:0.97 },
{ name:'CIFAR-100', base:{ymin:0.05,ymax:0.70,k:9.5,x0:0.60}, aug:{ymin:0.08,ymax:0.80,k:11.0,x0:0.45}, target:0.85 },
{ name:'ImageNet-1K', base:{ymin:0.02,ymax:0.68,k:8.5,x0:0.65}, aug:{ymin:0.04,ymax:0.75,k:9.5,x0:0.50}, target:0.82 }
];
var dsi = 0;
var yb = xs.map(function(x){ return logistic(x, datasets[dsi].base.ymin, datasets[dsi].base.ymax, datasets[dsi].base.k, datasets[dsi].base.x0); });
var ya = xs.map(function(x){ return logistic(x, datasets[dsi].aug.ymin, datasets[dsi].aug.ymax, datasets[dsi].aug.k, datasets[dsi].aug.x0); });
var yt = xs.map(function(){ return datasets[dsi].target; });
function applyAlpha(a){
var yi = yb.map(function(v,i){ return blend(v, ya[i], a); });
Plotly.restyle(gd, {y:[yi]}, [1]); // only Improved changes with α
if(valueEl) valueEl.textContent = a.toFixed(2);
}
function applyDataset(){
var d = datasets[dsi];
yb = xs.map(function(x){ return logistic(x, d.base.ymin, d.base.ymax, d.base.k, d.base.x0); });
ya = xs.map(function(x){ return logistic(x, d.aug.ymin, d.aug.ymax, d.aug.k, d.aug.x0); });
yt = xs.map(function(){ return d.target; });
var a = parseFloat(slider.value)||0;
var yi = yb.map(function(v,i){ return blend(v, ya[i], a); });
Plotly.restyle(gd, {y:[yb]}, [0]); // Baseline
Plotly.restyle(gd, {y:[yi]}, [1]); // Improved (blended)
Plotly.restyle(gd, {y:[yt]}, [2]); // Target
}
var initA = parseFloat(slider.value)||0;
slider.addEventListener('input', function(e){ applyAlpha(parseFloat(e.target.value)||0); });
dsSelect.addEventListener('change', function(e){ dsi = parseInt(e.target.value)||0; applyDataset(); });
setTimeout(function(){ applyDataset(); applyAlpha(initA); }, 0);
})();
</script>
'''
slider_html = (slider_tpl
.replace('__CID__', container_id)
.replace('__SID__', slider_id)
.replace('__A0__', f"{alpha0:.2f}")
.replace('__N__', str(N))
.replace('__PLOT__', html_plot)
)
with open("./plotly-line.html", "w", encoding="utf-8") as f:
f.write(slider_html)
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