Spaces:
Running
Running
File size: 7,883 Bytes
5846c4a fee9c1e 5846c4a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 |
import plotly.graph_objects as go
import plotly.io as pio
import numpy as np
import os
import uuid
"""
Interactive line chart example (3 curves + live slider)
The slider blends each curve from linear to exponential in real time (no mouseup required).
This fragment is safe to insert multiple times on the page (unique IDs per instance).
"""
# Grid (x) and parameterization
N = 240
x = np.linspace(0, 1, N)
# Linear baselines (increasing)
lin1 = 0.20 + 0.60 * x
lin2 = 0.15 + 0.70 * x
lin3 = 0.10 + 0.80 * x
# Helper: normalized exponential on [0,1]
def exp_norm(xv: np.ndarray, k: float) -> np.ndarray:
return (np.exp(k * xv) - 1.0) / (np.exp(k) - 1.0)
# Exponential counterparts (similar ranges)
exp1 = 0.20 + 0.60 * exp_norm(x, 3.0)
exp2 = 0.15 + 0.70 * exp_norm(x, 3.5)
exp3 = 0.10 + 0.80 * exp_norm(x, 2.8)
# Initial blend (alpha=0 ⇒ pure linear)
alpha0 = 0.0
blend = lambda l, e, a: (1 - a) * l + a * e
y1 = blend(lin1, exp1, alpha0)
y2 = blend(lin2, exp2, alpha0)
y3 = blend(lin3, exp3, alpha0)
color_base = "#64748b" # slate-500
color_improved = "#2563eb" # blue-600
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=28, r=12, t=8, b=28),
hovermode="x unified",
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,
),
)
# Écrit le fragment de manière robuste à côté de ce fichier, dans src/fragments/line.html
output_path = os.path.join(os.path.dirname(__file__), "fragments", "line.html")
os.makedirs(os.path.dirname(output_path), exist_ok=True)
# Injecte un petit script post-rendu pour arrondir les coins de la boîte de hover
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:10px; display:flex; gap:14px; align-items:center;">
<label style="font-size:12px;color:rgba(0,0,0,.65); display:flex; align-items:center; gap:6px; white-space:nowrap;">
Dataset
<select id="__DSID__" style="font-size:12px; padding:2px 6px;">
<option value="0">Dataset A</option>
<option value="1">Dataset B</option>
<option value="2">Dataset C</option>
</select>
</label>
<label style="font-size:12px;color:rgba(0,0,0,.65);display:flex;align-items:center;gap:8px; flex:1;">
Nonlinearity
<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 expNorm(x,k){ return (Math.exp(k*x)-1)/(Math.exp(k)-1); }
function blend(l,e,a){ return (1-a)*l + a*e; }
var datasets = [
{ curves: [ {o:0.20,s:0.60,k:3.0}, {o:0.15,s:0.70,k:3.5}, {o:0.10,s:0.80,k:2.8} ] },
{ curves: [ {o:0.30,s:0.55,k:2.2}, {o:0.18,s:0.65,k:2.8}, {o:0.12,s:0.70,k:2.0} ] },
{ curves: [ {o:0.10,s:0.85,k:3.8}, {o:0.12,s:0.80,k:3.2}, {o:0.08,s:0.90,k:3.0} ] }
];
var dsi = 0;
function makeY(a){
var cs = datasets[dsi].curves;
var y1 = xs.map(function(x){ return blend(cs[0].o + cs[0].s*x, cs[0].o + cs[0].s*expNorm(x,cs[0].k), a); });
var y2 = xs.map(function(x){ return blend(cs[1].o + cs[1].s*x, cs[1].o + cs[1].s*expNorm(x,cs[1].k), a); });
var y3 = xs.map(function(x){ return blend(cs[2].o + cs[2].s*x, cs[2].o + cs[2].s*expNorm(x,cs[2].k), a); });
return [y1,y2,y3];
}
function apply(a){
var ys = makeY(a);
Plotly.restyle(gd, {y:[ys[0]]}, [0]);
Plotly.restyle(gd, {y:[ys[1]]}, [1]);
Plotly.restyle(gd, {y:[ys[2]]}, [2]);
if(valueEl) valueEl.textContent = a.toFixed(2);
}
var initA = parseFloat(slider.value)||0;
slider.addEventListener('input', function(e){ apply(parseFloat(e.target.value)||0); });
dsSelect.addEventListener('change', function(e){ dsi = parseInt(e.target.value)||0; apply(parseFloat(slider.value)||0); });
setTimeout(function(){ apply(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)
)
fig.write_html("../app/src/fragments/line.html",
include_plotlyjs=False,
full_html=False,
config={
'displayModeBar': False,
'responsive': True,
'scrollZoom': False,
})
|