') + ')', 'gi'); if (regex.test(text)) { found = true; var frag = document.createDocumentFragment(); var parts = text.split(regex); parts.forEach(function(part, i) { if (i % 2 === 0) { frag.appendChild(document.createTextNode(part)); } else { var span = document.createElement('span'); span.className = 'userscript-highlight'; span.textContent = part; frag.appendChild(span); } }); node.parentNode.replaceChild(frag, node); } }); } else if (node.nodeType === 1 && node.childNodes) { // element var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT']; if (!skipTags.includes(node.tagName)) { Array.from(node.childNodes).forEach(highlight); } } } highlight(document.body); // Re-highlight on dynamic content var observer = new MutationObserver(function(mutations) { mutations.forEach(function(m) { m.addedNodes.forEach(function(node) { if (node.nodeType === 1 || node.nodeType === 3) highlight(node); }); }); }); observer.observe(document.body, { childList: true, subtree: true }); })(); } } catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + ', 'i'); if (__m === '*' || __re.test(location.href)) { // Strip utm_, fbclid, gclid, etc. from all links on page (function() { var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content', 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid', 'ref', 'ref_src', 'source', 'medium', 'campaign']; function cleanUrl(url) { try { var u = new URL(url, window.location.origin); var changed = false; trackingParams.forEach(function(p) { if (u.searchParams.has(p)) { u.searchParams.delete(p); changed = true; } }); return changed ? u.toString() : url; } catch (e) { return url; } } function cleanLinks() { document.querySelectorAll('a[href]').forEach(function(a) { var clean = cleanUrl(a.href); if (clean !== a.href) a.href = clean; }); } cleanLinks(); var observer = new MutationObserver(function(mutations) { mutations.forEach(function(m) { m.addedNodes.forEach(function(node) { if (node.nodeType === 1) { if (node.tagName === 'A') cleanLinks(); node.querySelectorAll('a[href]').forEach(function(a) { var clean = cleanUrl(a.href); if (clean !== a.href) a.href = clean; }); } }); }); }); observer.observe(document.body, { childList: true, subtree: true }); })(); } } catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); } })(); (function(){ try { var __m = "youtube.com"; var __re = new RegExp('^' + "youtube\\.com" + ', 'i'); if (__m === '*' || __re.test(location.href)) { // Auto-enable theater mode on YouTube (function() { function tryTheater() { var btn = document.querySelector('button[aria-label="Theater mode"], ytd-player #player button[title="Theater mode"]'); if (btn && !btn.classList.contains('activated')) { btn.click(); } } // Try immediately tryTheater(); // Try after navigation (SPA) var lastUrl = location.href; setInterval(function() { if (location.href !== lastUrl) { lastUrl = location.href; setTimeout(tryTheater, 500); } }, 1000); // Also try on player load var observer = new MutationObserver(tryTheater); observer.observe(document.body, { childList: true, subtree: true }); })(); } } catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + ', 'i'); if (__m === '*' || __re.test(location.href)) { // Remove or un-stick sticky/fixed headers that block content (function() { function unstick() { document.querySelectorAll('header, nav, [role="banner"], .header, .navbar, .sticky, .fixed-top, [style*="position: fixed"], [style*="position:sticky"]').forEach(function(el) { if (el.style.position === 'fixed' || el.style.position === 'sticky' || getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') { el.style.position = 'static'; el.style.top = 'auto'; el.style.zIndex = 'auto'; } }); } unstick(); var observer = new MutationObserver(unstick); observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] }); })(); } } catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); } })(); })(); Plotly 6.0.0 with Flask app no longer displays plot data with base 64 encoding · Issue #5045 · plotly/plotly.py · GitHub
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Plotly 6.0.0 with Flask app no longer displays plot data with base 64 encoding #5045

Description

@ParfaitG

Using the latest Plotly v6.0.0, my Flask web application no longer correctly displays a Plotly figure. While axes, background, and title renders, the plot displays an empty region with no data where a set of lines should be. The Flask UI receives a JSON string of a Plotly figure using json.dumps(fig, cls=plotly.utils.PlotlyJSONEncoder).

Comparing between prod (v5.24.1) and dev (6.0.0) version of app shows embedded base64 encoding in bdata elements within the JSON content. And release notes indicate the following changes:

Update plotly.py to use base64 encoding of typed arrays e.g. numpy in plotly JSON to keep precision intact and improve performance #4470.

With this update, should I use a different class other than PlotlyJSONEncoder for JSON string rendering of Plotly figures?

Reproducible Example

Python

importjsonimportpandasaspdimportplotlyimportplotly.graph_objectsasgomexico_frame=pd.DataFrame({'year': {0: 2015, 1: 2016, 2: 2017, 3: 2018, 4: 2019, 5: 2020, 6: 2021, 7: 2022, 8: 2015, 9: 2016, 10: 2017, 11: 2018, 12: 2019, 13: 2020, 14: 2021, 15: 2022, 16: 2015, 17: 2016, 18: 2017, 19: 2018, 20: 2019, 21: 2020, 22: 2021, 23: 2022, 24: 2015, 25: 2016, 26: 2017, 27: 2018, 28: 2019, 29: 2020, 30: 2021, 31: 2022, 32: 2015, 33: 2016, 34: 2017, 35: 2018, 36: 2019, 37: 2020, 38: 2021, 39: 2022, 40: 2015, 41: 2016, 42: 2017, 43: 2018, 44: 2019, 45: 2020, 46: 2021, 47: 2022, 48: 2015, 49: 2016, 50: 2017, 51: 2018, 52: 2019, 53: 2020, 54: 2021, 55: 2022}, 'iso2': {0: 'MX', 1: 'MX', 2: 'MX', 3: 'MX', 4: 'MX', 5: 'MX', 6: 'MX', 7: 'MX', 8: 'MX', 9: 'MX', 10: 'MX', 11: 'MX', 12: 'MX', 13: 'MX', 14: 'MX', 15: 'MX', 16: 'MX', 17: 'MX', 18: 'MX', 19: 'MX', 20: 'MX', 21: 'MX', 22: 'MX', 23: 'MX', 24: 'MX', 25: 'MX', 26: 'MX', 27: 'MX', 28: 'MX', 29: 'MX', 30: 'MX', 31: 'MX', 32: 'MX', 33: 'MX', 34: 'MX', 35: 'MX', 36: 'MX', 37: 'MX', 38: 'MX', 39: 'MX', 40: 'MX', 41: 'MX', 42: 'MX', 43: 'MX', 44: 'MX', 45: 'MX', 46: 'MX', 47: 'MX', 48: 'MX', 49: 'MX', 50: 'MX', 51: 'MX', 52: 'MX', 53: 'MX', 54: 'MX', 55: 'MX'}, 'country': {0: 'Mexico', 1: 'Mexico', 2: 'Mexico', 3: 'Mexico', 4: 'Mexico', 5: 'Mexico', 6: 'Mexico', 7: 'Mexico', 8: 'Mexico', 9: 'Mexico', 10: 'Mexico', 11: 'Mexico', 12: 'Mexico', 13: 'Mexico', 14: 'Mexico', 15: 'Mexico', 16: 'Mexico', 17: 'Mexico', 18: 'Mexico', 19: 'Mexico', 20: 'Mexico', 21: 'Mexico', 22: 'Mexico', 23: 'Mexico', 24: 'Mexico', 25: 'Mexico', 26: 'Mexico', 27: 'Mexico', 28: 'Mexico', 29: 'Mexico', 30: 'Mexico', 31: 'Mexico', 32: 'Mexico', 33: 'Mexico', 34: 'Mexico', 35: 'Mexico', 36: 'Mexico', 37: 'Mexico', 38: 'Mexico', 39: 'Mexico', 40: 'Mexico', 41: 'Mexico', 42: 'Mexico', 43: 'Mexico', 44: 'Mexico', 45: 'Mexico', 46: 'Mexico', 47: 'Mexico', 48: 'Mexico', 49: 'Mexico', 50: 'Mexico', 51: 'Mexico', 52: 'Mexico', 53: 'Mexico', 54: 'Mexico', 55: 'Mexico'}, 'metric_id': {0: 9, 1: 9, 2: 9, 3: 9, 4: 9, 5: 9, 6: 9, 7: 9, 8: 9, 9: 9, 10: 9, 11: 9, 12: 9, 13: 9, 14: 9, 15: 9, 16: 9, 17: 9, 18: 9, 19: 9, 20: 9, 21: 9, 22: 9, 23: 9, 24: 9, 25: 9, 26: 9, 27: 9, 28: 9, 29: 9, 30: 9, 31: 9, 32: 9, 33: 9, 34: 9, 35: 9, 36: 9, 37: 9, 38: 9, 39: 9, 40: 9, 41: 9, 42: 9, 43: 9, 44: 9, 45: 9, 46: 9, 47: 9, 48: 9, 49: 9, 50: 9, 51: 9, 52: 9, 53: 9, 54: 9, 55: 9}, 'metric': {0: 'Ecological Footprint per person', 1: 'Ecological Footprint per person', 2: 'Ecological Footprint per person', 3: 'Ecological Footprint per person', 4: 'Ecological Footprint per person', 5: 'Ecological Footprint per person', 6: 'Ecological Footprint per person', 7: 'Ecological Footprint per person', 8: 'Ecological Footprint per person', 9: 'Ecological Footprint per person', 10: 'Ecological Footprint per person', 11: 'Ecological Footprint per person', 12: 'Ecological Footprint per person', 13: 'Ecological Footprint per person', 14: 'Ecological Footprint per person', 15: 'Ecological Footprint per person', 16: 'Ecological Footprint per person', 17: 'Ecological Footprint per person', 18: 'Ecological Footprint per person', 19: 'Ecological Footprint per person', 20: 'Ecological Footprint per person', 21: 'Ecological Footprint per person', 22: 'Ecological Footprint per person', 23: 'Ecological Footprint per person', 24: 'Ecological Footprint per person', 25: 'Ecological Footprint per person', 26: 'Ecological Footprint per person', 27: 'Ecological Footprint per person', 28: 'Ecological Footprint per person', 29: 'Ecological Footprint per person', 30: 'Ecological Footprint per person', 31: 'Ecological Footprint per person', 32: 'Ecological Footprint per person', 33: 'Ecological Footprint per person', 34: 'Ecological Footprint per person', 35: 'Ecological Footprint per person', 36: 'Ecological Footprint per person', 37: 'Ecological Footprint per person', 38: 'Ecological Footprint per person', 39: 'Ecological Footprint per person', 40: 'Ecological Footprint per person', 41: 'Ecological Footprint per person', 42: 'Ecological Footprint per person', 43: 'Ecological Footprint per person', 44: 'Ecological Footprint per person', 45: 'Ecological Footprint per person', 46: 'Ecological Footprint per person', 47: 'Ecological Footprint per person', 48: 'Ecological Footprint per person', 49: 'Ecological Footprint per person', 50: 'Ecological Footprint per person', 51: 'Ecological Footprint per person', 52: 'Ecological Footprint per person', 53: 'Ecological Footprint per person', 54: 'Ecological Footprint per person', 55: 'Ecological Footprint per person'}, 'note': {0: 'Ecological Footprint of consumption in global hectares (gha) divided by population', 1: 'Ecological Footprint of consumption in global hectares (gha) divided by population', 2: 'Ecological Footprint of consumption in global hectares (gha) divided by population', 3: 'Ecological Footprint of consumption in global hectares (gha) divided by population', 4: 'Ecological Footprint of consumption in global hectares (gha) divided by population', 5: 'Ecological Footprint of consumption in global hectares (gha) divided by population', 6: 'Ecological Footprint of consumption in global hectares (gha) divided by population', 7: 'Ecological Footprint of consumption in global hectares (gha) divided by population', 8: 'Ecological Footprint of consumption in global hectares (gha) divided by population', 9: 'Ecological Footprint of consumption in global hectares (gha) divided by population', 10: 'Ecological Footprint of consumption in global hectares (gha) divided by population', 11: 'Ecological Footprint of consumption in global hectares (gha) divided by population', 12: 'Ecological Footprint of consumption in global hectares (gha) divided by population', 13: 'Ecological Footprint of consumption in global hectares (gha) divided by population', 14: 'Ecological Footprint of consumption in global hectares (gha) divided by population', 15: 'Ecological Footprint of consumption in global hectares (gha) divided by population', 16: 'Ecological Footprint of consumption in global hectares (gha) divided by population', 17: 'Ecological Footprint of consumption in global hectares (gha) divided by population', 18: 'Ecological Footprint of consumption in global hectares (gha) divided by population', 19: 'Ecological Footprint of consumption in global hectares (gha) divided by population', 20: 'Ecological Footprint of consumption in global hectares (gha) divided by population', 21: 'Ecological Footprint of consumption in global hectares (gha) divided by population', 22: 'Ecological Footprint of consumption in global hectares (gha) divided by population', 23: 'Ecological Footprint of consumption in global hectares (gha) divided by population', 24: 'Ecological Footprint of consumption in global hectares (gha) divided by population', 25: 'Ecological Footprint of consumption in global hectares (gha) divided by population', 26: 'Ecological Footprint of consumption in global hectares (gha) divided by population', 27: 'Ecological Footprint of consumption in global hectares (gha) divided by population', 28: 'Ecological Footprint of consumption in global hectares (gha) divided by population', 29: 'Ecological Footprint of consumption in global hectares (gha) divided by population', 30: 'Ecological Footprint of consumption in global hectares (gha) divided by population', 31: 'Ecological Footprint of consumption in global hectares (gha) divided by population', 32: 'Ecological Footprint of consumption in global hectares (gha) divided by population', 33: 'Ecological Footprint of consumption in global hectares (gha) divided by population', 34: 'Ecological Footprint of consumption in global hectares (gha) divided by population', 35: 'Ecological Footprint of consumption in global hectares (gha) divided by population', 36: 'Ecological Footprint of consumption in global hectares (gha) divided by population', 37: 'Ecological Footprint of consumption in global hectares (gha) divided by population', 38: 'Ecological Footprint of consumption in global hectares (gha) divided by population', 39: 'Ecological Footprint of consumption in global hectares (gha) divided by population', 40: 'Ecological Footprint of consumption in global hectares (gha) divided by population', 41: 'Ecological Footprint of consumption in global hectares (gha) divided by population', 42: 'Ecological Footprint of consumption in global hectares (gha) divided by population', 43: 'Ecological Footprint of consumption in global hectares (gha) divided by population', 44: 'Ecological Footprint of consumption in global hectares (gha) divided by population', 45: 'Ecological Footprint of consumption in global hectares (gha) divided by population', 46: 'Ecological Footprint of consumption in global hectares (gha) divided by population', 47: 'Ecological Footprint of consumption in global hectares (gha) divided by population', 48: 'Ecological Footprint of consumption in global hectares (gha) divided by population', 49: 'Ecological Footprint of consumption in global hectares (gha) divided by population', 50: 'Ecological Footprint of consumption in global hectares (gha) divided by population', 51: 'Ecological Footprint of consumption in global hectares (gha) divided by population', 52: 'Ecological Footprint of consumption in global hectares (gha) divided by population', 53: 'Ecological Footprint of consumption in global hectares (gha) divided by population', 54: 'Ecological Footprint of consumption in global hectares (gha) divided by population', 55: 'Ecological Footprint of consumption in global hectares (gha) divided by population'}, 'footprint_type': {0: 'builtupLand', 1: 'builtupLand', 2: 'builtupLand', 3: 'builtupLand', 4: 'builtupLand', 5: 'builtupLand', 6: 'builtupLand', 7: 'builtupLand', 8: 'carbon', 9: 'carbon', 10: 'carbon', 11: 'carbon', 12: 'carbon', 13: 'carbon', 14: 'carbon', 15: 'carbon', 16: 'cropLand', 17: 'cropLand', 18: 'cropLand', 19: 'cropLand', 20: 'cropLand', 21: 'cropLand', 22: 'cropLand', 23: 'cropLand', 24: 'fishingGround', 25: 'fishingGround', 26: 'fishingGround', 27: 'fishingGround', 28: 'fishingGround', 29: 'fishingGround', 30: 'fishingGround', 31: 'fishingGround', 32: 'forestLand', 33: 'forestLand', 34: 'forestLand', 35: 'forestLand', 36: 'forestLand', 37: 'forestLand', 38: 'forestLand', 39: 'forestLand', 40: 'grazingLand', 41: 'grazingLand', 42: 'grazingLand', 43: 'grazingLand', 44: 'grazingLand', 45: 'grazingLand', 46: 'grazingLand', 47: 'grazingLand', 48: 'total', 49: 'total', 50: 'total', 51: 'total', 52: 'total', 53: 'total', 54: 'total', 55: 'total'}, 'footprint_value': {0: 0.06998590647491501, 1: 0.07470568681825901, 2: 0.08089834521584001, 3: 0.08131535835414301, 4: 0.08437062872542, 5: 0.081877766078983, 6: 0.08226062967613301, 7: 0.08256117303348401, 8: 1.45299322354663, 9: 1.39674909997343, 10: 1.39675783668407, 11: 1.30509984067737, 12: 1.28630084494473, 13: 1.03612887068416, 14: 1.18958351222326, 15: 1.14254018173385, 16: 0.5597860304132211, 17: 0.5790942193074811, 18: 0.571234212047664, 19: 0.5711844221103021, 20: 0.560660759884935, 21: 0.49932535845387405, 22: 0.496539177731512, 23: 0.49342762983491606, 24: 0.116064280674846, 25: 0.11975863871968, 26: 0.11903431489980601, 27: 0.11361905819041701, 28: 0.10693797175441101, 29: 0.10230798395573201, 30: 0.10173711662881701, 31: 0.101099584032299, 32: 0.260319967669921, 33: 0.26654267401659404, 34: 0.281096560106174, 35: 0.278314696587597, 36: 0.26435168814146504, 37: 0.267845144452164, 38: 0.266350597929709, 39: 0.26468151987924904, 40: 0.23192434947102, 41: 0.23252356700818103, 42: 0.22898262872433803, 43: 0.222846437626832, 44: 0.21798902382889102, 45: 0.20332102496109902, 46: 0.202186515946873, 47: 0.200919520195826, 48: 2.69107375825056, 49: 2.66937388584362, 50: 2.67800389767789, 51: 2.57237981354666, 52: 2.52061091727985, 53: 2.19080614858601, 54: 2.3386575501363, 55: 2.28522960870963}})
defbuild_graph(frame: pd.DataFrame) ->str:
trace=go.Scatter(
x=frame.query("footprint_type == 'total'")["year"],
y=frame.query("footprint_type == 'total'")["footprint_value"],
dx=1,
dy=1,
text=frame.query("footprint_type == 'total'")["footprint_type"],
name="total",
mode="lines+markers",
hoverinfo="x+y+text",
textposition="top center",
marker=dict(showscale=False, size=2, line_width=0),
)
layout=go.Layout(
title=dict(
text=f'{frame["country"].iat[0]}: {frame["metric"].iat[0]}',
font_size=16
),
showlegend=False,
hovermode="closest",
margin=dict(b=20, l=5, r=5, t=40),
title_x=0.5,
font_family="Arial",
title_font_family="Arial",
legend_title_font_family="Arial",
yaxis=dict(tickformat=".2f"),
annotations=[
dict(
text=frame["note"].iat[0],
showarrow=False,
xref="paper",
yref="paper",
x=0.0,
y=-0.1,
)
],
)
fig=go.Figure(data=[trace], layout=layout)
forftypein [
"builtupLand",
"cropLand",
"forestLand",
"fishingGround",
"grazingLand",
"carbon",
]:
q="footprint_type == @ftype"fig.add_trace(
go.Scatter(
x=frame.query(q)["year"],
y=frame.query(q)["footprint_value"],
text=frame.query(q)["footprint_type"],
hoverinfo="x+y+text",
name=(ftype.replace("Land", " land").replace("Ground", " ground")),
mode="lines+markers",
connectgaps=True,
marker=dict(showscale=False, size=2, line_width=0),
)
)
fig.update_layout(
showlegend=True,
legend=dict(orientation="h", xanchor="center", yanchor="top", x=0.5, y=1.0),
margin=dict(l=20, r=20, t=35, b=40),
)
returnjson.dumps(fig, cls=plotly.utils.PlotlyJSONEncoder)
print(build_graph(mexico_frame)) 

Production (v5.24.1) vs Development (v6.0.0)

Comparing between my production and development version of same code generates following differences viewable on https://jsfiddle.net.

Prod (no issue with desired plot display)

HTML

<body><divid="cons-footprint-plot" class="chart"></div></body>

JS

vargraphs={"data": [{"dx": 1,"dy": 1,"hoverinfo": "x+y+text","marker": {"line": {"width": 0},"showscale": false,"size": 2},"mode": "lines+markers","name": "total","text": ["total","total","total","total","total","total","total","total","total","total","total","total","total","total","total","total","total","total","total","total","total","total","total","total","total","total","total","total","total","total","total","total","total","total","total","total","total","total","total","total","total","total","total","total","total","total","total","total","total","total","total","total","total","total","total","total","total","total","total","total","total","total"],"textposition": "top center","x": [1961,1962,1963,1964,1965,1966,1967,1968,1969,1970,1971,1972,1973,1974,1975,1976,1977,1978,1979,1980,1981,1982,1983,1984,1985,1986,1987,1988,1989,1990,1991,1992,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021,2022],"y": [2.07630830849152,2.00874943595944,1.99068211445437,2.01641382915592,1.91629237308816,1.9328807751123,2.01675331932545,2.00197643427104,1.98674011917152,2.1370388480144,2.10368681058998,2.07989181194314,2.26105928162359,2.37987775148131,2.37551912379596,2.25931223567278,2.24999993624751,2.321226207037,2.42122022921399,2.6040626570312497,2.70161183018488,2.50231694850833,2.33261784868637,2.44373322624395,2.52960889670879,2.3545055176894802,2.37820010662579,2.50396443379012,2.5807674937113,2.59314295364264,2.6398760268405,2.60358360545381,2.6386090283306,2.83561656481768,2.64108842432268,3.06268626752021,3.03825301080676,3.03017845674158,4.17126059815816,3.09137710985913,3.5684140915108,3.19017969374877,2.83935084717272,2.76359946386932,3.02830088722433,3.12622191610183,3.21184358080122,3.26084690768218,3.00079136419209,3.32717386995855,2.92856662511405,3.0615947042148,2.81671344682771,2.67579216065001,2.69107375825056,2.66937388584362,2.67800389767789,2.57237981354666,2.52061091727985,2.19080614858601,2.3386575501363,2.28522960870963],"type": "scatter"},{"connectgaps": true,"hoverinfo": "x+y+text","marker": {"line": {"width": 0},"showscale": false,"size": 2},"mode": "lines+markers","name": "builtup land","text": ["builtupLand","builtupLand","builtupLand","builtupLand","builtupLand","builtupLand","builtupLand","builtupLand","builtupLand","builtupLand","builtupLand","builtupLand","builtupLand","builtupLand","builtupLand","builtupLand","builtupLand","builtupLand","builtupLand","builtupLand","builtupLand","builtupLand","builtupLand","builtupLand","builtupLand","builtupLand","builtupLand","builtupLand","builtupLand","builtupLand","builtupLand","builtupLand","builtupLand","builtupLand","builtupLand","builtupLand","builtupLand","builtupLand","builtupLand","builtupLand","builtupLand","builtupLand","builtupLand","builtupLand","builtupLand","builtupLand","builtupLand","builtupLand","builtupLand","builtupLand","builtupLand","builtupLand","builtupLand","builtupLand","builtupLand","builtupLand","builtupLand","builtupLand","builtupLand","builtupLand","builtupLand","builtupLand"],"x": 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