Error Bar and Best Fit Line Slope Simulator<\/title>\n <style>\n body {\n font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;\n background-color: #f4f7f6;\n color: #333;\n margin: 0;\n padding: 20px;\n display: flex;\n flex-direction: column;\n align-items: center;\n }\n\n h1 {\n color: #2c3e50;\n }\n\n .container {\n background: white;\n padding: 20px;\n border-radius: 10px;\n box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);\n display: flex;\n flex-direction: column;\n max-width: 800px;\n width: 100%;\n }\n\n canvas {\n border: 1px solid #ddd;\n background-color: #fafbfc;\n border-radius: 5px;\n margin-bottom: 20px;\n cursor: crosshair;\n }\n\n .controls {\n display: flex;\n justify-content: space-between;\n gap: 20px;\n background: #f8f9fa;\n padding: 15px;\n border-radius: 8px;\n border: 1px solid #eee;\n }\n\n .slider-group {\n flex: 1;\n display: flex;\n flex-direction: column;\n }\n\n .slider-group label {\n font-weight: bold;\n margin-bottom: 8px;\n color: #495057;\n }\n\n input[type=\"range\"] {\n width: 100%;\n }\n\n .results {\n margin-top: 20px;\n padding: 15px;\n background-color: #e9ecef;\n border-radius: 8px;\n border-left: 5px solid #0d6efd;\n }\n\n .results p {\n margin: 5px 0;\n font-size: 16px;\n }\n\n .highlight {\n font-weight: bold;\n color: #0d6efd;\n font-size: 1.1em;\n }\n <\/style>\n<\/head>\n\n<body>\n\n <h1>Error Bar and Slope Uncertainty Simulator<\/h1>\n\n <div class=\"container\">\n <canvas id=\"graphCanvas\" width=\"760\" height=\"400\"><\/canvas>\n\n <div class=\"controls\">\n <div class=\"slider-group\">\n <label for=\"errX\">Horizontal Error Bar (\u03b4X): <span id=\"valX\">0.20<\/span><\/label>\n <input type=\"range\" id=\"errX\" min=\"0\" max=\"1\" step=\"0.05\" value=\"0.2\">\n <\/div>\n <div class=\"slider-group\">\n <label for=\"errY\">Vertical Error Bar (\u03b4Y): <span id=\"valY\">0.50<\/span><\/label>\n <input type=\"range\" id=\"errY\" min=\"0\" max=\"2\" step=\"0.1\" value=\"0.5\">\n <\/div>\n <\/div>\n\n <div class=\"results\">\n <p>Best Fit Slope (Solid Line): <span id=\"resBest\" class=\"highlight\">0.00<\/span><\/p>\n <p>Max Possible Slope (Blue Dashed Line): <span id=\"resMax\">0.00<\/span><\/p>\n <p>Min Possible Slope (Red Dotted Line): <span id=\"resMin\">0.00<\/span><\/p>\n <p><strong>Final Slope and Uncertainty (m \u00b1 \u03b4m): <span id=\"resFinal\" class=\"highlight\">0.00 \u00b1 0.00<\/span><\/strong><\/p>\n <p style=\"font-size: 0.9em; color: #666; margin-top: 10px;\">Formula: \u03b4m = (Max Slope – Min Slope) \/ 2<\/p>\n <\/div>\n <\/div>\n\n <script>\n const canvas = document.getElementById('graphCanvas');\n const ctx = canvas.getContext('2d');\n const errXSlider = document.getElementById('errX');\n const errYSlider = document.getElementById('errY');\n\n \/\/ Sample data points (X, Y)\n let dataPoints = [\n { x: 1, y: 2.1 }, { x: 2, y: 4.3 }, { x: 3, y: 6.5 }, { x: 4, y: 8.7 }, { x: 5, y: 11.0 }\n ];\n\n \/\/ Drawing parameters\n const padding = 50;\n const width = canvas.width;\n const height = canvas.height;\n let scaleX, scaleY;\n\n function update() {\n const errX = parseFloat(errXSlider.value);\n const errY = parseFloat(errYSlider.value);\n document.getElementById('valX').innerText = errX.toFixed(2);\n document.getElementById('valY').innerText = errY.toFixed(2);\n\n drawGraph(errX, errY);\n }\n\n function calculateLinearRegression(data) {\n let n = data.length, sumX = 0, sumY = 0, sumXY = 0, sumXX = 0;\n for (let i = 0; i < n; i++) {\n sumX += data[i].x; sumY += data[i].y; sumXY += data[i].x * data[i].y; sumXX += data[i].x * data[i].x;\n }\n let slope = (n * sumXY - sumX * sumY) \/ (n * sumXX - sumX * sumX);\n let intercept = (sumY - slope * sumX) \/ n;\n return { slope, intercept };\n }\n\n function drawGraph(errX, errY) {\n ctx.clearRect(0, 0, width, height);\n\n \/\/ Determine coordinate axis range\n let minX = 0, maxX = 6;\n let minY = 0, maxY = 14;\n scaleX = (width - padding * 2) \/ (maxX - minX);\n scaleY = (height - padding * 2) \/ (maxY - minY);\n\n function toCanvas(x, y) {\n return { cx: padding + (x - minX) * scaleX, cy: height - padding - (y - minY) * scaleY };\n }\n\n \/\/ Draw grid and coordinate axes\n ctx.strokeStyle = '#eee'; ctx.lineWidth = 1;\n for (let i = minX; i <= maxX; i++) {\n let p1 = toCanvas(i, minY), p2 = toCanvas(i, maxY);\n ctx.beginPath(); ctx.moveTo(p1.cx, p1.cy); ctx.lineTo(p2.cx, p2.cy); ctx.stroke();\n }\n for (let i = minY; i <= maxY; i += 2) {\n let p1 = toCanvas(minX, i), p2 = toCanvas(maxX, i);\n ctx.beginPath(); ctx.moveTo(p1.cx, p1.cy); ctx.lineTo(p2.cx, p2.cy); ctx.stroke();\n }\n ctx.strokeStyle = '#333'; ctx.lineWidth = 2;\n let origin = toCanvas(0, 0); let axisXEnd = toCanvas(maxX, 0); let axisYEnd = toCanvas(0, maxY);\n ctx.beginPath(); ctx.moveTo(origin.cx, axisYEnd.cy); ctx.lineTo(origin.cx, origin.cy); ctx.lineTo(axisXEnd.cx, origin.cy); ctx.stroke();\n\n \/\/ Calculate best fit line\n const bestFit = calculateLinearRegression(dataPoints);\n\n \/\/ Calculate max\/min slope lines (based on error bounds of first and last data points)\n const firstPoint = dataPoints[0];\n const lastPoint = dataPoints[dataPoints.length - 1];\n\n \/\/ Max slope: passing through bottom-right of first point and top-left of last point\n let m_max = ((lastPoint.y + errY) - (firstPoint.y - errY)) \/ ((lastPoint.x - errX) - (firstPoint.x + errX));\n let b_max = (lastPoint.y + errY) - m_max * (lastPoint.x - errX);\n\n \/\/ Min slope: passing through top-left of first point and bottom-right of last point\n let m_min = ((lastPoint.y - errY) - (firstPoint.y + errY)) \/ ((lastPoint.x + errX) - (firstPoint.x - errX));\n let b_min = (lastPoint.y - errY) - m_min * (lastPoint.x + errX);\n\n \/\/ Line drawing function\n function drawLine(slope, intercept, color, dash) {\n ctx.beginPath();\n ctx.strokeStyle = color;\n ctx.setLineDash(dash);\n let p1 = toCanvas(minX, slope * minX + intercept);\n let p2 = toCanvas(maxX, slope * maxX + intercept);\n ctx.moveTo(p1.cx, p1.cy); ctx.lineTo(p2.cx, p2.cy);\n ctx.stroke();\n ctx.setLineDash([]); \/\/ reset\n }\n\n \/\/ Draw three lines\n ctx.lineWidth = 2;\n drawLine(m_max, b_max, 'rgba(13, 110, 253, 0.6)', [5, 5]); \/\/ Blue dashed line (Max)\n drawLine(m_min, b_min, 'rgba(220, 53, 69, 0.6)', [2, 4]); \/\/ Red dotted line (Min)\n ctx.lineWidth = 3;\n drawLine(bestFit.slope, bestFit.intercept, '#212529', []); \/\/ Black solid line (Best Fit)\n\n \/\/ Draw data points and error bars\n ctx.fillStyle = '#000';\n ctx.strokeStyle = '#000';\n ctx.lineWidth = 1.5;\n dataPoints.forEach(p => {\n let center = toCanvas(p.x, p.y);\n\n \/\/ Draw point\n ctx.beginPath(); ctx.arc(center.cx, center.cy, 4, 0, Math.PI * 2); ctx.fill();\n\n \/\/ Draw vertical error bars\n let yTop = toCanvas(p.x, p.y + errY).cy;\n let yBottom = toCanvas(p.x, p.y - errY).cy;\n ctx.beginPath(); ctx.moveTo(center.cx, yTop); ctx.lineTo(center.cx, yBottom); ctx.stroke();\n ctx.beginPath(); ctx.moveTo(center.cx - 5, yTop); ctx.lineTo(center.cx + 5, yTop); ctx.stroke();\n ctx.beginPath(); ctx.moveTo(center.cx - 5, yBottom); ctx.lineTo(center.cx + 5, yBottom); ctx.stroke();\n\n \/\/ Draw horizontal error bars\n let xLeft = toCanvas(p.x - errX, p.y).cx;\n let xRight = toCanvas(p.x + errX, p.y).cx;\n ctx.beginPath(); ctx.moveTo(xLeft, center.cy); ctx.lineTo(xRight, center.cy); ctx.stroke();\n ctx.beginPath(); ctx.moveTo(xLeft, center.cy - 5); ctx.lineTo(xLeft, center.cy + 5); ctx.stroke();\n ctx.beginPath(); ctx.moveTo(xRight, center.cy - 5); ctx.lineTo(xRight, center.cy + 5); ctx.stroke();\n });\n\n \/\/ Update panel data\n let uncertainty = (m_max - m_min) \/ 2;\n document.getElementById('resBest').innerText = bestFit.slope.toFixed(3);\n document.getElementById('resMax').innerText = m_max.toFixed(3);\n document.getElementById('resMin').innerText = m_min.toFixed(3);\n document.getElementById('resFinal').innerText = `${bestFit.slope.toFixed(3)} \u00b1 ${uncertainty.toFixed(3)}`;\n }\n\n \/\/ Listen to slider events\n errXSlider.addEventListener('input', update);\n errYSlider.addEventListener('input', update);\n\n \/\/ Initial rendering\n update();\n <\/script>\n<\/body>\n\n<\/html>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Real-time interactive modeling of statistical error margins and probability density fields.<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[6],"tags":[],"class_list":["post-83","post","type-post","status-publish","format-standard","hentry","category-visual-learning-tools"],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/flowxiom.com\/index.php\/wp-json\/wp\/v2\/posts\/83","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/flowxiom.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/flowxiom.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/flowxiom.com\/index.php\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/flowxiom.com\/index.php\/wp-json\/wp\/v2\/comments?post=83"}],"version-history":[{"count":1,"href":"https:\/\/flowxiom.com\/index.php\/wp-json\/wp\/v2\/posts\/83\/revisions"}],"predecessor-version":[{"id":84,"href":"https:\/\/flowxiom.com\/index.php\/wp-json\/wp\/v2\/posts\/83\/revisions\/84"}],"wp:attachment":[{"href":"https:\/\/flowxiom.com\/index.php\/wp-json\/wp\/v2\/media?parent=83"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/flowxiom.com\/index.php\/wp-json\/wp\/v2\/categories?post=83"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/flowxiom.com\/index.php\/wp-json\/wp\/v2\/tags?post=83"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}

{"id":83,"date":"2026-05-20T17:37:38","date_gmt":"2026-05-20T17:37:38","guid":{"rendered":"https:\/\/flowxiom.com\/?p=83"},"modified":"2026-05-20T17:37:40","modified_gmt":"2026-05-20T17:37:40","slug":"uncertainty-visualization","status":"publish","type":"post","link":"https:\/\/flowxiom.com\/index.php\/2026\/05\/20\/uncertainty-visualization\/","title":{"rendered":"Uncertainty Visualization"},"content":{"rendered":"\n\n\n\n\n \n Error Bar and Best Fit Line Slope Simulator<\/title>\n <style>\n body {\n font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;\n background-color: #f4f7f6;\n color: #333;\n margin: 0;\n padding: 20px;\n display: flex;\n flex-direction: column;\n align-items: center;\n }\n\n h1 {\n color: #2c3e50;\n }\n\n .container {\n background: white;\n padding: 20px;\n border-radius: 10px;\n box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);\n display: flex;\n flex-direction: column;\n max-width: 800px;\n width: 100%;\n }\n\n canvas {\n border: 1px solid #ddd;\n background-color: #fafbfc;\n border-radius: 5px;\n margin-bottom: 20px;\n cursor: crosshair;\n }\n\n .controls {\n display: flex;\n justify-content: space-between;\n gap: 20px;\n background: #f8f9fa;\n padding: 15px;\n border-radius: 8px;\n border: 1px solid #eee;\n }\n\n .slider-group {\n flex: 1;\n display: flex;\n flex-direction: column;\n }\n\n .slider-group label {\n font-weight: bold;\n margin-bottom: 8px;\n color: #495057;\n }\n\n input[type=\"range\"] {\n width: 100%;\n }\n\n .results {\n margin-top: 20px;\n padding: 15px;\n background-color: #e9ecef;\n border-radius: 8px;\n border-left: 5px solid #0d6efd;\n }\n\n .results p {\n margin: 5px 0;\n font-size: 16px;\n }\n\n .highlight {\n font-weight: bold;\n color: #0d6efd;\n font-size: 1.1em;\n }\n <\/style>\n<\/head>\n\n<body>\n\n <h1>Error Bar and Slope Uncertainty Simulator<\/h1>\n\n <div class=\"container\">\n <canvas id=\"graphCanvas\" width=\"760\" height=\"400\"><\/canvas>\n\n <div class=\"controls\">\n <div class=\"slider-group\">\n <label for=\"errX\">Horizontal Error Bar (\u03b4X): <span id=\"valX\">0.20<\/span><\/label>\n <input type=\"range\" id=\"errX\" min=\"0\" max=\"1\" step=\"0.05\" value=\"0.2\">\n <\/div>\n <div class=\"slider-group\">\n <label for=\"errY\">Vertical Error Bar (\u03b4Y): <span id=\"valY\">0.50<\/span><\/label>\n <input type=\"range\" id=\"errY\" min=\"0\" max=\"2\" step=\"0.1\" value=\"0.5\">\n <\/div>\n <\/div>\n\n <div class=\"results\">\n <p>Best Fit Slope (Solid Line): <span id=\"resBest\" class=\"highlight\">0.00<\/span><\/p>\n <p>Max Possible Slope (Blue Dashed Line): <span id=\"resMax\">0.00<\/span><\/p>\n <p>Min Possible Slope (Red Dotted Line): <span id=\"resMin\">0.00<\/span><\/p>\n <p><strong>Final Slope and Uncertainty (m \u00b1 \u03b4m): <span id=\"resFinal\" class=\"highlight\">0.00 \u00b1 0.00<\/span><\/strong><\/p>\n <p style=\"font-size: 0.9em; color: #666; margin-top: 10px;\">Formula: \u03b4m = (Max Slope – Min Slope) \/ 2<\/p>\n <\/div>\n <\/div>\n\n <script>\n const canvas = document.getElementById('graphCanvas');\n const ctx = canvas.getContext('2d');\n const errXSlider = document.getElementById('errX');\n const errYSlider = document.getElementById('errY');\n\n \/\/ Sample data points (X, Y)\n let dataPoints = [\n { x: 1, y: 2.1 }, { x: 2, y: 4.3 }, { x: 3, y: 6.5 }, { x: 4, y: 8.7 }, { x: 5, y: 11.0 }\n ];\n\n \/\/ Drawing parameters\n const padding = 50;\n const width = canvas.width;\n const height = canvas.height;\n let scaleX, scaleY;\n\n function update() {\n const errX = parseFloat(errXSlider.value);\n const errY = parseFloat(errYSlider.value);\n document.getElementById('valX').innerText = errX.toFixed(2);\n document.getElementById('valY').innerText = errY.toFixed(2);\n\n drawGraph(errX, errY);\n }\n\n function calculateLinearRegression(data) {\n let n = data.length, sumX = 0, sumY = 0, sumXY = 0, sumXX = 0;\n for (let i = 0; i < n; i++) {\n sumX += data[i].x; sumY += data[i].y; sumXY += data[i].x * data[i].y; sumXX += data[i].x * data[i].x;\n }\n let slope = (n * sumXY - sumX * sumY) \/ (n * sumXX - sumX * sumX);\n let intercept = (sumY - slope * sumX) \/ n;\n return { slope, intercept };\n }\n\n function drawGraph(errX, errY) {\n ctx.clearRect(0, 0, width, height);\n\n \/\/ Determine coordinate axis range\n let minX = 0, maxX = 6;\n let minY = 0, maxY = 14;\n scaleX = (width - padding * 2) \/ (maxX - minX);\n scaleY = (height - padding * 2) \/ (maxY - minY);\n\n function toCanvas(x, y) {\n return { cx: padding + (x - minX) * scaleX, cy: height - padding - (y - minY) * scaleY };\n }\n\n \/\/ Draw grid and coordinate axes\n ctx.strokeStyle = '#eee'; ctx.lineWidth = 1;\n for (let i = minX; i <= maxX; i++) {\n let p1 = toCanvas(i, minY), p2 = toCanvas(i, maxY);\n ctx.beginPath(); ctx.moveTo(p1.cx, p1.cy); ctx.lineTo(p2.cx, p2.cy); ctx.stroke();\n }\n for (let i = minY; i <= maxY; i += 2) {\n let p1 = toCanvas(minX, i), p2 = toCanvas(maxX, i);\n ctx.beginPath(); ctx.moveTo(p1.cx, p1.cy); ctx.lineTo(p2.cx, p2.cy); ctx.stroke();\n }\n ctx.strokeStyle = '#333'; ctx.lineWidth = 2;\n let origin = toCanvas(0, 0); let axisXEnd = toCanvas(maxX, 0); let axisYEnd = toCanvas(0, maxY);\n ctx.beginPath(); ctx.moveTo(origin.cx, axisYEnd.cy); ctx.lineTo(origin.cx, origin.cy); ctx.lineTo(axisXEnd.cx, origin.cy); ctx.stroke();\n\n \/\/ Calculate best fit line\n const bestFit = calculateLinearRegression(dataPoints);\n\n \/\/ Calculate max\/min slope lines (based on error bounds of first and last data points)\n const firstPoint = dataPoints[0];\n const lastPoint = dataPoints[dataPoints.length - 1];\n\n \/\/ Max slope: passing through bottom-right of first point and top-left of last point\n let m_max = ((lastPoint.y + errY) - (firstPoint.y - errY)) \/ ((lastPoint.x - errX) - (firstPoint.x + errX));\n let b_max = (lastPoint.y + errY) - m_max * (lastPoint.x - errX);\n\n \/\/ Min slope: passing through top-left of first point and bottom-right of last point\n let m_min = ((lastPoint.y - errY) - (firstPoint.y + errY)) \/ ((lastPoint.x + errX) - (firstPoint.x - errX));\n let b_min = (lastPoint.y - errY) - m_min * (lastPoint.x + errX);\n\n \/\/ Line drawing function\n function drawLine(slope, intercept, color, dash) {\n ctx.beginPath();\n ctx.strokeStyle = color;\n ctx.setLineDash(dash);\n let p1 = toCanvas(minX, slope * minX + intercept);\n let p2 = toCanvas(maxX, slope * maxX + intercept);\n ctx.moveTo(p1.cx, p1.cy); ctx.lineTo(p2.cx, p2.cy);\n ctx.stroke();\n ctx.setLineDash([]); \/\/ reset\n }\n\n \/\/ Draw three lines\n ctx.lineWidth = 2;\n drawLine(m_max, b_max, 'rgba(13, 110, 253, 0.6)', [5, 5]); \/\/ Blue dashed line (Max)\n drawLine(m_min, b_min, 'rgba(220, 53, 69, 0.6)', [2, 4]); \/\/ Red dotted line (Min)\n ctx.lineWidth = 3;\n drawLine(bestFit.slope, bestFit.intercept, '#212529', []); \/\/ Black solid line (Best Fit)\n\n \/\/ Draw data points and error bars\n ctx.fillStyle = '#000';\n ctx.strokeStyle = '#000';\n ctx.lineWidth = 1.5;\n dataPoints.forEach(p => {\n let center = toCanvas(p.x, p.y);\n\n \/\/ Draw point\n ctx.beginPath(); ctx.arc(center.cx, center.cy, 4, 0, Math.PI * 2); ctx.fill();\n\n \/\/ Draw vertical error bars\n let yTop = toCanvas(p.x, p.y + errY).cy;\n let yBottom = toCanvas(p.x, p.y - errY).cy;\n ctx.beginPath(); ctx.moveTo(center.cx, yTop); ctx.lineTo(center.cx, yBottom); ctx.stroke();\n ctx.beginPath(); ctx.moveTo(center.cx - 5, yTop); ctx.lineTo(center.cx + 5, yTop); ctx.stroke();\n ctx.beginPath(); ctx.moveTo(center.cx - 5, yBottom); ctx.lineTo(center.cx + 5, yBottom); ctx.stroke();\n\n \/\/ Draw horizontal error bars\n let xLeft = toCanvas(p.x - errX, p.y).cx;\n let xRight = toCanvas(p.x + errX, p.y).cx;\n ctx.beginPath(); ctx.moveTo(xLeft, center.cy); ctx.lineTo(xRight, center.cy); ctx.stroke();\n ctx.beginPath(); ctx.moveTo(xLeft, center.cy - 5); ctx.lineTo(xLeft, center.cy + 5); ctx.stroke();\n ctx.beginPath(); ctx.moveTo(xRight, center.cy - 5); ctx.lineTo(xRight, center.cy + 5); ctx.stroke();\n });\n\n \/\/ Update panel data\n let uncertainty = (m_max - m_min) \/ 2;\n document.getElementById('resBest').innerText = bestFit.slope.toFixed(3);\n document.getElementById('resMax').innerText = m_max.toFixed(3);\n document.getElementById('resMin').innerText = m_min.toFixed(3);\n document.getElementById('resFinal').innerText = `${bestFit.slope.toFixed(3)} \u00b1 ${uncertainty.toFixed(3)}`;\n }\n\n \/\/ Listen to slider events\n errXSlider.addEventListener('input', update);\n errYSlider.addEventListener('input', update);\n\n \/\/ Initial rendering\n update();\n <\/script>\n<\/body>\n\n<\/html>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Real-time interactive modeling of statistical error margins and probability density fields.<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[6],"tags":[],"class_list":["post-83","post","type-post","status-publish","format-standard","hentry","category-visual-learning-tools"],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/flowxiom.com\/index.php\/wp-json\/wp\/v2\/posts\/83","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/flowxiom.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/flowxiom.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/flowxiom.com\/index.php\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/flowxiom.com\/index.php\/wp-json\/wp\/v2\/comments?post=83"}],"version-history":[{"count":1,"href":"https:\/\/flowxiom.com\/index.php\/wp-json\/wp\/v2\/posts\/83\/revisions"}],"predecessor-version":[{"id":84,"href":"https:\/\/flowxiom.com\/index.php\/wp-json\/wp\/v2\/posts\/83\/revisions\/84"}],"wp:attachment":[{"href":"https:\/\/flowxiom.com\/index.php\/wp-json\/wp\/v2\/media?parent=83"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/flowxiom.com\/index.php\/wp-json\/wp\/v2\/categories?post=83"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/flowxiom.com\/index.php\/wp-json\/wp\/v2\/tags?post=83"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}