diff --git a/grafana/dashboards/SustronicsPliot3_3.json b/grafana/dashboards/SustronicsPliot3_3.json
index e671470c1ac149cda2f33ab1b6e60367ef291018..54ec873036f5eef74a92ca49c6979bdc0bf3da31 100644
--- a/grafana/dashboards/SustronicsPliot3_3.json
+++ b/grafana/dashboards/SustronicsPliot3_3.json
@@ -19,8 +19,9 @@
"editable": true,
"fiscalYearStartMonth": 0,
"graphTooltip": 0,
- "id": 1,
+ "id": 110,
"links": [],
+ "liveNow": false,
"panels": [
{
"collapsed": false,
@@ -38,19 +39,15 @@
{
"datasource": {
"type": "influxdb",
- "uid": "P951FEA4DE68E13C5"
- },
- "fieldConfig": {
- "defaults": {},
- "overrides": []
+ "uid": "b3860bbb-0b28-4a3c-b8d8-0723cb4f180f"
},
"gridPos": {
"h": 10,
- "w": 12,
+ "w": 24,
"x": 0,
"y": 1
},
- "id": 14,
+ "id": 15,
"options": {
"allData": {},
"config": {},
@@ -60,11 +57,6 @@
"font": {
"family": "Inter, Helvetica, Arial, sans-serif"
},
- "layout": {
- "legend": {
- "orientation": "h"
- }
- },
"legend": {
"orientation": "h"
},
@@ -79,7 +71,7 @@
},
"xaxis": {
"automargin": true,
- "autorange": true,
+ "autorange": false,
"tickmode": "auto",
"type": "date"
},
@@ -87,39 +79,31 @@
"automargin": true,
"autorange": false,
"range": [
- 10,
- 45
- ],
- "tickvals": [
- 10,
- 15,
- 20,
- 25,
- 30,
- 35,
- 40
+ -2,
+ 12
],
+ "tickmode": "auto",
"type": "linear"
}
},
"onclick": "// Event handling\n/*\n// 'data', 'variables', 'options', 'utils', and 'event' are passed as arguments\n\ntry {\n const { type: eventType, data: eventData } = event;\n const { timeZone, dayjs, locationService, getTemplateSrv } = utils;\n\n switch (eventType) {\n case 'click':\n console.log('Click event:', eventData.points);\n break;\n case 'select':\n console.log('Selection event:', eventData.range);\n break;\n case 'zoom':\n console.log('Zoom event:', eventData);\n break;\n default:\n console.log('Unhandled event type:', eventType, eventData);\n }\n\n console.log('Current time zone:', timeZone);\n console.log('From time:', dayjs(variables.__from).format());\n console.log('To time:', dayjs(variables.__to).format());\n\n // Example of using locationService\n // locationService.partial({ 'var-example': 'test' }, true);\n\n} catch (error) {\n console.error('Error in onclick handler:', error);\n}\n*/\n ",
"resScale": 2,
- "script": "//console.log(data)\nfunction polynominalValue(coefs, arg) {\n let val = 0;\n let pow = 0;\n for (const coef of coefs) {\n val += coef * Math.pow(arg, pow);\n pow += 1;\n }\n return val;\n}\n\n// Retrieve polynomial coefficients\nlet tempCoefficient = variables.Temp_coefs;\nlet coefs = tempCoefficient.current.text.split(';').map(parseFloat).reverse();\n\n// Predefined colors for series\nconst colors = [\"#DD9040\", \"#407DDD\", \"#40DD72\", \"#DD40A6\", \"#DDBB40\", \"#40C8DD\"];\n\n// Prepare data for each series\nlet seriesData = [];\n\nfor (let i = 0; i < data.series.length; i++) {\n let series = data.series[i];\n let x = series.fields[0].values; // X-axis values (time)\n let v = series.fields[1].values; // Input values (Y-axis raw data)\n let y = [];\n\n // Calculate Y values using the polynomial function\n for (const element of v) {\n y.push(polynominalValue(coefs, element));\n }\n\n // Determine color for this series\n let color = colors[i % colors.length];\n\n // Add the processed data of this series to the result\n seriesData.push({\n x: x,\n y: y,\n type: 'scatter',\n mode: \"lines+markers\",\n line: {\n color: color\n },\n marker: {\n symbol: \"x\",\n color: color,\n size: 8\n },\n name: `${series.name}` // Unique name for each series\n });\n}\n\n// Return data and layout configuration for the plot\nreturn {\n data: seriesData,\n layout: {\n xaxis: {\n title: 'Time',\n type: 'date',\n tickformat: '%H:%M:%S'\n },\n yaxis: { title: 'Temperature [°C]'}\n }\n};\n",
+ "script": "//console.log(data)\nfunction polynominalValue(coefs, arg) {\n let val = 0;\n let pow = 0;\n for (const coef of coefs) {\n val += coef * Math.pow(arg, pow);\n pow += 1;\n }\n return val;\n}\n\n// Retrieve polynomial coefficients\nlet pHCoefficient = variables.pH_coefs;\nlet coefs = pHCoefficient.current.text.split(';').map(parseFloat).reverse();\n\n// Predefined colors for series\nconst colors = [\"#F2CC0C\", \"#73BF69\", \"#40DD72\", \"#DD40A6\", \"#DDBB40\", \"#40C8DD\"];\n\n// Prepare data for each series\nlet seriesData = [];\n\nlet t_min = 0;\nlet t_max = 0;\n\nfor (let i = 0; i < data.series.length; i++) {\n let series = data.series[i];\n let x = series.fields[0].values; // X-axis values (time)\n let v = series.fields[1].values; // Input values (Y-axis raw data)\n let y = [];\n\n // Calculate Y values using the polynomial function\n for (const element of v) {\n y.push(polynominalValue(coefs, element));\n }\n\n // Determine color for this series\n let color = colors[i % colors.length];\n\n t_min = x[0]\n t_max = x.slice(-1).pop()\n\n // Add the processed data of this series to the result\n seriesData.push({\n x: x,\n y: y,\n type: 'scatter',\n mode: \"lines+markers\",\n line: {\n color: color\n },\n marker: {\n symbol: \"x\",\n color: color,\n size: 8\n },\n name: `${series.name}` // Unique name for each series\n });\n}\n\nconsole.clear();\nconsole.log(t_min);\n\nt_min = new Date(new Date(t_min) - new Date(t_min).getTimezoneOffset() * 60000).toISOString()\nt_max = new Date(new Date(t_max) - new Date(t_max).getTimezoneOffset() * 60000).toISOString()\n\n// Return data and layout configuration for the plot\nreturn {\n data: seriesData,\n layout: {\n xaxis: {\n title: 'Time',\n type: 'date',\n tickformat: '%H:%M:%S',\n range: [t_min, t_max]\n },\n yaxis: { title: 'pH [ ]'}\n }\n};\n",
"syncTimeRange": false,
"timeCol": ""
},
"pluginVersion": "1.8.1",
"targets": [
{
- "alias": "temp@ROUnit_$tag_beacon_t",
+ "alias": "pH@ROUnit_$tag_beacon_t",
"datasource": {
"type": "influxdb",
- "uid": "P951FEA4DE68E13C5"
+ "uid": "b3860bbb-0b28-4a3c-b8d8-0723cb4f180f"
},
"groupBy": [
{
"params": [
- "$interval"
+ "$interval2"
],
"type": "time"
},
@@ -128,10 +112,16 @@
"beacon_t::tag"
],
"type": "tag"
+ },
+ {
+ "params": [
+ "none"
+ ],
+ "type": "fill"
}
],
"measurement": "sustronics",
- "orderByTime": "DESC",
+ "orderByTime": "ASC",
"policy": "default",
"query": "SELECT * FROM /^$Temp_coeff_A$/",
"rawQuery": false,
@@ -141,7 +131,7 @@
[
{
"params": [
- "temp"
+ "pH"
],
"type": "field"
},
@@ -160,25 +150,22 @@
]
}
],
- "title": "Temperature history",
+ "title": "pH history",
"type": "nline-plotlyjs-panel"
},
{
"datasource": {
"type": "influxdb",
- "uid": "P951FEA4DE68E13C5"
- },
- "fieldConfig": {
- "defaults": {},
- "overrides": []
+ "uid": "b3860bbb-0b28-4a3c-b8d8-0723cb4f180f"
},
+ "description": "",
"gridPos": {
"h": 10,
- "w": 12,
- "x": 12,
- "y": 1
+ "w": 24,
+ "x": 0,
+ "y": 11
},
- "id": 15,
+ "id": 18,
"options": {
"allData": {},
"config": {},
@@ -202,7 +189,7 @@
},
"xaxis": {
"automargin": true,
- "autorange": true,
+ "autorange": false,
"tickmode": "auto",
"type": "date"
},
@@ -211,43 +198,30 @@
"autorange": false,
"range": [
0,
- 12
+ 4096
],
"tickmode": "auto",
- "tickvals": [
- 0,
- 0.1,
- 0.2,
- 0.3,
- 0.4,
- 0.5,
- 0.6,
- 0.7,
- 0.8,
- 0.9,
- 1
- ],
"type": "linear"
}
},
"onclick": "// Event handling\n/*\n// 'data', 'variables', 'options', 'utils', and 'event' are passed as arguments\n\ntry {\n const { type: eventType, data: eventData } = event;\n const { timeZone, dayjs, locationService, getTemplateSrv } = utils;\n\n switch (eventType) {\n case 'click':\n console.log('Click event:', eventData.points);\n break;\n case 'select':\n console.log('Selection event:', eventData.range);\n break;\n case 'zoom':\n console.log('Zoom event:', eventData);\n break;\n default:\n console.log('Unhandled event type:', eventType, eventData);\n }\n\n console.log('Current time zone:', timeZone);\n console.log('From time:', dayjs(variables.__from).format());\n console.log('To time:', dayjs(variables.__to).format());\n\n // Example of using locationService\n // locationService.partial({ 'var-example': 'test' }, true);\n\n} catch (error) {\n console.error('Error in onclick handler:', error);\n}\n*/\n ",
"resScale": 2,
- "script": "//console.log(data)\nfunction polynominalValue(coefs, arg) {\n let val = 0;\n let pow = 0;\n for (const coef of coefs) {\n val += coef * Math.pow(arg, pow);\n pow += 1;\n }\n return val;\n}\n\n// Retrieve polynomial coefficients\nlet pHCoefficient = variables.pH_coefs;\nlet coefs = pHCoefficient.current.text.split(';').map(parseFloat).reverse();\n\n// Predefined colors for series\nconst colors = [\"#DD9040\", \"#407DDD\", \"#40DD72\", \"#DD40A6\", \"#DDBB40\", \"#40C8DD\"];\n\n// Prepare data for each series\nlet seriesData = [];\n\nfor (let i = 0; i < data.series.length; i++) {\n let series = data.series[i];\n let x = series.fields[0].values; // X-axis values (time)\n let v = series.fields[1].values; // Input values (Y-axis raw data)\n let y = [];\n\n // Calculate Y values using the polynomial function\n for (const element of v) {\n y.push(polynominalValue(coefs, element));\n }\n\n // Determine color for this series\n let color = colors[i % colors.length];\n\n // Add the processed data of this series to the result\n seriesData.push({\n x: x,\n y: y,\n type: 'scatter',\n mode: \"lines+markers\",\n line: {\n color: color\n },\n marker: {\n symbol: \"x\",\n color: color,\n size: 8\n },\n name: `${series.name}` // Unique name for each series\n });\n}\n\n// Return data and layout configuration for the plot\nreturn {\n data: seriesData,\n layout: {\n xaxis: {\n title: 'Time',\n type: 'date',\n tickformat: '%H:%M:%S'\n },\n yaxis: { title: 'pH [ ]'}\n }\n};\n",
+ "script": "// Predefined colors for series\nconst colors = [\"#F2CC0C\", \"#73BF69\", \"#40DD72\", \"#DD40A6\", \"#DDBB40\", \"#40C8DD\"];\n\n// Prepare data for each series\nlet seriesData = [];\n\nlet t_min = 0;\nlet t_max = 0;\n\nfor (let i = 0; i < data.series.length; i++) {\n let series = data.series[i];\n let x = series.fields[0].values; // X-axis values (time)\n let v = series.fields[1].values; // Input values (Y-axis raw data)\n let y = [];\n\n // Determine color for this series\n let color = colors[i % colors.length];\n\n t_min = x[0]\n t_max = x.slice(-1).pop()\n\n // Add the processed data of this series to the result\n seriesData.push({\n x: x,\n y: v,\n type: 'scatter',\n mode: \"lines+markers\",\n line: {\n color: color\n },\n marker: {\n symbol: \"x\",\n color: color,\n size: 8\n },\n name: `${series.name}` // Unique name for each series\n });\n}\n\nconsole.clear();\nconsole.log(t_min);\n\nt_min = new Date(new Date(t_min) - new Date(t_min).getTimezoneOffset() * 60000).toISOString()\nt_max = new Date(new Date(t_max) - new Date(t_max).getTimezoneOffset() * 60000).toISOString()\n\n// Return data and layout configuration for the plot\nreturn {\n data: seriesData,\n layout: {\n xaxis: {\n title: 'Time',\n type: 'date',\n tickformat: '%H:%M:%S',\n range: [t_min, t_max]\n },\n yaxis: { title: 'ADC'}\n }\n};\n",
"syncTimeRange": false,
"timeCol": ""
},
"pluginVersion": "1.8.1",
"targets": [
{
- "alias": "pH@ROUnit_$tag_beacon_t",
+ "alias": "ADC_pH@ROUnit_$tag_beacon_t",
"datasource": {
"type": "influxdb",
- "uid": "P951FEA4DE68E13C5"
+ "uid": "b3860bbb-0b28-4a3c-b8d8-0723cb4f180f"
},
"groupBy": [
{
"params": [
- "$interval"
+ "$interval2"
],
"type": "time"
},
@@ -256,6 +230,12 @@
"beacon_t::tag"
],
"type": "tag"
+ },
+ {
+ "params": [
+ "none"
+ ],
+ "type": "fill"
}
],
"measurement": "sustronics",
@@ -288,77 +268,21 @@
]
}
],
- "title": "pH history",
+ "title": "pH@ADC",
"type": "nline-plotlyjs-panel"
},
{
"datasource": {
"type": "influxdb",
- "uid": "P951FEA4DE68E13C5"
- },
- "fieldConfig": {
- "defaults": {
- "color": {
- "mode": "palette-classic"
- },
- "custom": {
- "axisBorderShow": false,
- "axisCenteredZero": false,
- "axisColorMode": "text",
- "axisLabel": "",
- "axisPlacement": "auto",
- "barAlignment": 0,
- "barWidthFactor": 0.6,
- "drawStyle": "line",
- "fillOpacity": 10,
- "gradientMode": "none",
- "hideFrom": {
- "legend": false,
- "tooltip": false,
- "viz": false
- },
- "insertNulls": false,
- "lineInterpolation": "linear",
- "lineWidth": 1,
- "pointSize": 3,
- "scaleDistribution": {
- "type": "linear"
- },
- "showPoints": "always",
- "spanNulls": false,
- "stacking": {
- "group": "A",
- "mode": "none"
- },
- "thresholdsStyle": {
- "mode": "off"
- }
- },
- "links": [],
- "mappings": [],
- "thresholds": {
- "mode": "absolute",
- "steps": [
- {
- "color": "green",
- "value": null
- },
- {
- "color": "red",
- "value": 80
- }
- ]
- }
- },
- "overrides": []
+ "uid": "b3860bbb-0b28-4a3c-b8d8-0723cb4f180f"
},
"gridPos": {
"h": 10,
"w": 24,
"x": 0,
- "y": 11
+ "y": 21
},
- "id": 15,
+ "id": 13,
"options": {
"allData": {},
"config": {},
@@ -368,9 +292,6 @@
"font": {
"family": "Inter, Helvetica, Arial, sans-serif"
},
- "legend": {
- "orientation": "h"
- },
"margin": {
"b": 0,
"l": 0,
@@ -383,74 +304,45 @@
"xaxis": {
"automargin": true,
"autorange": true,
+ "range": [
+ 0,
+ 4096
+ ],
"tickmode": "auto",
- "type": "date"
+ "type": "linear"
},
"yaxis": {
"automargin": true,
"autorange": false,
"range": [
- -2,
- 12
- ],
- "tickmode": "auto",
- "tickvals": [
0,
- 0.1,
- 0.2,
- 0.3,
- 0.4,
- 0.5,
- 0.6,
- 0.7,
- 0.8,
- 0.9,
- 1
+ 10
],
+ "tickmode": "auto",
"type": "linear"
}
},
"onclick": "// Event handling\n/*\n// 'data', 'variables', 'options', 'utils', and 'event' are passed as arguments\n\ntry {\n const { type: eventType, data: eventData } = event;\n const { timeZone, dayjs, locationService, getTemplateSrv } = utils;\n\n switch (eventType) {\n case 'click':\n console.log('Click event:', eventData.points);\n break;\n case 'select':\n console.log('Selection event:', eventData.range);\n break;\n case 'zoom':\n console.log('Zoom event:', eventData);\n break;\n default:\n console.log('Unhandled event type:', eventType, eventData);\n }\n\n console.log('Current time zone:', timeZone);\n console.log('From time:', dayjs(variables.__from).format());\n console.log('To time:', dayjs(variables.__to).format());\n\n // Example of using locationService\n // locationService.partial({ 'var-example': 'test' }, true);\n\n} catch (error) {\n console.error('Error in onclick handler:', error);\n}\n*/\n ",
"resScale": 2,
- "script": "//console.log(data)\nfunction polynominalValue(coefs, arg) {\n let val = 0;\n let pow = 0;\n for (const coef of coefs) {\n val += coef * Math.pow(arg, pow);\n pow += 1;\n }\n return val;\n}\n\n// Retrieve polynomial coefficients\nlet pHCoefficient = variables.pH_coefs;\nlet coefs = pHCoefficient.current.text.split(';').map(parseFloat).reverse();\n\n// Predefined colors for series\nconst colors = [\"#DD9040\", \"#407DDD\", \"#40DD72\", \"#DD40A6\", \"#DDBB40\", \"#40C8DD\"];\n\n// Prepare data for each series\nlet seriesData = [];\n\nfor (let i = 0; i < data.series.length; i++) {\n let series = data.series[i];\n let x = series.fields[0].values; // X-axis values (time)\n let v = series.fields[1].values; // Input values (Y-axis raw data)\n let y = [];\n\n // Calculate Y values using the polynomial function\n for (const element of v) {\n y.push(polynominalValue(coefs, element));\n }\n\n // Determine color for this series\n let color = colors[i % colors.length];\n\n // Add the processed data of this series to the result\n seriesData.push({\n x: x,\n y: y,\n type: 'scatter',\n mode: \"lines+markers\",\n line: {\n color: color\n },\n marker: {\n symbol: \"x\",\n color: color,\n size: 8\n },\n name: `${series.name}` // Unique name for each series\n });\n}\n\n// Return data and layout configuration for the plot\nreturn {\n data: seriesData,\n layout: {\n xaxis: {\n title: 'Time',\n type: 'date',\n tickformat: '%H:%M:%S'\n },\n yaxis: { title: 'pH [ ]'}\n }\n};\n",
+ "script": "function polynominalValue(coefs, arg) {\n val = 0;\n pow = 0;\n for (const coef of coefs) {\n val += coef * Math.pow(arg, pow);\n pow += 1;\n }\n return val;\n}\n\nlet phCoefficient = variables.pH_coefs;\nvar coefs = phCoefficient.current.text.split(';').map(parseFloat).reverse();\n\nlet current_x = data.series[0].fields[1].values[0]\nlet current_y = polynominalValue(coefs, current_x)\n\nlet x = Array.from({length: 4096}, (_, i) => (i));\nvar y = [];\n\nfor (const element of x) {\n y.push(polynominalValue(coefs, element))\n}\n\n\nreturn {\n data: [{\n x: x,\n y: y,\n type: 'scatter',\n mode: 'lines',\n name: 'Polynominal'\n },\n {\n x: [current_x, current_x],\n y: [current_y, current_y],\n type: 'scatter',\n mode: \"markers\",\n marker: {\n symbol: \"x\",\n color: \"#4090DD\",\n size: 8\n },\n name: 'Current readout'\n }],\n layout: {\n xaxis: { title: 'ADC [LSB]'},\n yaxis: { title: 'pH'}\n }\n}\n\nreturn {}\n ",
"syncTimeRange": false,
"timeCol": ""
},
"pluginVersion": "1.8.1",
"targets": [
{
- "alias": "pH@ROUnit_$tag_beacon_t",
"datasource": {
"type": "influxdb",
- "uid": "P951FEA4DE68E13C5"
+ "uid": "b3860bbb-0b28-4a3c-b8d8-0723cb4f180f"
},
- "groupBy": [
- {
- "params": [
- "$interval"
- ],
- "type": "time"
- },
- {
- "params": [
- "beacon_t::tag"
- ],
- "type": "tag"
- },
- {
- "params": [
- "none"
- ],
- "type": "fill"
- }
- ],
+ "groupBy": [],
"measurement": "sustronics",
"orderByTime": "ASC",
"policy": "default",
"query": "SELECT * FROM /^$Temp_coeff_A$/",
"rawQuery": false,
"refId": "A",
- "resultFormat": "time_series",
+ "resultFormat": "logs",
"select": [
[
{
@@ -465,146 +357,303 @@
}
]
],
- "tags": [
- {
- "key": "beacon_t::tag",
- "operator": "=~",
- "value": "/^$ROUnit$/"
- }
- ]
+ "tags": []
}
],
-<<<<<<< HEAD
- "title": "Temperature@ADC",
- "type": "timeseries"
-=======
- "title": "pH history",
+ "title": "pH calibration",
"type": "nline-plotlyjs-panel"
->>>>>>> bea82a1 (Fix dhasboard)
},
{
- "datasource": {
- "type": "influxdb",
- "uid": "P951FEA4DE68E13C5"
- },
- "fieldConfig": {
- "defaults": {
- "color": {
- "mode": "palette-classic"
- },
- "custom": {
- "axisBorderShow": false,
- "axisCenteredZero": false,
- "axisColorMode": "text",
- "axisLabel": "",
- "axisPlacement": "auto",
- "barAlignment": 0,
- "barWidthFactor": 0.6,
- "drawStyle": "line",
- "fillOpacity": 10,
- "gradientMode": "none",
- "hideFrom": {
- "legend": false,
- "tooltip": false,
- "viz": false
- },
- "insertNulls": false,
- "lineInterpolation": "linear",
- "lineWidth": 1,
- "pointSize": 5,
- "scaleDistribution": {
- "type": "linear"
- },
- "showPoints": "always",
- "spanNulls": false,
- "stacking": {
- "group": "A",
- "mode": "none"
- },
- "thresholdsStyle": {
- "mode": "off"
- }
- },
- "links": [],
- "mappings": [],
- "thresholds": {
- "mode": "absolute",
- "steps": [
- {
- "color": "green",
- "value": null
- },
- {
- "color": "red",
- "value": 80
- }
- ]
- }
- },
- "overrides": []
+ "collapsed": false,
+ "gridPos": {
+ "h": 1,
+ "w": 24,
+ "x": 0,
+ "y": 31
},
+ "id": 7,
+ "panels": [],
+ "title": "Sensors calibration (work in progress)",
+ "type": "row"
+ },
+ {
+ "collapsed": true,
"gridPos": {
- "h": 11,
+ "h": 1,
"w": 24,
"x": 0,
-<<<<<<< HEAD
- "y": 20
-=======
- "y": 11
->>>>>>> bea82a1 (Fix dhasboard)
+ "y": 32
},
- "id": 2,
- "options": {
- "alertThreshold": true,
- "legend": {
- "calcs": [
- "mean",
- "lastNotNull",
- "max",
- "min"
+ "id": 9,
+ "panels": [
+ {
+ "datasource": {
+ "type": "influxdb",
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/>",
+ "mode": "html"
+ },
+ "pluginVersion": "11.4.0-208376",
+ "title": "RH calib",
+ "type": "text"
}
+ ],
+ "title": "Eg chart",
+ "type": "row"
+ },
+ {
+ "collapsed": false,
+ "gridPos": {
+ "h": 1,
+ "w": 24,
+ "x": 0,
+ "y": 34
},
- "pluginVersion": "11.4.0-208313",
+ "id": 17,
+ "panels": [],
+ "title": "Real-time measurment - temperature",
+ "type": "row"
+ },
+ {
+ "datasource": {
+ "type": "influxdb",
+ "uid": "b3860bbb-0b28-4a3c-b8d8-0723cb4f180f"
+ },
+ "gridPos": {
+ "h": 10,
+ "w": 11,
+ "x": 0,
+ "y": 35
+ },
+ "id": 14,
+ "options": {
+ "allData": {},
+ "config": {},
+ "data": [],
+ "imgFormat": "png",
+ "layout": {
+ "font": {
+ "family": "Inter, Helvetica, Arial, sans-serif"
+ },
+ "layout": {
+ "legend": {
+ "orientation": "h"
+ }
+ },
+ "legend": {
+ "orientation": "h"
+ },
+ "margin": {
+ "b": 0,
+ "l": 0,
+ "r": 0,
+ "t": 0
+ },
+ "title": {
+ "automargin": true
+ },
+ "xaxis": {
+ "automargin": true,
+ "autorange": true,
+ "tickmode": "auto",
+ "type": "date"
+ },
+ "yaxis": {
+ "automargin": true,
+ "autorange": false,
+ "range": [
+ 10,
+ 45
+ ],
+ "tickvals": [
+ 10,
+ 15,
+ 20,
+ 25,
+ 30,
+ 35,
+ 40
+ ],
+ "type": "linear"
+ }
+ },
+ "onclick": "// Event handling\n/*\n// 'data', 'variables', 'options', 'utils', and 'event' are passed as arguments\n\ntry {\n const { type: eventType, data: eventData } = event;\n const { timeZone, dayjs, locationService, getTemplateSrv } = utils;\n\n switch (eventType) {\n case 'click':\n console.log('Click event:', eventData.points);\n break;\n case 'select':\n console.log('Selection event:', eventData.range);\n break;\n case 'zoom':\n console.log('Zoom event:', eventData);\n break;\n default:\n console.log('Unhandled event type:', eventType, eventData);\n }\n\n console.log('Current time zone:', timeZone);\n console.log('From time:', dayjs(variables.__from).format());\n console.log('To time:', dayjs(variables.__to).format());\n\n // Example of using locationService\n // locationService.partial({ 'var-example': 'test' }, true);\n\n} catch (error) {\n console.error('Error in onclick handler:', error);\n}\n*/\n ",
+ "resScale": 2,
+ "script": "//console.log(data)\nfunction polynominalValue(coefs, arg) {\n let val = 0;\n let pow = 0;\n for (const coef of coefs) {\n val += coef * Math.pow(arg, pow);\n pow += 1;\n }\n return val;\n}\n\n// Retrieve polynomial coefficients\nlet tempCoefficient = variables.Temp_coefs;\nlet coefs = tempCoefficient.current.text.split(';').map(parseFloat).reverse();\n\n// Predefined colors for series\nconst colors = [\"#DD9040\", \"#407DDD\", \"#40DD72\", \"#DD40A6\", \"#DDBB40\", \"#40C8DD\"];\n\n// Prepare data for each series\nlet seriesData = [];\n\nfor (let i = 0; i < data.series.length; i++) {\n let series = data.series[i];\n let x = series.fields[0].values; // X-axis values (time)\n let v = series.fields[1].values; // Input values (Y-axis raw data)\n let y = [];\n\n // Calculate Y values using the polynomial function\n for (const element of v) {\n y.push(polynominalValue(coefs, element));\n }\n\n // Determine color for this series\n let color = colors[i % colors.length];\n\n // Add the processed data of this series to the result\n seriesData.push({\n x: x,\n y: y,\n type: 'scatter',\n mode: \"lines+markers\",\n line: {\n color: color\n },\n marker: {\n symbol: \"x\",\n color: color,\n size: 8\n },\n name: `${series.name}` // Unique name for each series\n });\n}\n\n// Return data and layout configuration for the plot\nreturn {\n data: seriesData,\n layout: {\n xaxis: {\n title: 'Time',\n type: 'date',\n tickformat: '%H:%M:%S'\n },\n yaxis: { title: 'Temperature [°C]'}\n }\n};\n",
+ "syncTimeRange": false,
+ "timeCol": ""
+ },
+ "pluginVersion": "1.8.1",
"targets": [
{
- "alias": "pH@ROUnit_$tag_beacon_t",
+ "alias": "temp@ROUnit_$tag_beacon_t",
"datasource": {
"type": "influxdb",
- "uid": "xNdqUIZHz"
+ "uid": "b3860bbb-0b28-4a3c-b8d8-0723cb4f180f"
},
"groupBy": [
{
"params": [
- "$interval"
+ "$interval2"
],
"type": "time"
},
{
"params": [
- "beacon_t"
+ "beacon_t::tag"
],
"type": "tag"
}
],
- "hide": false,
"measurement": "sustronics",
- "orderByTime": "ASC",
+ "orderByTime": "DESC",
"policy": "default",
+ "query": "SELECT * FROM /^$Temp_coeff_A$/",
+ "rawQuery": false,
"refId": "A",
"resultFormat": "time_series",
"select": [
[
{
"params": [
- "pH"
+ "temp"
],
"type": "field"
},
@@ -619,53 +668,23 @@
"key": "beacon_t::tag",
"operator": "=~",
"value": "/^$ROUnit$/"
- },
- {
- "condition": "AND",
- "key": "espar_t::tag",
- "operator": "=~",
- "value": "/^$Gateway$/"
}
]
}
],
- "title": "pH@ADC",
- "type": "timeseries"
- },
- {
- "datasource": {
- "type": "influxdb",
- "uid": "P951FEA4DE68E13C5"
- },
- "fieldConfig": {
- "defaults": {},
- "overrides": []
- },
- "gridPos": {
- "h": 10,
- "w": 24,
- "x": 0,
- "y": 31
- },
- "id": 7,
- "panels": [],
- "title": "Sensors calibration (work in progress)",
- "type": "row"
+ "title": "Temperature history",
+ "type": "nline-plotlyjs-panel"
},
{
"datasource": {
"type": "influxdb",
- "uid": "P951FEA4DE68E13C5"
- },
- "fieldConfig": {
- "defaults": {},
- "overrides": []
+ "uid": "b3860bbb-0b28-4a3c-b8d8-0723cb4f180f"
},
"gridPos": {
"h": 10,
"w": 13,
- "x": 0,
- "y": 32
+ "x": 11,
+ "y": 35
},
"id": 12,
"options": {
@@ -739,7 +758,7 @@
{
"datasource": {
"type": "influxdb",
- "uid": "P951FEA4DE68E13C5"
+ "uid": "b3860bbb-0b28-4a3c-b8d8-0723cb4f180f"
},
"groupBy": [],
"measurement": "sustronics",
@@ -772,84 +791,118 @@
{
"datasource": {
"type": "influxdb",
- "uid": "P951FEA4DE68E13C5"
+ "uid": "b3860bbb-0b28-4a3c-b8d8-0723cb4f180f"
},
"fieldConfig": {
- "defaults": {},
- "overrides": []
- },
- "gridPos": {
- "h": 10,
- "w": 11,
- "x": 13,
- "y": 32
- },
- "id": 13,
- "options": {
- "allData": {},
- "config": {},
- "data": [],
- "imgFormat": "png",
- "layout": {
- "font": {
- "family": "Inter, Helvetica, Arial, sans-serif"
- },
- "margin": {
- "b": 0,
- "l": 0,
- "r": 0,
- "t": 0
- },
- "title": {
- "automargin": true
+ "defaults": {
+ "color": {
+ "mode": "palette-classic"
},
- "xaxis": {
- "automargin": true,
- "autorange": true,
- "range": [
- 0,
- 4096
- ],
- "tickmode": "auto",
- "type": "linear"
+ "custom": {
+ "axisCenteredZero": false,
+ "axisColorMode": "text",
+ "axisLabel": "",
+ "axisPlacement": "auto",
+ "barAlignment": 0,
+ "drawStyle": "line",
+ "fillOpacity": 10,
+ "gradientMode": "none",
+ "hideFrom": {
+ "legend": false,
+ "tooltip": false,
+ "viz": false
+ },
+ "lineInterpolation": "linear",
+ "lineWidth": 1,
+ "pointSize": 3,
+ "scaleDistribution": {
+ "type": "linear"
+ },
+ "showPoints": "always",
+ "spanNulls": false,
+ "stacking": {
+ "group": "A",
+ "mode": "none"
+ },
+ "thresholdsStyle": {
+ "mode": "off"
+ }
},
- "yaxis": {
- "automargin": true,
- "autorange": false,
- "range": [
- 0,
- 10
- ],
- "tickmode": "auto",
- "type": "linear"
+ "links": [],
+ "mappings": [],
+ "thresholds": {
+ "mode": "absolute",
+ "steps": [
+ {
+ "color": "green",
+ "value": null
+ },
+ {
+ "color": "red",
+ "value": 80
+ }
+ ]
}
},
- "onclick": "// Event handling\n/*\n// 'data', 'variables', 'options', 'utils', and 'event' are passed as arguments\n\ntry {\n const { type: eventType, data: eventData } = event;\n const { timeZone, dayjs, locationService, getTemplateSrv } = utils;\n\n switch (eventType) {\n case 'click':\n console.log('Click event:', eventData.points);\n break;\n case 'select':\n console.log('Selection event:', eventData.range);\n break;\n case 'zoom':\n console.log('Zoom event:', eventData);\n break;\n default:\n console.log('Unhandled event type:', eventType, eventData);\n }\n\n console.log('Current time zone:', timeZone);\n console.log('From time:', dayjs(variables.__from).format());\n console.log('To time:', dayjs(variables.__to).format());\n\n // Example of using locationService\n // locationService.partial({ 'var-example': 'test' }, true);\n\n} catch (error) {\n console.error('Error in onclick handler:', error);\n}\n*/\n ",
- "resScale": 2,
- "script": "function polynominalValue(coefs, arg) {\n val = 0;\n pow = 0;\n for (const coef of coefs) {\n val += coef * Math.pow(arg, pow);\n pow += 1;\n }\n return val;\n}\n\nlet phCoefficient = variables.pH_coefs;\nvar coefs = phCoefficient.current.text.split(';').map(parseFloat).reverse();\n\nlet current_x = data.series[0].fields[1].values[0]\nlet current_y = polynominalValue(coefs, current_x)\n\nlet x = Array.from({length: 4096}, (_, i) => (i));\nvar y = [];\n\nfor (const element of x) {\n y.push(polynominalValue(coefs, element))\n}\n\n\nreturn {\n data: [{\n x: x,\n y: y,\n type: 'scatter',\n mode: 'lines',\n name: 'Polynominal'\n },\n {\n x: [current_x, current_x],\n y: [current_y, current_y],\n type: 'scatter',\n mode: \"markers\",\n marker: {\n symbol: \"x\",\n color: \"#4090DD\",\n size: 8\n },\n name: 'Current readout'\n }],\n layout: {\n xaxis: { title: 'ADC [LSB]'},\n yaxis: { title: 'pH'}\n }\n}\n\nreturn {}\n ",
- "syncTimeRange": false,
- "timeCol": ""
+ "overrides": []
},
- "pluginVersion": "1.8.1",
+ "gridPos": {
+ "h": 9,
+ "w": 24,
+ "x": 0,
+ "y": 45
+ },
+ "id": 4,
+ "options": {
+ "alertThreshold": true,
+ "legend": {
+ "calcs": [
+ "mean",
+ "lastNotNull",
+ "max",
+ "min"
+ ],
+ "displayMode": "table",
+ "placement": "bottom",
+ "showLegend": true
+ },
+ "tooltip": {
+ "mode": "multi",
+ "sort": "asc"
+ }
+ },
+ "pluginVersion": "11.4.0-208313",
"targets": [
{
+ "alias": "Temp@ROUnit_$tag_beacon_t",
"datasource": {
"type": "influxdb",
- "uid": "P951FEA4DE68E13C5"
+ "uid": "b3860bbb-0b28-4a3c-b8d8-0723cb4f180f"
},
- "groupBy": [],
+ "groupBy": [
+ {
+ "params": [
+ "$interval2"
+ ],
+ "type": "time"
+ },
+ {
+ "params": [
+ "beacon_t::tag"
+ ],
+ "type": "tag"
+ }
+ ],
"measurement": "sustronics",
"orderByTime": "ASC",
"policy": "default",
- "query": "SELECT * FROM /^$Temp_coeff_A$/",
- "rawQuery": false,
"refId": "A",
- "resultFormat": "logs",
+ "resultFormat": "time_series",
"select": [
[
{
"params": [
- "pH"
+ "temp"
],
"type": "field"
},
@@ -859,187 +912,33 @@
}
]
],
- "tags": []
- }
- ],
- "title": "pH calibration",
- "type": "nline-plotlyjs-panel"
- },
- {
- "collapsed": true,
- "gridPos": {
- "h": 1,
- "w": 24,
- "x": 0,
- "y": 33
- },
- "id": 9,
- "panels": [
- {
- "datasource": {
- "type": "influxdb",
- "uid": "xNdqUIZHz"
- },
- "fieldConfig": {
- "defaults": {
- "color": {
- "mode": "thresholds"
- },
- "mappings": [],
- "thresholds": {
- "mode": "absolute",
- "steps": [
- {
- "color": "green"
- },
- {
- "color": "red",
- "value": 80
- }
- ]
- }
- },
- "overrides": []
- },
- "gridPos": {
- "h": 3,
- "w": 2,
- "x": 1,
- "y": 67
- },
- "id": 6,
- "options": {
- "colorMode": "value",
- "graphMode": "area",
- "justifyMode": "auto",
- "orientation": "auto",
- "percentChangeColorMode": "standard",
- "reduceOptions": {
- "calcs": [
- "lastNotNull"
- ],
- "fields": "",
- "values": false
- },
- "showPercentChange": false,
- "text": {},
- "textMode": "auto",
- "wideLayout": true
- },
- "pluginVersion": "11.4.0-204552",
- "targets": [
+ "tags": [
{
- "datasource": {
- "type": "influxdb",
- "uid": "xNdqUIZHz"
- },
- "groupBy": [],
- "measurement": "sustronics_doa_c",
- "orderByTime": "ASC",
- "policy": "default",
- "refId": "A",
- "resultFormat": "table",
- "select": [
- [
- {
- "params": [
- "angle"
- ],
- "type": "field"
- },
- {
- "params": [],
- "type": "last"
- }
- ]
- ],
- "tags": []
+ "key": "beacon_t::tag",
+ "operator": "=~",
+ "value": "/^$ROUnit$/"
}
- ],
- "title": "DoA",
- "type": "stat"
- }
- ],
- "title": "Direction of arrival (work in progress)",
- "type": "row"
- },
- {
- "collapsed": true,
- "gridPos": {
- "h": 1,
- "w": 24,
- "x": 0,
- "y": 34
- },
- "id": 16,
- "panels": [
- {
- "fieldConfig": {
- "defaults": {},
- "overrides": []
- },
- "gridPos": {
- "h": 14,
- "w": 12,
- "x": 0,
- "y": 44
- },
- "id": 10,
- "options": {
- "code": {
- "language": "plaintext",
- "showLineNumbers": false,
- "showMiniMap": false
- },
- "content": "<img src='data:image/png;base64, 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' />",
- "mode": "html"
- },
- "pluginVersion": "11.4.0-208376",
- "title": "RH calib",
- "type": "text"
+ ]
}
],
- "title": "Eg chart",
- "type": "row"
+ "title": "Temperature@ADC",
+ "type": "timeseries"
}
],
- "preload": false,
- "refresh": "1s",
- "schemaVersion": 40,
+ "refresh": "1m",
+ "schemaVersion": 38,
+ "style": "dark",
"tags": [],
"templating": {
"list": [
{
"current": {
+ "selected": true,
"text": "0.003695;32.5788",
"value": "0.003695;32.5788"
},
"description": "Temperature polynominal coeffinients as numbers separated with semicolons in descending powers order",
+ "hide": 0,
"label": "Temperature coeffinients",
"name": "Temp_coefs",
"options": [
@@ -1050,14 +949,17 @@
}
],
"query": "0.003695;32.5788",
+ "skipUrlSync": false,
"type": "textbox"
},
{
"current": {
+ "selected": true,
"text": "-0.003286;11.8311",
"value": "-0.003286;11.8311"
},
"description": "pH polynominal coeffinients as numbers separated with semicolons in descending powers order",
+ "hide": 0,
"label": "pH coefficients",
"name": "pH_coefs",
"options": [
@@ -1068,48 +970,61 @@
}
],
"query": "-0.003286;11.8311",
+ "skipUrlSync": false,
"type": "textbox"
},
{
"current": {
+ "selected": true,
"text": [
- "c00000000104",
- "c00000000103"
+ "c00000000104"
],
"value": [
- "c00000000104",
- "c00000000103"
+ "c00000000104"
]
},
+ "datasource": {
+ "type": "influxdb",
+ "uid": "b3860bbb-0b28-4a3c-b8d8-0723cb4f180f"
+ },
"definition": "select DISTINCT(\"beacon_t\") from (select \"rssi\",\"beacon_t\" from sustronics WHERE $timeFilter)",
+ "hide": 0,
"includeAll": true,
"multi": true,
"name": "ROUnit",
"options": [],
- "query": {
- "query": "select DISTINCT(\"beacon_t\") from (select \"rssi\",\"beacon_t\" from sustronics WHERE $timeFilter)",
- "refId": "InfluxVariableQueryEditor-VariableQuery"
- },
+ "query": "select DISTINCT(\"beacon_t\") from (select \"rssi\",\"beacon_t\" from sustronics WHERE $timeFilter)",
"refresh": 2,
"regex": "",
+ "skipUrlSync": false,
+ "sort": 0,
"type": "query"
},
{
"current": {
- "text": "All",
- "value": "$__all"
+ "selected": true,
+ "text": [
+ "c10000000e17"
+ ],
+ "value": [
+ "c10000000e17"
+ ]
+ },
+ "datasource": {
+ "type": "influxdb",
+ "uid": "b3860bbb-0b28-4a3c-b8d8-0723cb4f180f"
},
"definition": "select DISTINCT(\"espar_t\") from (select \"rssi\",\"espar_t\" from sustronics WHERE $timeFilter)",
+ "hide": 0,
"includeAll": true,
"multi": true,
"name": "Gateway",
"options": [],
- "query": {
- "query": "select DISTINCT(\"espar_t\") from (select \"rssi\",\"espar_t\" from sustronics WHERE $timeFilter)",
- "refId": "InfluxVariableQueryEditor-VariableQuery"
- },
+ "query": "select DISTINCT(\"espar_t\") from (select \"rssi\",\"espar_t\" from sustronics WHERE $timeFilter)",
"refresh": 1,
"regex": "",
+ "skipUrlSync": false,
+ "sort": 0,
"type": "query"
},
{
@@ -1117,14 +1032,16 @@
"auto_count": 30,
"auto_min": "10s",
"current": {
- "text": "1s",
- "value": "1s"
+ "selected": false,
+ "text": "1m",
+ "value": "1m"
},
+ "hide": 0,
"label": "Measurment interval",
- "name": "interval",
+ "name": "interval2",
"options": [
{
- "selected": true,
+ "selected": false,
"text": "1s",
"value": "1s"
},
@@ -1175,13 +1092,15 @@
}
],
"query": "1s,5s,10s,30s,1m,5m,10m,30m,1h,6h",
+ "queryValue": "",
"refresh": 2,
+ "skipUrlSync": false,
"type": "interval"
}
]
},
"time": {
- "from": "now-30m",
+ "from": "now-2d",
"to": "now"
},
"timepicker": {
@@ -1202,6 +1121,6 @@
"timezone": "",
"title": "SustronicsPilot3.3",
"uid": "1Mar-DTiz",
- "version": 21,
+ "version": 28,
"weekStart": ""
-}
+}
\ No newline at end of file