From 1f31d29c97364e1833ea10cc153e2f4c4f6c5627 Mon Sep 17 00:00:00 2001 From: Felipe Adachi Date: Mon, 18 Apr 2022 18:09:03 -0300 Subject: [PATCH 1/2] changing NotebookProfileViewer to NotebookProfileVisualizer: definitions, notebooks, tests. removing/merging/renaming existent notebooks: segmentation,constraints,profilevisualizer --- examples/Auto Segmentation.ipynb | 266 - .../Constraints_Distributional_Measures.ipynb | 866 +- ... => Creating_Customized_Constraints.ipynb} | 33 +- ...pynb => Notebook_Profile_Visualizer.ipynb} | 3864 ++++----- ...b => Profile_Visualizer_In_Notebook.ipynb} | 7100 ++++++++--------- examples/segmentation/segments.ipynb | 669 +- src/whylogs/viz/__init__.py | 4 +- src/whylogs/viz/jupyter_notebook_viz.py | 2 +- test_notebooks/notebook_tests.py | 1 + tests/unit/viz/test_jupyter_notebook_viz.py | 34 +- 10 files changed, 6410 insertions(+), 6429 deletions(-) delete mode 100755 examples/Auto Segmentation.ipynb rename examples/{Constraints.ipynb => Creating_Customized_Constraints.ipynb} (95%) rename examples/{Notebook_Profile_Viewer.ipynb => Notebook_Profile_Visualizer.ipynb} (82%) rename examples/{Profile_Viewer_In_Notebook.ipynb => Profile_Visualizer_In_Notebook.ipynb} (58%) diff --git a/examples/Auto Segmentation.ipynb b/examples/Auto Segmentation.ipynb deleted file mode 100755 index 00fd85d54c..0000000000 --- a/examples/Auto Segmentation.ipynb +++ /dev/null @@ -1,266 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Auto Segmentation Example" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First let us create a sample dataset, like an output you might get from a classification model. " - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "df = pd.DataFrame({\"target_var\": [\"cat\", \"dog\", \"pig\", \"dog\", \"cat\", \"dog\"],\n", - " \"confidence\": [1.2, 3.4, 4.5, 0.1, 4.5, -0.19],\n", - " \"prediction\": [\"good boy\", \"good boy\", \"good boy\", \"good boy\", \"bad\", \"bad\"]})" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this case one interesting segmentation would be to see how the `confidence` could be segmented by the other features." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Whylogs Auto Segmentation" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "from whylogs import get_or_create_session\n", - "_session=None\n", - "whylogs_session = get_or_create_session()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Before we start logging the data, we first run autosegmentation to infer what are the important segments in the data" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['prediction']" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "whylogs_session.estimate_segments(df, name= \"demo1\", target_field=\"target_var\", max_segments=3)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This creates a segmentation file `segments.json` in default path `output/{name}` where the `name` we gave above is `demo1`" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "zsh:1: no matches found: output/demo1/metadata/*\r\n" - ] - } - ], - "source": [ - "!ls output/demo1/metadata/*" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Containing the features to be segmented." - ] - }, - { - "cell_type": "code", - "execution_count": 83, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1;39m[\r\n", - " \u001b[0;32m\"prediction\"\u001b[0m\u001b[1;39m\r\n", - "\u001b[1;39m]\u001b[0m\r\n" - ] - } - ], - "source": [ - "!jq . output/demo1/metadata/segments.json" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Log Segmented data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can then proced loging the data, whylogs will segment each of the features above automatically." - ] - }, - { - "cell_type": "code", - "execution_count": 84, - "metadata": {}, - "outputs": [], - "source": [ - "whylogs_session.log_dataframe(df, dataset_name=\"demo1\")" - ] - }, - { - "cell_type": "code", - "execution_count": 85, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "cat: output/demo1/: Is a directory\r\n" - ] - } - ], - "source": [ - "!cat output/demo1/" - ] - }, - { - "cell_type": "code", - "execution_count": 86, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "output/demo1/27cbaff0-c7c7-4805-ae89-9ed4f548eda7/flat_table/dataset_profile.csv\r\n", - "output/demo1/27cbaff0-c7c7-4805-ae89-9ed4f548eda7/freq_numbers/dataset_profile.json\r\n", - "output/demo1/27cbaff0-c7c7-4805-ae89-9ed4f548eda7/frequent_strings/dataset_profile.json\r\n", - "output/demo1/27cbaff0-c7c7-4805-ae89-9ed4f548eda7/histogram/dataset_profile.json\r\n", - "output/demo1/27cbaff0-c7c7-4805-ae89-9ed4f548eda7/json/dataset_profile.json\r\n", - "output/demo1/27cbaff0-c7c7-4805-ae89-9ed4f548eda7/protobuf/dataset_profile.bin\r\n", - "output/demo1/metadata/segments.json\r\n", - "\r\n", - "output/demo1/27cbaff0-c7c7-4805-ae89-9ed4f548eda7:\r\n", - "\u001b[1m\u001b[36mflat_table\u001b[m\u001b[m \u001b[1m\u001b[36mfrequent_strings\u001b[m\u001b[m \u001b[1m\u001b[36mjson\u001b[m\u001b[m\r\n", - "\u001b[1m\u001b[36mfreq_numbers\u001b[m\u001b[m \u001b[1m\u001b[36mhistogram\u001b[m\u001b[m \u001b[1m\u001b[36mprotobuf\u001b[m\u001b[m\r\n", - "\r\n", - "output/demo1/27cbaff0-c7c7-4805-ae89-9ed4f548eda7/flat_table:\r\n", - "dataset_profile.csv\r\n", - "\r\n", - "output/demo1/27cbaff0-c7c7-4805-ae89-9ed4f548eda7/freq_numbers:\r\n", - "dataset_profile.json\r\n", - "\r\n", - "output/demo1/27cbaff0-c7c7-4805-ae89-9ed4f548eda7/frequent_strings:\r\n", - "dataset_profile.json\r\n", - "\r\n", - "output/demo1/27cbaff0-c7c7-4805-ae89-9ed4f548eda7/histogram:\r\n", - "dataset_profile.json\r\n", - "\r\n", - "output/demo1/27cbaff0-c7c7-4805-ae89-9ed4f548eda7/json:\r\n", - "dataset_profile.json\r\n", - "\r\n", - "output/demo1/27cbaff0-c7c7-4805-ae89-9ed4f548eda7/protobuf:\r\n", - "dataset_profile.bin\r\n", - "\r\n", - "output/demo1/metadata:\r\n", - "segments.json\r\n" - ] - } - ], - "source": [ - "!ls output/demo1/**/*" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "metadata": {}, - "outputs": [], - "source": [ - "whylogs_session.close()" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [], - "source": [ - "!rm -rf output/demo1/**/*" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "whylogs-dev", - "language": "python", - "name": "whylogs-dev" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.5" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/Constraints_Distributional_Measures.ipynb b/examples/Constraints_Distributional_Measures.ipynb index d7efbd7828..0e4896b65a 100644 --- a/examples/Constraints_Distributional_Measures.ipynb +++ b/examples/Constraints_Distributional_Measures.ipynb @@ -33,7 +33,26 @@ "execution_count": 1, "id": "507332c6", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: seaborn in /home/felipeadachi/miniconda3/envs/viewer-rename/lib/python3.8/site-packages (0.11.2)\n", + "Requirement already satisfied: matplotlib>=2.2 in /home/felipeadachi/miniconda3/envs/viewer-rename/lib/python3.8/site-packages (from seaborn) (3.3.4)\n", + "Requirement already satisfied: numpy>=1.15 in /home/felipeadachi/miniconda3/envs/viewer-rename/lib/python3.8/site-packages (from seaborn) (1.19.5)\n", + "Requirement already satisfied: scipy>=1.0 in /home/felipeadachi/miniconda3/envs/viewer-rename/lib/python3.8/site-packages (from seaborn) (1.5.4)\n", + "Requirement already satisfied: pandas>=0.23 in /home/felipeadachi/miniconda3/envs/viewer-rename/lib/python3.8/site-packages (from seaborn) (1.1.5)\n", + "Requirement already satisfied: pillow>=6.2.0 in /home/felipeadachi/miniconda3/envs/viewer-rename/lib/python3.8/site-packages (from matplotlib>=2.2->seaborn) (8.4.0)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /home/felipeadachi/miniconda3/envs/viewer-rename/lib/python3.8/site-packages (from matplotlib>=2.2->seaborn) (1.3.1)\n", + "Requirement already satisfied: cycler>=0.10 in /home/felipeadachi/miniconda3/envs/viewer-rename/lib/python3.8/site-packages (from matplotlib>=2.2->seaborn) (0.11.0)\n", + "Requirement already satisfied: python-dateutil>=2.1 in /home/felipeadachi/miniconda3/envs/viewer-rename/lib/python3.8/site-packages (from matplotlib>=2.2->seaborn) (2.8.2)\n", + "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in /home/felipeadachi/miniconda3/envs/viewer-rename/lib/python3.8/site-packages (from matplotlib>=2.2->seaborn) (3.0.7)\n", + "Requirement already satisfied: pytz>=2017.2 in /home/felipeadachi/miniconda3/envs/viewer-rename/lib/python3.8/site-packages (from pandas>=0.23->seaborn) (2022.1)\n", + "Requirement already satisfied: six>=1.5 in /home/felipeadachi/miniconda3/envs/viewer-rename/lib/python3.8/site-packages (from python-dateutil>=2.1->matplotlib>=2.2->seaborn) (1.16.0)\n" + ] + } + ], "source": [ "!pip install seaborn\n", "import pandas as pd\n", @@ -380,7 +399,7 @@ "id": "fce5bcc3", "metadata": {}, "source": [ - "For visual inspection of the data we are going to use whylogs' NotebookProfileViewer. The assumption is that visually exploring the production data each time a new batch arrives is too expensive, but for the purpose of this notebook and for better understanding of the constraints, we are going to include visual representation." + "For visual inspection of the data we are going to use whylogs' NotebookProfileVisualizer. The assumption is that visually exploring the production data each time a new batch arrives is too expensive, but for the purpose of this notebook and for better understanding of the constraints, we are going to include visual representation." ] }, { @@ -388,7 +407,7 @@ "id": "da4101ea", "metadata": {}, "source": [ - "### Define the NotebookProfileViewer and set the target and reference profiles" + "### Define the NotebookProfileVisualizer and set the target and reference profiles" ] }, { @@ -398,7 +417,7 @@ "metadata": {}, "outputs": [], "source": [ - "from whylogs.viz import NotebookProfileViewer" + "from whylogs.viz import NotebookProfileVisualizer" ] }, { @@ -408,7 +427,7 @@ "metadata": {}, "outputs": [], "source": [ - "visualization = NotebookProfileViewer() # create the profile viewer\n", + "visualization = NotebookProfileVisualizer() # create the profile visualizer\n", "# set the training data profile as the refrence profile, and the production data profile as the target profile\n", "# we want to use the training data features' distributions as the reference for incoming data\n", "visualization.set_profiles(target_profile=test_profile, reference_profile=profile) " @@ -588,15 +607,15 @@ " color: #4F595B;\n", " }\n", "\n", - " .bar.positive {\n", - " fill: #369BACB2;\n", - " }\n", - "\n", - " .bar.negative {\n", - " fill: #2683C9E5;\n", + " .error-message {\n", + " display: flex;\n", + " justify-content: center;\n", + " align-items: center;\n", + " color: rgb(255, 114, 71);\n", + " font-size: 30px;\n", + " font-weight: 900;\n", " }\n", "\n", - "\n", " @media screen and (min-width: 500px) {\n", " .desktop-content {\n", " display: block;\n", @@ -617,11 +636,11 @@ " <p>{{@key}}</p>\n", " <div class="display-flex">\n", " <div class="colors-for-distingushing-charts">\n", - " <div class="circle-color" style="background: #369BAC"></div>\n", - " <text alignment-baseline="middle" style="font-size: 15px;">Current</text>\n", + " <div class="circle-color" style="background: #44C0E7;"></div>\n", + " <text alignment-baseline="middle" style="font-size: 15px;">Target</text>\n", " </div>\n", " <div class="colors-for-distingushing-charts">\n", - " <div class="circle-color" style="background: #2683C9"></div>\n", + " <div class="circle-color" style="background: #F5843C"></div>\n", " <text alignment-baseline="middle" style="font-size: 15px;">Reference</text>\n", " </div>\n", " </div></div>\n", @@ -649,9 +668,15 @@ "\n", " <script>\n", " function registerHandlebarHelperFunctions() {\n", + " \n", " const findAndDeleteUndefined = (axisData) => {\n", " const undefinedAxisIndex = axisData.findIndex((axis) => axis === undefined)\n", - " return [...axisData.slice(0, undefinedAxisIndex), ...axisData.slice(undefinedAxisIndex + 1)]\n", + " if (undefinedAxisIndex == -1) {\n", + " return axisData;\n", + " }\n", + "\n", + " const result = [...axisData.slice(0, undefinedAxisIndex), ...axisData.slice(undefinedAxisIndex + 1)]\n", + " return result\n", " }\n", "\n", " const filterAndSortChartData = (overlappedHistogramData, histogramData) => {\n", @@ -690,10 +715,10 @@ " .attr("preserveAspectRatio", "xMinYMin meet")\n", " .attr("viewBox", `0 0 ${$(window).width()} ${$(window).height()-30}`)\n", " .classed("svg-content-responsive", true)\n", - " this.maxYValue = d3.max(data, (d) => Math.abs(d.axisY));\n", + " this.maxYValue = d3.max([...data, ...referenceData], (d) => Math.abs(d.axisY));\n", " this.xScale = d3\n", " .scaleBand()\n", - " .domain(filterAndSortChartData(data, referenceData).map((sortedCounts) => sortedCounts.axisX))\n", + " .domain(filterAndSortChartData(data, referenceData).map((sortedCounts) => sortedCounts?.axisX))\n", " .range([this.MARGIN.LEFT, this.MARGIN.LEFT + this.CHART_WIDTH]);\n", " this.yScale = d3\n", " .scaleLinear()\n", @@ -704,15 +729,34 @@ "\n", " function chartData(column) {\n", " const data = [];\n", + " if (column.stringSummary?.frequent) {\n", " Object.entries(column.stringSummary.frequent.items).forEach(([key, {value, estimate}], index) => {\n", " data.push({\n", " axisY: estimate,\n", " axisX: value,\n", " });\n", " });\n", + " } else if (column.stringSummary?.charPosTracker){\n", + " Object.entries(column.stringSummary.charPosTracker.charPosMap).forEach(([key, {count}], index) => {\n", + " data.push({\n", + " axisY: count,\n", + " axisX: key,\n", + " });\n", + " });\n", + " } else {\n", + " $(document).ready(() => \n", + " $(".desktop-content").html(`\n", + " <p style="height: ${$(window).height()}px" class="error-message"> \n", + " Something went wrong. Please try again.\n", + " </p>\n", + " `)\n", + " )\n", + " }\n", + "\n", " return data\n", " }\n", "\n", + "\n", " function CheckNumberSummary(column) {\n", " if (column.numberSummary) {\n", " return true\n", @@ -725,14 +769,17 @@ " let yFormat,\n", " xFormat;\n", " const data = filterAndSortChartData(chartData(histogramData), chartData(overlappedHistogramData)).map((axis, index) => {\n", - " const findIndex = chartData(histogramData).findIndex((value) => value.axisX === axis.axisX)\n", - " return {\n", - " group: axis.axisX,\n", - " profile: axis.axisY,\n", - " reference_profile: chartData(histogramData)[findIndex].axisY\n", + " if (axis) {\n", + " const findIndex = chartData(histogramData).findIndex((value) => value.axisX === axis.axisX)\n", + " return {\n", + " group: axis.axisX,\n", + " profile: axis.axisY,\n", + " reference_profile: chartData(histogramData)[findIndex].axisY\n", + " }\n", " }\n", + " return 0;\n", " })\n", - "\n", + " \n", " const sizes = new GenerateChartParams($(window).height()-60, $(window).width(), chartData(histogramData), chartData(overlappedHistogramData))\n", " let {\n", " MARGIN,\n", @@ -744,17 +791,19 @@ " xScale,\n", " yScale\n", " } = sizes\n", - "\n", + " \n", + " const rectColors = ["#44C0E7", "#F5843C"]\n", " const subgroups = ['reference_profile', 'profile']\n", - "\n", + " \n", " xScale.padding([0.3])\n", - "\n", + " \n", " const xAxis = d3.axisBottom(xScale).ticks(SVG_WIDTH / 80, xFormat).tickSizeOuter(0);\n", " const yAxis = d3.axisLeft(yScale).ticks(SVG_HEIGHT / 40, yFormat);\n", " yFormat = yScale.tickFormat(100, yFormat);\n", - "\n", + " \n", " svgEl.append("g")\n", " .attr("transform", `translate(${MARGIN.LEFT}, 0)`)\n", + " .attr("id", "g1")\n", " .call(yAxis)\n", " .call(g => g.select(".domain").remove())\n", " .call(g => g.selectAll(".tick line")\n", @@ -768,13 +817,13 @@ "\n", " svgEl.append("text")\n", " .attr("transform",\n", - " "translate(" + (CHART_WIDTH/2) + " ," +\n", + " "translate(" + (CHART_WIDTH/2) + " ," + \n", " (CHART_HEIGHT + MARGIN.TOP + 40) + ")")\n", " .style("text-anchor", "middle")\n", " .text("Values")\n", " .style("font-size", "15")\n", " .style("opacity", "0.6")\n", - "\n", + " \n", " svgEl.append("text")\n", " .attr("transform", "rotate(-90)")\n", " .attr("y", 0)\n", @@ -782,11 +831,12 @@ " .attr("dy", "1em")\n", " .style("text-anchor", "middle")\n", " .text("Counts")\n", - " .style("font-size", "15")\n", + " .style("font-size", "15") \n", " .style("opacity", "0.6")\n", - "\n", + " \n", " svgEl.append("g")\n", " .attr("transform", `translate(0,${SVG_HEIGHT - MARGIN.BOTTOM})`)\n", + " .attr("id", "g2")\n", " .call(xAxis)\n", " .call(g => g.select(".domain").remove())\n", " .call(g => g.selectAll(".tick line").remove())\n", @@ -795,46 +845,57 @@ " .attr("y", 27)\n", " .attr("fill", "currentColor")\n", " .attr("text-anchor", "end"));\n", - " // Another scale for subgroup position?\n", + "\n", " const xSubgroup = d3.scaleBand()\n", " .domain(subgroups)\n", " .range([0, xScale.bandwidth()])\n", "\n", - " // color palette = one color per subgroup\n", " const color = d3.scaleOrdinal()\n", " .domain(subgroups)\n", - " .range(['#2683C9', '#369BAC'])\n", - "\n", + " .range(rectColors)\n", + " \n", " svgEl.append("g")\n", + " .attr("id", "g3")\n", " .selectAll("g")\n", - " // Enter in data = loop group per group\n", " .data(data)\n", " .enter()\n", " .append("g")\n", - " .attr("transform", function(d) { return "translate(" + xScale(d.group) + ",0)"; })\n", + " .attr("transform", function(d) { return "translate(" + xScale(d?.group) + ",0)"; })\n", + " .attr("id", "g4")\n", " .selectAll("rect")\n", - " .data(function(d) { return subgroups.map(function(key) { return {key: key, value: d[key]}; }); })\n", + " .data(function(d) { return subgroups.map(function(key) { return {key: key, value: d && d[key]}; }); })\n", " .enter().append("rect")\n", " .attr("x", function(d) { return xSubgroup(d.key); })\n", " .attr("y", function(d) { return yScale(d.value); })\n", " .attr("width", xSubgroup.bandwidth())\n", " .attr("height", function(d) { return (CHART_HEIGHT - yScale(d.value)); })\n", " .attr("fill", function(d) { return color(d.key); })\n", - " .style("opacity", "0.6");\n", - "\n", - "\n", + " .style("opacity", "0.8");\n", + " \n", + " \n", " return svgEl._groups[0][0].outerHTML;\n", - " }\n", + " } \n", "\n", - " const profileFromCSVfile = {"grade": {"counters": {"count": "800"}, "schema": {"inferredType": {"type": "STRING", "ratio": 1.0}, "typeCounts": {"STRING": "800"}}, "stringSummary": {"uniqueCount": {"estimate": 7.0, "upper": 7.0, "lower": 7.0}, "frequent": {"items": [{"value": "C", "estimate": 291.0}, {"value": "D", "estimate": 221.0}, {"value": "B", "estimate": 177.0}, {"value": "A", "estimate": 56.0}, {"value": "E", "estimate": 30.0}, {"value": "F", "estimate": 23.0}, {"value": "G", "estimate": 2.0}]}, "length": {"count": "800", "min": 1.0, "max": 1.0, "mean": 1.0, "histogram": {"start": 1.0, "end": 1.0000001, "counts": ["800"], "max": 1.0, "min": 1.0, "bins": [1.0, 1.0000001], "n": "800", "width": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, "isDiscrete": true, "stddev": 0.0}, "tokenLength": {"count": "800", "min": 1.0, "max": 1.0, "mean": 1.0, "histogram": {"start": 1.0, "end": 1.0000001, "counts": ["800"], "max": 1.0, "min": 1.0, "bins": [1.0, 1.0000001], "n": "800", "width": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, "isDiscrete": true, "stddev": 0.0}, "charPosTracker": {"characterList": "!#$%&()*+,-./0123456789?@[]^_abcdefghijklmnopqrstuvwyz{}", "charPosMap": {"d": {"count": "221", "histogram": {"counts": ["221"], "bins": [0.0, 0.0], "n": "221", "start": 0.0, "end": 0.0, "width": 0.0, "max": 0.0, "min": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}, "isDiscrete": true, "min": 0.0, "max": 0.0, "mean": 0.0, "stddev": 0.0}, "e": {"count": "30", "histogram": {"counts": ["30"], "bins": [0.0, 0.0], "n": "30", "start": 0.0, "end": 0.0, "width": 0.0, "max": 0.0, "min": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}, "isDiscrete": true, "min": 0.0, "max": 0.0, "mean": 0.0, "stddev": 0.0}, "b": {"count": "177", "histogram": {"counts": ["177"], "bins": [0.0, 0.0], "n": "177", "start": 0.0, "end": 0.0, "width": 0.0, "max": 0.0, "min": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}, "isDiscrete": true, "min": 0.0, "max": 0.0, "mean": 0.0, "stddev": 0.0}, "f": {"count": "23", "histogram": {"counts": ["23"], "bins": [0.0, 0.0], "n": "23", "start": 0.0, "end": 0.0, "width": 0.0, "max": 0.0, "min": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}, "isDiscrete": true, "min": 0.0, "max": 0.0, "mean": 0.0, "stddev": 0.0}, "a": {"count": "56", "histogram": {"counts": ["56"], "bins": [0.0, 0.0], "n": "56", "start": 0.0, "end": 0.0, "width": 0.0, "max": 0.0, "min": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}, "isDiscrete": true, "min": 0.0, "max": 0.0, "mean": 0.0, "stddev": 0.0}, "c": {"count": "291", "histogram": {"counts": ["291"], "bins": [0.0, 0.0], "n": "291", "start": 0.0, "end": 0.0, "width": 0.0, "max": 0.0, "min": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}, "isDiscrete": true, "min": 0.0, "max": 0.0, "mean": 0.0, "stddev": 0.0}, "g": {"count": "2", "histogram": {"counts": ["2"], "bins": [0.0, 0.0], "n": "2", "start": 0.0, "end": 0.0, "width": 0.0, "max": 0.0, "min": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}, "min": 0.0, "max": 0.0, "mean": 0.0, "stddev": 0.0, "isDiscrete": false}}}}, "frequentItems": {"items": [{"estimate": "291", "jsonValue": "C"}, {"estimate": "221", "jsonValue": "D"}, {"estimate": "177", "jsonValue": "B"}, {"estimate": "56", "jsonValue": "A"}, {"estimate": "30", "jsonValue": "E"}, {"estimate": "23", "jsonValue": "F"}, {"estimate": "2", "jsonValue": "G"}]}, "uniqueCount": {"estimate": 7.000000104308129, "upper": 7.000349609067664, "lower": 7.0}}}\n", + "\n", + " const profileFromCSVfile = {"grade": {"counters": {"count": "800"}, "schema": {"inferredType": {"type": "STRING", "ratio": 1.0}, "typeCounts": {"STRING": "800"}}, "stringSummary": {"uniqueCount": {"estimate": 7.0, "upper": 7.0, "lower": 7.0}, "frequent": {"items": [{"value": "C", "estimate": 291.0}, {"value": "D", "estimate": 221.0}, {"value": "B", "estimate": 177.0}, {"value": "A", "estimate": 56.0}, {"value": "E", "estimate": 30.0}, {"value": "F", "estimate": 23.0}, {"value": "G", "estimate": 2.0}]}, "length": {"count": "800", "min": 1.0, "max": 1.0, "mean": 1.0, "histogram": {"start": 1.0, "end": 1.0000001, "counts": ["800"], "max": 1.0, "min": 1.0, "bins": [1.0, 1.0000001], "n": "800", "width": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, "isDiscrete": true, "stddev": 0.0}, "tokenLength": {"count": "800", "min": 1.0, "max": 1.0, "mean": 1.0, "histogram": {"start": 1.0, "end": 1.0000001, "counts": ["800"], "max": 1.0, "min": 1.0, "bins": [1.0, 1.0000001], "n": "800", "width": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, "isDiscrete": true, "stddev": 0.0}, "charPosTracker": {"characterList": "!#$%&()*+,-./0123456789?@[]^_abcdefghijklmnopqrstuvwyz{}", "charPosMap": {"c": {"count": "291", "histogram": {"counts": ["291"], "bins": [0.0, 0.0], "n": "291", "start": 0.0, "end": 0.0, "width": 0.0, "max": 0.0, "min": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}, "isDiscrete": true, "min": 0.0, "max": 0.0, "mean": 0.0, "stddev": 0.0}, "g": {"count": "2", "histogram": {"counts": ["2"], "bins": [0.0, 0.0], "n": "2", "start": 0.0, "end": 0.0, "width": 0.0, "max": 0.0, "min": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}, "min": 0.0, "max": 0.0, "mean": 0.0, "stddev": 0.0, "isDiscrete": false}, "d": {"count": "221", "histogram": {"counts": ["221"], "bins": [0.0, 0.0], "n": "221", "start": 0.0, "end": 0.0, "width": 0.0, "max": 0.0, "min": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}, "isDiscrete": true, "min": 0.0, "max": 0.0, "mean": 0.0, "stddev": 0.0}, "a": {"count": "56", "histogram": {"counts": ["56"], "bins": [0.0, 0.0], "n": "56", "start": 0.0, "end": 0.0, "width": 0.0, "max": 0.0, "min": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}, "isDiscrete": true, "min": 0.0, "max": 0.0, "mean": 0.0, "stddev": 0.0}, "e": {"count": "30", "histogram": {"counts": ["30"], "bins": [0.0, 0.0], "n": "30", "start": 0.0, "end": 0.0, "width": 0.0, "max": 0.0, "min": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}, "isDiscrete": true, "min": 0.0, "max": 0.0, "mean": 0.0, "stddev": 0.0}, "f": {"count": "23", "histogram": {"counts": ["23"], "bins": [0.0, 0.0], "n": "23", "start": 0.0, "end": 0.0, "width": 0.0, "max": 0.0, "min": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}, "isDiscrete": true, "min": 0.0, "max": 0.0, "mean": 0.0, "stddev": 0.0}, "b": {"count": "177", "histogram": {"counts": ["177"], "bins": [0.0, 0.0], "n": "177", "start": 0.0, "end": 0.0, "width": 0.0, "max": 0.0, "min": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}, "isDiscrete": true, "min": 0.0, "max": 0.0, "mean": 0.0, "stddev": 0.0}}}}, "frequentItems": {"items": [{"estimate": "291", "jsonValue": "C"}, {"estimate": "221", "jsonValue": "D"}, {"estimate": "177", "jsonValue": "B"}, {"estimate": "56", "jsonValue": "A"}, {"estimate": "30", "jsonValue": "E"}, {"estimate": "23", "jsonValue": "F"}, {"estimate": "2", "jsonValue": "G"}]}, "uniqueCount": {"estimate": 7.000000104308129, "upper": 7.000349609067664, "lower": 7.0}}}\n", "\n", " Handlebars.registerHelper("getDoubleHistogramChart",(column,key) => {\n", " const columnKey = key.data.key\n", - " if (profileFromCSVfile) {\n", - " return generateBarChart(\n", - " column,\n", - " profileFromCSVfile[columnKey]\n", - " )\n", + " try {\n", + " if (profileFromCSVfile) {\n", + " return generateBarChart(\n", + " column,\n", + " profileFromCSVfile[columnKey]\n", + " )\n", + " }\n", + " } catch (err) {\n", + " $(document).ready(() => \n", + " $(".desktop-content").html(`\n", + " <p style="height: ${$(window).height()}px" class="error-message"> \n", + " Something went wrong. Please try again.\n", + " </p>\n", + " `)\n", + " )\n", " }\n", " });\n", " }\n", @@ -845,7 +906,7 @@ "\n", " function initHandlebarsTemplate() {\n", " // Replace this context with JSON from .py file\n", - " const context = {"grade": {"counters": {"count": "200", "nullCount": "2"}, "schema": {"inferredType": {"type": "STRING", "ratio": 1.0}, "typeCounts": {"STRING": "198", "NULL": "2"}}, "stringSummary": {"uniqueCount": {"estimate": 6.0, "upper": 6.0, "lower": 6.0}, "frequent": {"items": [{"value": "C", "estimate": 109.0}, {"value": "B", "estimate": 70.0}, {"value": "D", "estimate": 14.0}, {"value": "A", "estimate": 3.0}, {"value": "E", "estimate": 1.0}, {"value": "F", "estimate": 1.0}]}, "length": {"count": "198", "min": 1.0, "max": 1.0, "mean": 1.0, "histogram": {"start": 1.0, "end": 1.0000001, "counts": ["198"], "max": 1.0, "min": 1.0, "bins": [1.0, 1.0000001], "n": "198", "width": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, "isDiscrete": true, "stddev": 0.0}, "tokenLength": {"count": "198", "min": 1.0, "max": 1.0, "mean": 1.0, "histogram": {"start": 1.0, "end": 1.0000001, "counts": ["198"], "max": 1.0, "min": 1.0, "bins": [1.0, 1.0000001], "n": "198", "width": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, "isDiscrete": true, "stddev": 0.0}, "charPosTracker": {"characterList": "!#$%&()*+,-./0123456789?@[]^_abcdefghijklmnopqrstuvwyz{}", "charPosMap": {"c": {"count": "109", "histogram": {"counts": ["109"], "bins": [0.0, 0.0], "n": "109", "start": 0.0, "end": 0.0, "width": 0.0, "max": 0.0, "min": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}, "isDiscrete": true, "min": 0.0, "max": 0.0, "mean": 0.0, "stddev": 0.0}, "a": {"count": "3", "histogram": {"counts": ["3"], "bins": [0.0, 0.0], "n": "3", "start": 0.0, "end": 0.0, "width": 0.0, "max": 0.0, "min": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}, "min": 0.0, "max": 0.0, "mean": 0.0, "stddev": 0.0, "isDiscrete": false}, "e": {"count": "1", "histogram": {"counts": ["1"], "bins": [0.0, 0.0], "n": "1", "start": 0.0, "end": 0.0, "width": 0.0, "max": 0.0, "min": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}, "min": 0.0, "max": 0.0, "mean": 0.0, "stddev": 0.0, "isDiscrete": false}, "b": {"count": "70", "histogram": {"counts": ["70"], "bins": [0.0, 0.0], "n": "70", "start": 0.0, "end": 0.0, "width": 0.0, "max": 0.0, "min": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}, "isDiscrete": true, "min": 0.0, "max": 0.0, "mean": 0.0, "stddev": 0.0}, "d": {"count": "14", "histogram": {"counts": ["14"], "bins": [0.0, 0.0], "n": "14", "start": 0.0, "end": 0.0, "width": 0.0, "max": 0.0, "min": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}, "isDiscrete": true, "min": 0.0, "max": 0.0, "mean": 0.0, "stddev": 0.0}, "f": {"count": "1", "histogram": {"counts": ["1"], "bins": [0.0, 0.0], "n": "1", "start": 0.0, "end": 0.0, "width": 0.0, "max": 0.0, "min": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}, "min": 0.0, "max": 0.0, "mean": 0.0, "stddev": 0.0, "isDiscrete": false}}}}, "frequentItems": {"items": [{"estimate": "109", "jsonValue": "C"}, {"estimate": "70", "jsonValue": "B"}, {"estimate": "14", "jsonValue": "D"}, {"estimate": "3", "jsonValue": "A"}, {"estimate": "1", "jsonValue": "E"}, {"estimate": "1", "jsonValue": "F"}]}, "uniqueCount": {"estimate": 6.000000074505807, "upper": 6.000299650013235, "lower": 6.0}}};\n", + " const context = {"grade": {"counters": {"count": "200", "nullCount": "2"}, "schema": {"inferredType": {"type": "STRING", "ratio": 1.0}, "typeCounts": {"STRING": "198", "NULL": "2"}}, "stringSummary": {"uniqueCount": {"estimate": 6.0, "upper": 6.0, "lower": 6.0}, "frequent": {"items": [{"value": "C", "estimate": 109.0}, {"value": "B", "estimate": 70.0}, {"value": "D", "estimate": 14.0}, {"value": "A", "estimate": 3.0}, {"value": "E", "estimate": 1.0}, {"value": "F", "estimate": 1.0}]}, "length": {"count": "198", "min": 1.0, "max": 1.0, "mean": 1.0, "histogram": {"start": 1.0, "end": 1.0000001, "counts": ["198"], "max": 1.0, "min": 1.0, "bins": [1.0, 1.0000001], "n": "198", "width": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, "isDiscrete": true, "stddev": 0.0}, "tokenLength": {"count": "198", "min": 1.0, "max": 1.0, "mean": 1.0, "histogram": {"start": 1.0, "end": 1.0000001, "counts": ["198"], "max": 1.0, "min": 1.0, "bins": [1.0, 1.0000001], "n": "198", "width": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, "isDiscrete": true, "stddev": 0.0}, "charPosTracker": {"characterList": "!#$%&()*+,-./0123456789?@[]^_abcdefghijklmnopqrstuvwyz{}", "charPosMap": {"c": {"count": "109", "histogram": {"counts": ["109"], "bins": [0.0, 0.0], "n": "109", "start": 0.0, "end": 0.0, "width": 0.0, "max": 0.0, "min": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}, "isDiscrete": true, "min": 0.0, "max": 0.0, "mean": 0.0, "stddev": 0.0}, "b": {"count": "70", "histogram": {"counts": ["70"], "bins": [0.0, 0.0], "n": "70", "start": 0.0, "end": 0.0, "width": 0.0, "max": 0.0, "min": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}, "isDiscrete": true, "min": 0.0, "max": 0.0, "mean": 0.0, "stddev": 0.0}, "e": {"count": "1", "histogram": {"counts": ["1"], "bins": [0.0, 0.0], "n": "1", "start": 0.0, "end": 0.0, "width": 0.0, "max": 0.0, "min": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}, "min": 0.0, "max": 0.0, "mean": 0.0, "stddev": 0.0, "isDiscrete": false}, "a": {"count": "3", "histogram": {"counts": ["3"], "bins": [0.0, 0.0], "n": "3", "start": 0.0, "end": 0.0, "width": 0.0, "max": 0.0, "min": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}, "min": 0.0, "max": 0.0, "mean": 0.0, "stddev": 0.0, "isDiscrete": false}, "d": {"count": "14", "histogram": {"counts": ["14"], "bins": [0.0, 0.0], "n": "14", "start": 0.0, "end": 0.0, "width": 0.0, "max": 0.0, "min": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}, "isDiscrete": true, "min": 0.0, "max": 0.0, "mean": 0.0, "stddev": 0.0}, "f": {"count": "1", "histogram": {"counts": ["1"], "bins": [0.0, 0.0], "n": "1", "start": 0.0, "end": 0.0, "width": 0.0, "max": 0.0, "min": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}, "min": 0.0, "max": 0.0, "mean": 0.0, "stddev": 0.0, "isDiscrete": false}}}}, "frequentItems": {"items": [{"estimate": "109", "jsonValue": "C"}, {"estimate": "70", "jsonValue": "B"}, {"estimate": "14", "jsonValue": "D"}, {"estimate": "3", "jsonValue": "A"}, {"estimate": "1", "jsonValue": "E"}, {"estimate": "1", "jsonValue": "F"}]}, "uniqueCount": {"estimate": 6.000000074505807, "upper": 6.000299650013235, "lower": 6.0}}};\n", " // Config handlebars and pass data to HBS template\n", " const source = document.getElementById("entry-template").innerHTML;\n", " const template = Handlebars.compile(source);\n", @@ -1105,15 +1166,15 @@ " color: #4F595B;\n", " }\n", "\n", - " .bar.positive {\n", - " fill: #369BACB2;\n", - " }\n", - "\n", - " .bar.negative {\n", - " fill: #2683C9E5;\n", + " .error-message {\n", + " display: flex;\n", + " justify-content: center;\n", + " align-items: center;\n", + " color: rgb(255, 114, 71);\n", + " font-size: 30px;\n", + " font-weight: 900;\n", " }\n", "\n", - "\n", " @media screen and (min-width: 500px) {\n", " .desktop-content {\n", " display: block;\n", @@ -1134,11 +1195,11 @@ " <p>{{@key}}</p>\n", " <div class="display-flex">\n", " <div class="colors-for-distingushing-charts">\n", - " <div class="circle-color" style="background: #369BAC"></div>\n", - " <text alignment-baseline="middle" style="font-size: 15px;">Current</text>\n", + " <div class="circle-color" style="background: #44C0E7;"></div>\n", + " <text alignment-baseline="middle" style="font-size: 15px;">Target</text>\n", " </div>\n", " <div class="colors-for-distingushing-charts">\n", - " <div class="circle-color" style="background: #2683C9"></div>\n", + " <div class="circle-color" style="background: #F5843C"></div>\n", " <text alignment-baseline="middle" style="font-size: 15px;">Reference</text>\n", " </div>\n", " </div></div>\n", @@ -1166,9 +1227,15 @@ "\n", " <script>\n", " function registerHandlebarHelperFunctions() {\n", + " \n", " const findAndDeleteUndefined = (axisData) => {\n", " const undefinedAxisIndex = axisData.findIndex((axis) => axis === undefined)\n", - " return [...axisData.slice(0, undefinedAxisIndex), ...axisData.slice(undefinedAxisIndex + 1)]\n", + " if (undefinedAxisIndex == -1) {\n", + " return axisData;\n", + " }\n", + "\n", + " const result = [...axisData.slice(0, undefinedAxisIndex), ...axisData.slice(undefinedAxisIndex + 1)]\n", + " return result\n", " }\n", "\n", " const filterAndSortChartData = (overlappedHistogramData, histogramData) => {\n", @@ -1207,10 +1274,10 @@ " .attr("preserveAspectRatio", "xMinYMin meet")\n", " .attr("viewBox", `0 0 ${$(window).width()} ${$(window).height()-30}`)\n", " .classed("svg-content-responsive", true)\n", - " this.maxYValue = d3.max(data, (d) => Math.abs(d.axisY));\n", + " this.maxYValue = d3.max([...data, ...referenceData], (d) => Math.abs(d.axisY));\n", " this.xScale = d3\n", " .scaleBand()\n", - " .domain(filterAndSortChartData(data, referenceData).map((sortedCounts) => sortedCounts.axisX))\n", + " .domain(filterAndSortChartData(data, referenceData).map((sortedCounts) => sortedCounts?.axisX))\n", " .range([this.MARGIN.LEFT, this.MARGIN.LEFT + this.CHART_WIDTH]);\n", " this.yScale = d3\n", " .scaleLinear()\n", @@ -1221,15 +1288,34 @@ "\n", " function chartData(column) {\n", " const data = [];\n", + " if (column.stringSummary?.frequent) {\n", " Object.entries(column.stringSummary.frequent.items).forEach(([key, {value, estimate}], index) => {\n", " data.push({\n", " axisY: estimate,\n", " axisX: value,\n", " });\n", " });\n", + " } else if (column.stringSummary?.charPosTracker){\n", + " Object.entries(column.stringSummary.charPosTracker.charPosMap).forEach(([key, {count}], index) => {\n", + " data.push({\n", + " axisY: count,\n", + " axisX: key,\n", + " });\n", + " });\n", + " } else {\n", + " $(document).ready(() => \n", + " $(".desktop-content").html(`\n", + " <p style="height: ${$(window).height()}px" class="error-message"> \n", + " Something went wrong. Please try again.\n", + " </p>\n", + " `)\n", + " )\n", + " }\n", + "\n", " return data\n", " }\n", "\n", + "\n", " function CheckNumberSummary(column) {\n", " if (column.numberSummary) {\n", " return true\n", @@ -1242,14 +1328,17 @@ " let yFormat,\n", " xFormat;\n", " const data = filterAndSortChartData(chartData(histogramData), chartData(overlappedHistogramData)).map((axis, index) => {\n", - " const findIndex = chartData(histogramData).findIndex((value) => value.axisX === axis.axisX)\n", - " return {\n", - " group: axis.axisX,\n", - " profile: axis.axisY,\n", - " reference_profile: chartData(histogramData)[findIndex].axisY\n", + " if (axis) {\n", + " const findIndex = chartData(histogramData).findIndex((value) => value.axisX === axis.axisX)\n", + " return {\n", + " group: axis.axisX,\n", + " profile: axis.axisY,\n", + " reference_profile: chartData(histogramData)[findIndex].axisY\n", + " }\n", " }\n", + " return 0;\n", " })\n", - "\n", + " \n", " const sizes = new GenerateChartParams($(window).height()-60, $(window).width(), chartData(histogramData), chartData(overlappedHistogramData))\n", " let {\n", " MARGIN,\n", @@ -1261,17 +1350,19 @@ " xScale,\n", " yScale\n", " } = sizes\n", - "\n", + " \n", + " const rectColors = ["#44C0E7", "#F5843C"]\n", " const subgroups = ['reference_profile', 'profile']\n", - "\n", + " \n", " xScale.padding([0.3])\n", - "\n", + " \n", " const xAxis = d3.axisBottom(xScale).ticks(SVG_WIDTH / 80, xFormat).tickSizeOuter(0);\n", " const yAxis = d3.axisLeft(yScale).ticks(SVG_HEIGHT / 40, yFormat);\n", " yFormat = yScale.tickFormat(100, yFormat);\n", - "\n", + " \n", " svgEl.append("g")\n", " .attr("transform", `translate(${MARGIN.LEFT}, 0)`)\n", + " .attr("id", "g1")\n", " .call(yAxis)\n", " .call(g => g.select(".domain").remove())\n", " .call(g => g.selectAll(".tick line")\n", @@ -1285,13 +1376,13 @@ "\n", " svgEl.append("text")\n", " .attr("transform",\n", - " "translate(" + (CHART_WIDTH/2) + " ," +\n", + " "translate(" + (CHART_WIDTH/2) + " ," + \n", " (CHART_HEIGHT + MARGIN.TOP + 40) + ")")\n", " .style("text-anchor", "middle")\n", " .text("Values")\n", " .style("font-size", "15")\n", " .style("opacity", "0.6")\n", - "\n", + " \n", " svgEl.append("text")\n", " .attr("transform", "rotate(-90)")\n", " .attr("y", 0)\n", @@ -1299,11 +1390,12 @@ " .attr("dy", "1em")\n", " .style("text-anchor", "middle")\n", " .text("Counts")\n", - " .style("font-size", "15")\n", + " .style("font-size", "15") \n", " .style("opacity", "0.6")\n", - "\n", + " \n", " svgEl.append("g")\n", " .attr("transform", `translate(0,${SVG_HEIGHT - MARGIN.BOTTOM})`)\n", + " .attr("id", "g2")\n", " .call(xAxis)\n", " .call(g => g.select(".domain").remove())\n", " .call(g => g.selectAll(".tick line").remove())\n", @@ -1312,46 +1404,57 @@ " .attr("y", 27)\n", " .attr("fill", "currentColor")\n", " .attr("text-anchor", "end"));\n", - " // Another scale for subgroup position?\n", + "\n", " const xSubgroup = d3.scaleBand()\n", " .domain(subgroups)\n", " .range([0, xScale.bandwidth()])\n", "\n", - " // color palette = one color per subgroup\n", " const color = d3.scaleOrdinal()\n", " .domain(subgroups)\n", - " .range(['#2683C9', '#369BAC'])\n", - "\n", + " .range(rectColors)\n", + " \n", " svgEl.append("g")\n", + " .attr("id", "g3")\n", " .selectAll("g")\n", - " // Enter in data = loop group per group\n", " .data(data)\n", " .enter()\n", " .append("g")\n", - " .attr("transform", function(d) { return "translate(" + xScale(d.group) + ",0)"; })\n", + " .attr("transform", function(d) { return "translate(" + xScale(d?.group) + ",0)"; })\n", + " .attr("id", "g4")\n", " .selectAll("rect")\n", - " .data(function(d) { return subgroups.map(function(key) { return {key: key, value: d[key]}; }); })\n", + " .data(function(d) { return subgroups.map(function(key) { return {key: key, value: d && d[key]}; }); })\n", " .enter().append("rect")\n", " .attr("x", function(d) { return xSubgroup(d.key); })\n", " .attr("y", function(d) { return yScale(d.value); })\n", " .attr("width", xSubgroup.bandwidth())\n", " .attr("height", function(d) { return (CHART_HEIGHT - yScale(d.value)); })\n", " .attr("fill", function(d) { return color(d.key); })\n", - " .style("opacity", "0.6");\n", - "\n", - "\n", + " .style("opacity", "0.8");\n", + " \n", + " \n", " return svgEl._groups[0][0].outerHTML;\n", - " }\n", + " } \n", "\n", - " const profileFromCSVfile = {"term": {"counters": {"count": "800"}, "schema": {"inferredType": {"type": "STRING", "ratio": 1.0}, "typeCounts": {"STRING": "800"}}, "stringSummary": {"uniqueCount": {"estimate": 2.0, "upper": 2.0, "lower": 2.0}, "frequent": {"items": [{"value": "36 months", "estimate": 578.0}, {"value": "60 months", "estimate": 222.0}]}, "length": {"count": "800", "min": 9.0, "max": 9.0, "mean": 9.0, "histogram": {"start": 9.0, "end": 9.0000009, "counts": ["800"], "max": 9.0, "min": 9.0, "bins": [9.0, 9.0000009], "n": "800", "width": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [9.0, 9.0, 9.0, 9.0, 9.0, 9.0, 9.0, 9.0, 9.0]}, "isDiscrete": true, "stddev": 0.0}, "tokenLength": {"count": "800", "min": 2.0, "max": 2.0, "mean": 2.0, "histogram": {"start": 2.0, "end": 2.0000002, "counts": ["800"], "max": 2.0, "min": 2.0, "bins": [2.0, 2.0000002], "n": "800", "width": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0]}, "isDiscrete": true, "stddev": 0.0}, "charPosTracker": {"characterList": "!#$%&()*+,-./0123456789?@[]^_abcdefghijklmnopqrstuvwyz{}", "charPosMap": {"NITL": {"count": "800", "min": 2.0, "max": 2.0, "mean": 2.0, "histogram": {"start": 2.0, "end": 2.0000002, "counts": ["800"], "max": 2.0, "min": 2.0, "bins": [2.0, 2.0000002], "n": "800", "width": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0]}, "isDiscrete": true, "stddev": 0.0}, "t": {"count": "800", "min": 6.0, "max": 6.0, "mean": 6.0, "histogram": {"start": 6.0, "end": 6.0000006, "counts": ["800"], "max": 6.0, "min": 6.0, "bins": [6.0, 6.0000006], "n": "800", "width": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0]}, "isDiscrete": true, "stddev": 0.0}, "m": {"count": "800", "min": 3.0, "max": 3.0, "mean": 3.0, "histogram": {"start": 3.0, "end": 3.0000003, "counts": ["800"], "max": 3.0, "min": 3.0, "bins": [3.0, 3.0000003], "n": "800", "width": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0]}, "isDiscrete": true, "stddev": 0.0}, "3": {"count": "578", "histogram": {"counts": ["578"], "bins": [0.0, 0.0], "n": "578", "start": 0.0, "end": 0.0, "width": 0.0, "max": 0.0, "min": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}, "isDiscrete": true, "min": 0.0, "max": 0.0, "mean": 0.0, "stddev": 0.0}, "n": {"count": "800", "min": 5.0, "max": 5.0, "mean": 5.0, "histogram": {"start": 5.0, "end": 5.0000005, "counts": ["800"], "max": 5.0, "min": 5.0, "bins": [5.0, 5.0000005], "n": "800", "width": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0]}, "isDiscrete": true, "stddev": 0.0}, "h": {"count": "800", "min": 7.0, "max": 7.0, "mean": 7.0, "histogram": {"start": 7.0, "end": 7.0000007, "counts": ["800"], "max": 7.0, "min": 7.0, "bins": [7.0, 7.0000007], "n": "800", "width": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0]}, "isDiscrete": true, "stddev": 0.0}, "6": {"count": "800", "max": 1.0, "mean": 0.7225, "stddev": 0.44804540043511626, "histogram": {"end": 1.0000001, "counts": ["223", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "577"], "max": 1.0, "bins": [0.0, 0.03333333666666667, 0.06666667333333334, 0.10000001000000001, 0.13333334666666669, 0.16666668333333334, 0.20000002000000003, 0.2333333566666667, 0.26666669333333337, 0.30000003000000003, 0.3333333666666667, 0.3666667033333334, 0.40000004000000006, 0.4333333766666667, 0.4666667133333334, 0.50000005, 0.5333333866666667, 0.5666667233333335, 0.6000000600000001, 0.6333333966666668, 0.6666667333333334, 0.7000000700000001, 0.7333334066666668, 0.7666667433333334, 0.8000000800000001, 0.8333334166666668, 0.8666667533333334, 0.9000000900000001, 0.9333334266666669, 0.9666667633333335, 1.0000001], "n": "800", "start": 0.0, "width": 0.0, "min": 0.0}, "uniqueCount": {"estimate": 2.0, "upper": 2.0, "lower": 2.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, "isDiscrete": true, "min": 0.0}, "0": {"count": "222", "min": 1.0, "max": 1.0, "mean": 1.0, "histogram": {"start": 1.0, "end": 1.0000001, "counts": ["222"], "max": 1.0, "min": 1.0, "bins": [1.0, 1.0000001], "n": "222", "width": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, "isDiscrete": true, "stddev": 0.0}, "s": {"count": "800", "min": 8.0, "max": 8.0, "mean": 8.0, "histogram": {"start": 8.0, "end": 8.0000008, "counts": ["800"], "max": 8.0, "min": 8.0, "bins": [8.0, 8.0000008], "n": "800", "width": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0]}, "isDiscrete": true, "stddev": 0.0}, "o": {"count": "800", "min": 4.0, "max": 4.0, "mean": 4.0, "histogram": {"start": 4.0, "end": 4.0000004, "counts": ["800"], "max": 4.0, "min": 4.0, "bins": [4.0, 4.0000004], "n": "800", "width": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0]}, "isDiscrete": true, "stddev": 0.0}}}}, "frequentItems": {"items": [{"estimate": "578", "jsonValue": "36 months"}, {"estimate": "222", "jsonValue": "60 months"}]}, "uniqueCount": {"estimate": 2.000000004967054, "upper": 2.000099863468538, "lower": 2.0}}}\n", + "\n", + " const profileFromCSVfile = {"term": {"counters": {"count": "800"}, "schema": {"inferredType": {"type": "STRING", "ratio": 1.0}, "typeCounts": {"STRING": "800"}}, "stringSummary": {"uniqueCount": {"estimate": 2.0, "upper": 2.0, "lower": 2.0}, "frequent": {"items": [{"value": "36 months", "estimate": 578.0}, {"value": "60 months", "estimate": 222.0}]}, "length": {"count": "800", "min": 9.0, "max": 9.0, "mean": 9.0, "histogram": {"start": 9.0, "end": 9.0000009, "counts": ["800"], "max": 9.0, "min": 9.0, "bins": [9.0, 9.0000009], "n": "800", "width": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [9.0, 9.0, 9.0, 9.0, 9.0, 9.0, 9.0, 9.0, 9.0]}, "isDiscrete": true, "stddev": 0.0}, "tokenLength": {"count": "800", "min": 2.0, "max": 2.0, "mean": 2.0, "histogram": {"start": 2.0, "end": 2.0000002, "counts": ["800"], "max": 2.0, "min": 2.0, "bins": [2.0, 2.0000002], "n": "800", "width": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0]}, "isDiscrete": true, "stddev": 0.0}, "charPosTracker": {"characterList": "!#$%&()*+,-./0123456789?@[]^_abcdefghijklmnopqrstuvwyz{}", "charPosMap": {"NITL": {"count": "800", "min": 2.0, "max": 2.0, "mean": 2.0, "histogram": {"start": 2.0, "end": 2.0000002, "counts": ["800"], "max": 2.0, "min": 2.0, "bins": [2.0, 2.0000002], "n": "800", "width": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0]}, "isDiscrete": true, "stddev": 0.0}, "3": {"count": "578", "histogram": {"counts": ["578"], "bins": [0.0, 0.0], "n": "578", "start": 0.0, "end": 0.0, "width": 0.0, "max": 0.0, "min": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}, "isDiscrete": true, "min": 0.0, "max": 0.0, "mean": 0.0, "stddev": 0.0}, "t": {"count": "800", "min": 6.0, "max": 6.0, "mean": 6.0, "histogram": {"start": 6.0, "end": 6.0000006, "counts": ["800"], "max": 6.0, "min": 6.0, "bins": [6.0, 6.0000006], "n": "800", "width": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0]}, "isDiscrete": true, "stddev": 0.0}, "o": {"count": "800", "min": 4.0, "max": 4.0, "mean": 4.0, "histogram": {"start": 4.0, "end": 4.0000004, "counts": ["800"], "max": 4.0, "min": 4.0, "bins": [4.0, 4.0000004], "n": "800", "width": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0]}, "isDiscrete": true, "stddev": 0.0}, "h": {"count": "800", "min": 7.0, "max": 7.0, "mean": 7.0, "histogram": {"start": 7.0, "end": 7.0000007, "counts": ["800"], "max": 7.0, "min": 7.0, "bins": [7.0, 7.0000007], "n": "800", "width": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0]}, "isDiscrete": true, "stddev": 0.0}, "n": {"count": "800", "min": 5.0, "max": 5.0, "mean": 5.0, "histogram": {"start": 5.0, "end": 5.0000005, "counts": ["800"], "max": 5.0, "min": 5.0, "bins": [5.0, 5.0000005], "n": "800", "width": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0]}, "isDiscrete": true, "stddev": 0.0}, "s": {"count": "800", "min": 8.0, "max": 8.0, "mean": 8.0, "histogram": {"start": 8.0, "end": 8.0000008, "counts": ["800"], "max": 8.0, "min": 8.0, "bins": [8.0, 8.0000008], "n": "800", "width": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0]}, "isDiscrete": true, "stddev": 0.0}, "6": {"count": "800", "max": 1.0, "mean": 0.7225, "stddev": 0.44804540043511626, "histogram": {"end": 1.0000001, "counts": ["219", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "581"], "max": 1.0, "bins": [0.0, 0.03333333666666667, 0.06666667333333334, 0.10000001000000001, 0.13333334666666669, 0.16666668333333334, 0.20000002000000003, 0.2333333566666667, 0.26666669333333337, 0.30000003000000003, 0.3333333666666667, 0.3666667033333334, 0.40000004000000006, 0.4333333766666667, 0.4666667133333334, 0.50000005, 0.5333333866666667, 0.5666667233333335, 0.6000000600000001, 0.6333333966666668, 0.6666667333333334, 0.7000000700000001, 0.7333334066666668, 0.7666667433333334, 0.8000000800000001, 0.8333334166666668, 0.8666667533333334, 0.9000000900000001, 0.9333334266666669, 0.9666667633333335, 1.0000001], "n": "800", "start": 0.0, "width": 0.0, "min": 0.0}, "uniqueCount": {"estimate": 2.0, "upper": 2.0, "lower": 2.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, "isDiscrete": true, "min": 0.0}, "0": {"count": "222", "min": 1.0, "max": 1.0, "mean": 1.0, "histogram": {"start": 1.0, "end": 1.0000001, "counts": ["222"], "max": 1.0, "min": 1.0, "bins": [1.0, 1.0000001], "n": "222", "width": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, "isDiscrete": true, "stddev": 0.0}, "m": {"count": "800", "min": 3.0, "max": 3.0, "mean": 3.0, "histogram": {"start": 3.0, "end": 3.0000003, "counts": ["800"], "max": 3.0, "min": 3.0, "bins": [3.0, 3.0000003], "n": "800", "width": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0]}, "isDiscrete": true, "stddev": 0.0}}}}, "frequentItems": {"items": [{"estimate": "578", "jsonValue": "36 months"}, {"estimate": "222", "jsonValue": "60 months"}]}, "uniqueCount": {"estimate": 2.000000004967054, "upper": 2.000099863468538, "lower": 2.0}}}\n", "\n", " Handlebars.registerHelper("getDoubleHistogramChart",(column,key) => {\n", " const columnKey = key.data.key\n", - " if (profileFromCSVfile) {\n", - " return generateBarChart(\n", - " column,\n", - " profileFromCSVfile[columnKey]\n", - " )\n", + " try {\n", + " if (profileFromCSVfile) {\n", + " return generateBarChart(\n", + " column,\n", + " profileFromCSVfile[columnKey]\n", + " )\n", + " }\n", + " } catch (err) {\n", + " $(document).ready(() => \n", + " $(".desktop-content").html(`\n", + " <p style="height: ${$(window).height()}px" class="error-message"> \n", + " Something went wrong. Please try again.\n", + " </p>\n", + " `)\n", + " )\n", " }\n", " });\n", " }\n", @@ -1362,7 +1465,7 @@ "\n", " function initHandlebarsTemplate() {\n", " // Replace this context with JSON from .py file\n", - " const context = {"term": {"counters": {"count": "200", "nullCount": "2"}, "schema": {"inferredType": {"type": "STRING", "ratio": 1.0}, "typeCounts": {"NULL": "2", "STRING": "198"}}, "stringSummary": {"uniqueCount": {"estimate": 2.0, "upper": 2.0, "lower": 2.0}, "frequent": {"items": [{"value": "60 months", "estimate": 159.0}, {"value": "36 months", "estimate": 39.0}]}, "length": {"count": "198", "min": 9.0, "max": 9.0, "mean": 9.0, "histogram": {"start": 9.0, "end": 9.0000009, "counts": ["198"], "max": 9.0, "min": 9.0, "bins": [9.0, 9.0000009], "n": "198", "width": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [9.0, 9.0, 9.0, 9.0, 9.0, 9.0, 9.0, 9.0, 9.0]}, "isDiscrete": true, "stddev": 0.0}, "tokenLength": {"count": "198", "min": 2.0, "max": 2.0, "mean": 2.0, "histogram": {"start": 2.0, "end": 2.0000002, "counts": ["198"], "max": 2.0, "min": 2.0, "bins": [2.0, 2.0000002], "n": "198", "width": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0]}, "isDiscrete": true, "stddev": 0.0}, "charPosTracker": {"characterList": "!#$%&()*+,-./0123456789?@[]^_abcdefghijklmnopqrstuvwyz{}", "charPosMap": {"6": {"count": "198", "max": 1.0, "mean": 0.19696969696969696, "stddev": 0.3987173713600565, "histogram": {"end": 1.0000001, "counts": ["159", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "39"], "max": 1.0, "bins": [0.0, 0.03333333666666667, 0.06666667333333334, 0.10000001000000001, 0.13333334666666669, 0.16666668333333334, 0.20000002000000003, 0.2333333566666667, 0.26666669333333337, 0.30000003000000003, 0.3333333666666667, 0.3666667033333334, 0.40000004000000006, 0.4333333766666667, 0.4666667133333334, 0.50000005, 0.5333333866666667, 0.5666667233333335, 0.6000000600000001, 0.6333333966666668, 0.6666667333333334, 0.7000000700000001, 0.7333334066666668, 0.7666667433333334, 0.8000000800000001, 0.8333334166666668, 0.8666667533333334, 0.9000000900000001, 0.9333334266666669, 0.9666667633333335, 1.0000001], "n": "198", "start": 0.0, "width": 0.0, "min": 0.0}, "uniqueCount": {"estimate": 2.0, "upper": 2.0, "lower": 2.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0]}, "isDiscrete": true, "min": 0.0}, "NITL": {"count": "198", "min": 2.0, "max": 2.0, "mean": 2.0, "histogram": {"start": 2.0, "end": 2.0000002, "counts": ["198"], "max": 2.0, "min": 2.0, "bins": [2.0, 2.0000002], "n": "198", "width": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0]}, "isDiscrete": true, "stddev": 0.0}, "m": {"count": "198", "min": 3.0, "max": 3.0, "mean": 3.0, "histogram": {"start": 3.0, "end": 3.0000003, "counts": ["198"], "max": 3.0, "min": 3.0, "bins": [3.0, 3.0000003], "n": "198", "width": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0]}, "isDiscrete": true, "stddev": 0.0}, "s": {"count": "198", "min": 8.0, "max": 8.0, "mean": 8.0, "histogram": {"start": 8.0, "end": 8.0000008, "counts": ["198"], "max": 8.0, "min": 8.0, "bins": [8.0, 8.0000008], "n": "198", "width": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0]}, "isDiscrete": true, "stddev": 0.0}, "3": {"count": "39", "histogram": {"counts": ["39"], "bins": [0.0, 0.0], "n": "39", "start": 0.0, "end": 0.0, "width": 0.0, "max": 0.0, "min": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}, "isDiscrete": true, "min": 0.0, "max": 0.0, "mean": 0.0, "stddev": 0.0}, "o": {"count": "198", "min": 4.0, "max": 4.0, "mean": 4.0, "histogram": {"start": 4.0, "end": 4.0000004, "counts": ["198"], "max": 4.0, "min": 4.0, "bins": [4.0, 4.0000004], "n": "198", "width": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0]}, "isDiscrete": true, "stddev": 0.0}, "n": {"count": "198", "min": 5.0, "max": 5.0, "mean": 5.0, "histogram": {"start": 5.0, "end": 5.0000005, "counts": ["198"], "max": 5.0, "min": 5.0, "bins": [5.0, 5.0000005], "n": "198", "width": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0]}, "isDiscrete": true, "stddev": 0.0}, "t": {"count": "198", "min": 6.0, "max": 6.0, "mean": 6.0, "histogram": {"start": 6.0, "end": 6.0000006, "counts": ["198"], "max": 6.0, "min": 6.0, "bins": [6.0, 6.0000006], "n": "198", "width": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0]}, "isDiscrete": true, "stddev": 0.0}, "h": {"count": "198", "min": 7.0, "max": 7.0, "mean": 7.0, "histogram": {"start": 7.0, "end": 7.0000007, "counts": ["198"], "max": 7.0, "min": 7.0, "bins": [7.0, 7.0000007], "n": "198", "width": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0]}, "isDiscrete": true, "stddev": 0.0}, "0": {"count": "159", "min": 1.0, "max": 1.0, "mean": 1.0, "histogram": {"start": 1.0, "end": 1.0000001, "counts": ["159"], "max": 1.0, "min": 1.0, "bins": [1.0, 1.0000001], "n": "159", "width": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, "isDiscrete": true, "stddev": 0.0}}}}, "frequentItems": {"items": [{"estimate": "159", "jsonValue": "60 months"}, {"estimate": "39", "jsonValue": "36 months"}]}, "uniqueCount": {"estimate": 2.000000004967054, "upper": 2.000099863468538, "lower": 2.0}}};\n", + " const context = {"term": {"counters": {"count": "200", "nullCount": "2"}, "schema": {"inferredType": {"type": "STRING", "ratio": 1.0}, "typeCounts": {"STRING": "198", "NULL": "2"}}, "stringSummary": {"uniqueCount": {"estimate": 2.0, "upper": 2.0, "lower": 2.0}, "frequent": {"items": [{"value": "60 months", "estimate": 159.0}, {"value": "36 months", "estimate": 39.0}]}, "length": {"count": "198", "min": 9.0, "max": 9.0, "mean": 9.0, "histogram": {"start": 9.0, "end": 9.0000009, "counts": ["198"], "max": 9.0, "min": 9.0, "bins": [9.0, 9.0000009], "n": "198", "width": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [9.0, 9.0, 9.0, 9.0, 9.0, 9.0, 9.0, 9.0, 9.0]}, "isDiscrete": true, "stddev": 0.0}, "tokenLength": {"count": "198", "min": 2.0, "max": 2.0, "mean": 2.0, "histogram": {"start": 2.0, "end": 2.0000002, "counts": ["198"], "max": 2.0, "min": 2.0, "bins": [2.0, 2.0000002], "n": "198", "width": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0]}, "isDiscrete": true, "stddev": 0.0}, "charPosTracker": {"characterList": "!#$%&()*+,-./0123456789?@[]^_abcdefghijklmnopqrstuvwyz{}", "charPosMap": {"3": {"count": "39", "histogram": {"counts": ["39"], "bins": [0.0, 0.0], "n": "39", "start": 0.0, "end": 0.0, "width": 0.0, "max": 0.0, "min": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}, "isDiscrete": true, "min": 0.0, "max": 0.0, "mean": 0.0, "stddev": 0.0}, "h": {"count": "198", "min": 7.0, "max": 7.0, "mean": 7.0, "histogram": {"start": 7.0, "end": 7.0000007, "counts": ["198"], "max": 7.0, "min": 7.0, "bins": [7.0, 7.0000007], "n": "198", "width": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0]}, "isDiscrete": true, "stddev": 0.0}, "o": {"count": "198", "min": 4.0, "max": 4.0, "mean": 4.0, "histogram": {"start": 4.0, "end": 4.0000004, "counts": ["198"], "max": 4.0, "min": 4.0, "bins": [4.0, 4.0000004], "n": "198", "width": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0]}, "isDiscrete": true, "stddev": 0.0}, "n": {"count": "198", "min": 5.0, "max": 5.0, "mean": 5.0, "histogram": {"start": 5.0, "end": 5.0000005, "counts": ["198"], "max": 5.0, "min": 5.0, "bins": [5.0, 5.0000005], "n": "198", "width": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0]}, "isDiscrete": true, "stddev": 0.0}, "0": {"count": "159", "min": 1.0, "max": 1.0, "mean": 1.0, "histogram": {"start": 1.0, "end": 1.0000001, "counts": ["159"], "max": 1.0, "min": 1.0, "bins": [1.0, 1.0000001], "n": "159", "width": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, "isDiscrete": true, "stddev": 0.0}, "6": {"count": "198", "max": 1.0, "mean": 0.19696969696969696, "stddev": 0.3987173713600565, "histogram": {"end": 1.0000001, "counts": ["159", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "39"], "max": 1.0, "bins": [0.0, 0.03333333666666667, 0.06666667333333334, 0.10000001000000001, 0.13333334666666669, 0.16666668333333334, 0.20000002000000003, 0.2333333566666667, 0.26666669333333337, 0.30000003000000003, 0.3333333666666667, 0.3666667033333334, 0.40000004000000006, 0.4333333766666667, 0.4666667133333334, 0.50000005, 0.5333333866666667, 0.5666667233333335, 0.6000000600000001, 0.6333333966666668, 0.6666667333333334, 0.7000000700000001, 0.7333334066666668, 0.7666667433333334, 0.8000000800000001, 0.8333334166666668, 0.8666667533333334, 0.9000000900000001, 0.9333334266666669, 0.9666667633333335, 1.0000001], "n": "198", "start": 0.0, "width": 0.0, "min": 0.0}, "uniqueCount": {"estimate": 2.0, "upper": 2.0, "lower": 2.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0]}, "isDiscrete": true, "min": 0.0}, "s": {"count": "198", "min": 8.0, "max": 8.0, "mean": 8.0, "histogram": {"start": 8.0, "end": 8.0000008, "counts": ["198"], "max": 8.0, "min": 8.0, "bins": [8.0, 8.0000008], "n": "198", "width": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0]}, "isDiscrete": true, "stddev": 0.0}, "NITL": {"count": "198", "min": 2.0, "max": 2.0, "mean": 2.0, "histogram": {"start": 2.0, "end": 2.0000002, "counts": ["198"], "max": 2.0, "min": 2.0, "bins": [2.0, 2.0000002], "n": "198", "width": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0]}, "isDiscrete": true, "stddev": 0.0}, "m": {"count": "198", "min": 3.0, "max": 3.0, "mean": 3.0, "histogram": {"start": 3.0, "end": 3.0000003, "counts": ["198"], "max": 3.0, "min": 3.0, "bins": [3.0, 3.0000003], "n": "198", "width": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0]}, "isDiscrete": true, "stddev": 0.0}, "t": {"count": "198", "min": 6.0, "max": 6.0, "mean": 6.0, "histogram": {"start": 6.0, "end": 6.0000006, "counts": ["198"], "max": 6.0, "min": 6.0, "bins": [6.0, 6.0000006], "n": "198", "width": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0]}, "isDiscrete": true, "stddev": 0.0}}}}, "frequentItems": {"items": [{"estimate": "159", "jsonValue": "60 months"}, {"estimate": "39", "jsonValue": "36 months"}]}, "uniqueCount": {"estimate": 2.000000004967054, "upper": 2.000099863468538, "lower": 2.0}}};\n", " // Config handlebars and pass data to HBS template\n", " const source = document.getElementById("entry-template").innerHTML;\n", " const template = Handlebars.compile(source);\n", @@ -1549,7 +1652,7 @@ " }\n", "\n", " .chart-box-title {\n", - " width: 98%;\n", + " width: 88%;\n", " justify-content: space-between;\n", " margin: 10px;\n", " margin-top: 15px;\n", @@ -1564,6 +1667,26 @@ " line-height: 16px;\n", " color: #4F595B;\n", " }\n", + " \n", + " .bar {\n", + " font: 10px sans-serif;\n", + " }\n", + " \n", + " .bar path,\n", + " .bar line {\n", + " fill: none;\n", + " stroke: #000;\n", + " shape-rendering: crispEdges;\n", + " }\n", + "\n", + " .error-message {\n", + " display: flex;\n", + " justify-content: center;\n", + " align-items: center;\n", + " color: rgb(255, 114, 71);\n", + " font-size: 30px;\n", + " font-weight: 900;\n", + " }\n", "\n", " @media screen and (min-width: 500px) {\n", " .desktop-content {\n", @@ -1585,11 +1708,11 @@ " <p>{{@key}}</p>\n", " <div class="display-flex">\n", " <div class="colors-for-distingushing-charts">\n", - " <div class="circle-color" style="background: #369BAC"></div>\n", - " <text alignment-baseline="middle" style="font-size: 15px;">Current</text>\n", + " <div class="circle-color" style="background: #44C0E7;"></div>\n", + " <text alignment-baseline="middle" style="font-size: 15px;">Target</text>\n", " </div>\n", " <div class="colors-for-distingushing-charts">\n", - " <div class="circle-color" style="background: #2683C9"></div>\n", + " <div class="circle-color" style="background: #F5843C"></div>\n", " <text alignment-baseline="middle" style="font-size: 15px;">Reference</text>\n", " </div>\n", " </div></div>\n", @@ -1631,57 +1754,71 @@ " this.CHART_HEIGHT = this.SVG_HEIGHT - this.MARGIN.TOP - this.MARGIN.BOTTOM;\n", " this.svgEl = d3.create("svg")\n", " .attr("preserveAspectRatio", "xMinYMin meet")\n", - " .attr("viewBox", `20 0 ${$(window).width()} ${$(window).height()-30}`)\n", + " .attr("viewBox", `0 0 ${$(window).width()+100} ${$(window).height()-30}`)\n", " .classed("svg-content-responsive", true)\n", " this.maxYValue = d3.max(targetData, (d) => Math.abs(d.axisY));\n", - " const mergedData = targetData.concat(referenceData).map(a => {\n", - " return a.axisX\n", + " this.minYValue = d3.min(targetData, (d) => Math.abs(d.axisY));\n", + " const mergedReferenceData = referenceData.map(({axisX, axisY}) => {\n", + " return {axisX, axisY}\n", " })\n", - " const mergedAndSortedData = mergedData.sort(function(a, b) { return a - b; });\n", + " const mergedTargetedData = targetData.map(({axisX, axisY}) => {\n", + " return {axisX, axisY}\n", + " })\n", + "\n", + " this.charts2 = mergedReferenceData.concat(mergedTargetedData)\n", + " this.charts2 = this.charts2.sort(function(a, b) { return a - b; });\n", + "\n", + " this.targetBinWidth = targetData[1]?.axisX - targetData[0]?.axisX\n", + " this.referenceBinWidth = referenceData[1]?.axisX - referenceData[0]?.axisX\n", + " this.maxTargetXValue = d3.max(targetData, (d) => d.axisX);\n", + "\n", + " this.maxReferenceXValue = d3.max(referenceData, (d) => d.axisX);\n", + "\n", " this.xScale = d3\n", - " .scaleBand()\n", - " .domain(mergedAndSortedData.map((d) => d))\n", - " .range([this.MARGIN.LEFT, this.MARGIN.LEFT + this.CHART_WIDTH]);\n", - " this.yScale = d3\n", - " .scaleLinear()\n", - " .domain([0, this.maxYValue * 1.2])\n", - " .range([this.CHART_HEIGHT, 0]);\n", + " .scaleLinear()\n", + " .domain([d3.min(this.charts2, function(d) { return parseFloat(d.axisX); }),(this.maxTargetXValue+this.targetBinWidth >= this.maxReferenceXValue+this.referenceBinWidth) ? this.maxTargetXValue+this.targetBinWidth:this.maxReferenceXValue+this.referenceBinWidth]) // I was too lazy to do basic math of max(bin_start + width for each distribution)\n", + " .range([0, this.CHART_WIDTH ]);\n", + "\n", + " this.svgEl.append("g")\n", + " .attr("transform", "translate("+ this.MARGIN.LEFT +"," + this.SVG_HEIGHT + ")")\n", + " .call(d3.axisBottom(this.xScale));\n", + " this.yScale = d3.scaleLinear()\n", + " .range([this.CHART_HEIGHT , 0])\n", + " this.yScale.domain([d3.min(this.charts2, function(d) { return parseFloat(d.axisY); }), d3.max(this.charts2, function(d) { return parseFloat(d.axisY); })]);\n", " }\n", " }\n", "\n", - " function chartData(column, startIndex) {\n", + " function chartData(column) {\n", " const data = [];\n", - " for (let i = 0; i<column.numberSummary.histogram.counts.length; i++) {\n", - " data.push({\n", - " axisY: column.numberSummary.histogram.counts[i] || 0,\n", - " axisX: parseFloat(column.numberSummary.histogram.bins[i]).toFixed(2),\n", - " });\n", - " }\n", + " if (column.numberSummary?.histogram) {\n", + " for (let i = 0; i<column.numberSummary.histogram.bins.length; i++) {\n", + " data.push({\n", + " axisY: column.numberSummary.histogram.counts[i] || 0,\n", + " axisX: column.numberSummary.histogram.bins[i] || 0,\n", + " });\n", + " }\n", + " } else {\n", + " $(document).ready(() => \n", + " $(".desktop-content").html(`\n", + " <p style="height: ${$(window).height()}px" class="error-message"> \n", + " Something went wrong. Please try again.\n", + " </p>\n", + " `)\n", + " )\n", + " }\n", + "\n", " return data\n", " }\n", "\n", " function generateDoubleHistogramChart(targetData, referenceData) {\n", " let histogramData = [],\n", " overlappedHistogramData = [];\n", + " let yFormat;\n", "\n", - " const targetedProfileMin = targetData.numberSummary.histogram.min < 0 ?\n", - " targetData.numberSummary.histogram.min.toFixed(1) * 10 * -1 :\n", - " targetData.numberSummary.histogram.min.toFixed(1) * 10\n", - "\n", - " const referenceProfileMin = referenceData.numberSummary.histogram.min < 0 ?\n", - " referenceData.numberSummary.histogram.min.toFixed(1) * 10 * -1 :\n", - " referenceData.numberSummary.histogram.min.toFixed(1) * 10\n", - "\n", - " const chartsStartPoint = []\n", - " targetedProfileMin > referenceProfileMin ?\n", - " chartsStartPoint.push(Math.ceil(targetedProfileMin-referenceProfileMin), 0):\n", - " chartsStartPoint.push(0, Math.ceil(referenceProfileMin-targetedProfileMin))\n", - " histogramData = chartData(targetData, chartsStartPoint[0], 'original_profile')\n", - " overlappedHistogramData = chartData(referenceData, chartsStartPoint[1], 'ref_profile')\n", + " histogramData = chartData(targetData)\n", + " overlappedHistogramData = chartData(referenceData)\n", "\n", - " let yFormat,\n", - " xFormat;\n", - " const sizes = new GenerateChartParams($(window).height()-55, $(window).width(), histogramData, overlappedHistogramData)\n", + " const sizes = new GenerateChartParams($(window).height()-80, $(window).width(), histogramData, overlappedHistogramData)\n", " const {\n", " MARGIN,\n", " SVG_WIDTH,\n", @@ -1690,16 +1827,17 @@ " CHART_HEIGHT,\n", " svgEl,\n", " maxYValue,\n", + " minYValue,\n", " xScale,\n", " yScale\n", " } = sizes\n", "\n", - " const xAxis = d3.axisBottom(xScale).ticks(SVG_WIDTH, xFormat).tickSizeOuter(0);\n", + " const rectColors = ["#44C0E7", "#F5843C"]\n", " const yAxis = d3.axisLeft(yScale).ticks(SVG_HEIGHT / 40, yFormat);\n", " yFormat = yScale.tickFormat(100, yFormat);\n", "\n", " svgEl.append("g")\n", - " .attr("transform", `translate(${MARGIN.LEFT}, ${MARGIN.TOP})`)\n", + " .attr("transform", `translate(${MARGIN.LEFT}, ${MARGIN.BOTTOM})`)\n", " .call(yAxis)\n", " .call(g => g.select(".domain").remove())\n", " .call(g => g.selectAll(".tick line")\n", @@ -1709,61 +1847,82 @@ " .attr("x", -MARGIN.LEFT)\n", " .attr("y", 10)\n", " .attr("fill", "currentColor")\n", - " .attr("text-anchor", "start"));\n", + " .attr("text-anchor", "start"))\n", "\n", - " svgEl.append("g")\n", - " .attr("transform", `translate(0,${SVG_HEIGHT - MARGIN.BOTTOM})`)\n", - " .call(xAxis)\n", - " .selectAll("text")\n", - " .attr("dy", "-.6em")\n", - " .attr("dx", "-.8em")\n", - " .attr("transform", "rotate(-90)")\n", - " .style("text-anchor", "end")\n", - " .call(g => g.select(".domain").remove())\n", - " .call(g => g.selectAll(".tick line").remove())\n", - " .call(g => g.append("text")\n", - " .attr("fill", "currentColor")\n", - " .attr("text-anchor", "end"))\n", - " .style("font-size", "10")\n", + " svgEl.append("text")\n", + " .attr("transform",\n", + " "translate(" + (CHART_WIDTH/2) + " ," +\n", + " (CHART_HEIGHT + MARGIN.TOP + 75) + ")")\n", + " .style("text-anchor", "middle")\n", + " .text("Values")\n", + " .style("font-size", "15")\n", + " .style("opacity", "0.6")\n", + "\n", + " svgEl.append("text")\n", + " .attr("transform", "rotate(-90)")\n", + " .attr("y", 0)\n", + " .attr("x", 0 - (SVG_HEIGHT / 2))\n", + " .attr("dy", "1em")\n", + " .style("text-anchor", "middle")\n", + " .text("Counts")\n", + " .style("font-size", "15")\n", + " .style("opacity", "0.6")\n", "\n", " const gChart = svgEl.append("g");\n", " gChart\n", + " .attr("transform", "translate("+ MARGIN.LEFT +",0)")\n", " .selectAll(".bar")\n", " .data(histogramData)\n", " .enter()\n", " .append("rect")\n", + " .style("stroke", "#021826")\n", " .classed("bar", true)\n", - " .attr("width", xScale.bandwidth())\n", - " .attr("height", (d) => ((CHART_HEIGHT - yScale(d.axisY)) < 0 || d.axisY === 0 ? 0 : (CHART_HEIGHT - yScale(d.axisY))))\n", - " .attr("x", (d) => xScale(d.axisX))\n", - " .attr("y", (d) => yScale(d.axisY) + MARGIN.TOP)\n", - " .attr("fill", "#2683C9")\n", - " .style("opacity","0.6");\n", + " .attr("width", function(d) { return xScale(histogramData[1]?.axisX)-xScale(histogramData[0]?.axisX); })\n", + " .attr("height", (d) => CHART_HEIGHT - yScale(d.axisY))\n", + " .attr("x", 1)\n", + " .attr("transform", function(d) { return "translate(" + xScale(d.axisX) + "," + 0 + ")"; })\n", + " .attr("y", (d) => yScale(d.axisY) + MARGIN.TOP + MARGIN.BOTTOM)\n", + " .attr("fill", rectColors[0])\n", + " .style("opacity","0.6")\n", "\n", " const gChart1 = svgEl.append("g");\n", " gChart1\n", - " .selectAll(".bar")\n", - " .data(overlappedHistogramData)\n", - " .enter()\n", - " .append("rect")\n", - " .classed("bar", true)\n", - " .attr("width", xScale.bandwidth())\n", - " .attr("height", (d) => ((CHART_HEIGHT - yScale(d.axisY)) < 0 || d.axisY === 0 ? 0 : (CHART_HEIGHT - yScale(d.axisY))))\n", - " .attr("x", (d) => xScale(d.axisX))\n", - " .attr("y", (d) => yScale(d.axisY) + MARGIN.TOP)\n", - " .attr("fill", "#369BAC")\n", - " .style("opacity", "0.6");\n", + " .attr("transform", "translate("+ MARGIN.LEFT +",0)")\n", + " .selectAll(".bar")\n", + " .data(overlappedHistogramData)\n", + " .enter()\n", + " .append("rect")\n", + " .style("stroke", "#021826")\n", + " .classed("bar", true)\n", + " .attr("width", function(d) { return xScale(overlappedHistogramData[1]?.axisX)-xScale(overlappedHistogramData[0]?.axisX); })\n", + " .attr("height", (d) => CHART_HEIGHT - yScale(d.axisY))\n", + " .attr("x", 1)\n", + " .attr("transform", function(d) { return "translate(" + xScale(d.axisX) + "," + 0 + ")"; })\n", + " .attr("y", (d) => yScale(d.axisY) + MARGIN.TOP + MARGIN.BOTTOM)\n", + " .attr("fill", rectColors[1])\n", + " .style("opacity","0.6")\n", + "\n", " return svgEl._groups[0][0].outerHTML;\n", " }\n", "\n", - " const profileFromCSVfile = {"int_rate": {"counters": {"count": "800"}, "schema": {"inferredType": {"type": "FRACTIONAL", "ratio": 1.0}, "typeCounts": {"FRACTIONAL": "800"}}, "numberSummary": {"count": "800", "min": 5.32, "max": 29.96, "mean": 14.713449999999902, "stddev": 4.69498948404777, "histogram": {"start": 5.320000171661377, "end": 29.960002080472563, "counts": ["10", "0", "28", "22", "46", "47", "54", "30", "0", "113", "57", "67", "51", "0", "63", "68", "42", "18", "0", "31", "8", "0", "4", "14", "12", "4", "5", "6", "0", "0"], "max": 29.959999084472656, "min": 5.320000171661377, "bins": [5.320000171661377, 6.14133356862175, 6.962666965582123, 7.784000362542495, 8.60533375950287, 9.426667156463242, 10.248000553423614, 11.069333950383987, 11.89066734734436, 12.712000744304733, 13.533334141265106, 14.35466753822548, 15.17600093518585, 15.997334332146224, 16.818667729106597, 17.64000112606697, 18.461334523027343, 19.282667919987716, 20.10400131694809, 20.925334713908462, 21.746668110868836, 22.56800150782921, 23.38933490478958, 24.210668301749955, 25.032001698710324, 25.853335095670698, 26.67466849263107, 27.496001889591444, 28.317335286551817, 29.13866868351219, 29.960002080472563], "n": "800", "width": 0.0}, "uniqueCount": {"estimate": 32.0, "upper": 32.0, "lower": 32.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [5.320000171661377, 5.320000171661377, 7.989999771118164, 10.989999771118164, 13.989999771118164, 17.989999771118164, 24.489999771118164, 26.989999771118164, 29.959999084472656]}, "isDiscrete": false}, "frequentItems": {"items": [{"estimate": "68", "jsonValue": "14.49"}, {"estimate": "66", "jsonValue": "17.99"}, {"estimate": "63", "jsonValue": "16.99"}, {"estimate": "62", "jsonValue": "12.79"}, {"estimate": "57", "jsonValue": "13.99"}, {"estimate": "52", "jsonValue": "15.59"}, {"estimate": "52", "jsonValue": "13.49"}, {"estimate": "48", "jsonValue": "18.99"}, {"estimate": "48", "jsonValue": "8.99"}, {"estimate": "47", "jsonValue": "9.49"}, {"estimate": "35", "jsonValue": "21.49"}, {"estimate": "30", "jsonValue": "19.99"}, {"estimate": "30", "jsonValue": "10.49"}, {"estimate": "27", "jsonValue": "8.59"}, {"estimate": "27", "jsonValue": "25.69"}, {"estimate": "27", "jsonValue": "10.99"}, {"estimate": "26", "jsonValue": "11.49"}, {"estimate": "26", "jsonValue": "27.79"}]}, "uniqueCount": {"estimate": 32.00000246365887, "upper": 32.00160019980166, "lower": 32.0}}}\n", + " const profileFromCSVfile = {"int_rate": {"counters": {"count": "800"}, "schema": {"inferredType": {"type": "FRACTIONAL", "ratio": 1.0}, "typeCounts": {"FRACTIONAL": "800"}}, "numberSummary": {"count": "800", "min": 5.32, "max": 29.96, "mean": 14.713449999999902, "stddev": 4.69498948404777, "histogram": {"start": 5.320000171661377, "end": 29.960002080472563, "counts": ["10", "0", "24", "22", "50", "47", "54", "26", "0", "117", "57", "67", "51", "0", "63", "64", "46", "18", "0", "31", "8", "0", "4", "14", "8", "4", "9", "6", "0", "0"], "max": 29.959999084472656, "min": 5.320000171661377, "bins": [5.320000171661377, 6.14133356862175, 6.962666965582123, 7.784000362542495, 8.60533375950287, 9.426667156463242, 10.248000553423614, 11.069333950383987, 11.89066734734436, 12.712000744304733, 13.533334141265106, 14.35466753822548, 15.17600093518585, 15.997334332146224, 16.818667729106597, 17.64000112606697, 18.461334523027343, 19.282667919987716, 20.10400131694809, 20.925334713908462, 21.746668110868836, 22.56800150782921, 23.38933490478958, 24.210668301749955, 25.032001698710324, 25.853335095670698, 26.67466849263107, 27.496001889591444, 28.317335286551817, 29.13866868351219, 29.960002080472563], "n": "800", "width": 0.0}, "uniqueCount": {"estimate": 32.0, "upper": 32.0, "lower": 32.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [5.320000171661377, 5.320000171661377, 7.989999771118164, 10.989999771118164, 13.989999771118164, 17.989999771118164, 24.489999771118164, 26.989999771118164, 29.959999084472656]}, "isDiscrete": false}, "frequentItems": {"items": [{"estimate": "68", "jsonValue": "14.49"}, {"estimate": "66", "jsonValue": "17.99"}, {"estimate": "63", "jsonValue": "16.99"}, {"estimate": "62", "jsonValue": "12.79"}, {"estimate": "57", "jsonValue": "13.99"}, {"estimate": "52", "jsonValue": "15.59"}, {"estimate": "52", "jsonValue": "13.49"}, {"estimate": "48", "jsonValue": "18.99"}, {"estimate": "48", "jsonValue": "8.99"}, {"estimate": "47", "jsonValue": "9.49"}, {"estimate": "35", "jsonValue": "21.49"}, {"estimate": "30", "jsonValue": "19.99"}, {"estimate": "30", "jsonValue": "10.49"}, {"estimate": "27", "jsonValue": "8.59"}, {"estimate": "27", "jsonValue": "25.69"}, {"estimate": "27", "jsonValue": "10.99"}, {"estimate": "26", "jsonValue": "11.49"}, {"estimate": "26", "jsonValue": "27.79"}]}, "uniqueCount": {"estimate": 32.00000246365887, "upper": 32.00160019980166, "lower": 32.0}}}\n", "\n", " Handlebars.registerHelper("getDoubleHistogramChart",(column,key) => {\n", - " const columnKey = key.data.key\n", - " if (profileFromCSVfile) {\n", - " return generateDoubleHistogramChart (\n", - " column,\n", - " profileFromCSVfile[columnKey]\n", + " const columnKey = key.data.key\n", + " try {\n", + " if (profileFromCSVfile) {\n", + " return generateDoubleHistogramChart (\n", + " column,\n", + " profileFromCSVfile[columnKey]\n", + " )\n", + " }\n", + " } catch (err) {\n", + " $(document).ready(() => \n", + " $(".desktop-content").html(`\n", + " <p style="height: ${$(window).height()}px" class="error-message"> \n", + " Something went wrong. Please try again.\n", + " </p>\n", + " `)\n", " )\n", " }\n", " });\n", @@ -1775,7 +1934,7 @@ "\n", " function initHandlebarsTemplate() {\n", " // Replace this context with JSON from .py file\n", - " const context = {"int_rate": {"counters": {"count": "200", "nullCount": "2"}, "schema": {"inferredType": {"type": "FRACTIONAL", "ratio": 1.0}, "typeCounts": {"NULL": "2", "FRACTIONAL": "198"}}, "numberSummary": {"count": "198", "min": 6.99, "max": 26.49, "mean": 13.170303030302986, "stddev": 3.081443710207697, "histogram": {"start": 6.989999771118164, "end": 26.49000242011814, "counts": ["1", "0", "2", "30", "0", "18", "22", "0", "19", "17", "21", "24", "0", "28", "0", "3", "2", "0", "0", "6", "0", "0", "3", "0", "0", "0", "1", "0", "0", "1"], "max": 26.489999771118164, "min": 6.989999771118164, "bins": [6.989999771118164, 7.639999859418163, 8.289999947718162, 8.940000036018162, 9.590000124318161, 10.24000021261816, 10.89000030091816, 11.540000389218159, 12.190000477518158, 12.840000565818157, 13.490000654118155, 14.140000742418156, 14.790000830718155, 15.440000919018154, 16.090001007318154, 16.740001095618155, 17.39000118391815, 18.04000127221815, 18.69000136051815, 19.34000144881815, 19.990001537118147, 20.640001625418147, 21.290001713718148, 21.940001802018145, 22.590001890318145, 23.240001978618146, 23.890002066918143, 24.540002155218144, 25.190002243518144, 25.84000233181814, 26.49000242011814], "n": "198", "width": 0.0}, "uniqueCount": {"estimate": 18.0, "upper": 18.0, "lower": 18.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [6.989999771118164, 8.59000015258789, 8.989999771118164, 10.489999771118164, 13.489999771118164, 14.489999771118164, 19.989999771118164, 24.489999771118164, 26.489999771118164]}, "isDiscrete": false}, "frequentItems": {"items": [{"estimate": "28", "jsonValue": "15.59"}, {"estimate": "24", "jsonValue": "14.49"}, {"estimate": "21", "jsonValue": "13.99"}, {"estimate": "19", "jsonValue": "12.79"}, {"estimate": "18", "jsonValue": "10.99"}, {"estimate": "18", "jsonValue": "10.49"}, {"estimate": "17", "jsonValue": "13.49"}, {"estimate": "17", "jsonValue": "9.49"}, {"estimate": "13", "jsonValue": "8.99"}, {"estimate": "6", "jsonValue": "19.99"}, {"estimate": "4", "jsonValue": "11.49"}, {"estimate": "3", "jsonValue": "21.49"}, {"estimate": "3", "jsonValue": "16.99"}, {"estimate": "2", "jsonValue": "8.59"}, {"estimate": "2", "jsonValue": "17.99"}, {"estimate": "1", "jsonValue": "6.99"}, {"estimate": "1", "jsonValue": "26.49"}, {"estimate": "1", "jsonValue": "24.49"}]}, "uniqueCount": {"estimate": 18.00000075995926, "upper": 18.00089948650833, "lower": 18.0}}};\n", + " const context = {"int_rate": {"counters": {"count": "200", "nullCount": "2"}, "schema": {"inferredType": {"type": "FRACTIONAL", "ratio": 1.0}, "typeCounts": {"FRACTIONAL": "198", "NULL": "2"}}, "numberSummary": {"count": "198", "min": 6.99, "max": 26.49, "mean": 13.170303030302986, "stddev": 3.081443710207697, "histogram": {"start": 6.989999771118164, "end": 26.49000242011814, "counts": ["1", "0", "2", "30", "0", "18", "22", "0", "19", "17", "21", "24", "0", "28", "0", "3", "2", "0", "0", "6", "0", "0", "3", "0", "0", "0", "1", "0", "0", "1"], "max": 26.489999771118164, "min": 6.989999771118164, "bins": [6.989999771118164, 7.639999859418163, 8.289999947718162, 8.940000036018162, 9.590000124318161, 10.24000021261816, 10.89000030091816, 11.540000389218159, 12.190000477518158, 12.840000565818157, 13.490000654118155, 14.140000742418156, 14.790000830718155, 15.440000919018154, 16.090001007318154, 16.740001095618155, 17.39000118391815, 18.04000127221815, 18.69000136051815, 19.34000144881815, 19.990001537118147, 20.640001625418147, 21.290001713718148, 21.940001802018145, 22.590001890318145, 23.240001978618146, 23.890002066918143, 24.540002155218144, 25.190002243518144, 25.84000233181814, 26.49000242011814], "n": "198", "width": 0.0}, "uniqueCount": {"estimate": 18.0, "upper": 18.0, "lower": 18.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [6.989999771118164, 8.59000015258789, 8.989999771118164, 10.489999771118164, 13.489999771118164, 14.489999771118164, 19.989999771118164, 24.489999771118164, 26.489999771118164]}, "isDiscrete": false}, "frequentItems": {"items": [{"estimate": "28", "jsonValue": "15.59"}, {"estimate": "24", "jsonValue": "14.49"}, {"estimate": "21", "jsonValue": "13.99"}, {"estimate": "19", "jsonValue": "12.79"}, {"estimate": "18", "jsonValue": "10.99"}, {"estimate": "18", "jsonValue": "10.49"}, {"estimate": "17", "jsonValue": "13.49"}, {"estimate": "17", "jsonValue": "9.49"}, {"estimate": "13", "jsonValue": "8.99"}, {"estimate": "6", "jsonValue": "19.99"}, {"estimate": "4", "jsonValue": "11.49"}, {"estimate": "3", "jsonValue": "21.49"}, {"estimate": "3", "jsonValue": "16.99"}, {"estimate": "2", "jsonValue": "8.59"}, {"estimate": "2", "jsonValue": "17.99"}, {"estimate": "1", "jsonValue": "6.99"}, {"estimate": "1", "jsonValue": "26.49"}, {"estimate": "1", "jsonValue": "24.49"}]}, "uniqueCount": {"estimate": 18.00000075995926, "upper": 18.00089948650833, "lower": 18.0}}};\n", " // Config handlebars and pass data to HBS template\n", " const source = document.getElementById("entry-template").innerHTML;\n", " const template = Handlebars.compile(source);\n", @@ -1990,7 +2149,7 @@ " }\n", "\n", " .chart-box-title {\n", - " width: 98%;\n", + " width: 88%;\n", " justify-content: space-between;\n", " margin: 10px;\n", " margin-top: 15px;\n", @@ -2005,6 +2164,26 @@ " line-height: 16px;\n", " color: #4F595B;\n", " }\n", + " \n", + " .bar {\n", + " font: 10px sans-serif;\n", + " }\n", + " \n", + " .bar path,\n", + " .bar line {\n", + " fill: none;\n", + " stroke: #000;\n", + " shape-rendering: crispEdges;\n", + " }\n", + "\n", + " .error-message {\n", + " display: flex;\n", + " justify-content: center;\n", + " align-items: center;\n", + " color: rgb(255, 114, 71);\n", + " font-size: 30px;\n", + " font-weight: 900;\n", + " }\n", "\n", " @media screen and (min-width: 500px) {\n", " .desktop-content {\n", @@ -2026,11 +2205,11 @@ " <p>{{@key}}</p>\n", " <div class="display-flex">\n", " <div class="colors-for-distingushing-charts">\n", - " <div class="circle-color" style="background: #369BAC"></div>\n", - " <text alignment-baseline="middle" style="font-size: 15px;">Current</text>\n", + " <div class="circle-color" style="background: #44C0E7;"></div>\n", + " <text alignment-baseline="middle" style="font-size: 15px;">Target</text>\n", " </div>\n", " <div class="colors-for-distingushing-charts">\n", - " <div class="circle-color" style="background: #2683C9"></div>\n", + " <div class="circle-color" style="background: #F5843C"></div>\n", " <text alignment-baseline="middle" style="font-size: 15px;">Reference</text>\n", " </div>\n", " </div></div>\n", @@ -2072,57 +2251,71 @@ " this.CHART_HEIGHT = this.SVG_HEIGHT - this.MARGIN.TOP - this.MARGIN.BOTTOM;\n", " this.svgEl = d3.create("svg")\n", " .attr("preserveAspectRatio", "xMinYMin meet")\n", - " .attr("viewBox", `20 0 ${$(window).width()} ${$(window).height()-30}`)\n", + " .attr("viewBox", `0 0 ${$(window).width()+100} ${$(window).height()-30}`)\n", " .classed("svg-content-responsive", true)\n", " this.maxYValue = d3.max(targetData, (d) => Math.abs(d.axisY));\n", - " const mergedData = targetData.concat(referenceData).map(a => {\n", - " return a.axisX\n", + " this.minYValue = d3.min(targetData, (d) => Math.abs(d.axisY));\n", + " const mergedReferenceData = referenceData.map(({axisX, axisY}) => {\n", + " return {axisX, axisY}\n", + " })\n", + " const mergedTargetedData = targetData.map(({axisX, axisY}) => {\n", + " return {axisX, axisY}\n", " })\n", - " const mergedAndSortedData = mergedData.sort(function(a, b) { return a - b; });\n", + "\n", + " this.charts2 = mergedReferenceData.concat(mergedTargetedData)\n", + " this.charts2 = this.charts2.sort(function(a, b) { return a - b; });\n", + "\n", + " this.targetBinWidth = targetData[1]?.axisX - targetData[0]?.axisX\n", + " this.referenceBinWidth = referenceData[1]?.axisX - referenceData[0]?.axisX\n", + " this.maxTargetXValue = d3.max(targetData, (d) => d.axisX);\n", + "\n", + " this.maxReferenceXValue = d3.max(referenceData, (d) => d.axisX);\n", + "\n", " this.xScale = d3\n", - " .scaleBand()\n", - " .domain(mergedAndSortedData.map((d) => d))\n", - " .range([this.MARGIN.LEFT, this.MARGIN.LEFT + this.CHART_WIDTH]);\n", - " this.yScale = d3\n", - " .scaleLinear()\n", - " .domain([0, this.maxYValue * 1.2])\n", - " .range([this.CHART_HEIGHT, 0]);\n", + " .scaleLinear()\n", + " .domain([d3.min(this.charts2, function(d) { return parseFloat(d.axisX); }),(this.maxTargetXValue+this.targetBinWidth >= this.maxReferenceXValue+this.referenceBinWidth) ? this.maxTargetXValue+this.targetBinWidth:this.maxReferenceXValue+this.referenceBinWidth]) // I was too lazy to do basic math of max(bin_start + width for each distribution)\n", + " .range([0, this.CHART_WIDTH ]);\n", + "\n", + " this.svgEl.append("g")\n", + " .attr("transform", "translate("+ this.MARGIN.LEFT +"," + this.SVG_HEIGHT + ")")\n", + " .call(d3.axisBottom(this.xScale));\n", + " this.yScale = d3.scaleLinear()\n", + " .range([this.CHART_HEIGHT , 0])\n", + " this.yScale.domain([d3.min(this.charts2, function(d) { return parseFloat(d.axisY); }), d3.max(this.charts2, function(d) { return parseFloat(d.axisY); })]);\n", " }\n", " }\n", "\n", - " function chartData(column, startIndex) {\n", + " function chartData(column) {\n", " const data = [];\n", - " for (let i = 0; i<column.numberSummary.histogram.counts.length; i++) {\n", - " data.push({\n", - " axisY: column.numberSummary.histogram.counts[i] || 0,\n", - " axisX: parseFloat(column.numberSummary.histogram.bins[i]).toFixed(2),\n", - " });\n", - " }\n", + " if (column.numberSummary?.histogram) {\n", + " for (let i = 0; i<column.numberSummary.histogram.bins.length; i++) {\n", + " data.push({\n", + " axisY: column.numberSummary.histogram.counts[i] || 0,\n", + " axisX: column.numberSummary.histogram.bins[i] || 0,\n", + " });\n", + " }\n", + " } else {\n", + " $(document).ready(() => \n", + " $(".desktop-content").html(`\n", + " <p style="height: ${$(window).height()}px" class="error-message"> \n", + " Something went wrong. Please try again.\n", + " </p>\n", + " `)\n", + " )\n", + " }\n", + "\n", " return data\n", " }\n", "\n", " function generateDoubleHistogramChart(targetData, referenceData) {\n", " let histogramData = [],\n", " overlappedHistogramData = [];\n", + " let yFormat;\n", "\n", - " const targetedProfileMin = targetData.numberSummary.histogram.min < 0 ?\n", - " targetData.numberSummary.histogram.min.toFixed(1) * 10 * -1 :\n", - " targetData.numberSummary.histogram.min.toFixed(1) * 10\n", + " histogramData = chartData(targetData)\n", + " overlappedHistogramData = chartData(referenceData)\n", "\n", - " const referenceProfileMin = referenceData.numberSummary.histogram.min < 0 ?\n", - " referenceData.numberSummary.histogram.min.toFixed(1) * 10 * -1 :\n", - " referenceData.numberSummary.histogram.min.toFixed(1) * 10\n", - "\n", - " const chartsStartPoint = []\n", - " targetedProfileMin > referenceProfileMin ?\n", - " chartsStartPoint.push(Math.ceil(targetedProfileMin-referenceProfileMin), 0):\n", - " chartsStartPoint.push(0, Math.ceil(referenceProfileMin-targetedProfileMin))\n", - " histogramData = chartData(targetData, chartsStartPoint[0], 'original_profile')\n", - " overlappedHistogramData = chartData(referenceData, chartsStartPoint[1], 'ref_profile')\n", - "\n", - " let yFormat,\n", - " xFormat;\n", - " const sizes = new GenerateChartParams($(window).height()-55, $(window).width(), histogramData, overlappedHistogramData)\n", + " const sizes = new GenerateChartParams($(window).height()-80, $(window).width(), histogramData, overlappedHistogramData)\n", " const {\n", " MARGIN,\n", " SVG_WIDTH,\n", @@ -2131,16 +2324,17 @@ " CHART_HEIGHT,\n", " svgEl,\n", " maxYValue,\n", + " minYValue,\n", " xScale,\n", " yScale\n", " } = sizes\n", "\n", - " const xAxis = d3.axisBottom(xScale).ticks(SVG_WIDTH, xFormat).tickSizeOuter(0);\n", + " const rectColors = ["#44C0E7", "#F5843C"]\n", " const yAxis = d3.axisLeft(yScale).ticks(SVG_HEIGHT / 40, yFormat);\n", " yFormat = yScale.tickFormat(100, yFormat);\n", "\n", " svgEl.append("g")\n", - " .attr("transform", `translate(${MARGIN.LEFT}, ${MARGIN.TOP})`)\n", + " .attr("transform", `translate(${MARGIN.LEFT}, ${MARGIN.BOTTOM})`)\n", " .call(yAxis)\n", " .call(g => g.select(".domain").remove())\n", " .call(g => g.selectAll(".tick line")\n", @@ -2150,61 +2344,82 @@ " .attr("x", -MARGIN.LEFT)\n", " .attr("y", 10)\n", " .attr("fill", "currentColor")\n", - " .attr("text-anchor", "start"));\n", + " .attr("text-anchor", "start"))\n", "\n", - " svgEl.append("g")\n", - " .attr("transform", `translate(0,${SVG_HEIGHT - MARGIN.BOTTOM})`)\n", - " .call(xAxis)\n", - " .selectAll("text")\n", - " .attr("dy", "-.6em")\n", - " .attr("dx", "-.8em")\n", - " .attr("transform", "rotate(-90)")\n", - " .style("text-anchor", "end")\n", - " .call(g => g.select(".domain").remove())\n", - " .call(g => g.selectAll(".tick line").remove())\n", - " .call(g => g.append("text")\n", - " .attr("fill", "currentColor")\n", - " .attr("text-anchor", "end"))\n", - " .style("font-size", "10")\n", + " svgEl.append("text")\n", + " .attr("transform",\n", + " "translate(" + (CHART_WIDTH/2) + " ," +\n", + " (CHART_HEIGHT + MARGIN.TOP + 75) + ")")\n", + " .style("text-anchor", "middle")\n", + " .text("Values")\n", + " .style("font-size", "15")\n", + " .style("opacity", "0.6")\n", + "\n", + " svgEl.append("text")\n", + " .attr("transform", "rotate(-90)")\n", + " .attr("y", 0)\n", + " .attr("x", 0 - (SVG_HEIGHT / 2))\n", + " .attr("dy", "1em")\n", + " .style("text-anchor", "middle")\n", + " .text("Counts")\n", + " .style("font-size", "15")\n", + " .style("opacity", "0.6")\n", "\n", " const gChart = svgEl.append("g");\n", " gChart\n", + " .attr("transform", "translate("+ MARGIN.LEFT +",0)")\n", " .selectAll(".bar")\n", " .data(histogramData)\n", " .enter()\n", " .append("rect")\n", + " .style("stroke", "#021826")\n", " .classed("bar", true)\n", - " .attr("width", xScale.bandwidth())\n", - " .attr("height", (d) => ((CHART_HEIGHT - yScale(d.axisY)) < 0 || d.axisY === 0 ? 0 : (CHART_HEIGHT - yScale(d.axisY))))\n", - " .attr("x", (d) => xScale(d.axisX))\n", - " .attr("y", (d) => yScale(d.axisY) + MARGIN.TOP)\n", - " .attr("fill", "#2683C9")\n", - " .style("opacity","0.6");\n", + " .attr("width", function(d) { return xScale(histogramData[1]?.axisX)-xScale(histogramData[0]?.axisX); })\n", + " .attr("height", (d) => CHART_HEIGHT - yScale(d.axisY))\n", + " .attr("x", 1)\n", + " .attr("transform", function(d) { return "translate(" + xScale(d.axisX) + "," + 0 + ")"; })\n", + " .attr("y", (d) => yScale(d.axisY) + MARGIN.TOP + MARGIN.BOTTOM)\n", + " .attr("fill", rectColors[0])\n", + " .style("opacity","0.6")\n", "\n", " const gChart1 = svgEl.append("g");\n", " gChart1\n", - " .selectAll(".bar")\n", - " .data(overlappedHistogramData)\n", - " .enter()\n", - " .append("rect")\n", - " .classed("bar", true)\n", - " .attr("width", xScale.bandwidth())\n", - " .attr("height", (d) => ((CHART_HEIGHT - yScale(d.axisY)) < 0 || d.axisY === 0 ? 0 : (CHART_HEIGHT - yScale(d.axisY))))\n", - " .attr("x", (d) => xScale(d.axisX))\n", - " .attr("y", (d) => yScale(d.axisY) + MARGIN.TOP)\n", - " .attr("fill", "#369BAC")\n", - " .style("opacity", "0.6");\n", + " .attr("transform", "translate("+ MARGIN.LEFT +",0)")\n", + " .selectAll(".bar")\n", + " .data(overlappedHistogramData)\n", + " .enter()\n", + " .append("rect")\n", + " .style("stroke", "#021826")\n", + " .classed("bar", true)\n", + " .attr("width", function(d) { return xScale(overlappedHistogramData[1]?.axisX)-xScale(overlappedHistogramData[0]?.axisX); })\n", + " .attr("height", (d) => CHART_HEIGHT - yScale(d.axisY))\n", + " .attr("x", 1)\n", + " .attr("transform", function(d) { return "translate(" + xScale(d.axisX) + "," + 0 + ")"; })\n", + " .attr("y", (d) => yScale(d.axisY) + MARGIN.TOP + MARGIN.BOTTOM)\n", + " .attr("fill", rectColors[1])\n", + " .style("opacity","0.6")\n", + "\n", " return svgEl._groups[0][0].outerHTML;\n", " }\n", "\n", - " const profileFromCSVfile = {"loan_amnt": {"counters": {"count": "800"}, "schema": {"inferredType": {"type": "FRACTIONAL", "ratio": 1.0}, "typeCounts": {"FRACTIONAL": "800"}}, "numberSummary": {"count": "800", "min": 1000.0, "max": 40000.0, "mean": 16271.1875, "stddev": 10051.962904086211, "histogram": {"start": 1000.0, "end": 40000.004, "counts": ["10", "30", "17", "70", "46", "53", "78", "15", "57", "21", "59", "23", "18", "26", "50", "15", "9", "20", "29", "5", "16", "5", "28", "10", "2", "5", "69", "0", "0", "14"], "max": 40000.0, "min": 1000.0, "bins": [1000.0, 2300.0001333333335, 3600.000266666667, 4900.000400000001, 6200.000533333334, 7500.000666666667, 8800.000800000002, 10100.000933333335, 11400.001066666668, 12700.0012, 14000.001333333334, 15300.001466666668, 16600.001600000003, 17900.001733333334, 19200.00186666667, 20500.002, 21800.002133333335, 23100.00226666667, 24400.0024, 25700.002533333336, 27000.002666666667, 28300.002800000002, 29600.002933333337, 30900.003066666668, 32200.003200000003, 33500.00333333334, 34800.00346666667, 36100.003600000004, 37400.00373333334, 38700.00386666667, 40000.004], "n": "800", "width": 0.0}, "uniqueCount": {"estimate": 183.0, "upper": 183.0, "lower": 183.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [1000.0, 2000.0, 4000.0, 8000.0, 14025.0, 24000.0, 35000.0, 40000.0, 40000.0]}, "isDiscrete": false}, "frequentItems": {"items": [{"estimate": "61", "jsonValue": "35000.0"}, {"estimate": "57", "jsonValue": "10000.0"}, {"estimate": "45", "jsonValue": "15000.0"}, {"estimate": "44", "jsonValue": "12000.0"}, {"estimate": "42", "jsonValue": "20000.0"}, {"estimate": "40", "jsonValue": "8000.0"}, {"estimate": "32", "jsonValue": "5000.0"}, {"estimate": "31", "jsonValue": "25000.0"}, {"estimate": "29", "jsonValue": "18000.0"}, {"estimate": "28", "jsonValue": "30000.0"}, {"estimate": "27", "jsonValue": "28000.0"}, {"estimate": "27", "jsonValue": "21000.0"}, {"estimate": "27", "jsonValue": "24000.0"}, {"estimate": "26", "jsonValue": "7000.0"}, {"estimate": "26", "jsonValue": "12600.0"}, {"estimate": "26", "jsonValue": "40000.0"}, {"estimate": "26", "jsonValue": "12800.0"}, {"estimate": "26", "jsonValue": "16925.0"}, {"estimate": "26", "jsonValue": "16000.0"}, {"estimate": "26", "jsonValue": "36000.0"}, {"estimate": "26", "jsonValue": "14225.0"}, {"estimate": "26", "jsonValue": "3000.0"}, {"estimate": "26", "jsonValue": "17400.0"}]}, "uniqueCount": {"estimate": 183.00008271638887, "upper": 183.00921977338194, "lower": 183.0}}}\n", + " const profileFromCSVfile = {"loan_amnt": {"counters": {"count": "800"}, "schema": {"inferredType": {"type": "FRACTIONAL", "ratio": 1.0}, "typeCounts": {"FRACTIONAL": "800"}}, "numberSummary": {"count": "800", "min": 1000.0, "max": 40000.0, "mean": 16271.1875, "stddev": 10051.962904086211, "histogram": {"start": 1000.0, "end": 40000.004, "counts": ["10", "30", "17", "70", "46", "53", "78", "15", "57", "25", "55", "23", "18", "26", "50", "15", "9", "20", "29", "9", "16", "1", "32", "6", "2", "5", "69", "0", "0", "14"], "max": 40000.0, "min": 1000.0, "bins": [1000.0, 2300.0001333333335, 3600.000266666667, 4900.000400000001, 6200.000533333334, 7500.000666666667, 8800.000800000002, 10100.000933333335, 11400.001066666668, 12700.0012, 14000.001333333334, 15300.001466666668, 16600.001600000003, 17900.001733333334, 19200.00186666667, 20500.002, 21800.002133333335, 23100.00226666667, 24400.0024, 25700.002533333336, 27000.002666666667, 28300.002800000002, 29600.002933333337, 30900.003066666668, 32200.003200000003, 33500.00333333334, 34800.00346666667, 36100.003600000004, 37400.00373333334, 38700.00386666667, 40000.004], "n": "800", "width": 0.0}, "uniqueCount": {"estimate": 183.0, "upper": 183.0, "lower": 183.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [1000.0, 2000.0, 4000.0, 8000.0, 14000.0, 23350.0, 35000.0, 40000.0, 40000.0]}, "isDiscrete": false}, "frequentItems": {"items": [{"estimate": "61", "jsonValue": "35000.0"}, {"estimate": "57", "jsonValue": "10000.0"}, {"estimate": "45", "jsonValue": "15000.0"}, {"estimate": "44", "jsonValue": "12000.0"}, {"estimate": "42", "jsonValue": "20000.0"}, {"estimate": "40", "jsonValue": "8000.0"}, {"estimate": "32", "jsonValue": "5000.0"}, {"estimate": "31", "jsonValue": "25000.0"}, {"estimate": "29", "jsonValue": "18000.0"}, {"estimate": "28", "jsonValue": "30000.0"}, {"estimate": "27", "jsonValue": "28000.0"}, {"estimate": "27", "jsonValue": "21000.0"}, {"estimate": "27", "jsonValue": "24000.0"}, {"estimate": "26", "jsonValue": "7000.0"}, {"estimate": "26", "jsonValue": "12600.0"}, {"estimate": "26", "jsonValue": "40000.0"}, {"estimate": "26", "jsonValue": "12800.0"}, {"estimate": "26", "jsonValue": "16925.0"}, {"estimate": "26", "jsonValue": "16000.0"}, {"estimate": "26", "jsonValue": "36000.0"}, {"estimate": "26", "jsonValue": "14225.0"}, {"estimate": "26", "jsonValue": "3000.0"}, {"estimate": "26", "jsonValue": "17400.0"}]}, "uniqueCount": {"estimate": 183.00008271638887, "upper": 183.00921977338194, "lower": 183.0}}}\n", "\n", " Handlebars.registerHelper("getDoubleHistogramChart",(column,key) => {\n", - " const columnKey = key.data.key\n", - " if (profileFromCSVfile) {\n", - " return generateDoubleHistogramChart (\n", - " column,\n", - " profileFromCSVfile[columnKey]\n", + " const columnKey = key.data.key\n", + " try {\n", + " if (profileFromCSVfile) {\n", + " return generateDoubleHistogramChart (\n", + " column,\n", + " profileFromCSVfile[columnKey]\n", + " )\n", + " }\n", + " } catch (err) {\n", + " $(document).ready(() => \n", + " $(".desktop-content").html(`\n", + " <p style="height: ${$(window).height()}px" class="error-message"> \n", + " Something went wrong. Please try again.\n", + " </p>\n", + " `)\n", " )\n", " }\n", " });\n", @@ -2449,15 +2664,15 @@ " color: #4F595B;\n", " }\n", "\n", - " .bar.positive {\n", - " fill: #369BACB2;\n", - " }\n", - "\n", - " .bar.negative {\n", - " fill: #2683C9E5;\n", + " .error-message {\n", + " display: flex;\n", + " justify-content: center;\n", + " align-items: center;\n", + " color: rgb(255, 114, 71);\n", + " font-size: 30px;\n", + " font-weight: 900;\n", " }\n", "\n", - "\n", " @media screen and (min-width: 500px) {\n", " .desktop-content {\n", " display: block;\n", @@ -2478,11 +2693,11 @@ " <p>{{@key}}</p>\n", " <div class="display-flex">\n", " <div class="colors-for-distingushing-charts">\n", - " <div class="circle-color" style="background: #369BAC"></div>\n", - " <text alignment-baseline="middle" style="font-size: 15px;">Current</text>\n", + " <div class="circle-color" style="background: #44C0E7;"></div>\n", + " <text alignment-baseline="middle" style="font-size: 15px;">Target</text>\n", " </div>\n", " <div class="colors-for-distingushing-charts">\n", - " <div class="circle-color" style="background: #2683C9"></div>\n", + " <div class="circle-color" style="background: #F5843C"></div>\n", " <text alignment-baseline="middle" style="font-size: 15px;">Reference</text>\n", " </div>\n", " </div></div>\n", @@ -2510,9 +2725,15 @@ "\n", " <script>\n", " function registerHandlebarHelperFunctions() {\n", + " \n", " const findAndDeleteUndefined = (axisData) => {\n", " const undefinedAxisIndex = axisData.findIndex((axis) => axis === undefined)\n", - " return [...axisData.slice(0, undefinedAxisIndex), ...axisData.slice(undefinedAxisIndex + 1)]\n", + " if (undefinedAxisIndex == -1) {\n", + " return axisData;\n", + " }\n", + "\n", + " const result = [...axisData.slice(0, undefinedAxisIndex), ...axisData.slice(undefinedAxisIndex + 1)]\n", + " return result\n", " }\n", "\n", " const filterAndSortChartData = (overlappedHistogramData, histogramData) => {\n", @@ -2551,10 +2772,10 @@ " .attr("preserveAspectRatio", "xMinYMin meet")\n", " .attr("viewBox", `0 0 ${$(window).width()} ${$(window).height()-30}`)\n", " .classed("svg-content-responsive", true)\n", - " this.maxYValue = d3.max(data, (d) => Math.abs(d.axisY));\n", + " this.maxYValue = d3.max([...data, ...referenceData], (d) => Math.abs(d.axisY));\n", " this.xScale = d3\n", " .scaleBand()\n", - " .domain(filterAndSortChartData(data, referenceData).map((sortedCounts) => sortedCounts.axisX))\n", + " .domain(filterAndSortChartData(data, referenceData).map((sortedCounts) => sortedCounts?.axisX))\n", " .range([this.MARGIN.LEFT, this.MARGIN.LEFT + this.CHART_WIDTH]);\n", " this.yScale = d3\n", " .scaleLinear()\n", @@ -2565,15 +2786,34 @@ "\n", " function chartData(column) {\n", " const data = [];\n", + " if (column.stringSummary?.frequent) {\n", " Object.entries(column.stringSummary.frequent.items).forEach(([key, {value, estimate}], index) => {\n", " data.push({\n", " axisY: estimate,\n", " axisX: value,\n", " });\n", " });\n", + " } else if (column.stringSummary?.charPosTracker){\n", + " Object.entries(column.stringSummary.charPosTracker.charPosMap).forEach(([key, {count}], index) => {\n", + " data.push({\n", + " axisY: count,\n", + " axisX: key,\n", + " });\n", + " });\n", + " } else {\n", + " $(document).ready(() => \n", + " $(".desktop-content").html(`\n", + " <p style="height: ${$(window).height()}px" class="error-message"> \n", + " Something went wrong. Please try again.\n", + " </p>\n", + " `)\n", + " )\n", + " }\n", + "\n", " return data\n", " }\n", "\n", + "\n", " function CheckNumberSummary(column) {\n", " if (column.numberSummary) {\n", " return true\n", @@ -2586,14 +2826,17 @@ " let yFormat,\n", " xFormat;\n", " const data = filterAndSortChartData(chartData(histogramData), chartData(overlappedHistogramData)).map((axis, index) => {\n", - " const findIndex = chartData(histogramData).findIndex((value) => value.axisX === axis.axisX)\n", - " return {\n", - " group: axis.axisX,\n", - " profile: axis.axisY,\n", - " reference_profile: chartData(histogramData)[findIndex].axisY\n", + " if (axis) {\n", + " const findIndex = chartData(histogramData).findIndex((value) => value.axisX === axis.axisX)\n", + " return {\n", + " group: axis.axisX,\n", + " profile: axis.axisY,\n", + " reference_profile: chartData(histogramData)[findIndex].axisY\n", + " }\n", " }\n", + " return 0;\n", " })\n", - "\n", + " \n", " const sizes = new GenerateChartParams($(window).height()-60, $(window).width(), chartData(histogramData), chartData(overlappedHistogramData))\n", " let {\n", " MARGIN,\n", @@ -2605,17 +2848,19 @@ " xScale,\n", " yScale\n", " } = sizes\n", - "\n", + " \n", + " const rectColors = ["#44C0E7", "#F5843C"]\n", " const subgroups = ['reference_profile', 'profile']\n", - "\n", + " \n", " xScale.padding([0.3])\n", - "\n", + " \n", " const xAxis = d3.axisBottom(xScale).ticks(SVG_WIDTH / 80, xFormat).tickSizeOuter(0);\n", " const yAxis = d3.axisLeft(yScale).ticks(SVG_HEIGHT / 40, yFormat);\n", " yFormat = yScale.tickFormat(100, yFormat);\n", - "\n", + " \n", " svgEl.append("g")\n", " .attr("transform", `translate(${MARGIN.LEFT}, 0)`)\n", + " .attr("id", "g1")\n", " .call(yAxis)\n", " .call(g => g.select(".domain").remove())\n", " .call(g => g.selectAll(".tick line")\n", @@ -2629,13 +2874,13 @@ "\n", " svgEl.append("text")\n", " .attr("transform",\n", - " "translate(" + (CHART_WIDTH/2) + " ," +\n", + " "translate(" + (CHART_WIDTH/2) + " ," + \n", " (CHART_HEIGHT + MARGIN.TOP + 40) + ")")\n", " .style("text-anchor", "middle")\n", " .text("Values")\n", " .style("font-size", "15")\n", " .style("opacity", "0.6")\n", - "\n", + " \n", " svgEl.append("text")\n", " .attr("transform", "rotate(-90)")\n", " .attr("y", 0)\n", @@ -2643,11 +2888,12 @@ " .attr("dy", "1em")\n", " .style("text-anchor", "middle")\n", " .text("Counts")\n", - " .style("font-size", "15")\n", + " .style("font-size", "15") \n", " .style("opacity", "0.6")\n", - "\n", + " \n", " svgEl.append("g")\n", " .attr("transform", `translate(0,${SVG_HEIGHT - MARGIN.BOTTOM})`)\n", + " .attr("id", "g2")\n", " .call(xAxis)\n", " .call(g => g.select(".domain").remove())\n", " .call(g => g.selectAll(".tick line").remove())\n", @@ -2656,46 +2902,57 @@ " .attr("y", 27)\n", " .attr("fill", "currentColor")\n", " .attr("text-anchor", "end"));\n", - " // Another scale for subgroup position?\n", + "\n", " const xSubgroup = d3.scaleBand()\n", " .domain(subgroups)\n", " .range([0, xScale.bandwidth()])\n", "\n", - " // color palette = one color per subgroup\n", " const color = d3.scaleOrdinal()\n", " .domain(subgroups)\n", - " .range(['#2683C9', '#369BAC'])\n", - "\n", + " .range(rectColors)\n", + " \n", " svgEl.append("g")\n", + " .attr("id", "g3")\n", " .selectAll("g")\n", - " // Enter in data = loop group per group\n", " .data(data)\n", " .enter()\n", " .append("g")\n", - " .attr("transform", function(d) { return "translate(" + xScale(d.group) + ",0)"; })\n", + " .attr("transform", function(d) { return "translate(" + xScale(d?.group) + ",0)"; })\n", + " .attr("id", "g4")\n", " .selectAll("rect")\n", - " .data(function(d) { return subgroups.map(function(key) { return {key: key, value: d[key]}; }); })\n", + " .data(function(d) { return subgroups.map(function(key) { return {key: key, value: d && d[key]}; }); })\n", " .enter().append("rect")\n", " .attr("x", function(d) { return xSubgroup(d.key); })\n", " .attr("y", function(d) { return yScale(d.value); })\n", " .attr("width", xSubgroup.bandwidth())\n", " .attr("height", function(d) { return (CHART_HEIGHT - yScale(d.value)); })\n", " .attr("fill", function(d) { return color(d.key); })\n", - " .style("opacity", "0.6");\n", - "\n", - "\n", + " .style("opacity", "0.8");\n", + " \n", + " \n", " return svgEl._groups[0][0].outerHTML;\n", - " }\n", + " } \n", "\n", - " const profileFromCSVfile = {"debt_settlement_flag": {"counters": {"count": "800"}, "schema": {"inferredType": {"type": "STRING", "ratio": 1.0}, "typeCounts": {"STRING": "800"}}, "stringSummary": {"uniqueCount": {"estimate": 2.0, "upper": 2.0, "lower": 2.0}, "frequent": {"items": [{"value": "N", "estimate": 774.0}, {"value": "Y", "estimate": 26.0}]}, "length": {"count": "800", "min": 1.0, "max": 1.0, "mean": 1.0, "histogram": {"start": 1.0, "end": 1.0000001, "counts": ["800"], "max": 1.0, "min": 1.0, "bins": [1.0, 1.0000001], "n": "800", "width": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, "isDiscrete": true, "stddev": 0.0}, "tokenLength": {"count": "800", "min": 1.0, "max": 1.0, "mean": 1.0, "histogram": {"start": 1.0, "end": 1.0000001, "counts": ["800"], "max": 1.0, "min": 1.0, "bins": [1.0, 1.0000001], "n": "800", "width": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, "isDiscrete": true, "stddev": 0.0}, "charPosTracker": {"characterList": "!#$%&()*+,-./0123456789?@[]^_abcdefghijklmnopqrstuvwyz{}", "charPosMap": {"y": {"count": "26", "histogram": {"counts": ["26"], "bins": [0.0, 0.0], "n": "26", "start": 0.0, "end": 0.0, "width": 0.0, "max": 0.0, "min": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}, "isDiscrete": true, "min": 0.0, "max": 0.0, "mean": 0.0, "stddev": 0.0}, "n": {"count": "774", "histogram": {"counts": ["774"], "bins": [0.0, 0.0], "n": "774", "start": 0.0, "end": 0.0, "width": 0.0, "max": 0.0, "min": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}, "isDiscrete": true, "min": 0.0, "max": 0.0, "mean": 0.0, "stddev": 0.0}}}}, "frequentItems": {"items": [{"estimate": "774", "jsonValue": "N"}, {"estimate": "26", "jsonValue": "Y"}]}, "uniqueCount": {"estimate": 2.000000004967054, "upper": 2.000099863468538, "lower": 2.0}}}\n", + "\n", + " const profileFromCSVfile = {"debt_settlement_flag": {"counters": {"count": "800"}, "schema": {"inferredType": {"type": "STRING", "ratio": 1.0}, "typeCounts": {"STRING": "800"}}, "stringSummary": {"uniqueCount": {"estimate": 2.0, "upper": 2.0, "lower": 2.0}, "frequent": {"items": [{"value": "N", "estimate": 774.0}, {"value": "Y", "estimate": 26.0}]}, "length": {"count": "800", "min": 1.0, "max": 1.0, "mean": 1.0, "histogram": {"start": 1.0, "end": 1.0000001, "counts": ["800"], "max": 1.0, "min": 1.0, "bins": [1.0, 1.0000001], "n": "800", "width": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, "isDiscrete": true, "stddev": 0.0}, "tokenLength": {"count": "800", "min": 1.0, "max": 1.0, "mean": 1.0, "histogram": {"start": 1.0, "end": 1.0000001, "counts": ["800"], "max": 1.0, "min": 1.0, "bins": [1.0, 1.0000001], "n": "800", "width": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, "isDiscrete": true, "stddev": 0.0}, "charPosTracker": {"characterList": "!#$%&()*+,-./0123456789?@[]^_abcdefghijklmnopqrstuvwyz{}", "charPosMap": {"n": {"count": "774", "histogram": {"counts": ["774"], "bins": [0.0, 0.0], "n": "774", "start": 0.0, "end": 0.0, "width": 0.0, "max": 0.0, "min": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}, "isDiscrete": true, "min": 0.0, "max": 0.0, "mean": 0.0, "stddev": 0.0}, "y": {"count": "26", "histogram": {"counts": ["26"], "bins": [0.0, 0.0], "n": "26", "start": 0.0, "end": 0.0, "width": 0.0, "max": 0.0, "min": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}, "isDiscrete": true, "min": 0.0, "max": 0.0, "mean": 0.0, "stddev": 0.0}}}}, "frequentItems": {"items": [{"estimate": "774", "jsonValue": "N"}, {"estimate": "26", "jsonValue": "Y"}]}, "uniqueCount": {"estimate": 2.000000004967054, "upper": 2.000099863468538, "lower": 2.0}}}\n", "\n", " Handlebars.registerHelper("getDoubleHistogramChart",(column,key) => {\n", " const columnKey = key.data.key\n", - " if (profileFromCSVfile) {\n", - " return generateBarChart(\n", - " column,\n", - " profileFromCSVfile[columnKey]\n", - " )\n", + " try {\n", + " if (profileFromCSVfile) {\n", + " return generateBarChart(\n", + " column,\n", + " profileFromCSVfile[columnKey]\n", + " )\n", + " }\n", + " } catch (err) {\n", + " $(document).ready(() => \n", + " $(".desktop-content").html(`\n", + " <p style="height: ${$(window).height()}px" class="error-message"> \n", + " Something went wrong. Please try again.\n", + " </p>\n", + " `)\n", + " )\n", " }\n", " });\n", " }\n", @@ -2973,7 +3230,7 @@ " border: 1px solid #DBE5E7;\n", " box-sizing: border-box;\n", " border-radius: 4px;\n", - " width: 240px;\n", + " width: 170px;\n", " padding-left: 10px;\n", " }\n", "\n", @@ -3122,7 +3379,7 @@ " }\n", "\n", " .turquoise-background-color {\n", - " background-color: #369BAC;\n", + " background-color: #1DBB42;\n", " }\n", "\n", " .bordeaux-background-color {\n", @@ -3381,10 +3638,14 @@ " return column.properties.tags.name\n", " });\n", "\n", + "\n", " Handlebars.registerHelper("alertLIst", function (column) {\n", " let alertListItem = column.map((value) => {\n", " if (value[1][0]) {\n", - " return alertListElement(value[0], value[1][0][0], value[1][0][value[1][0].length - 1] === 0 || (failedConstraints++, false))\n", + " let alertListValue = value[1].map((cstr)=>{\n", + " return alertListElement(value[0],cstr[0],cstr[cstr.length - 1] === 0 || (failedConstraints++, false))\n", + " })\n", + " return alertListValue.join(' ')\n", " } else {\n", " return alertListElement('', value[0], value[value.length - 1] === 0 || (failedConstraints++, false))\n", " }\n", @@ -3396,6 +3657,7 @@ " })\n", " return alertListItem.join(' ')\n", " });\n", + "\n", " }\n", "\n", " function openFilter() {\n", @@ -3605,7 +3867,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.8.13" } }, "nbformat": 4, diff --git a/examples/Constraints.ipynb b/examples/Creating_Customized_Constraints.ipynb similarity index 95% rename from examples/Constraints.ipynb rename to examples/Creating_Customized_Constraints.ipynb index f55d7dad19..b0456fb6b0 100644 --- a/examples/Constraints.ipynb +++ b/examples/Creating_Customized_Constraints.ipynb @@ -1,5 +1,36 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Table of Contents" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this notebook, you'll learn how to:\n", + "\n", + "- Create customized constraints that chan operate on invidiual column values or on overall feature summaries\n", + "- Auto-generate constraints based on the profile's statistics" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Creating Customized Constraints" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> This notebook shows how you can create customized constraints for specific needs. There is a large number of common constraints that can be seen in the [Constraints Suite](Constraints_Suite.ipynb) example. Please refer to the list of existing constraints before going through the work of creating your own set of constraints." + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -741,7 +772,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.8.5" } }, "nbformat": 4, diff --git a/examples/Notebook_Profile_Viewer.ipynb b/examples/Notebook_Profile_Visualizer.ipynb similarity index 82% rename from examples/Notebook_Profile_Viewer.ipynb rename to examples/Notebook_Profile_Visualizer.ipynb index d19f2c95d8..2e0fdd9129 100644 --- a/examples/Notebook_Profile_Viewer.ipynb +++ b/examples/Notebook_Profile_Visualizer.ipynb @@ -4,14 +4,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Viewing Data Drift with the Notebook Profile Viewer" + "# Viewing Data Drift with the Notebook Profile Visualizer" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "In this notebook, we'll show how you can use whylog's Notebook Profile Viewer to compare two different sets of the same data. This includes:\n", + "In this notebook, we'll show how you can use whylog's Notebook Profile Visualizer to compare two different sets of the same data. This includes:\n", "- __Data Drift__: Detecting feature drift between two datasets' distributions\n", "- __Data Visualization__: Comparing feature's histograms and bar charts\n", "- __Summary Statistics__: Visualizing Summary Statistics of individual features\n", @@ -29,7 +29,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "To demonstrate the Profile Viewer, let's use [UCI's Wine Quality Dataset](https://archive.ics.uci.edu/ml/datasets/wine+quality), frequently used for learning purposes. Classification is one possible task, where we predict the wine's quality based on its features, like pH, density and percent alcohol content.\n", + "To demonstrate the Profile Visualizer, let's use [UCI's Wine Quality Dataset](https://archive.ics.uci.edu/ml/datasets/wine+quality), frequently used for learning purposes. Classification is one possible task, where we predict the wine's quality based on its features, like pH, density and percent alcohol content.\n", "\n", "In this example, we will split the available dataset in two groups: wines with alcohol content (`alcohol` feature) below and above 11. The first group is considered our baseline (or reference) dataset, while the second will be our target dataset. The goal here is to induce a case of __Sample Selection Bias__, where the training sample is not representative of the population.\n", "\n", @@ -65,7 +65,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -206,13 +206,14 @@ "4 9.4 5 " ] }, - "execution_count": 2, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", + "pd.options.mode.chained_assignment = None # Disabling false positive warning\n", "\n", "url = \"http://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv\"\n", "wine = pd.read_csv(url,sep=\";\")\n", @@ -221,7 +222,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -263,7 +264,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -278,12 +279,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Let's also add some missing values to `citric acid`, to see how this is reflected in the Profile Viewer later on." + "Let's also add some missing values to `citric acid`, to see how this is reflected in the Profile Visualizer later on." ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -300,12 +301,11 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", - "pd.options.mode.chained_assignment = None # Disabling false positive warning\n", "\n", "bins = (2, 6.5, 8)\n", "group_names = ['bad', 'good']\n", @@ -318,12 +318,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now, we can profile our dataframes with `whylogs`. We will keep these profiles in-memory to use them later as inputs for the `NotebookProfileViewer`" + "Now, we can profile our dataframes with `whylogs`. We will keep these profiles in-memory to use them later as inputs for the `NotebookProfileVisualizer`" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -349,7 +349,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -365,18 +365,18 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Let's instantiate `NotebookProfileViewer` and set the reference and target profiles:" + "Let's instantiate `NotebookProfileVisualizer` and set the reference and target profiles:" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ - "from whylogs.viz import NotebookProfileViewer\n", + "from whylogs.viz import NotebookProfileVisualizer\n", "\n", - "visualization = NotebookProfileViewer()\n", + "visualization = NotebookProfileVisualizer()\n", "visualization.set_profiles(target_profile=target_profile, reference_profile=reference_profile)" ] }, @@ -408,7 +408,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -1258,7 +1258,7 @@ " <script src="https://cdnjs.cloudflare.com/ajax/libs/d3/6.7.0/d3.min.js" integrity="sha512-cd6CHE+XWDQ33ElJqsi0MdNte3S+bQY819f7p3NUHgwQQLXSKjE4cPZTeGNI+vaxZynk1wVU3hoHmow3m089wA==" crossorigin="anonymous" referrerpolicy="no-referrer"></script>\n", "\n", " <script>\n", - " const getReferenceProfile = () => { return {"properties": {"schemaMajorVersion": 1, "schemaMinorVersion": 2, "sessionId": "d178db8d-5b0d-47bc-bf67-62fdd55ac34a", "sessionTimestamp": "1648734114995", "dataTimestamp": "1648647714996", "tags": {"name": "drift-test"}, "metadata": {}, "observations": 4896, "missing_cells": 10, "total_count": 4896, "missing_percentage": 0.20424836601307192}, "columns": {"fixed acidity": {"counters": {"count": "1191"}, "schema": {"inferredType": {"type": "FRACTIONAL", "ratio": 1.0}, "typeCounts": 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- " },\n", - " {\n", - " "estimate": "16",\n", - " "jsonValue": "26.0"\n", - " }\n", - " ]\n", - " },\n", - " "uniqueCount": {\n", - " "estimate": 45.000004917383826,\n", - " "upper": 45.00225173390716,\n", - " "lower": 45.0\n", - " }\n", " }\n", " }\n", "};\n", @@ -4644,7 +4644,7 @@ "" ] }, - "execution_count": 10, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -4685,7 +4685,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -5063,7 +5063,7 @@ " return svgEl._groups[0][0].outerHTML;\n", " }\n", "\n", - " const profileFromCSVfile = {"density": {"counters": {"count": "1191"}, "schema": {"inferredType": {"type": "FRACTIONAL", "ratio": 1.0}, "typeCounts": {"FRACTIONAL": "1191"}}, "numberSummary": {"count": "1191", "min": 0.99236, "max": 1.00369, "mean": 0.9972326448362713, "stddev": 0.0014946134006660365, "histogram": {"start": 0.99235999584198, "end": 1.0036901047177553, "counts": ["0", "2", 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0.95, 0.99, 1.0], "quantileValues": [0.99235999584198, 0.9943000078201294, 0.9950199723243713, 0.9962000250816345, 0.9970800280570984, 0.9980300068855286, 1.0, 1.0013999938964844, 1.0036900043487549]}, "isDiscrete": false}, "frequentItems": {"items": [{"estimate": "56", "jsonValue": "0.99546"}, {"estimate": "55", "jsonValue": "0.99622"}, {"estimate": "54", "jsonValue": "0.99627"}, {"estimate": "54", "jsonValue": "0.99574"}, {"estimate": "53", "jsonValue": "0.99547"}, {"estimate": "53", "jsonValue": "0.99768"}, {"estimate": "53", "jsonValue": "0.99549"}, {"estimate": "53", "jsonValue": "0.99588"}, {"estimate": "53", "jsonValue": "0.99651"}, {"estimate": "53", "jsonValue": "0.99489"}, {"estimate": "53", "jsonValue": "0.99787"}, {"estimate": "53", "jsonValue": "0.99677"}, {"estimate": "53", "jsonValue": "0.99783"}, {"estimate": "53", "jsonValue": "0.99629"}, {"estimate": "53", "jsonValue": "0.9949"}, {"estimate": "53", "jsonValue": "0.99483"}, {"estimate": "53", "jsonValue": "0.99714"}, {"estimate": "53", "jsonValue": "0.9977"}, {"estimate": "53", "jsonValue": "0.99636"}, {"estimate": "53", "jsonValue": "0.99514"}, {"estimate": "53", "jsonValue": "0.99632"}, {"estimate": "53", "jsonValue": "0.99578"}, {"estimate": "53", "jsonValue": "0.99765"}]}, "uniqueCount": {"estimate": 312.0002409817926, "upper": 312.01581892001747, "lower": 312.0}}}\n", + " const profileFromCSVfile = {"density": {"counters": {"count": "1191"}, "schema": {"inferredType": {"type": "FRACTIONAL", "ratio": 1.0}, "typeCounts": {"FRACTIONAL": "1191"}}, "numberSummary": {"count": "1191", "min": 0.99236, "max": 1.00369, "mean": 0.9972326448362713, "stddev": 0.001494613400666039, "histogram": {"start": 0.99235999584198, "end": 1.0036901047177553, "counts": ["0", "2", "2", "2", "4", "12", "36", "40", "75", "92", "135", "136", "126", "129", "102", "82", "42", "42", "34", "34", "26", "18", "4", "8", "0", "0", "6", "0", "0", "2"], "max": 1.0036900043487549, "min": 0.99235999584198, "bins": [0.99235999584198, 0.9927376661378392, 0.9931153364336983, 0.9934930067295575, 0.9938706770254166, 0.9942483473212759, 0.9946260176171351, 0.9950036879129942, 0.9953813582088534, 0.9957590285047125, 0.9961366988005718, 0.996514369096431, 0.9968920393922901, 0.9972697096881493, 0.9976473799840084, 0.9980250502798677, 0.9984027205757269, 0.998780390871586, 0.9991580611674452, 0.9995357314633043, 0.9999134017591635, 1.0002910720550227, 1.000668742350882, 1.001046412646741, 1.0014240829426002, 1.0018017532384595, 1.0021794235343187, 1.0025570938301778, 1.0029347641260369, 1.0033124344218962, 1.0036901047177553], "n": "1191", "width": 0.0}, "uniqueCount": {"estimate": 312.0, "upper": 312.0, "lower": 312.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [0.99235999584198, 0.9943000078201294, 0.9950199723243713, 0.9962000250816345, 0.9970800280570984, 0.9980300068855286, 1.0, 1.0013999938964844, 1.0036900043487549]}, "isDiscrete": false}, "frequentItems": {"items": [{"estimate": "56", "jsonValue": "0.99546"}, {"estimate": "55", "jsonValue": "0.9962200000000001"}, {"estimate": "54", "jsonValue": "0.9962700000000001"}, {"estimate": "54", "jsonValue": "0.99574"}, {"estimate": "53", "jsonValue": "0.9951399999999999"}, {"estimate": "53", "jsonValue": "0.99629"}, {"estimate": "53", "jsonValue": "0.9954700000000001"}, {"estimate": "53", "jsonValue": "0.9948299999999999"}, {"estimate": "53", "jsonValue": "0.9976799999999999"}, {"estimate": "53", "jsonValue": "0.9977"}, {"estimate": "53", "jsonValue": "0.99787"}, {"estimate": "53", "jsonValue": "0.9963200000000001"}, {"estimate": "53", "jsonValue": "0.99651"}, {"estimate": "53", "jsonValue": "0.9949"}, {"estimate": "53", "jsonValue": "0.99677"}, {"estimate": "53", "jsonValue": "0.9958799999999999"}, {"estimate": "53", "jsonValue": "0.99636"}, {"estimate": "53", "jsonValue": "0.9948899999999999"}, {"estimate": "53", "jsonValue": "0.99549"}, {"estimate": "53", "jsonValue": "0.9957799999999999"}, {"estimate": "53", "jsonValue": "0.9978299999999999"}, {"estimate": "53", "jsonValue": "0.9971399999999999"}, {"estimate": "53", "jsonValue": "0.99765"}]}, "uniqueCount": {"estimate": 312.0002409817926, "upper": 312.01581892001747, "lower": 312.0}}}\n", "\n", " Handlebars.registerHelper("getDoubleHistogramChart",(column,key) => {\n", " const columnKey = key.data.key\n", @@ -5092,7 +5092,7 @@ "\n", " function initHandlebarsTemplate() {\n", " // Replace this context with JSON from .py file\n", - " const context = {"density": {"counters": {"count": "408"}, "schema": {"inferredType": {"type": "FRACTIONAL", "ratio": 1.0}, "typeCounts": {"FRACTIONAL": "408"}}, "numberSummary": {"count": "408", "min": 0.99007, "max": 1.0032, "mean": 0.9953280882352936, "stddev": 0.002178457013634714, "histogram": {"start": 0.9900699853897095, "end": 1.0032001544887066, "counts": ["4", "4", "0", "10", "4", "11", "8", "32", "32", "37", "38", "49", "46", "25", "25", "18", "15", "8", "4", "8", "6", "6", "2", "4", "4", "2", "0", "2", "0", "4"], "max": 1.0032000541687012, "min": 0.9900699853897095, "bins": [0.9900699853897095, 0.9905076576930094, 0.9909453299963092, 0.9913830022996092, 0.9918206746029091, 0.992258346906209, 0.9926960192095089, 0.9931336915128088, 0.9935713638161087, 0.9940090361194086, 0.9944467084227085, 0.9948843807260084, 0.9953220530293083, 0.9957597253326083, 0.9961973976359081, 0.996635069939208, 0.997072742242508, 0.9975104145458078, 0.9979480868491077, 0.9983857591524077, 0.9988234314557075, 0.9992611037590075, 0.9996987760623074, 1.0001364483656072, 1.0005741206689072, 1.001011792972207, 1.001449465275507, 1.001887137578807, 1.0023248098821067, 1.0027624821854066, 1.0032001544887066], "n": "408", "width": 0.0}, "uniqueCount": {"estimate": 228.0, "upper": 228.0, "lower": 228.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [0.9900699853897095, 0.9906399846076965, 0.9922000169754028, 0.993939995765686, 0.9951599836349487, 0.9962999820709229, 0.9994000196456909, 1.0020999908447266, 1.0032000541687012]}, "isDiscrete": false}, "frequentItems": {"items": [{"estimate": "18", "jsonValue": "0.99402"}, {"estimate": "17", "jsonValue": "0.99314"}, {"estimate": "17", "jsonValue": "0.99512"}, {"estimate": "17", "jsonValue": "0.99492"}, {"estimate": "17", "jsonValue": "0.99494"}, {"estimate": "17", "jsonValue": "0.99414"}, {"estimate": "17", "jsonValue": "0.9947"}]}, "uniqueCount": {"estimate": 228.00012853749982, "upper": 228.0115124130585, "lower": 228.0}}};\n", + " const context = {"density": {"counters": {"count": "408"}, "schema": {"inferredType": {"type": "FRACTIONAL", "ratio": 1.0}, "typeCounts": {"FRACTIONAL": "408"}}, "numberSummary": {"count": "408", "min": 0.9900700000000001, "max": 1.0032, "mean": 0.9953280882352936, "stddev": 0.002178457013634713, "histogram": {"start": 0.9900699853897095, "end": 1.0032001544887066, "counts": ["4", "4", "0", "10", "4", "11", "8", "32", "32", "37", "38", "49", "46", "25", "25", "18", "15", "8", "4", "8", "6", "6", "2", "4", "4", "2", "0", "2", "0", "4"], "max": 1.0032000541687012, "min": 0.9900699853897095, "bins": [0.9900699853897095, 0.9905076576930094, 0.9909453299963092, 0.9913830022996092, 0.9918206746029091, 0.992258346906209, 0.9926960192095089, 0.9931336915128088, 0.9935713638161087, 0.9940090361194086, 0.9944467084227085, 0.9948843807260084, 0.9953220530293083, 0.9957597253326083, 0.9961973976359081, 0.996635069939208, 0.997072742242508, 0.9975104145458078, 0.9979480868491077, 0.9983857591524077, 0.9988234314557075, 0.9992611037590075, 0.9996987760623074, 1.0001364483656072, 1.0005741206689072, 1.001011792972207, 1.001449465275507, 1.001887137578807, 1.0023248098821067, 1.0027624821854066, 1.0032001544887066], "n": "408", "width": 0.0}, "uniqueCount": {"estimate": 228.0, "upper": 228.0, "lower": 228.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [0.9900699853897095, 0.9906399846076965, 0.9922000169754028, 0.993939995765686, 0.9951599836349487, 0.9962999820709229, 0.9994000196456909, 1.0020999908447266, 1.0032000541687012]}, "isDiscrete": false}, "frequentItems": {"items": [{"estimate": "18", "jsonValue": "0.9940200000000001"}, {"estimate": "17", "jsonValue": "0.9941399999999999"}, {"estimate": "17", "jsonValue": "0.9951200000000001"}, {"estimate": "17", "jsonValue": "0.99492"}, {"estimate": "17", "jsonValue": "0.9949399999999999"}, {"estimate": "17", "jsonValue": "0.9947"}, {"estimate": "17", "jsonValue": "0.9931399999999999"}]}, "uniqueCount": {"estimate": 228.00012853749982, "upper": 228.0115124130585, "lower": 228.0}}};\n", " // Config handlebars and pass data to HBS template\n", " const source = document.getElementById("entry-template").innerHTML;\n", " const template = Handlebars.compile(source);\n", @@ -5113,7 +5113,7 @@ "" ] }, - "execution_count": 11, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -5140,7 +5140,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -5518,7 +5518,7 @@ " return svgEl._groups[0][0].outerHTML;\n", " }\n", "\n", - " const profileFromCSVfile = {"alcohol": {"counters": {"count": "1191"}, "schema": {"inferredType": {"type": "FRACTIONAL", "ratio": 1.0}, "typeCounts": {"FRACTIONAL": "1191"}}, "numberSummary": {"count": "1191", "min": 8.4, "max": 11.0, "mean": 9.910383431290208, "stddev": 0.5788373617222901, "histogram": {"start": 8.399999618530273, "end": 11.0000011, "counts": ["2", "0", "0", "0", "2", "0", "30", "0", "26", "70", "62", "104", "138", "62", "52", "0", "79", "50", "72", "47", "44", "36", "0", "36", "71", "26", "28", "42", "51", "61"], "max": 11.0, "min": 8.399999618530273, "bins": [8.399999618530273, 8.486666334579265, 8.573333050628255, 8.659999766677245, 8.746666482726237, 8.833333198775229, 8.919999914824219, 9.006666630873209, 9.0933333469222, 9.180000062971192, 9.266666779020182, 9.353333495069172, 9.440000211118164, 9.526666927167156, 9.613333643216146, 9.700000359265136, 9.786667075314128, 9.87333379136312, 9.96000050741211, 10.0466672234611, 10.133333939510091, 10.220000655559083, 10.306667371608073, 10.393334087657063, 10.480000803706055, 10.566667519755047, 10.653334235804037, 10.740000951853027, 10.826667667902019, 10.91333438395101, 11.0000011], "n": "1191", "width": 0.0}, "uniqueCount": {"estimate": 34.0, "upper": 34.0, "lower": 34.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [8.399999618530273, 9.0, 9.100000381469727, 9.5, 9.800000190734863, 10.399999618530273, 11.0, 11.0, 11.0]}, "isDiscrete": false}, "frequentItems": {"items": [{"estimate": "139", "jsonValue": "9.5"}, {"estimate": "103", "jsonValue": "9.4"}, {"estimate": "78", "jsonValue": "9.8"}, {"estimate": "72", "jsonValue": "9.2"}, {"estimate": "67", "jsonValue": "10.5"}, {"estimate": "67", "jsonValue": "10.0"}, {"estimate": "59", "jsonValue": "9.6"}, {"estimate": "59", "jsonValue": "9.3"}, {"estimate": "59", "jsonValue": "11.0"}, {"estimate": "54", "jsonValue": "9.7"}, {"estimate": "51", "jsonValue": "10.1"}, {"estimate": "51", "jsonValue": "9.9"}, {"estimate": "49", "jsonValue": "10.2"}, {"estimate": "49", "jsonValue": "10.6"}, {"estimate": "49", "jsonValue": "10.9"}, {"estimate": "48", "jsonValue": "10.4"}, {"estimate": "47", "jsonValue": "10.55"}, {"estimate": "47", "jsonValue": "10.75"}, {"estimate": "47", "jsonValue": "10.3"}, {"estimate": "47", "jsonValue": "10.8"}]}, "uniqueCount": {"estimate": 34.00000278651741, "upper": 34.00170038117756, "lower": 34.0}}}\n", + " const profileFromCSVfile = {"alcohol": {"counters": {"count": "1191"}, "schema": {"inferredType": {"type": "FRACTIONAL", "ratio": 1.0}, "typeCounts": {"FRACTIONAL": "1191"}}, "numberSummary": {"count": "1191", "min": 8.4, "max": 11.0, "mean": 9.910383431290208, "stddev": 0.5788373617222901, "histogram": {"start": 8.399999618530273, "end": 11.0000011, "counts": ["2", "0", "0", "0", "2", "0", "34", "0", "22", "74", "58", "104", "138", "62", "52", "0", "79", "54", "68", "47", "44", "36", "0", "40", "67", "26", "28", "46", "51", "57"], "max": 11.0, "min": 8.399999618530273, "bins": [8.399999618530273, 8.486666334579265, 8.573333050628255, 8.659999766677245, 8.746666482726237, 8.833333198775229, 8.919999914824219, 9.006666630873209, 9.0933333469222, 9.180000062971192, 9.266666779020182, 9.353333495069172, 9.440000211118164, 9.526666927167156, 9.613333643216146, 9.700000359265136, 9.786667075314128, 9.87333379136312, 9.96000050741211, 10.0466672234611, 10.133333939510091, 10.220000655559083, 10.306667371608073, 10.393334087657063, 10.480000803706055, 10.566667519755047, 10.653334235804037, 10.740000951853027, 10.826667667902019, 10.91333438395101, 11.0000011], "n": "1191", "width": 0.0}, "uniqueCount": {"estimate": 34.0, "upper": 34.0, "lower": 34.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [8.399999618530273, 9.0, 9.100000381469727, 9.5, 9.800000190734863, 10.399999618530273, 10.899999618530273, 11.0, 11.0]}, "isDiscrete": false}, "frequentItems": {"items": [{"estimate": "139", "jsonValue": "9.5"}, {"estimate": "103", "jsonValue": "9.4"}, {"estimate": "78", "jsonValue": "9.8"}, {"estimate": "72", "jsonValue": "9.2"}, {"estimate": "67", "jsonValue": "10.5"}, {"estimate": "67", "jsonValue": "10.0"}, {"estimate": "59", "jsonValue": "9.6"}, {"estimate": "59", "jsonValue": "9.3"}, {"estimate": "59", "jsonValue": "11.0"}, {"estimate": "54", "jsonValue": "9.7"}, {"estimate": "51", "jsonValue": "10.1"}, {"estimate": "51", "jsonValue": "9.9"}, {"estimate": "49", "jsonValue": "10.2"}, {"estimate": "49", "jsonValue": "10.6"}, {"estimate": "49", "jsonValue": "10.9"}, {"estimate": "48", "jsonValue": "10.4"}, {"estimate": "47", "jsonValue": "10.55"}, {"estimate": "47", "jsonValue": "10.75"}, {"estimate": "47", "jsonValue": "10.3"}, {"estimate": "47", "jsonValue": "10.8"}]}, "uniqueCount": {"estimate": 34.00000278651741, "upper": 34.00170038117756, "lower": 34.0}}}\n", "\n", " Handlebars.registerHelper("getDoubleHistogramChart",(column,key) => {\n", " const columnKey = key.data.key\n", @@ -5547,7 +5547,7 @@ "\n", " function initHandlebarsTemplate() {\n", " // Replace this context with JSON from .py file\n", - " const context = {"alcohol": {"counters": {"count": "408"}, "schema": {"inferredType": {"type": "FRACTIONAL", "ratio": 1.0}, "typeCounts": {"FRACTIONAL": "408"}}, "numberSummary": {"count": "408", "min": 11.0666666666667, "max": 14.9, "mean": 11.919321895424847, "stddev": 0.6824986635512771, "histogram": {"start": 11.066666603088379, "end": 14.900001108530235, "counts": ["28", "68", "32", "30", "37", "30", "21", "20", "26", "11", "13", "21", "15", "17", "10", "8", "0", "4", "2", "7", "0", "0", "6", "0", "0", "0", "0", "0", "0", "2"], "max": 14.899999618530273, "min": 11.066666603088379, "bins": [11.066666603088379, 11.19444441993644, 11.322222236784503, 11.450000053632564, 11.577777870480626, 11.705555687328689, 11.83333350417675, 11.961111321024813, 12.088889137872874, 12.216666954720935, 12.344444771568998, 12.47222258841706, 12.60000040526512, 12.727778222113184, 12.855556038961245, 12.983333855809306, 13.111111672657369, 13.23888948950543, 13.366667306353492, 13.494445123201555, 13.622222940049616, 13.750000756897677, 13.87777857374574, 14.005556390593801, 14.133334207441862, 14.261112024289925, 14.388889841137987, 14.51666765798605, 14.64444547483411, 14.772223291682174, 14.900001108530235], "n": "408", "width": 0.0}, "uniqueCount": {"estimate": 31.0, "upper": 31.0, "lower": 31.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [11.066666603088379, 11.100000381469727, 11.100000381469727, 11.399999618530273, 11.800000190734863, 12.399999618530273, 13.300000190734863, 14.0, 14.899999618530273]}, "isDiscrete": false}, "frequentItems": {"items": [{"estimate": "36", "jsonValue": "11.2"}, {"estimate": "32", "jsonValue": "11.3"}, {"estimate": "32", "jsonValue": "11.4"}, {"estimate": "30", "jsonValue": "11.5"}, {"estimate": "29", "jsonValue": "11.8"}, {"estimate": "27", "jsonValue": "11.1"}, {"estimate": "23", "jsonValue": "11.7"}, {"estimate": "21", "jsonValue": "12.0"}, {"estimate": "21", "jsonValue": "12.5"}, {"estimate": "21", "jsonValue": "11.9"}, {"estimate": "17", "jsonValue": "12.8"}, {"estimate": "17", "jsonValue": "11.6"}, {"estimate": "16", "jsonValue": "12.4"}, {"estimate": "14", "jsonValue": "11.95"}, {"estimate": "14", "jsonValue": "12.1"}, {"estimate": "14", "jsonValue": "13.6"}]}, "uniqueCount": {"estimate": 31.000002309680188, "upper": 31.00155011656467, "lower": 31.0}}};\n", + " const context = {"alcohol": {"counters": {"count": "408"}, "schema": {"inferredType": {"type": "FRACTIONAL", "ratio": 1.0}, "typeCounts": {"FRACTIONAL": "408"}}, "numberSummary": {"count": "408", "min": 11.066666666666698, "max": 14.9, "mean": 11.919321895424847, "stddev": 0.6824986635512771, "histogram": {"start": 11.066666603088379, "end": 14.900001108530235, "counts": ["28", "68", "32", "30", "37", "30", "21", "20", "26", "11", "13", "21", "15", "17", "10", "8", "0", "4", "2", "7", "0", "0", "6", "0", "0", "0", "0", "0", "0", "2"], "max": 14.899999618530273, "min": 11.066666603088379, "bins": [11.066666603088379, 11.19444441993644, 11.322222236784503, 11.450000053632564, 11.577777870480626, 11.705555687328689, 11.83333350417675, 11.961111321024813, 12.088889137872874, 12.216666954720935, 12.344444771568998, 12.47222258841706, 12.60000040526512, 12.727778222113184, 12.855556038961245, 12.983333855809306, 13.111111672657369, 13.23888948950543, 13.366667306353492, 13.494445123201555, 13.622222940049616, 13.750000756897677, 13.87777857374574, 14.005556390593801, 14.133334207441862, 14.261112024289925, 14.388889841137987, 14.51666765798605, 14.64444547483411, 14.772223291682174, 14.900001108530235], "n": "408", "width": 0.0}, "uniqueCount": {"estimate": 31.0, "upper": 31.0, "lower": 31.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [11.066666603088379, 11.100000381469727, 11.100000381469727, 11.399999618530273, 11.800000190734863, 12.399999618530273, 13.300000190734863, 14.0, 14.899999618530273]}, "isDiscrete": false}, "frequentItems": {"items": [{"estimate": "36", "jsonValue": "11.2"}, {"estimate": "32", "jsonValue": "11.3"}, {"estimate": "32", "jsonValue": "11.4"}, {"estimate": "30", "jsonValue": "11.5"}, {"estimate": "29", "jsonValue": "11.8"}, {"estimate": "27", "jsonValue": "11.1"}, {"estimate": "23", "jsonValue": "11.7"}, {"estimate": "21", "jsonValue": "12.0"}, {"estimate": "21", "jsonValue": "12.5"}, {"estimate": "21", "jsonValue": "11.9"}, {"estimate": "17", "jsonValue": "12.8"}, {"estimate": "17", "jsonValue": "11.6"}, {"estimate": "16", "jsonValue": "12.4"}, {"estimate": "14", "jsonValue": "11.95"}, {"estimate": "14", "jsonValue": "12.1"}, {"estimate": "14", "jsonValue": "13.6"}]}, "uniqueCount": {"estimate": 31.000002309680188, "upper": 31.00155011656467, "lower": 31.0}}};\n", " // Config handlebars and pass data to HBS template\n", " const source = document.getElementById("entry-template").innerHTML;\n", " const template = Handlebars.compile(source);\n", @@ -5568,7 +5568,7 @@ "" ] }, - "execution_count": 12, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -5586,7 +5586,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -6001,7 +6001,7 @@ " } \n", "\n", "\n", - " const profileFromCSVfile = {"quality": {"counters": {"count": "1191"}, "schema": {"inferredType": {"type": "STRING", "ratio": 1.0}, "typeCounts": {"STRING": "1191"}}, "stringSummary": {"uniqueCount": {"estimate": 2.0, "upper": 2.0, "lower": 2.0}, "frequent": {"items": [{"value": "bad", "estimate": 1115.0}, {"value": "good", "estimate": 76.0}]}, "length": {"count": "1191", "min": 3.0, "max": 4.0, "mean": 3.063811922753988, "stddev": 0.2445202709726784, "histogram": {"start": 3.0, "end": 4.0000004, "counts": ["1115", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "76"], "max": 4.0, "min": 3.0, "bins": [3.0, 3.0333333466666668, 3.0666666933333335, 3.10000004, 3.1333333866666666, 3.1666667333333334, 3.20000008, 3.233333426666667, 3.266666773333333, 3.30000012, 3.3333334666666667, 3.3666668133333335, 3.4000001600000003, 3.4333335066666666, 3.4666668533333334, 3.5000002, 3.533333546666667, 3.566666893333333, 3.60000024, 3.6333335866666667, 3.6666669333333335, 3.7000002800000003, 3.733333626666667, 3.7666669733333333, 3.80000032, 3.833333666666667, 3.8666670133333336, 3.90000036, 3.9333337066666667, 3.9666670533333335, 4.0000004], "n": "1191", "width": 0.0}, "uniqueCount": {"estimate": 2.0, "upper": 2.0, "lower": 2.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 4.0, 4.0, 4.0]}, "isDiscrete": true}, "tokenLength": {"count": "1191", "min": 1.0, "max": 1.0, "mean": 1.0, "histogram": {"start": 1.0, "end": 1.0000001, "counts": ["1191"], "max": 1.0, "min": 1.0, "bins": [1.0, 1.0000001], "n": "1191", "width": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, "isDiscrete": true, "stddev": 0.0}, "charPosTracker": {"characterList": "!#$%&()*+,-./0123456789?@[]^_abcdefghijklmnopqrstuvwyz{}", "charPosMap": {"g": {"count": "76", "histogram": {"counts": ["76"], "bins": [0.0, 0.0], "n": "76", "start": 0.0, "end": 0.0, "width": 0.0, "max": 0.0, "min": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}, "isDiscrete": true, "min": 0.0, "max": 0.0, "mean": 0.0, "stddev": 0.0}, "d": {"count": "1191", "min": 2.0, "max": 3.0, "mean": 2.063811922753988, "stddev": 0.2445202709726784, "histogram": {"start": 2.0, "end": 3.0000003, "counts": ["1115", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "76"], "max": 3.0, "min": 2.0, "bins": [2.0, 2.033333343333333, 2.0666666866666668, 2.10000003, 2.1333333733333335, 2.1666667166666667, 2.20000006, 2.2333334033333334, 2.2666667466666666, 2.30000009, 2.3333334333333333, 2.3666667766666665, 2.40000012, 2.4333334633333332, 2.466666806666667, 2.50000015, 2.533333493333333, 2.5666668366666667, 2.60000018, 2.6333335233333335, 2.6666668666666666, 2.70000021, 2.7333335533333334, 2.7666668966666665, 2.80000024, 2.8333335833333333, 2.8666669266666664, 2.90000027, 2.9333336133333336, 2.966666956666667, 3.0000003], "n": "1191", "width": 0.0}, "uniqueCount": {"estimate": 2.0, "upper": 2.0, "lower": 2.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 3.0, 3.0, 3.0]}, "isDiscrete": true}, "b": {"count": "1115", "histogram": {"counts": ["1115"], "bins": [0.0, 0.0], "n": "1115", "start": 0.0, "end": 0.0, "width": 0.0, "max": 0.0, "min": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}, "isDiscrete": true, "min": 0.0, "max": 0.0, "mean": 0.0, "stddev": 0.0}, "a": {"count": "1115", "min": 1.0, "max": 1.0, "mean": 1.0, "histogram": {"start": 1.0, "end": 1.0000001, "counts": ["1115"], "max": 1.0, "min": 1.0, "bins": [1.0, 1.0000001], "n": "1115", "width": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, "isDiscrete": true, "stddev": 0.0}, "o": {"count": "152", "min": 1.0, "max": 2.0, "mean": 1.5, "stddev": 0.5016528970703477, "histogram": {"start": 1.0, "end": 2.0000002, "counts": ["76", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "76"], "max": 2.0, "min": 1.0, "bins": [1.0, 1.03333334, 1.06666668, 1.10000002, 1.13333336, 1.1666667, 1.20000004, 1.23333338, 1.26666672, 1.30000006, 1.3333334, 1.36666674, 1.40000008, 1.43333342, 1.46666676, 1.5000001, 1.53333344, 1.56666678, 1.60000012, 1.63333346, 1.6666668, 1.7000001400000002, 1.7333334800000002, 1.7666668200000002, 1.8000001600000002, 1.8333335000000002, 1.8666668400000002, 1.9000001800000001, 1.9333335200000001, 1.9666668600000001, 2.0000002], "n": "152", "width": 0.0}, "uniqueCount": {"estimate": 2.0, "upper": 2.0, "lower": 2.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 2.0, 2.0]}, "isDiscrete": true}}}}, "frequentItems": {"items": [{"estimate": "1115", "jsonValue": "bad"}, {"estimate": "76", "jsonValue": "good"}]}, "uniqueCount": {"estimate": 2.000000004967054, "upper": 2.000099863468538, "lower": 2.0}}}\n", + " const profileFromCSVfile = {"quality": {"counters": {"count": "1191"}, "schema": {"inferredType": {"type": "STRING", "ratio": 1.0}, "typeCounts": {"STRING": "1191"}}, "stringSummary": {"uniqueCount": {"estimate": 2.0, "upper": 2.0, "lower": 2.0}, "frequent": {"items": [{"value": "bad", "estimate": 1115.0}, {"value": "good", "estimate": 76.0}]}, "length": {"count": "1191", "min": 3.0, "max": 4.0, "mean": 3.063811922753988, "stddev": 0.2445202709726784, "histogram": {"start": 3.0, "end": 4.0000004, "counts": ["1115", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "76"], "max": 4.0, "min": 3.0, "bins": [3.0, 3.0333333466666668, 3.0666666933333335, 3.10000004, 3.1333333866666666, 3.1666667333333334, 3.20000008, 3.233333426666667, 3.266666773333333, 3.30000012, 3.3333334666666667, 3.3666668133333335, 3.4000001600000003, 3.4333335066666666, 3.4666668533333334, 3.5000002, 3.533333546666667, 3.566666893333333, 3.60000024, 3.6333335866666667, 3.6666669333333335, 3.7000002800000003, 3.733333626666667, 3.7666669733333333, 3.80000032, 3.833333666666667, 3.8666670133333336, 3.90000036, 3.9333337066666667, 3.9666670533333335, 4.0000004], "n": "1191", "width": 0.0}, "uniqueCount": {"estimate": 2.0, "upper": 2.0, "lower": 2.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 4.0, 4.0, 4.0]}, "isDiscrete": true}, "tokenLength": {"count": "1191", "min": 1.0, "max": 1.0, "mean": 1.0, "histogram": {"start": 1.0, "end": 1.0000001, "counts": ["1191"], "max": 1.0, "min": 1.0, "bins": [1.0, 1.0000001], "n": "1191", "width": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, "isDiscrete": true, "stddev": 0.0}, "charPosTracker": {"characterList": "!#$%&()*+,-./0123456789?@[]^_abcdefghijklmnopqrstuvwyz{}", "charPosMap": {"d": {"count": "1191", "min": 2.0, "max": 3.0, "mean": 2.063811922753988, "stddev": 0.2445202709726784, "histogram": {"start": 2.0, "end": 3.0000003, "counts": ["1115", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "76"], "max": 3.0, "min": 2.0, "bins": [2.0, 2.033333343333333, 2.0666666866666668, 2.10000003, 2.1333333733333335, 2.1666667166666667, 2.20000006, 2.2333334033333334, 2.2666667466666666, 2.30000009, 2.3333334333333333, 2.3666667766666665, 2.40000012, 2.4333334633333332, 2.466666806666667, 2.50000015, 2.533333493333333, 2.5666668366666667, 2.60000018, 2.6333335233333335, 2.6666668666666666, 2.70000021, 2.7333335533333334, 2.7666668966666665, 2.80000024, 2.8333335833333333, 2.8666669266666664, 2.90000027, 2.9333336133333336, 2.966666956666667, 3.0000003], "n": "1191", "width": 0.0}, "uniqueCount": {"estimate": 2.0, "upper": 2.0, "lower": 2.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 3.0, 3.0, 3.0]}, "isDiscrete": true}, "o": {"count": "152", "min": 1.0, "max": 2.0, "mean": 1.5, "stddev": 0.5016528970703477, "histogram": {"start": 1.0, "end": 2.0000002, "counts": ["76", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "76"], "max": 2.0, "min": 1.0, "bins": [1.0, 1.03333334, 1.06666668, 1.10000002, 1.13333336, 1.1666667, 1.20000004, 1.23333338, 1.26666672, 1.30000006, 1.3333334, 1.36666674, 1.40000008, 1.43333342, 1.46666676, 1.5000001, 1.53333344, 1.56666678, 1.60000012, 1.63333346, 1.6666668, 1.7000001400000002, 1.7333334800000002, 1.7666668200000002, 1.8000001600000002, 1.8333335000000002, 1.8666668400000002, 1.9000001800000001, 1.9333335200000001, 1.9666668600000001, 2.0000002], "n": "152", "width": 0.0}, "uniqueCount": {"estimate": 2.0, "upper": 2.0, "lower": 2.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 2.0, 2.0]}, "isDiscrete": true}, "g": {"count": "76", "histogram": {"counts": ["76"], "bins": [0.0, 0.0], "n": "76", "start": 0.0, "end": 0.0, "width": 0.0, "max": 0.0, "min": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}, "isDiscrete": true, "min": 0.0, "max": 0.0, "mean": 0.0, "stddev": 0.0}, "b": {"count": "1115", "histogram": {"counts": ["1115"], "bins": [0.0, 0.0], "n": "1115", "start": 0.0, "end": 0.0, "width": 0.0, "max": 0.0, "min": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}, "isDiscrete": true, "min": 0.0, "max": 0.0, "mean": 0.0, "stddev": 0.0}, "a": {"count": "1115", "min": 1.0, "max": 1.0, "mean": 1.0, "histogram": {"start": 1.0, "end": 1.0000001, "counts": ["1115"], "max": 1.0, "min": 1.0, "bins": [1.0, 1.0000001], "n": "1115", "width": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, "isDiscrete": true, "stddev": 0.0}}}}, "frequentItems": {"items": [{"estimate": "1115", "jsonValue": "bad"}, {"estimate": "76", "jsonValue": "good"}]}, "uniqueCount": {"estimate": 2.000000004967054, "upper": 2.000099863468538, "lower": 2.0}}}\n", "\n", " Handlebars.registerHelper("getDoubleHistogramChart",(column,key) => {\n", " const columnKey = key.data.key\n", @@ -6030,7 +6030,7 @@ "\n", " function initHandlebarsTemplate() {\n", " // Replace this context with JSON from .py file\n", - " const context = {"quality": {"counters": {"count": "408"}, "schema": {"inferredType": {"type": "STRING", "ratio": 1.0}, "typeCounts": {"STRING": "408"}}, "stringSummary": {"uniqueCount": {"estimate": 2.0, "upper": 2.0, "lower": 2.0}, "frequent": {"items": [{"value": "bad", "estimate": 267.0}, {"value": "good", "estimate": 141.0}]}, "length": {"count": "408", "min": 3.0, "max": 4.0, "mean": 3.3455882352941178, "stddev": 0.4761435447867452, "histogram": {"start": 3.0, "end": 4.0000004, "counts": ["266", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "142"], "max": 4.0, "min": 3.0, "bins": [3.0, 3.0333333466666668, 3.0666666933333335, 3.10000004, 3.1333333866666666, 3.1666667333333334, 3.20000008, 3.233333426666667, 3.266666773333333, 3.30000012, 3.3333334666666667, 3.3666668133333335, 3.4000001600000003, 3.4333335066666666, 3.4666668533333334, 3.5000002, 3.533333546666667, 3.566666893333333, 3.60000024, 3.6333335866666667, 3.6666669333333335, 3.7000002800000003, 3.733333626666667, 3.7666669733333333, 3.80000032, 3.833333666666667, 3.8666670133333336, 3.90000036, 3.9333337066666667, 3.9666670533333335, 4.0000004], "n": "408", "width": 0.0}, "uniqueCount": {"estimate": 2.0, "upper": 2.0, "lower": 2.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [3.0, 3.0, 3.0, 3.0, 3.0, 4.0, 4.0, 4.0, 4.0]}, "isDiscrete": true}, "tokenLength": {"count": "408", "min": 1.0, "max": 1.0, "mean": 1.0, "histogram": {"start": 1.0, "end": 1.0000001, "counts": ["408"], "max": 1.0, "min": 1.0, "bins": [1.0, 1.0000001], "n": "408", "width": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, "isDiscrete": true, "stddev": 0.0}, "charPosTracker": {"characterList": "!#$%&()*+,-./0123456789?@[]^_abcdefghijklmnopqrstuvwyz{}", "charPosMap": {"g": {"count": "141", "histogram": {"counts": ["141"], "bins": [0.0, 0.0], "n": "141", "start": 0.0, "end": 0.0, "width": 0.0, "max": 0.0, "min": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}, "isDiscrete": true, "min": 0.0, "max": 0.0, "mean": 0.0, "stddev": 0.0}, "a": {"count": "267", "min": 1.0, "max": 1.0, "mean": 1.0, "histogram": {"start": 1.0, "end": 1.0000001, "counts": ["267"], "max": 1.0, "min": 1.0, "bins": [1.0, 1.0000001], "n": "267", "width": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, "isDiscrete": true, "stddev": 0.0}, "o": {"count": "282", "min": 1.0, "max": 2.0, "mean": 1.5, "stddev": 0.5008888895905983, "histogram": {"start": 1.0, "end": 2.0000002, "counts": ["141", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "141"], "max": 2.0, "min": 1.0, "bins": [1.0, 1.03333334, 1.06666668, 1.10000002, 1.13333336, 1.1666667, 1.20000004, 1.23333338, 1.26666672, 1.30000006, 1.3333334, 1.36666674, 1.40000008, 1.43333342, 1.46666676, 1.5000001, 1.53333344, 1.56666678, 1.60000012, 1.63333346, 1.6666668, 1.7000001400000002, 1.7333334800000002, 1.7666668200000002, 1.8000001600000002, 1.8333335000000002, 1.8666668400000002, 1.9000001800000001, 1.9333335200000001, 1.9666668600000001, 2.0000002], "n": "282", "width": 0.0}, "uniqueCount": {"estimate": 2.0, "upper": 2.0, "lower": 2.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 2.0, 2.0]}, "isDiscrete": true}, "d": {"count": "408", "min": 2.0, "max": 3.0, "mean": 2.3455882352941178, "stddev": 0.4761435447867452, "histogram": {"start": 2.0, "end": 3.0000003, "counts": ["268", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "140"], "max": 3.0, "min": 2.0, "bins": [2.0, 2.033333343333333, 2.0666666866666668, 2.10000003, 2.1333333733333335, 2.1666667166666667, 2.20000006, 2.2333334033333334, 2.2666667466666666, 2.30000009, 2.3333334333333333, 2.3666667766666665, 2.40000012, 2.4333334633333332, 2.466666806666667, 2.50000015, 2.533333493333333, 2.5666668366666667, 2.60000018, 2.6333335233333335, 2.6666668666666666, 2.70000021, 2.7333335533333334, 2.7666668966666665, 2.80000024, 2.8333335833333333, 2.8666669266666664, 2.90000027, 2.9333336133333336, 2.966666956666667, 3.0000003], "n": "408", "width": 0.0}, "uniqueCount": {"estimate": 2.0, "upper": 2.0, "lower": 2.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [2.0, 2.0, 2.0, 2.0, 2.0, 3.0, 3.0, 3.0, 3.0]}, "isDiscrete": true}, "b": {"count": "267", "histogram": {"counts": ["267"], "bins": [0.0, 0.0], "n": "267", "start": 0.0, "end": 0.0, "width": 0.0, "max": 0.0, "min": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}, "isDiscrete": true, "min": 0.0, "max": 0.0, "mean": 0.0, "stddev": 0.0}}}}, "frequentItems": {"items": [{"estimate": "267", "jsonValue": "bad"}, {"estimate": "141", "jsonValue": "good"}]}, "uniqueCount": {"estimate": 2.000000004967054, "upper": 2.000099863468538, "lower": 2.0}}};\n", + " const context = {"quality": {"counters": {"count": "408"}, "schema": {"inferredType": {"type": "STRING", "ratio": 1.0}, "typeCounts": {"STRING": "408"}}, "stringSummary": {"uniqueCount": {"estimate": 2.0, "upper": 2.0, "lower": 2.0}, "frequent": {"items": [{"value": "bad", "estimate": 267.0}, {"value": "good", "estimate": 141.0}]}, "length": {"count": "408", "min": 3.0, "max": 4.0, "mean": 3.3455882352941178, "stddev": 0.4761435447867452, "histogram": {"start": 3.0, "end": 4.0000004, "counts": ["268", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "140"], "max": 4.0, "min": 3.0, "bins": [3.0, 3.0333333466666668, 3.0666666933333335, 3.10000004, 3.1333333866666666, 3.1666667333333334, 3.20000008, 3.233333426666667, 3.266666773333333, 3.30000012, 3.3333334666666667, 3.3666668133333335, 3.4000001600000003, 3.4333335066666666, 3.4666668533333334, 3.5000002, 3.533333546666667, 3.566666893333333, 3.60000024, 3.6333335866666667, 3.6666669333333335, 3.7000002800000003, 3.733333626666667, 3.7666669733333333, 3.80000032, 3.833333666666667, 3.8666670133333336, 3.90000036, 3.9333337066666667, 3.9666670533333335, 4.0000004], "n": "408", "width": 0.0}, "uniqueCount": {"estimate": 2.0, "upper": 2.0, "lower": 2.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [3.0, 3.0, 3.0, 3.0, 3.0, 4.0, 4.0, 4.0, 4.0]}, "isDiscrete": true}, "tokenLength": {"count": "408", "min": 1.0, "max": 1.0, "mean": 1.0, "histogram": {"start": 1.0, "end": 1.0000001, "counts": ["408"], "max": 1.0, "min": 1.0, "bins": [1.0, 1.0000001], "n": "408", "width": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, "isDiscrete": true, "stddev": 0.0}, "charPosTracker": {"characterList": "!#$%&()*+,-./0123456789?@[]^_abcdefghijklmnopqrstuvwyz{}", "charPosMap": {"g": {"count": "141", "histogram": {"counts": ["141"], "bins": [0.0, 0.0], "n": "141", "start": 0.0, "end": 0.0, "width": 0.0, "max": 0.0, "min": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}, "isDiscrete": true, "min": 0.0, "max": 0.0, "mean": 0.0, "stddev": 0.0}, "b": {"count": "267", "histogram": {"counts": ["267"], "bins": [0.0, 0.0], "n": "267", "start": 0.0, "end": 0.0, "width": 0.0, "max": 0.0, "min": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}, "isDiscrete": true, "min": 0.0, "max": 0.0, "mean": 0.0, "stddev": 0.0}, "a": {"count": "267", "min": 1.0, "max": 1.0, "mean": 1.0, "histogram": {"start": 1.0, "end": 1.0000001, "counts": ["267"], "max": 1.0, "min": 1.0, "bins": [1.0, 1.0000001], "n": "267", "width": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, "isDiscrete": true, "stddev": 0.0}, "o": {"count": "282", "min": 1.0, "max": 2.0, "mean": 1.5, "stddev": 0.5008888895905983, "histogram": {"start": 1.0, "end": 2.0000002, "counts": ["141", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "141"], "max": 2.0, "min": 1.0, "bins": [1.0, 1.03333334, 1.06666668, 1.10000002, 1.13333336, 1.1666667, 1.20000004, 1.23333338, 1.26666672, 1.30000006, 1.3333334, 1.36666674, 1.40000008, 1.43333342, 1.46666676, 1.5000001, 1.53333344, 1.56666678, 1.60000012, 1.63333346, 1.6666668, 1.7000001400000002, 1.7333334800000002, 1.7666668200000002, 1.8000001600000002, 1.8333335000000002, 1.8666668400000002, 1.9000001800000001, 1.9333335200000001, 1.9666668600000001, 2.0000002], "n": "282", "width": 0.0}, "uniqueCount": {"estimate": 2.0, "upper": 2.0, "lower": 2.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 2.0, 2.0]}, "isDiscrete": true}, "d": {"count": "408", "min": 2.0, "max": 3.0, "mean": 2.3455882352941178, "stddev": 0.4761435447867452, "histogram": {"start": 2.0, "end": 3.0000003, "counts": ["268", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "140"], "max": 3.0, "min": 2.0, "bins": [2.0, 2.033333343333333, 2.0666666866666668, 2.10000003, 2.1333333733333335, 2.1666667166666667, 2.20000006, 2.2333334033333334, 2.2666667466666666, 2.30000009, 2.3333334333333333, 2.3666667766666665, 2.40000012, 2.4333334633333332, 2.466666806666667, 2.50000015, 2.533333493333333, 2.5666668366666667, 2.60000018, 2.6333335233333335, 2.6666668666666666, 2.70000021, 2.7333335533333334, 2.7666668966666665, 2.80000024, 2.8333335833333333, 2.8666669266666664, 2.90000027, 2.9333336133333336, 2.966666956666667, 3.0000003], "n": "408", "width": 0.0}, "uniqueCount": {"estimate": 2.0, "upper": 2.0, "lower": 2.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [2.0, 2.0, 2.0, 2.0, 2.0, 3.0, 3.0, 3.0, 3.0]}, "isDiscrete": true}}}}, "frequentItems": {"items": [{"estimate": "267", "jsonValue": "bad"}, {"estimate": "141", "jsonValue": "good"}]}, "uniqueCount": {"estimate": 2.000000004967054, "upper": 2.000099863468538, "lower": 2.0}}};\n", " // Config handlebars and pass data to HBS template\n", " const source = document.getElementById("entry-template").innerHTML;\n", " const template = Handlebars.compile(source);\n", @@ -6051,7 +6051,7 @@ "" ] }, - "execution_count": 13, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -6069,7 +6069,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -6468,7 +6468,7 @@ " return svgEl._groups[0][0].outerHTML;\n", " }\n", "\n", - " const profileFromCSVfile = {"quality": {"counters": {"count": "1191"}, "schema": {"inferredType": {"type": "STRING", "ratio": 1.0}, "typeCounts": {"STRING": "1191"}}, "stringSummary": {"uniqueCount": {"estimate": 2.0, "upper": 2.0, "lower": 2.0}, "frequent": {"items": [{"value": "bad", "estimate": 1115.0}, {"value": "good", "estimate": 76.0}]}, "length": {"count": "1191", "min": 3.0, "max": 4.0, "mean": 3.063811922753988, "stddev": 0.2445202709726784, "histogram": {"start": 3.0, "end": 4.0000004, "counts": ["1115", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "76"], "max": 4.0, "min": 3.0, "bins": [3.0, 3.0333333466666668, 3.0666666933333335, 3.10000004, 3.1333333866666666, 3.1666667333333334, 3.20000008, 3.233333426666667, 3.266666773333333, 3.30000012, 3.3333334666666667, 3.3666668133333335, 3.4000001600000003, 3.4333335066666666, 3.4666668533333334, 3.5000002, 3.533333546666667, 3.566666893333333, 3.60000024, 3.6333335866666667, 3.6666669333333335, 3.7000002800000003, 3.733333626666667, 3.7666669733333333, 3.80000032, 3.833333666666667, 3.8666670133333336, 3.90000036, 3.9333337066666667, 3.9666670533333335, 4.0000004], "n": "1191", "width": 0.0}, "uniqueCount": {"estimate": 2.0, "upper": 2.0, "lower": 2.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 4.0, 4.0, 4.0]}, "isDiscrete": true}, "tokenLength": {"count": "1191", "min": 1.0, "max": 1.0, "mean": 1.0, "histogram": {"start": 1.0, "end": 1.0000001, "counts": ["1191"], "max": 1.0, "min": 1.0, "bins": [1.0, 1.0000001], "n": "1191", "width": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, "isDiscrete": true, "stddev": 0.0}, "charPosTracker": {"characterList": "!#$%&()*+,-./0123456789?@[]^_abcdefghijklmnopqrstuvwyz{}", "charPosMap": {"g": {"count": "76", "histogram": {"counts": ["76"], "bins": [0.0, 0.0], "n": "76", "start": 0.0, "end": 0.0, "width": 0.0, "max": 0.0, "min": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}, "isDiscrete": true, "min": 0.0, "max": 0.0, "mean": 0.0, "stddev": 0.0}, "d": {"count": "1191", "min": 2.0, "max": 3.0, "mean": 2.063811922753988, "stddev": 0.2445202709726784, "histogram": {"start": 2.0, "end": 3.0000003, "counts": ["1115", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "76"], "max": 3.0, "min": 2.0, "bins": [2.0, 2.033333343333333, 2.0666666866666668, 2.10000003, 2.1333333733333335, 2.1666667166666667, 2.20000006, 2.2333334033333334, 2.2666667466666666, 2.30000009, 2.3333334333333333, 2.3666667766666665, 2.40000012, 2.4333334633333332, 2.466666806666667, 2.50000015, 2.533333493333333, 2.5666668366666667, 2.60000018, 2.6333335233333335, 2.6666668666666666, 2.70000021, 2.7333335533333334, 2.7666668966666665, 2.80000024, 2.8333335833333333, 2.8666669266666664, 2.90000027, 2.9333336133333336, 2.966666956666667, 3.0000003], "n": "1191", "width": 0.0}, "uniqueCount": {"estimate": 2.0, "upper": 2.0, "lower": 2.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 3.0, 3.0, 3.0]}, "isDiscrete": true}, "b": {"count": "1115", "histogram": {"counts": ["1115"], "bins": [0.0, 0.0], "n": "1115", "start": 0.0, "end": 0.0, "width": 0.0, "max": 0.0, "min": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}, "isDiscrete": true, "min": 0.0, "max": 0.0, "mean": 0.0, "stddev": 0.0}, "a": {"count": "1115", "min": 1.0, "max": 1.0, "mean": 1.0, "histogram": {"start": 1.0, "end": 1.0000001, "counts": ["1115"], "max": 1.0, "min": 1.0, "bins": [1.0, 1.0000001], "n": "1115", "width": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, "isDiscrete": true, "stddev": 0.0}, "o": {"count": "152", "min": 1.0, "max": 2.0, "mean": 1.5, "stddev": 0.5016528970703477, "histogram": {"start": 1.0, "end": 2.0000002, "counts": ["76", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "76"], "max": 2.0, "min": 1.0, "bins": [1.0, 1.03333334, 1.06666668, 1.10000002, 1.13333336, 1.1666667, 1.20000004, 1.23333338, 1.26666672, 1.30000006, 1.3333334, 1.36666674, 1.40000008, 1.43333342, 1.46666676, 1.5000001, 1.53333344, 1.56666678, 1.60000012, 1.63333346, 1.6666668, 1.7000001400000002, 1.7333334800000002, 1.7666668200000002, 1.8000001600000002, 1.8333335000000002, 1.8666668400000002, 1.9000001800000001, 1.9333335200000001, 1.9666668600000001, 2.0000002], "n": "152", "width": 0.0}, "uniqueCount": {"estimate": 2.0, "upper": 2.0, "lower": 2.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 2.0, 2.0]}, "isDiscrete": true}}}}, "frequentItems": {"items": [{"estimate": "1115", "jsonValue": "bad"}, {"estimate": "76", "jsonValue": "good"}]}, "uniqueCount": {"estimate": 2.000000004967054, "upper": 2.000099863468538, "lower": 2.0}}}\n", + " const profileFromCSVfile = {"quality": {"counters": {"count": "1191"}, "schema": {"inferredType": {"type": "STRING", "ratio": 1.0}, "typeCounts": {"STRING": "1191"}}, "stringSummary": {"uniqueCount": {"estimate": 2.0, "upper": 2.0, "lower": 2.0}, "frequent": {"items": [{"value": "bad", "estimate": 1115.0}, {"value": "good", "estimate": 76.0}]}, "length": {"count": "1191", "min": 3.0, "max": 4.0, "mean": 3.063811922753988, "stddev": 0.2445202709726784, "histogram": {"start": 3.0, "end": 4.0000004, "counts": ["1115", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "76"], "max": 4.0, "min": 3.0, "bins": [3.0, 3.0333333466666668, 3.0666666933333335, 3.10000004, 3.1333333866666666, 3.1666667333333334, 3.20000008, 3.233333426666667, 3.266666773333333, 3.30000012, 3.3333334666666667, 3.3666668133333335, 3.4000001600000003, 3.4333335066666666, 3.4666668533333334, 3.5000002, 3.533333546666667, 3.566666893333333, 3.60000024, 3.6333335866666667, 3.6666669333333335, 3.7000002800000003, 3.733333626666667, 3.7666669733333333, 3.80000032, 3.833333666666667, 3.8666670133333336, 3.90000036, 3.9333337066666667, 3.9666670533333335, 4.0000004], "n": "1191", "width": 0.0}, "uniqueCount": {"estimate": 2.0, "upper": 2.0, "lower": 2.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 4.0, 4.0, 4.0]}, "isDiscrete": true}, "tokenLength": {"count": "1191", "min": 1.0, "max": 1.0, "mean": 1.0, "histogram": {"start": 1.0, "end": 1.0000001, "counts": ["1191"], "max": 1.0, "min": 1.0, "bins": [1.0, 1.0000001], "n": "1191", "width": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, "isDiscrete": true, "stddev": 0.0}, "charPosTracker": {"characterList": "!#$%&()*+,-./0123456789?@[]^_abcdefghijklmnopqrstuvwyz{}", "charPosMap": {"d": {"count": "1191", "min": 2.0, "max": 3.0, "mean": 2.063811922753988, "stddev": 0.2445202709726784, "histogram": {"start": 2.0, "end": 3.0000003, "counts": ["1115", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "76"], "max": 3.0, "min": 2.0, "bins": [2.0, 2.033333343333333, 2.0666666866666668, 2.10000003, 2.1333333733333335, 2.1666667166666667, 2.20000006, 2.2333334033333334, 2.2666667466666666, 2.30000009, 2.3333334333333333, 2.3666667766666665, 2.40000012, 2.4333334633333332, 2.466666806666667, 2.50000015, 2.533333493333333, 2.5666668366666667, 2.60000018, 2.6333335233333335, 2.6666668666666666, 2.70000021, 2.7333335533333334, 2.7666668966666665, 2.80000024, 2.8333335833333333, 2.8666669266666664, 2.90000027, 2.9333336133333336, 2.966666956666667, 3.0000003], "n": "1191", "width": 0.0}, "uniqueCount": {"estimate": 2.0, "upper": 2.0, "lower": 2.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 3.0, 3.0, 3.0]}, "isDiscrete": true}, "o": {"count": "152", "min": 1.0, "max": 2.0, "mean": 1.5, "stddev": 0.5016528970703477, "histogram": {"start": 1.0, "end": 2.0000002, "counts": ["76", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "76"], "max": 2.0, "min": 1.0, "bins": [1.0, 1.03333334, 1.06666668, 1.10000002, 1.13333336, 1.1666667, 1.20000004, 1.23333338, 1.26666672, 1.30000006, 1.3333334, 1.36666674, 1.40000008, 1.43333342, 1.46666676, 1.5000001, 1.53333344, 1.56666678, 1.60000012, 1.63333346, 1.6666668, 1.7000001400000002, 1.7333334800000002, 1.7666668200000002, 1.8000001600000002, 1.8333335000000002, 1.8666668400000002, 1.9000001800000001, 1.9333335200000001, 1.9666668600000001, 2.0000002], "n": "152", "width": 0.0}, "uniqueCount": {"estimate": 2.0, "upper": 2.0, "lower": 2.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 2.0, 2.0]}, "isDiscrete": true}, "g": {"count": "76", "histogram": {"counts": ["76"], "bins": [0.0, 0.0], "n": "76", "start": 0.0, "end": 0.0, "width": 0.0, "max": 0.0, "min": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}, "isDiscrete": true, "min": 0.0, "max": 0.0, "mean": 0.0, "stddev": 0.0}, "b": {"count": "1115", "histogram": {"counts": ["1115"], "bins": [0.0, 0.0], "n": "1115", "start": 0.0, "end": 0.0, "width": 0.0, "max": 0.0, "min": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}, "isDiscrete": true, "min": 0.0, "max": 0.0, "mean": 0.0, "stddev": 0.0}, "a": {"count": "1115", "min": 1.0, "max": 1.0, "mean": 1.0, "histogram": {"start": 1.0, "end": 1.0000001, "counts": ["1115"], "max": 1.0, "min": 1.0, "bins": [1.0, 1.0000001], "n": "1115", "width": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, "isDiscrete": true, "stddev": 0.0}}}}, "frequentItems": {"items": [{"estimate": "1115", "jsonValue": "bad"}, {"estimate": "76", "jsonValue": "good"}]}, "uniqueCount": {"estimate": 2.000000004967054, "upper": 2.000099863468538, "lower": 2.0}}}\n", "\n", " Handlebars.registerHelper("getDoubleHistogramChart",(column,key) => {\n", " const columnKey = key.data.key\n", @@ -6497,7 +6497,7 @@ "\n", " function initHandlebarsTemplate() {\n", " // Replace this context with JSON from .py file\n", - " const context = {"quality": {"counters": {"count": "408"}, "schema": {"inferredType": {"type": "STRING", "ratio": 1.0}, "typeCounts": {"STRING": "408"}}, "stringSummary": {"uniqueCount": {"estimate": 2.0, "upper": 2.0, "lower": 2.0}, "frequent": {"items": [{"value": "bad", "estimate": 267.0}, {"value": "good", "estimate": 141.0}]}, "length": {"count": "408", "min": 3.0, "max": 4.0, "mean": 3.3455882352941178, "stddev": 0.4761435447867452, "histogram": {"start": 3.0, "end": 4.0000004, "counts": ["266", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "142"], "max": 4.0, "min": 3.0, "bins": [3.0, 3.0333333466666668, 3.0666666933333335, 3.10000004, 3.1333333866666666, 3.1666667333333334, 3.20000008, 3.233333426666667, 3.266666773333333, 3.30000012, 3.3333334666666667, 3.3666668133333335, 3.4000001600000003, 3.4333335066666666, 3.4666668533333334, 3.5000002, 3.533333546666667, 3.566666893333333, 3.60000024, 3.6333335866666667, 3.6666669333333335, 3.7000002800000003, 3.733333626666667, 3.7666669733333333, 3.80000032, 3.833333666666667, 3.8666670133333336, 3.90000036, 3.9333337066666667, 3.9666670533333335, 4.0000004], "n": "408", "width": 0.0}, "uniqueCount": {"estimate": 2.0, "upper": 2.0, "lower": 2.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [3.0, 3.0, 3.0, 3.0, 3.0, 4.0, 4.0, 4.0, 4.0]}, "isDiscrete": true}, "tokenLength": {"count": "408", "min": 1.0, "max": 1.0, "mean": 1.0, "histogram": {"start": 1.0, "end": 1.0000001, "counts": ["408"], "max": 1.0, "min": 1.0, "bins": [1.0, 1.0000001], "n": "408", "width": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, "isDiscrete": true, "stddev": 0.0}, "charPosTracker": {"characterList": "!#$%&()*+,-./0123456789?@[]^_abcdefghijklmnopqrstuvwyz{}", "charPosMap": {"g": {"count": "141", "histogram": {"counts": ["141"], "bins": [0.0, 0.0], "n": "141", "start": 0.0, "end": 0.0, "width": 0.0, "max": 0.0, "min": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}, "isDiscrete": true, "min": 0.0, "max": 0.0, "mean": 0.0, "stddev": 0.0}, "a": {"count": "267", "min": 1.0, "max": 1.0, "mean": 1.0, "histogram": {"start": 1.0, "end": 1.0000001, "counts": ["267"], "max": 1.0, "min": 1.0, "bins": [1.0, 1.0000001], "n": "267", "width": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, "isDiscrete": true, "stddev": 0.0}, "o": {"count": "282", "min": 1.0, "max": 2.0, "mean": 1.5, "stddev": 0.5008888895905983, "histogram": {"start": 1.0, "end": 2.0000002, "counts": ["141", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "141"], "max": 2.0, "min": 1.0, "bins": [1.0, 1.03333334, 1.06666668, 1.10000002, 1.13333336, 1.1666667, 1.20000004, 1.23333338, 1.26666672, 1.30000006, 1.3333334, 1.36666674, 1.40000008, 1.43333342, 1.46666676, 1.5000001, 1.53333344, 1.56666678, 1.60000012, 1.63333346, 1.6666668, 1.7000001400000002, 1.7333334800000002, 1.7666668200000002, 1.8000001600000002, 1.8333335000000002, 1.8666668400000002, 1.9000001800000001, 1.9333335200000001, 1.9666668600000001, 2.0000002], "n": "282", "width": 0.0}, "uniqueCount": {"estimate": 2.0, "upper": 2.0, "lower": 2.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 2.0, 2.0]}, "isDiscrete": true}, "d": {"count": "408", "min": 2.0, "max": 3.0, "mean": 2.3455882352941178, "stddev": 0.4761435447867452, "histogram": {"start": 2.0, "end": 3.0000003, "counts": ["268", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "140"], "max": 3.0, "min": 2.0, "bins": [2.0, 2.033333343333333, 2.0666666866666668, 2.10000003, 2.1333333733333335, 2.1666667166666667, 2.20000006, 2.2333334033333334, 2.2666667466666666, 2.30000009, 2.3333334333333333, 2.3666667766666665, 2.40000012, 2.4333334633333332, 2.466666806666667, 2.50000015, 2.533333493333333, 2.5666668366666667, 2.60000018, 2.6333335233333335, 2.6666668666666666, 2.70000021, 2.7333335533333334, 2.7666668966666665, 2.80000024, 2.8333335833333333, 2.8666669266666664, 2.90000027, 2.9333336133333336, 2.966666956666667, 3.0000003], "n": "408", "width": 0.0}, "uniqueCount": {"estimate": 2.0, "upper": 2.0, "lower": 2.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [2.0, 2.0, 2.0, 2.0, 2.0, 3.0, 3.0, 3.0, 3.0]}, "isDiscrete": true}, "b": {"count": "267", "histogram": {"counts": ["267"], "bins": [0.0, 0.0], "n": "267", "start": 0.0, "end": 0.0, "width": 0.0, "max": 0.0, "min": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}, "isDiscrete": true, "min": 0.0, "max": 0.0, "mean": 0.0, "stddev": 0.0}}}}, "frequentItems": {"items": [{"estimate": "267", "jsonValue": "bad"}, {"estimate": "141", "jsonValue": "good"}]}, "uniqueCount": {"estimate": 2.000000004967054, "upper": 2.000099863468538, "lower": 2.0}}};\n", + " const context = {"quality": {"counters": {"count": "408"}, "schema": {"inferredType": {"type": "STRING", "ratio": 1.0}, "typeCounts": {"STRING": "408"}}, "stringSummary": {"uniqueCount": {"estimate": 2.0, "upper": 2.0, "lower": 2.0}, "frequent": {"items": [{"value": "bad", "estimate": 267.0}, {"value": "good", "estimate": 141.0}]}, "length": {"count": "408", "min": 3.0, "max": 4.0, "mean": 3.3455882352941178, "stddev": 0.4761435447867452, "histogram": {"start": 3.0, "end": 4.0000004, "counts": ["268", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "140"], "max": 4.0, "min": 3.0, "bins": [3.0, 3.0333333466666668, 3.0666666933333335, 3.10000004, 3.1333333866666666, 3.1666667333333334, 3.20000008, 3.233333426666667, 3.266666773333333, 3.30000012, 3.3333334666666667, 3.3666668133333335, 3.4000001600000003, 3.4333335066666666, 3.4666668533333334, 3.5000002, 3.533333546666667, 3.566666893333333, 3.60000024, 3.6333335866666667, 3.6666669333333335, 3.7000002800000003, 3.733333626666667, 3.7666669733333333, 3.80000032, 3.833333666666667, 3.8666670133333336, 3.90000036, 3.9333337066666667, 3.9666670533333335, 4.0000004], "n": "408", "width": 0.0}, "uniqueCount": {"estimate": 2.0, "upper": 2.0, "lower": 2.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [3.0, 3.0, 3.0, 3.0, 3.0, 4.0, 4.0, 4.0, 4.0]}, "isDiscrete": true}, "tokenLength": {"count": "408", "min": 1.0, "max": 1.0, "mean": 1.0, "histogram": {"start": 1.0, "end": 1.0000001, "counts": ["408"], "max": 1.0, "min": 1.0, "bins": [1.0, 1.0000001], "n": "408", "width": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, "isDiscrete": true, "stddev": 0.0}, "charPosTracker": {"characterList": "!#$%&()*+,-./0123456789?@[]^_abcdefghijklmnopqrstuvwyz{}", "charPosMap": {"g": {"count": "141", "histogram": {"counts": ["141"], "bins": [0.0, 0.0], "n": "141", "start": 0.0, "end": 0.0, "width": 0.0, "max": 0.0, "min": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}, "isDiscrete": true, "min": 0.0, "max": 0.0, "mean": 0.0, "stddev": 0.0}, "b": {"count": "267", "histogram": {"counts": ["267"], "bins": [0.0, 0.0], "n": "267", "start": 0.0, "end": 0.0, "width": 0.0, "max": 0.0, "min": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}, "isDiscrete": true, "min": 0.0, "max": 0.0, "mean": 0.0, "stddev": 0.0}, "a": {"count": "267", "min": 1.0, "max": 1.0, "mean": 1.0, "histogram": {"start": 1.0, "end": 1.0000001, "counts": ["267"], "max": 1.0, "min": 1.0, "bins": [1.0, 1.0000001], "n": "267", "width": 0.0}, "uniqueCount": {"estimate": 1.0, "upper": 1.0, "lower": 1.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}, "isDiscrete": true, "stddev": 0.0}, "o": {"count": "282", "min": 1.0, "max": 2.0, "mean": 1.5, "stddev": 0.5008888895905983, "histogram": {"start": 1.0, "end": 2.0000002, "counts": ["141", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "141"], "max": 2.0, "min": 1.0, "bins": [1.0, 1.03333334, 1.06666668, 1.10000002, 1.13333336, 1.1666667, 1.20000004, 1.23333338, 1.26666672, 1.30000006, 1.3333334, 1.36666674, 1.40000008, 1.43333342, 1.46666676, 1.5000001, 1.53333344, 1.56666678, 1.60000012, 1.63333346, 1.6666668, 1.7000001400000002, 1.7333334800000002, 1.7666668200000002, 1.8000001600000002, 1.8333335000000002, 1.8666668400000002, 1.9000001800000001, 1.9333335200000001, 1.9666668600000001, 2.0000002], "n": "282", "width": 0.0}, "uniqueCount": {"estimate": 2.0, "upper": 2.0, "lower": 2.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 2.0, 2.0]}, "isDiscrete": true}, "d": {"count": "408", "min": 2.0, "max": 3.0, "mean": 2.3455882352941178, "stddev": 0.4761435447867452, "histogram": {"start": 2.0, "end": 3.0000003, "counts": ["268", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "140"], "max": 3.0, "min": 2.0, "bins": [2.0, 2.033333343333333, 2.0666666866666668, 2.10000003, 2.1333333733333335, 2.1666667166666667, 2.20000006, 2.2333334033333334, 2.2666667466666666, 2.30000009, 2.3333334333333333, 2.3666667766666665, 2.40000012, 2.4333334633333332, 2.466666806666667, 2.50000015, 2.533333493333333, 2.5666668366666667, 2.60000018, 2.6333335233333335, 2.6666668666666666, 2.70000021, 2.7333335533333334, 2.7666668966666665, 2.80000024, 2.8333335833333333, 2.8666669266666664, 2.90000027, 2.9333336133333336, 2.966666956666667, 3.0000003], "n": "408", "width": 0.0}, "uniqueCount": {"estimate": 2.0, "upper": 2.0, "lower": 2.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [2.0, 2.0, 2.0, 2.0, 2.0, 3.0, 3.0, 3.0, 3.0]}, "isDiscrete": true}}}}, "frequentItems": {"items": [{"estimate": "267", "jsonValue": "bad"}, {"estimate": "141", "jsonValue": "good"}]}, "uniqueCount": {"estimate": 2.000000004967054, "upper": 2.000099863468538, "lower": 2.0}}};\n", " // Config handlebars and pass data to HBS template\n", " const source = document.getElementById("entry-template").innerHTML;\n", " const template = Handlebars.compile(source);\n", @@ -6518,7 +6518,7 @@ "" ] }, - "execution_count": 14, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -6550,7 +6550,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -7017,7 +7017,7 @@ "\n", " function initHandlebarsTemplate() {\n", " // Replace this context with JSON from .py file\n", - " const context = {"properties": {"schemaMajorVersion": 1, "schemaMinorVersion": 2, "sessionId": "d178db8d-5b0d-47bc-bf67-62fdd55ac34a", "sessionTimestamp": "1648734114995", "dataTimestamp": "1648734115375", "tags": {"name": "drift-test"}, "metadata": {}}, "citric acid": {"counters": {"count": "408", "nullCount": "10"}, "schema": {"inferredType": {"type": "FRACTIONAL", "ratio": 1.0}, "typeCounts": {"NULL": "10", "FRACTIONAL": "398"}}, "numberSummary": {"count": "398", "max": 0.79, "mean": 0.3150753768844221, "stddev": 0.20859943016152155, "histogram": {"end": 0.7900001004576742, "counts": ["58", "13", "7", "22", "11", "8", "5", "5", "7", "10", "13", "13", "28", "9", "24", "36", "10", "24", "30", "9", "9", "9", "5", "4", "7", "12", "2", "4", "4", "0"], "max": 0.7900000214576721, "bins": [0.0, 0.026333336681922472, 0.052666673363844944, 0.07900001004576741, 0.10533334672768989, 0.13166668340961235, 0.15800002009153483, 0.1843333567734573, 0.21066669345537978, 0.23700003013730225, 0.2633333668192247, 0.2896667035011472, 0.31600004018306965, 0.34233337686499216, 0.3686667135469146, 0.3950000502288371, 0.42133338691075956, 0.447666723592682, 0.4740000602746045, 0.500333396956527, 0.5266667336384494, 0.5530000703203719, 0.5793334070022944, 0.6056667436842169, 0.6320000803661393, 0.6583334170480618, 0.6846667537299843, 0.7110000904119067, 0.7373334270938292, 0.7636667637757517, 0.7900001004576742], "n": "398", "start": 0.0, "width": 0.0, "min": 0.0}, "uniqueCount": {"estimate": 72.0, "upper": 72.0, "lower": 72.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [0.0, 0.0, 0.0, 0.10000000149011612, 0.3400000035762787, 0.46000000834465027, 0.6600000262260437, 0.75, 0.7900000214576721]}, "min": 0.0, "isDiscrete": false}, "frequentItems": {"items": [{"estimate": "39", "jsonValue": "0.0"}, {"estimate": "19", "jsonValue": "0.49"}, {"estimate": "18", "jsonValue": "0.09"}, {"estimate": "18", "jsonValue": "0.15"}, {"estimate": "17", "jsonValue": "0.14"}, {"estimate": "17", "jsonValue": "0.44"}, {"estimate": "17", "jsonValue": "0.1"}, {"estimate": "17", "jsonValue": "0.01"}, {"estimate": "17", "jsonValue": "0.33"}, {"estimate": "17", "jsonValue": "0.02"}, {"estimate": "17", "jsonValue": "0.42"}, {"estimate": "16", "jsonValue": "0.32"}, {"estimate": "16", "jsonValue": "0.2"}, {"estimate": "16", "jsonValue": "0.41"}, {"estimate": "16", "jsonValue": "0.4"}, {"estimate": "16", "jsonValue": "0.53"}]}, "uniqueCount": {"estimate": 72.00001269579195, "upper": 72.00360760247034, "lower": 72.0}, "sum": 128.55075376884423, "variance": 0.04351372226371151, "coefficient_of_variation": 0.6620619872750046, "quantile_statistics": {"fifth_percentile": 0.0, "q1": 0.10000000149011612, "median": 0.3400000035762787, "q3": 0.46000000834465027, "ninety_fifth_percentile": 0.6600000262260437, "range": 0.79, "iqr": 0.36000000685453415}}};\n", + " const context = {"properties": {"schemaMajorVersion": 1, "schemaMinorVersion": 2, "sessionId": "e28d9f1a-3285-40c7-b947-f25c29437753", "sessionTimestamp": "1650302333424", "dataTimestamp": "1650302336378", "tags": {"name": "drift-test"}, "metadata": {}}, "citric acid": {"counters": {"count": "408", "nullCount": "10"}, "schema": {"inferredType": {"type": "FRACTIONAL", "ratio": 1.0}, "typeCounts": {"FRACTIONAL": "398", "NULL": "10"}}, "numberSummary": {"count": "398", "max": 0.79, "mean": 0.3150753768844221, "stddev": 0.20859943016152155, "histogram": {"end": 0.7900001004576742, "counts": ["58", "13", "7", "22", "11", "8", "5", "5", "7", "10", "13", "13", "28", "9", "24", "36", "10", "24", "30", "9", "9", "9", "5", "4", "7", "12", "2", "4", "4", "0"], "max": 0.7900000214576721, "bins": [0.0, 0.026333336681922472, 0.052666673363844944, 0.07900001004576741, 0.10533334672768989, 0.13166668340961235, 0.15800002009153483, 0.1843333567734573, 0.21066669345537978, 0.23700003013730225, 0.2633333668192247, 0.2896667035011472, 0.31600004018306965, 0.34233337686499216, 0.3686667135469146, 0.3950000502288371, 0.42133338691075956, 0.447666723592682, 0.4740000602746045, 0.500333396956527, 0.5266667336384494, 0.5530000703203719, 0.5793334070022944, 0.6056667436842169, 0.6320000803661393, 0.6583334170480618, 0.6846667537299843, 0.7110000904119067, 0.7373334270938292, 0.7636667637757517, 0.7900001004576742], "n": "398", "start": 0.0, "width": 0.0, "min": 0.0}, "uniqueCount": {"estimate": 72.0, "upper": 72.0, "lower": 72.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [0.0, 0.0, 0.0, 0.10000000149011612, 0.3400000035762787, 0.46000000834465027, 0.6600000262260437, 0.75, 0.7900000214576721]}, "min": 0.0, "isDiscrete": false}, "frequentItems": {"items": [{"estimate": "39", "jsonValue": "0.0"}, {"estimate": "19", "jsonValue": "0.49"}, {"estimate": "18", "jsonValue": "0.09"}, {"estimate": "18", "jsonValue": "0.15"}, {"estimate": "17", "jsonValue": "0.14"}, {"estimate": "17", "jsonValue": "0.44"}, {"estimate": "17", "jsonValue": "0.1"}, {"estimate": "17", "jsonValue": "0.01"}, {"estimate": "17", "jsonValue": "0.33"}, {"estimate": "17", "jsonValue": "0.02"}, {"estimate": "17", "jsonValue": "0.42"}, {"estimate": "16", "jsonValue": "0.32"}, {"estimate": "16", "jsonValue": "0.2"}, {"estimate": "16", "jsonValue": "0.41"}, {"estimate": "16", "jsonValue": "0.4"}, {"estimate": "16", "jsonValue": "0.53"}]}, "uniqueCount": {"estimate": 72.00001269579195, "upper": 72.00360760247034, "lower": 72.0}, "sum": 128.55075376884423, "variance": 0.04351372226371151, "coefficient_of_variation": 0.6620619872750046, "quantile_statistics": {"fifth_percentile": 0.0, "q1": 0.10000000149011612, "median": 0.3400000035762787, "q3": 0.46000000834465027, "ninety_fifth_percentile": 0.6600000262260437, "range": 0.79, "iqr": 0.36000000685453415}}};\n", " // Config handlebars and pass data to HBS template\n", " const source = document.getElementById("entry-template").innerHTML;\n", " const template = Handlebars.compile(source);\n", @@ -7037,7 +7037,7 @@ "" ] }, - "execution_count": 15, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -7066,7 +7066,7 @@ "source": [ "whylogs also enables data quality checks, called `constraints`. This is a topic on its own and will be explored in details in a separate example.\n", "\n", - "There are a lot of different types of constraints, but for now we'll use a small number of them, as the goal is to show how you can generate a report of the applied constraints through the Profile Viewer." + "There are a lot of different types of constraints, but for now we'll use a small number of them, as the goal is to show how you can generate a report of the applied constraints through the Profile Visualizer." ] }, { @@ -7090,7 +7090,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -7122,7 +7122,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -7148,18 +7148,18 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[('quality', [(\"distinct values are equal to the set {'good', 'bad'}\", 1, 0)]),\n", + "[('quality', [(\"distinct values are equal to the set {'bad', 'good'}\", 1, 0)]),\n", " ('pH', [('maximum is less than or equal to 4.5', 1, 0)]),\n", " ('volatile acidity', [('minimum is greater than or equal to 0.14', 1, 1)])]" ] }, - "execution_count": 18, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -7172,18 +7172,18 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Let's set up the NotebookProfileViewer once again:" + "Let's set up the NotebookProfileVisualizer once again:" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ - "from whylogs.viz import NotebookProfileViewer\n", + "from whylogs.viz import NotebookProfileVisualizer\n", "\n", - "visualization = NotebookProfileViewer()\n", + "visualization = NotebookProfileVisualizer()\n", "visualization.set_profiles(target_profile=target_profile)" ] }, @@ -7198,7 +7198,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -7793,7 +7793,7 @@ "\n", " function initHandlebarsTemplate() {\n", " // Replace this context with JSON from .py file\n", - " const context = [["quality", [["distinct values are equal to the set {'good', 'bad'}", 1, 0]]], ["pH", [["maximum is less than or equal to 4.5", 1, 0]]], ["volatile acidity", [["minimum is greater than or equal to 0.14", 1, 1]]], ["The columns of the table are equal to the set {'density', 'total sulfur dioxide', 'citric acid', 'pH', 'residual sugar', 'volatile acidity', 'free sulfur dioxide', 'fixed acidity', 'chlorides', 'quality', 'alcohol', 'sulphates'}", 0, 0]];\n", + " const context = [["quality", [["distinct values are equal to the set {'bad', 'good'}", 1, 0]]], ["pH", [["maximum is less than or equal to 4.5", 1, 0]]], ["volatile acidity", [["minimum is greater than or equal to 0.14", 1, 1]]], ["The columns of the table are equal to the set {'total sulfur dioxide', 'residual sugar', 'pH', 'sulphates', 'alcohol', 'volatile acidity', 'quality', 'density', 'chlorides', 'citric acid', 'free sulfur dioxide', 'fixed acidity'}", 0, 0]];\n", " // Config handlebars and pass data to HBS template\n", " const source = document.getElementById("entry-template").innerHTML;\n", " const template = Handlebars.compile(source);\n", @@ -7956,7 +7956,7 @@ "" ] }, - "execution_count": 20, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -7981,7 +7981,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -8035,7 +8035,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.10" + "version": "3.8.13" }, "orig_nbformat": 4 }, diff --git a/examples/Profile_Viewer_In_Notebook.ipynb b/examples/Profile_Visualizer_In_Notebook.ipynb similarity index 58% rename from examples/Profile_Viewer_In_Notebook.ipynb rename to examples/Profile_Visualizer_In_Notebook.ipynb index c72a920f27..3e592f89d7 100644 --- a/examples/Profile_Viewer_In_Notebook.ipynb +++ b/examples/Profile_Visualizer_In_Notebook.ipynb @@ -22,7 +22,19 @@ "execution_count": 1, "id": "29e48da5", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: faker in /home/felipeadachi/miniconda3/envs/viewer-rename/lib/python3.8/site-packages (13.3.4)\n", + "Requirement already satisfied: python-dateutil>=2.4 in /home/felipeadachi/miniconda3/envs/viewer-rename/lib/python3.8/site-packages (from faker) (2.8.2)\n", + "Requirement already satisfied: six>=1.5 in /home/felipeadachi/miniconda3/envs/viewer-rename/lib/python3.8/site-packages (from python-dateutil>=2.4->faker) (1.16.0)\n", + "Requirement already satisfied: pybars3 in /home/felipeadachi/miniconda3/envs/viewer-rename/lib/python3.8/site-packages (0.9.7)\n", + "Requirement already satisfied: PyMeta3>=0.5.1 in /home/felipeadachi/miniconda3/envs/viewer-rename/lib/python3.8/site-packages (from pybars3) (0.5.1)\n" + ] + } + ], "source": [ "!pip install faker\n", "!pip install pybars3" @@ -52,7 +64,7 @@ "outputs": [], "source": [ "from whylogs import get_or_create_session\n", - "from whylogs.viz import NotebookProfileViewer\n", + "from whylogs.viz import NotebookProfileVisualizer\n", "from whylogs.core.statistics.constraints import (\n", " columnValuesInSetConstraint,\n", " containsEmailConstraint,\n", @@ -88,7 +100,6 @@ " ('ja_JP', 2),\n", "])\n", "fake = Faker(locales)\n", - "Faker.seed(42)\n", "distribution = np.concatenate((np.random.normal(0.1, 0.1, 500), np.random.normal(0.6, 0.2, 500)))" ] }, @@ -152,7 +163,7 @@ "metadata": {}, "source": [ "### Initialization\n", - "Initialize Profile viewer by passing profiles for which you want to get the visualizations" + "Initialize Profile visualizer by passing profiles for which you want to get the visualizations" ] }, { @@ -162,7 +173,7 @@ "metadata": {}, "outputs": [], "source": [ - "visualization = NotebookProfileViewer()\n", + "visualization = NotebookProfileVisualizer()\n", "visualization.set_profiles(target_profile=target_profile, reference_profile=reference_profile)" ] }, @@ -977,7 +988,7 @@ " <div class="wl-table-heading">\n", " <div class="wl-table-row wl-table-row--bottom-shadow">\n", " <div class="wl-table-head wl-table-head-wraper">\n", - " <div class="wl-table-head table-border-none graph-table-head">Feature</div>\n", + " <div class="wl-table-head table-border-none graph-table-head">Target</div>\n", " <div class="wl-table-head table-border-none reference-table-head">Reference</div>\n", " </div>\n", " <div class="wl-table-head wl-sub-table-head text-align-center" id="diff-from-ref">\n", @@ -985,8 +996,7 @@ " <div class="tooltip-full-number">\n", " <span class="question-mark">?</span>\n", " <span class="tooltiptext">\n", - " <div class="mb-1">Lorem Ipsum is simply dummy</div> \n", - " <div>text of the printing and typesetting.</div>\n", + " <div class="mb-1">Distance between the reference and target profiles</div>\n", " </span>\n", " </div>\n", " </div>\n", @@ -1047,7 +1057,7 @@ " <script src="https://cdnjs.cloudflare.com/ajax/libs/d3/6.7.0/d3.min.js" integrity="sha512-cd6CHE+XWDQ33ElJqsi0MdNte3S+bQY819f7p3NUHgwQQLXSKjE4cPZTeGNI+vaxZynk1wVU3hoHmow3m089wA==" crossorigin="anonymous" referrerpolicy="no-referrer"></script>\n", "\n", " <script>\n", - " const getReferenceProfile = () => { return {"properties": {"schemaMajorVersion": 1, "schemaMinorVersion": 2, "sessionId": "2da09ec3-f565-441f-a3d8-5649e0a4340a", "sessionTimestamp": "1648032222786", "dataTimestamp": "1622592000000", "tags": {"name": "mytestytest"}, "metadata": {}, "observations": 4003, "missing_cells": 500, "total_count": 4003, "missing_percentage": 12.490632025980515}, "columns": {"nulls": {"counters": {"count": "500", "nullCount": "500"}, "schema": {"inferredType": {"type": "NULL", "ratio": 1.0}, "typeCounts": {"NULL": "500"}}, 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= {"uniform_integers": {"counters": {"count": "500"}, "schema": {"inferredType": {"type": "INTEGRAL", "ratio": 1.0}, "typeCounts": {"INTEGRAL": "500"}}, "numberSummary": {"count": "500", "max": 49.0, "mean": 24.232, "stddev": 14.149721029420352, "histogram": {"end": 49.0000049, "counts": ["22", "21", "8", "17", "23", "10", "23", "18", "13", "13", "9", "20", "28", "6", "17", "14", "14", "27", "23", "9", "17", "17", "19", "15", "14", "19", "29", "7", "20", "8"], "max": 49.0, "bins": [0.0, 1.6333334966666666, 3.266666993333333, 4.90000049, 6.533333986666666, 8.166667483333333, 9.80000098, 11.433334476666666, 13.066667973333333, 14.70000147, 16.333334966666666, 17.96666846333333, 19.60000196, 21.233335456666666, 22.86666895333333, 24.50000245, 26.133335946666666, 27.76666944333333, 29.40000294, 31.033336436666666, 32.66666993333333, 34.30000343, 35.93333692666666, 37.566670423333335, 39.20000392, 40.833337416666666, 42.46667091333333, 44.10000441, 45.73333790666666, 47.366671403333335, 49.0000049], "n": "500", "start": 0.0, "width": 0.0, "min": 0.0}, "uniqueCount": {"estimate": 50.0, "upper": 50.0, "lower": 50.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [0.0, 0.0, 2.0, 12.0, 25.0, 36.0, 46.0, 48.0, 49.0]}, "min": 0.0, "isDiscrete": false}, "frequentItems": {"items": [{"estimate": "23", "jsonValue": "44"}, {"estimate": "22", "jsonValue": "7"}, {"estimate": "21", "jsonValue": "40"}, {"estimate": "21", "jsonValue": "14"}, {"estimate": "21", "jsonValue": "6"}, {"estimate": "21", "jsonValue": "27"}, {"estimate": "20", "jsonValue": "33"}, {"estimate": "20", "jsonValue": "4"}, {"estimate": "20", "jsonValue": "20"}, {"estimate": "20", "jsonValue": "35"}, {"estimate": "20", "jsonValue": "47"}, {"estimate": "20", "jsonValue": "28"}, {"estimate": "20", "jsonValue": "0"}, {"estimate": "20", "jsonValue": "23"}, {"estimate": "20", "jsonValue": "34"}, {"estimate": "20", "jsonValue": "21"}]}, "uniqueCount": {"estimate": 50.00000608464168, "upper": 50.00250254747639, "lower": 50.0}}};\n", " // Config handlebars and pass data to HBS template\n", " const source = document.getElementById("entry-template").innerHTML;\n", " const template = Handlebars.compile(source);\n", @@ -6191,7 +6125,7 @@ " <div class="display-flex">\n", " <div class="colors-for-distingushing-charts">\n", " <div class="circle-color" style="background: #44C0E7;"></div>\n", - " <text alignment-baseline="middle" style="font-size: 15px;">Current</text>\n", + " <text alignment-baseline="middle" style="font-size: 15px;">Target</text>\n", " </div>\n", " <div class="colors-for-distingushing-charts">\n", " <div class="circle-color" style="background: #F5843C"></div>\n", @@ -6225,7 +6159,12 @@ " \n", " const findAndDeleteUndefined = (axisData) => {\n", " const undefinedAxisIndex = axisData.findIndex((axis) => axis === undefined)\n", - " return [...axisData.slice(0, undefinedAxisIndex), ...axisData.slice(undefinedAxisIndex + 1)]\n", + " if (undefinedAxisIndex == -1) {\n", + " return axisData;\n", + " }\n", + "\n", + " const result = [...axisData.slice(0, undefinedAxisIndex), ...axisData.slice(undefinedAxisIndex + 1)]\n", + " return result\n", " }\n", "\n", " const filterAndSortChartData = (overlappedHistogramData, histogramData) => {\n", @@ -6264,7 +6203,7 @@ " .attr("preserveAspectRatio", "xMinYMin meet")\n", " .attr("viewBox", `0 0 ${$(window).width()} ${$(window).height()-30}`)\n", " .classed("svg-content-responsive", true)\n", - " this.maxYValue = d3.max(data, (d) => Math.abs(d.axisY));\n", + " this.maxYValue = d3.max([...data, ...referenceData], (d) => Math.abs(d.axisY));\n", " this.xScale = d3\n", " .scaleBand()\n", " .domain(filterAndSortChartData(data, referenceData).map((sortedCounts) => sortedCounts?.axisX))\n", @@ -6352,6 +6291,7 @@ " \n", " svgEl.append("g")\n", " .attr("transform", `translate(${MARGIN.LEFT}, 0)`)\n", + " .attr("id", "g1")\n", " .call(yAxis)\n", " .call(g => g.select(".domain").remove())\n", " .call(g => g.selectAll(".tick line")\n", @@ -6363,7 +6303,7 @@ " .attr("fill", "currentColor")\n", " .attr("text-anchor", "start"));\n", "\n", - " svgEl.append("text") \n", + " svgEl.append("text")\n", " .attr("transform",\n", " "translate(" + (CHART_WIDTH/2) + " ," + \n", " (CHART_HEIGHT + MARGIN.TOP + 40) + ")")\n", @@ -6384,6 +6324,7 @@ " \n", " svgEl.append("g")\n", " .attr("transform", `translate(0,${SVG_HEIGHT - MARGIN.BOTTOM})`)\n", + " .attr("id", "g2")\n", " .call(xAxis)\n", " .call(g => g.select(".domain").remove())\n", " .call(g => g.selectAll(".tick line").remove())\n", @@ -6402,11 +6343,13 @@ " .range(rectColors)\n", " \n", " svgEl.append("g")\n", + " .attr("id", "g3")\n", " .selectAll("g")\n", " .data(data)\n", " .enter()\n", " .append("g")\n", " .attr("transform", function(d) { return "translate(" + xScale(d?.group) + ",0)"; })\n", + " .attr("id", "g4")\n", " .selectAll("rect")\n", " .data(function(d) { return subgroups.map(function(key) { return {key: key, value: d && d[key]}; }); })\n", " .enter().append("rect")\n", @@ -6421,7 +6364,8 @@ " return svgEl._groups[0][0].outerHTML;\n", " } \n", "\n", - " const profileFromCSVfile = {"strings": {"counters": {"count": "500"}, "schema": {"inferredType": {"type": "STRING", "ratio": 1.0}, "typeCounts": {"STRING": "500"}}, "stringSummary": {"uniqueCount": {"estimate": 492.0, "upper": 492.0, "lower": 492.0}, "length": {"count": "500", "min": 4.0, "max": 30.0, "mean": 11.32, "stddev": 6.227613213155204, "histogram": {"start": 4.0, "end": 30.000003, "counts": ["49", "119", "31", "1", "0", "6", "12", "0", "23", "30", "41", "43", "32", "26", "14", "0", "10", "6", "3", "12", "11", "10", "0", "8", "5", "4", "2", "1", "0", "1"], "max": 30.0, "min": 4.0, "bins": [4.0, 4.8666667666666665, 5.733333533333333, 6.6000003, 7.466667066666666, 8.333333833333334, 9.2000006, 10.066667366666668, 10.933334133333332, 11.8000009, 12.666667666666667, 13.533334433333334, 14.4000012, 15.266667966666667, 16.133334733333335, 17.0000015, 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from .py file\n", - " const context = {"properties": {"schemaMajorVersion": 1, "schemaMinorVersion": 2, "sessionId": "2da09ec3-f565-441f-a3d8-5649e0a4340a", "sessionTimestamp": "1648032222786", "dataTimestamp": "1622592000000", "tags": {"name": "mytestytest"}, "metadata": {}}, "mixture_distribution": {"counters": {"count": "500"}, "schema": {"inferredType": {"type": "FRACTIONAL", "ratio": 1.0}, "typeCounts": {"FRACTIONAL": "500"}}, "numberSummary": {"count": "500", "min": -0.16197451040897445, "max": 1.1264764129674782, "mean": 0.355046313966549, "stddev": 0.2963055570649652, "histogram": {"start": -0.16197450459003448, "end": 1.1264765196987272, "counts": ["1", "10", "4", "22", "48", "32", "45", "41", "23", "18", "13", "13", "5", "15", "15", "34", "20", "33", "19", "16", "20", "13", "8", "3", "7", "5", "7", "3", "0", "7"], "max": 1.1264764070510864, "min": -0.16197450459003448, "bins": [-0.16197450459003448, -0.11902613711374244, -0.07607776963745037, -0.03312940216115831, 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"-0.09132802446577978"}, {"estimate": "20", "jsonValue": "0.018177931676652742"}, {"estimate": "20", "jsonValue": "0.2356240028570823"}, {"estimate": "20", "jsonValue": "0.15144388340587492"}, {"estimate": "20", "jsonValue": "0.5021121114963556"}, {"estimate": "20", "jsonValue": "0.5934493459568003"}, {"estimate": "20", "jsonValue": "0.1513267433113356"}, {"estimate": "20", "jsonValue": "0.7640964362394728"}, {"estimate": "20", "jsonValue": "0.2886741652952192"}, {"estimate": "20", "jsonValue": "0.5538130939582644"}, {"estimate": "20", "jsonValue": "0.12449665711087228"}, {"estimate": "20", "jsonValue": "-0.07630401553627339"}, {"estimate": "20", "jsonValue": "-0.10392321777601007"}, {"estimate": "20", "jsonValue": "0.4279173269432095"}, {"estimate": "20", "jsonValue": "0.2523124077269657"}, {"estimate": "20", "jsonValue": "-0.02369507108780819"}, {"estimate": "20", "jsonValue": "0.7265863635511022"}, {"estimate": "20", "jsonValue": "0.6096169893322764"}, {"estimate": "20", "jsonValue": "0.09444523011033813"}, {"estimate": "20", "jsonValue": "0.6052182100421667"}]}, "uniqueCount": {"estimate": 387.12974491049755, "upper": 392.19706114997956, "lower": 382.1856940789122}, "sum": 177.5231569832745, "variance": 0.08779698314757935, "coefficient_of_variation": 0.8345546634597139, "quantile_statistics": {"fifth_percentile": -0.016867803409695625, "q1": 0.10210038721561432, "median": 0.28867417573928833, "q3": 0.5930023193359375, "ninety_fifth_percentile": 0.8841008543968201, "range": 1.2884509233764527, "iqr": 0.4909019321203232}}};\n", " // Config handlebars and pass data to HBS template\n", " const source = document.getElementById("entry-template").innerHTML;\n", " const template = Handlebars.compile(source);\n", @@ -8133,10 +8078,14 @@ " return column.properties.tags.name\n", " });\n", "\n", + "\n", " Handlebars.registerHelper("alertLIst", function (column) {\n", " let alertListItem = column.map((value) => {\n", " if (value[1][0]) {\n", - " return alertListElement(value[0], value[1][0][0], value[1][0][value[1][0].length - 1] === 0 || (failedConstraints++, false))\n", + " let alertListValue = value[1].map((cstr)=>{\n", + " return alertListElement(value[0],cstr[0],cstr[cstr.length - 1] === 0 || (failedConstraints++, false))\n", + " })\n", + " return alertListValue.join(' ')\n", " } else {\n", " return alertListElement('', value[0], value[value.length - 1] === 0 || (failedConstraints++, false))\n", " }\n", @@ -8148,6 +8097,7 @@ " })\n", " return alertListItem.join(' ')\n", " });\n", + "\n", " }\n", "\n", " function openFilter() {\n", @@ -8404,7 +8354,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.10" + "version": "3.8.13" } }, "nbformat": 4, diff --git a/examples/segmentation/segments.ipynb b/examples/segmentation/segments.ipynb index eabf0b679c..466d80246c 100644 --- a/examples/segmentation/segments.ipynb +++ b/examples/segmentation/segments.ipynb @@ -62,17 +62,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ - "!pip install whylogs\n", - "!pip install pybars3" + "# !pip install whylogs\n", + "# !pip install pybars3" ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -82,7 +82,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -273,7 +273,7 @@ "4 0 0 " ] }, - "execution_count": 2, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -292,7 +292,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -301,7 +301,7 @@ "['M', 'F']" ] }, - "execution_count": 3, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -312,7 +312,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -321,7 +321,7 @@ "['Home and kitchen', 'Books', 'Electronics', 'Bags', 'Footwear', 'Clothing']" ] }, - "execution_count": 4, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -339,7 +339,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -377,7 +377,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -413,7 +413,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -421,37 +421,37 @@ "output_type": "stream", "text": [ " column count null_count bool_count numeric_count \\\n", - "0 City Code 66.0 0.0 0.0 66.0 \n", - "1 Store Type 66.0 0.0 0.0 0.0 \n", - "2 Age at Transaction Date 66.0 0.0 0.0 66.0 \n", - "3 Date of Birth 66.0 0.0 0.0 0.0 \n", + "0 Product Category Code 66.0 0.0 0.0 66.0 \n", + "1 Customer ID 66.0 0.0 0.0 0.0 \n", + "2 City Code 66.0 0.0 0.0 66.0 \n", + "3 Product Category 66.0 0.0 0.0 0.0 \n", "4 Product Subcategory Code 66.0 0.0 0.0 66.0 \n", "\n", - " max mean min stddev nunique_numbers ... stddev_token_length \\\n", - "0 10.0 5.469697 1.0 2.735632 10.0 ... 0.000000 \n", - "1 0.0 0.000000 0.0 0.000000 0.0 ... 0.361298 \n", - "2 41.0 29.803030 20.0 6.043992 22.0 ... 0.000000 \n", - "3 0.0 0.000000 0.0 0.000000 0.0 ... 0.000000 \n", - "4 4.0 2.530303 1.0 1.338423 3.0 ... 0.000000 \n", + " max mean min stddev nunique_numbers ... stddev_token_length \\\n", + "0 1.0 1.000000 1.0 0.000000 1.0 ... 0.0 \n", + "1 0.0 0.000000 0.0 0.000000 0.0 ... 0.0 \n", + "2 10.0 5.469697 1.0 2.735632 10.0 ... 0.0 \n", + "3 0.0 0.000000 0.0 0.000000 0.0 ... 0.0 \n", + "4 4.0 2.530303 1.0 1.338423 3.0 ... 0.0 \n", "\n", " quantile_0.0000 quantile_0.0100 quantile_0.0500 quantile_0.2500 \\\n", - "0 1.0 1.0 2.0 3.0 \n", + "0 1.0 1.0 1.0 1.0 \n", "1 NaN NaN NaN NaN \n", - "2 20.0 20.0 21.0 24.0 \n", + "2 1.0 1.0 2.0 3.0 \n", "3 NaN NaN NaN NaN \n", "4 1.0 1.0 1.0 1.0 \n", "\n", " quantile_0.5000 quantile_0.7500 quantile_0.9500 quantile_0.9900 \\\n", - "0 5.0 8.0 10.0 10.0 \n", + "0 1.0 1.0 1.0 1.0 \n", "1 NaN NaN NaN NaN \n", - "2 29.0 35.0 39.0 41.0 \n", + "2 5.0 8.0 10.0 10.0 \n", "3 NaN NaN NaN NaN \n", "4 3.0 4.0 4.0 4.0 \n", "\n", " quantile_1.0000 \n", - "0 10.0 \n", + "0 1.0 \n", "1 NaN \n", - "2 41.0 \n", + "2 10.0 \n", "3 NaN \n", "4 4.0 \n", "\n", @@ -473,13 +473,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Just to make it more readable, let's use one of the features of the `NotebookProfileViewer` to display simple statistics for a given feature.\n", + "Just to make it more readable, let's use one of the features of the `NotebookProfileVisualizer` to display simple statistics for a given feature.\n", "We might be interested in looking at the `Purchase Canceled` feature:" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -957,7 +957,7 @@ "\n", " function initHandlebarsTemplate() {\n", " // Replace this context with JSON from .py file\n", - " const context = {"properties": {"schemaMajorVersion": 1, "schemaMinorVersion": 2, "sessionId": "9c4f7a93-5c43-4d84-a070-40f5554c89e9", "sessionTimestamp": "1649932817631", "dataTimestamp": "1649943618287", "tags": {"whylogs.tag.Product Category": "Clothing", "whylogs.tag.Gender": "M", "name": "segment-test"}, "metadata": {}}, "Purchase Canceled": {"counters": {"count": "66", "nullCount": "3"}, "schema": {"inferredType": {"type": "FRACTIONAL", "ratio": 1.0}, "typeCounts": {"FRACTIONAL": "63", "NULL": "3"}}, "numberSummary": {"count": "63", "max": 1.0, "mean": 0.06349206349206349, "stddev": 0.24580452980260506, "histogram": {"end": 1.0000001, "counts": ["59", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "4"], "max": 1.0, "bins": [0.0, 0.06250000625, 0.1250000125, 0.18750001875, 0.250000025, 0.31250003125000003, 0.3750000375, 0.43750004375, 0.50000005, 0.56250005625, 0.6250000625000001, 0.68750006875, 0.750000075, 0.8125000812500001, 0.8750000875, 0.9375000937500001, 1.0000001], "n": "63", "start": 0.0, "width": 0.0, "min": 0.0}, "uniqueCount": {"estimate": 2.0, "upper": 2.0, "lower": 2.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0]}, "min": 0.0, "isDiscrete": false}, "frequentItems": {"items": [{"estimate": "59", "jsonValue": "0.0"}, {"estimate": "4", "jsonValue": "1.0"}]}, "uniqueCount": {"estimate": 2.000000004967054, "upper": 2.000099863468538, "lower": 2.0}, "sum": 4.19047619047619, "variance": 0.06041986687147976, "coefficient_of_variation": 3.8714213443910297, "quantile_statistics": {"fifth_percentile": 0.0, "q1": 0.0, "median": 0.0, "q3": 0.0, "ninety_fifth_percentile": 1.0, "range": 1.0, "iqr": 0.0}}};\n", + " const context = {"properties": {"schemaMajorVersion": 1, "schemaMinorVersion": 2, "sessionId": "89536e36-1f98-40e4-9b19-5a5d23d5654b", "sessionTimestamp": "1650301095603", "dataTimestamp": "1650311896675", "tags": {"whylogs.tag.Gender": "M", "name": "segment-test", "whylogs.tag.Product Category": "Clothing"}, "metadata": {}}, "Purchase Canceled": {"counters": {"count": "66", "nullCount": "3"}, "schema": {"inferredType": {"type": "FRACTIONAL", "ratio": 1.0}, "typeCounts": {"FRACTIONAL": "63", "NULL": "3"}}, "numberSummary": {"count": "63", "max": 1.0, "mean": 0.06349206349206349, "stddev": 0.24580452980260506, "histogram": {"end": 1.0000001, "counts": ["59", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "4"], "max": 1.0, "bins": [0.0, 0.06250000625, 0.1250000125, 0.18750001875, 0.250000025, 0.31250003125000003, 0.3750000375, 0.43750004375, 0.50000005, 0.56250005625, 0.6250000625000001, 0.68750006875, 0.750000075, 0.8125000812500001, 0.8750000875, 0.9375000937500001, 1.0000001], "n": "63", "start": 0.0, "width": 0.0, "min": 0.0}, "uniqueCount": {"estimate": 2.0, "upper": 2.0, "lower": 2.0}, "quantiles": {"quantiles": [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0], "quantileValues": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0]}, "min": 0.0, "isDiscrete": false}, "frequentItems": {"items": [{"estimate": "59", "jsonValue": "0.0"}, {"estimate": "4", "jsonValue": "1.0"}]}, "uniqueCount": {"estimate": 2.000000004967054, "upper": 2.000099863468538, "lower": 2.0}, "sum": 4.19047619047619, "variance": 0.06041986687147976, "coefficient_of_variation": 3.8714213443910297, "quantile_statistics": {"fifth_percentile": 0.0, "q1": 0.0, "median": 0.0, "q3": 0.0, "ninety_fifth_percentile": 1.0, "range": 1.0, "iqr": 0.0}}};\n", " // Config handlebars and pass data to HBS template\n", " const source = document.getElementById("entry-template").innerHTML;\n", " const template = Handlebars.compile(source);\n", @@ -977,17 +977,17 @@ "" ] }, - "execution_count": 8, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "from whylogs.viz import NotebookProfileViewer\n", + "from whylogs.viz import NotebookProfileVisualizer\n", "feature_name = \"Purchase Canceled\"\n", "\n", "print(\"Feature Statistics for:\\n Feature:{}\\nFor profile segment of:\\n Gender: {}\\n Product Category: {}\".format(feature_name,gender_label,product_label))\n", - "visualization = NotebookProfileViewer()\n", + "visualization = NotebookProfileVisualizer()\n", "visualization.set_profiles(target_profile=target_profile)\n", "visualization.feature_statistics(feature_name=feature_name)" ] @@ -1017,7 +1017,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -1030,7 +1030,7 @@ "Name: Store Type, dtype: int64" ] }, - "execution_count": 9, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -1048,7 +1048,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -1075,7 +1075,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -1100,7 +1100,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -1125,7 +1125,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -1134,7 +1134,7 @@ "['City Code']" ] }, - "execution_count": 13, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -1152,16 +1152,19 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Note that we can also specify the maximum number of segments. In this case, we specify a maximum of 20 segments.\n", + "`estimate_segments` returns a list of column names, which will be used as argument to `log_dataframe`.\n", "\n", - "If no target field is specified, the method will find a suitable field based on the maximum entropy column.\n", + "A few things to note:\n", "\n", - "`estimate_segments` returns a list of column names, which in turn can be used as argument to `log_dataframe`:" + "- `estimate_segmentes` returns the segments list in-memory, but it also outputs it as a json file as metadata in the default path `output/{name}/metadata`.\n", + "- We can specify the maximum number of segments. In this case, we specify a maximum of 20 segments.\n", + "- If no target field is specified, the method will find a suitable field based on the maximum entropy column.\n", + "\n" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -1185,26 +1188,9 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'whylogs.tag.City Code': '1.0', 'name': 'segment-test'}\n", - "{'whylogs.tag.City Code': '2.0', 'name': 'segment-test'}\n", - "{'whylogs.tag.City Code': '3.0', 'name': 'segment-test'}\n", - "{'whylogs.tag.City Code': '4.0', 'name': 'segment-test'}\n", - "{'whylogs.tag.City Code': '5.0', 'name': 'segment-test'}\n", - "{'whylogs.tag.City Code': '6.0', 'name': 'segment-test'}\n", - "{'whylogs.tag.City Code': '7.0', 'name': 'segment-test'}\n", - "{'whylogs.tag.City Code': '8.0', 'name': 'segment-test'}\n", - "{'whylogs.tag.City Code': '9.0', 'name': 'segment-test'}\n", - "{'whylogs.tag.City Code': '10.0', 'name': 'segment-test'}\n" - ] - } - ], + "outputs": [], "source": [ "for k, prof in profile_segments.items():\n", " print(prof.tags)" @@ -1228,9 +1214,26 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'whylogs.tag.City Code': '1.0', 'name': 'segment-test'}\n", + "{'whylogs.tag.City Code': '2.0', 'name': 'segment-test'}\n", + "{'whylogs.tag.City Code': '3.0', 'name': 'segment-test'}\n", + "{'whylogs.tag.City Code': '4.0', 'name': 'segment-test'}\n", + "{'whylogs.tag.City Code': '5.0', 'name': 'segment-test'}\n", + "{'whylogs.tag.City Code': '6.0', 'name': 'segment-test'}\n", + "{'whylogs.tag.City Code': '7.0', 'name': 'segment-test'}\n", + "{'whylogs.tag.City Code': '8.0', 'name': 'segment-test'}\n", + "{'whylogs.tag.City Code': '9.0', 'name': 'segment-test'}\n", + "{'whylogs.tag.City Code': '10.0', 'name': 'segment-test'}\n" + ] + } + ], "source": [ "from functools import reduce\n", "profiles = [prof for _,prof in profile_segments.items()]\n", @@ -1246,7 +1249,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -1296,7 +1299,31 @@ " \n", " \n", " 0\n", - " Date of Birth\n", + " Product Subcategory Code\n", + " 908.0\n", + " 0.0\n", + " 0.0\n", + " 908.0\n", + " 12.0000\n", + " 6.008811\n", + " 1.000\n", + " 3.756645\n", + " 12.0\n", + " ...\n", + " 0.000000\n", + " 1.000000\n", + " 1.000000\n", + " 1.000000\n", + " 3.000000\n", + " 5.000000\n", + " 10.000000\n", + " 12.000000\n", + " 12.000000\n", + " 12.000000\n", + " \n", + " \n", + " 1\n", + " Store Type\n", " 908.0\n", " 0.0\n", " 0.0\n", @@ -1307,7 +1334,7 @@ " 0.000000\n", " 0.0\n", " ...\n", - " 0.000000\n", + " 0.392934\n", " NaN\n", " NaN\n", " NaN\n", @@ -1319,32 +1346,8 @@ " NaN\n", " \n", " \n", - " 1\n", - " City Code\n", - " 908.0\n", - " 0.0\n", - " 0.0\n", - " 908.0\n", - " 10.0000\n", - " 5.390969\n", - " 1.000\n", - " 2.887491\n", - " 10.0\n", - " ...\n", - " 0.000000\n", - " 1.000000\n", - " 1.000000\n", - " 1.000000\n", - " 3.000000\n", - " 5.000000\n", - " 8.000000\n", - " 10.000000\n", - " 10.000000\n", - " 10.000000\n", - " \n", - " \n", " 2\n", - " Product Category\n", + " Transaction ID\n", " 908.0\n", " 0.0\n", " 0.0\n", @@ -1355,7 +1358,7 @@ " 0.000000\n", " 0.0\n", " ...\n", - " 0.767990\n", + " 0.000000\n", " NaN\n", " NaN\n", " NaN\n", @@ -1368,55 +1371,55 @@ " \n", " \n", " 3\n", - " Transaction Week\n", + " City Code\n", " 908.0\n", " 0.0\n", " 0.0\n", " 908.0\n", - " 0.0000\n", - " 0.000000\n", - " 0.000\n", - " 0.000000\n", - " 1.0\n", + " 10.0000\n", + " 5.390969\n", + " 1.000\n", + " 2.887491\n", + " 10.0\n", " ...\n", " 0.000000\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", + " 1.000000\n", + " 1.000000\n", + " 1.000000\n", + " 3.000000\n", + " 5.000000\n", + " 8.000000\n", + " 10.000000\n", + " 10.000000\n", + " 10.000000\n", " \n", " \n", " 4\n", - " Purchase Canceled\n", + " Product Category Code\n", " 908.0\n", - " 72.0\n", " 0.0\n", - " 836.0\n", - " 1.0000\n", - " 0.095694\n", - " 0.000\n", - " 0.294347\n", - " 2.0\n", + " 0.0\n", + " 908.0\n", + " 6.0000\n", + " 3.719163\n", + " 1.000\n", + " 1.705231\n", + " 6.0\n", " ...\n", " 0.000000\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", " 1.000000\n", " 1.000000\n", " 1.000000\n", + " 2.000000\n", + " 4.000000\n", + " 5.000000\n", + " 6.000000\n", + " 6.000000\n", + " 6.000000\n", " \n", " \n", " 5\n", - " Transaction Batch\n", + " Transaction Week\n", " 908.0\n", " 0.0\n", " 0.0\n", @@ -1440,31 +1443,31 @@ " \n", " \n", " 6\n", - " Product Subcategory Code\n", + " Item Price\n", " 908.0\n", " 0.0\n", " 0.0\n", " 908.0\n", - " 12.0000\n", - " 6.008811\n", - " 1.000\n", - " 3.756645\n", - " 12.0\n", + " 149.8000\n", + " 79.096916\n", + " 7.100\n", + " 42.087292\n", + " 664.0\n", " ...\n", " 0.000000\n", - " 1.000000\n", - " 1.000000\n", - " 1.000000\n", - " 3.000000\n", - " 5.000000\n", - " 10.000000\n", - " 12.000000\n", - " 12.000000\n", - " 12.000000\n", + " 7.100000\n", + " 9.000000\n", + " 13.000000\n", + " 43.200001\n", + " 80.099998\n", + " 116.199997\n", + " 141.399994\n", + " 148.300003\n", + " 149.800003\n", " \n", " \n", " 7\n", - " Transaction ID\n", + " Product Category\n", " 908.0\n", " 0.0\n", " 0.0\n", @@ -1475,7 +1478,7 @@ " 0.000000\n", " 0.0\n", " ...\n", - " 0.000000\n", + " 0.767990\n", " NaN\n", " NaN\n", " NaN\n", @@ -1488,6 +1491,30 @@ " \n", " \n", " 8\n", + " Customer ID\n", + " 908.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0000\n", + " 0.000000\n", + " 0.000\n", + " 0.000000\n", + " 0.0\n", + " ...\n", + " 0.000000\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", + " \n", + " 9\n", " Total Tax\n", " 908.0\n", " 0.0\n", @@ -1502,41 +1529,41 @@ " 0.000000\n", " 0.861000\n", " 1.701000\n", - " 3.454500\n", - " 9.922500\n", - " 20.643000\n", - " 36.855000\n", - " 61.698002\n", - " 75.809998\n", + " 3.234000\n", + " 9.765000\n", + " 20.380501\n", + " 36.014999\n", + " 61.582500\n", + " 74.182503\n", " 78.277496\n", " \n", " \n", - " 9\n", - " Product Subcategory\n", + " 10\n", + " Total Amount\n", " 908.0\n", " 0.0\n", " 0.0\n", - " 0.0\n", - " 0.0000\n", - " 0.000000\n", - " 0.000\n", - " 0.000000\n", - " 0.0\n", + " 908.0\n", + " 823.7775\n", + " 219.909604\n", + " -767.975\n", + " 240.507694\n", + " 830.0\n", " ...\n", - " 0.425731\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", + " 0.000000\n", + " -767.974976\n", + " -495.924011\n", + " -107.626999\n", + " 80.554497\n", + " 184.645493\n", + " 358.683014\n", + " 638.247986\n", + " 780.682495\n", + " 823.777527\n", " \n", " \n", - " 10\n", - " Customer ID\n", + " 11\n", + " Date of Birth\n", " 908.0\n", " 0.0\n", " 0.0\n", @@ -1559,7 +1586,7 @@ " NaN\n", " \n", " \n", - " 11\n", + " 12\n", " Age at Transaction Date\n", " 908.0\n", " 0.0\n", @@ -1583,56 +1610,56 @@ " 43.000000\n", " \n", " \n", - " 12\n", - " Product Category Code\n", + " 13\n", + " Purchase Canceled\n", " 908.0\n", + " 72.0\n", " 0.0\n", - " 0.0\n", - " 908.0\n", - " 6.0000\n", - " 3.719163\n", - " 1.000\n", - " 1.705231\n", - " 6.0\n", + " 836.0\n", + " 1.0000\n", + " 0.095694\n", + " 0.000\n", + " 0.294347\n", + " 2.0\n", " ...\n", " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", " 1.000000\n", " 1.000000\n", " 1.000000\n", - " 2.000000\n", - " 4.000000\n", - " 5.000000\n", - " 6.000000\n", - " 6.000000\n", - " 6.000000\n", " \n", " \n", - " 13\n", - " Gender\n", + " 14\n", + " Transaction Day of Week\n", " 908.0\n", " 0.0\n", " 0.0\n", - " 0.0\n", + " 908.0\n", " 0.0000\n", " 0.000000\n", " 0.000\n", " 0.000000\n", - " 0.0\n", + " 1.0\n", " ...\n", " 0.000000\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", " \n", " \n", - " 14\n", - " Store Type\n", + " 15\n", + " Gender\n", " 908.0\n", " 0.0\n", " 0.0\n", @@ -1643,7 +1670,7 @@ " 0.000000\n", " 0.0\n", " ...\n", - " 0.392934\n", + " 0.000000\n", " NaN\n", " NaN\n", " NaN\n", @@ -1655,56 +1682,8 @@ " NaN\n", " \n", " \n", - " 15\n", - " Item Price\n", - " 908.0\n", - " 0.0\n", - " 0.0\n", - " 908.0\n", - " 149.8000\n", - " 79.096916\n", - " 7.100\n", - " 42.087292\n", - " 664.0\n", - " ...\n", - " 0.000000\n", - " 7.100000\n", - " 9.000000\n", - " 13.200000\n", - " 43.400002\n", - " 80.099998\n", - " 116.199997\n", - " 141.500000\n", - " 148.300003\n", - " 149.800003\n", - " \n", - " \n", " 16\n", - " Total Amount\n", - " 908.0\n", - " 0.0\n", - " 0.0\n", - " 908.0\n", - " 823.7775\n", - " 219.909604\n", - " -767.975\n", - " 240.507694\n", - " 830.0\n", - " ...\n", - " 0.000000\n", - " -767.974976\n", - " -495.924011\n", - " -107.626999\n", - " 82.432999\n", - " 185.639999\n", - " 360.009003\n", - " 641.341980\n", - " 797.809998\n", - " 823.777527\n", - " \n", - " \n", - " 17\n", - " Transaction Day of Week\n", + " Transaction Batch\n", " 908.0\n", " 0.0\n", " 0.0\n", @@ -1726,6 +1705,30 @@ " 0.000000\n", " 0.000000\n", " \n", + " \n", + " 17\n", + " Product Subcategory\n", + " 908.0\n", + " 0.0\n", + " 0.0\n", + " 0.0\n", + " 0.0000\n", + " 0.000000\n", + " 0.000\n", + " 0.000000\n", + " 0.0\n", + " ...\n", + " 0.425731\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", " \n", "\n", "

18 rows × 40 columns

\n", @@ -1733,109 +1736,109 @@ ], "text/plain": [ " column count null_count bool_count numeric_count \\\n", - "0 Date of Birth 908.0 0.0 0.0 0.0 \n", - "1 City Code 908.0 0.0 0.0 908.0 \n", - "2 Product Category 908.0 0.0 0.0 0.0 \n", - "3 Transaction Week 908.0 0.0 0.0 908.0 \n", - "4 Purchase Canceled 908.0 72.0 0.0 836.0 \n", - "5 Transaction Batch 908.0 0.0 0.0 908.0 \n", - "6 Product Subcategory Code 908.0 0.0 0.0 908.0 \n", - "7 Transaction ID 908.0 0.0 0.0 0.0 \n", - "8 Total Tax 908.0 0.0 0.0 908.0 \n", - "9 Product Subcategory 908.0 0.0 0.0 0.0 \n", - "10 Customer ID 908.0 0.0 0.0 0.0 \n", - "11 Age at Transaction Date 908.0 0.0 0.0 908.0 \n", - "12 Product Category Code 908.0 0.0 0.0 908.0 \n", - "13 Gender 908.0 0.0 0.0 0.0 \n", - "14 Store Type 908.0 0.0 0.0 0.0 \n", - "15 Item Price 908.0 0.0 0.0 908.0 \n", - "16 Total Amount 908.0 0.0 0.0 908.0 \n", - "17 Transaction Day of Week 908.0 0.0 0.0 908.0 \n", + "0 Product Subcategory Code 908.0 0.0 0.0 908.0 \n", + "1 Store Type 908.0 0.0 0.0 0.0 \n", + "2 Transaction ID 908.0 0.0 0.0 0.0 \n", + "3 City Code 908.0 0.0 0.0 908.0 \n", + "4 Product Category Code 908.0 0.0 0.0 908.0 \n", + "5 Transaction Week 908.0 0.0 0.0 908.0 \n", + "6 Item Price 908.0 0.0 0.0 908.0 \n", + "7 Product Category 908.0 0.0 0.0 0.0 \n", + "8 Customer ID 908.0 0.0 0.0 0.0 \n", + "9 Total Tax 908.0 0.0 0.0 908.0 \n", + "10 Total Amount 908.0 0.0 0.0 908.0 \n", + "11 Date of Birth 908.0 0.0 0.0 0.0 \n", + "12 Age at Transaction Date 908.0 0.0 0.0 908.0 \n", + "13 Purchase Canceled 908.0 72.0 0.0 836.0 \n", + "14 Transaction Day of Week 908.0 0.0 0.0 908.0 \n", + "15 Gender 908.0 0.0 0.0 0.0 \n", + "16 Transaction Batch 908.0 0.0 0.0 908.0 \n", + "17 Product Subcategory 908.0 0.0 0.0 0.0 \n", "\n", " max mean min stddev nunique_numbers ... \\\n", - "0 0.0000 0.000000 0.000 0.000000 0.0 ... \n", - "1 10.0000 5.390969 1.000 2.887491 10.0 ... \n", + "0 12.0000 6.008811 1.000 3.756645 12.0 ... \n", + "1 0.0000 0.000000 0.000 0.000000 0.0 ... \n", "2 0.0000 0.000000 0.000 0.000000 0.0 ... \n", - "3 0.0000 0.000000 0.000 0.000000 1.0 ... \n", - "4 1.0000 0.095694 0.000 0.294347 2.0 ... \n", + "3 10.0000 5.390969 1.000 2.887491 10.0 ... \n", + "4 6.0000 3.719163 1.000 1.705231 6.0 ... \n", "5 0.0000 0.000000 0.000 0.000000 1.0 ... \n", - "6 12.0000 6.008811 1.000 3.756645 12.0 ... \n", + "6 149.8000 79.096916 7.100 42.087292 664.0 ... \n", "7 0.0000 0.000000 0.000 0.000000 0.0 ... \n", - "8 78.2775 24.808725 0.861 18.527800 809.0 ... \n", - "9 0.0000 0.000000 0.000 0.000000 0.0 ... \n", - "10 0.0000 0.000000 0.000 0.000000 0.0 ... \n", - "11 43.0000 30.579295 19.000 6.697701 25.0 ... \n", - "12 6.0000 3.719163 1.000 1.705231 6.0 ... \n", - "13 0.0000 0.000000 0.000 0.000000 0.0 ... \n", - "14 0.0000 0.000000 0.000 0.000000 0.0 ... \n", - "15 149.8000 79.096916 7.100 42.087292 664.0 ... \n", - "16 823.7775 219.909604 -767.975 240.507694 830.0 ... \n", - "17 0.0000 0.000000 0.000 0.000000 1.0 ... \n", + "8 0.0000 0.000000 0.000 0.000000 0.0 ... \n", + "9 78.2775 24.808725 0.861 18.527800 809.0 ... \n", + "10 823.7775 219.909604 -767.975 240.507694 830.0 ... \n", + "11 0.0000 0.000000 0.000 0.000000 0.0 ... \n", + "12 43.0000 30.579295 19.000 6.697701 25.0 ... \n", + "13 1.0000 0.095694 0.000 0.294347 2.0 ... \n", + "14 0.0000 0.000000 0.000 0.000000 1.0 ... \n", + "15 0.0000 0.000000 0.000 0.000000 0.0 ... \n", + "16 0.0000 0.000000 0.000 0.000000 1.0 ... \n", + "17 0.0000 0.000000 0.000 0.000000 0.0 ... \n", "\n", " stddev_token_length quantile_0.0000 quantile_0.0100 quantile_0.0500 \\\n", - "0 0.000000 NaN NaN NaN \n", - "1 0.000000 1.000000 1.000000 1.000000 \n", - "2 0.767990 NaN NaN NaN \n", - "3 0.000000 0.000000 0.000000 0.000000 \n", - "4 0.000000 0.000000 0.000000 0.000000 \n", + "0 0.000000 1.000000 1.000000 1.000000 \n", + "1 0.392934 NaN NaN NaN \n", + "2 0.000000 NaN NaN NaN \n", + "3 0.000000 1.000000 1.000000 1.000000 \n", + "4 0.000000 1.000000 1.000000 1.000000 \n", "5 0.000000 0.000000 0.000000 0.000000 \n", - "6 0.000000 1.000000 1.000000 1.000000 \n", - "7 0.000000 NaN NaN NaN \n", - "8 0.000000 0.861000 1.701000 3.454500 \n", - "9 0.425731 NaN NaN NaN \n", - "10 0.000000 NaN NaN NaN \n", - "11 0.000000 19.000000 20.000000 20.000000 \n", - "12 0.000000 1.000000 1.000000 1.000000 \n", - "13 0.000000 NaN NaN NaN \n", - "14 0.392934 NaN NaN NaN \n", - "15 0.000000 7.100000 9.000000 13.200000 \n", - "16 0.000000 -767.974976 -495.924011 -107.626999 \n", - "17 0.000000 0.000000 0.000000 0.000000 \n", + "6 0.000000 7.100000 9.000000 13.000000 \n", + "7 0.767990 NaN NaN NaN \n", + "8 0.000000 NaN NaN NaN \n", + "9 0.000000 0.861000 1.701000 3.234000 \n", + "10 0.000000 -767.974976 -495.924011 -107.626999 \n", + "11 0.000000 NaN NaN NaN \n", + "12 0.000000 19.000000 20.000000 20.000000 \n", + "13 0.000000 0.000000 0.000000 0.000000 \n", + "14 0.000000 0.000000 0.000000 0.000000 \n", + "15 0.000000 NaN NaN NaN \n", + "16 0.000000 0.000000 0.000000 0.000000 \n", + "17 0.425731 NaN NaN NaN \n", "\n", " quantile_0.2500 quantile_0.5000 quantile_0.7500 quantile_0.9500 \\\n", - "0 NaN NaN NaN NaN \n", - "1 3.000000 5.000000 8.000000 10.000000 \n", + "0 3.000000 5.000000 10.000000 12.000000 \n", + "1 NaN NaN NaN NaN \n", "2 NaN NaN NaN NaN \n", - "3 0.000000 0.000000 0.000000 0.000000 \n", - "4 0.000000 0.000000 0.000000 1.000000 \n", + "3 3.000000 5.000000 8.000000 10.000000 \n", + "4 2.000000 4.000000 5.000000 6.000000 \n", "5 0.000000 0.000000 0.000000 0.000000 \n", - "6 3.000000 5.000000 10.000000 12.000000 \n", + "6 43.200001 80.099998 116.199997 141.399994 \n", "7 NaN NaN NaN NaN \n", - "8 9.922500 20.643000 36.855000 61.698002 \n", - "9 NaN NaN NaN NaN \n", - "10 NaN NaN NaN NaN \n", - "11 25.000000 31.000000 36.000000 41.000000 \n", - "12 2.000000 4.000000 5.000000 6.000000 \n", - "13 NaN NaN NaN NaN \n", - "14 NaN NaN NaN NaN \n", - "15 43.400002 80.099998 116.199997 141.500000 \n", - "16 82.432999 185.639999 360.009003 641.341980 \n", - "17 0.000000 0.000000 0.000000 0.000000 \n", + "8 NaN NaN NaN NaN \n", + "9 9.765000 20.380501 36.014999 61.582500 \n", + "10 80.554497 184.645493 358.683014 638.247986 \n", + "11 NaN NaN NaN NaN \n", + "12 25.000000 31.000000 36.000000 41.000000 \n", + "13 0.000000 0.000000 0.000000 1.000000 \n", + "14 0.000000 0.000000 0.000000 0.000000 \n", + "15 NaN NaN NaN NaN \n", + "16 0.000000 0.000000 0.000000 0.000000 \n", + "17 NaN NaN NaN NaN \n", "\n", " quantile_0.9900 quantile_1.0000 \n", - "0 NaN NaN \n", - "1 10.000000 10.000000 \n", + "0 12.000000 12.000000 \n", + "1 NaN NaN \n", "2 NaN NaN \n", - "3 0.000000 0.000000 \n", - "4 1.000000 1.000000 \n", + "3 10.000000 10.000000 \n", + "4 6.000000 6.000000 \n", "5 0.000000 0.000000 \n", - "6 12.000000 12.000000 \n", + "6 148.300003 149.800003 \n", "7 NaN NaN \n", - "8 75.809998 78.277496 \n", - "9 NaN NaN \n", - "10 NaN NaN \n", - "11 42.000000 43.000000 \n", - "12 6.000000 6.000000 \n", - "13 NaN NaN \n", - "14 NaN NaN \n", - "15 148.300003 149.800003 \n", - "16 797.809998 823.777527 \n", - "17 0.000000 0.000000 \n", + "8 NaN NaN \n", + "9 74.182503 78.277496 \n", + "10 780.682495 823.777527 \n", + "11 NaN NaN \n", + "12 42.000000 43.000000 \n", + "13 1.000000 1.000000 \n", + "14 0.000000 0.000000 \n", + "15 NaN NaN \n", + "16 0.000000 0.000000 \n", + "17 NaN NaN \n", "\n", "[18 rows x 40 columns]" ] }, - "execution_count": 17, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -1847,10 +1850,10 @@ ], "metadata": { "interpreter": { - "hash": "323493c40bedb65fef2eec2a6e595ce0cca722dcb720da40c0d127c8422c938f" + "hash": "a19bf64db2f82b50417c12105371f5da8c66e73f54ebe00efd04d6d7ec0b69d4" }, "kernelspec": { - "display_name": "Python 3.8.10 ('base')", + "display_name": "py36", "language": "python", "name": "python3" }, diff --git a/src/whylogs/viz/__init__.py b/src/whylogs/viz/__init__.py index 0a6959984f..422133a97f 100644 --- a/src/whylogs/viz/__init__.py +++ b/src/whylogs/viz/__init__.py @@ -1,5 +1,5 @@ from .browser_viz import profile_viewer -from .jupyter_notebook_viz import NotebookProfileViewer +from .jupyter_notebook_viz import NotebookProfileVisualizer from .visualizer import BaseProfileVisualizer, ProfileVisualizer -__ALL__ = [ProfileVisualizer, BaseProfileVisualizer, profile_viewer, NotebookProfileViewer] +__ALL__ = [ProfileVisualizer, BaseProfileVisualizer, profile_viewer, NotebookProfileVisualizer] diff --git a/src/whylogs/viz/jupyter_notebook_viz.py b/src/whylogs/viz/jupyter_notebook_viz.py index c9bc848c20..9ebd00b0a9 100644 --- a/src/whylogs/viz/jupyter_notebook_viz.py +++ b/src/whylogs/viz/jupyter_notebook_viz.py @@ -21,7 +21,7 @@ numerical_types = (InferredType.Type.INTEGRAL, InferredType.Type.FRACTIONAL) -class NotebookProfileViewer: +class NotebookProfileVisualizer: SUMMARY_REPORT_TEMPLATE_NAME = "index-hbs-cdn-all-in-for-jupyter-notebook.html" DOUBLE_HISTOGRAM_TEMPLATE_NAME = "index-hbs-cdn-all-in-jupyter-distribution-chart.html" DISTRIBUTION_CHART_TEMPLATE_NAME = "index-hbs-cdn-all-in-jupyter-bar-chart.html" diff --git a/test_notebooks/notebook_tests.py b/test_notebooks/notebook_tests.py index 1e3870115a..2567bbe980 100644 --- a/test_notebooks/notebook_tests.py +++ b/test_notebooks/notebook_tests.py @@ -9,6 +9,7 @@ skip_notebooks = [ "Guest Session Demo.ipynb", + "Auto Segmentation.ipynb", "RAPIDS GPU Integration Example.ipynb", "Analysis.ipynb", "ROV-whylogs.ipynb", diff --git a/tests/unit/viz/test_jupyter_notebook_viz.py b/tests/unit/viz/test_jupyter_notebook_viz.py index 9a4b06b959..97be9effae 100644 --- a/tests/unit/viz/test_jupyter_notebook_viz.py +++ b/tests/unit/viz/test_jupyter_notebook_viz.py @@ -16,7 +16,7 @@ columnValuesUniqueWithinRow, sumOfRowValuesOfMultipleColumnsEqualsConstraint, ) -from whylogs.viz import NotebookProfileViewer +from whylogs.viz import NotebookProfileVisualizer def __generate_target_profile(): @@ -96,17 +96,17 @@ def _get_sample_dataset_constraints(): ) -def test_notebook_profile_viewer_set_profiles(): +def test_notebook_profile_visualizer_set_profiles(): target_profile = __generate_target_profile() reference_profile = __generate_reference_profile() - viz = NotebookProfileViewer() + viz = NotebookProfileVisualizer() viz.set_profiles(target_profile=target_profile, reference_profile=reference_profile) def test_summary_drift_report_without_preferred_height(): target_profile = __generate_target_profile() reference_profile = __generate_reference_profile() - viz = NotebookProfileViewer() + viz = NotebookProfileVisualizer() viz.set_profiles(target_profile=target_profile, reference_profile=reference_profile) viz.summary_drift_report() @@ -114,7 +114,7 @@ def test_summary_drift_report_without_preferred_height(): def test_summary_drift_report_with_preferred_height(): target_profile = __generate_target_profile() reference_profile = __generate_reference_profile() - viz = NotebookProfileViewer() + viz = NotebookProfileVisualizer() viz.set_profiles(target_profile=target_profile, reference_profile=reference_profile) viz.summary_drift_report() @@ -122,7 +122,7 @@ def test_summary_drift_report_with_preferred_height(): def test_feature_statistics_not_passing_profile_type(): target_profile = __generate_target_profile() reference_profile = __generate_reference_profile() - viz = NotebookProfileViewer() + viz = NotebookProfileVisualizer() viz.set_profiles(target_profile=target_profile, reference_profile=reference_profile) viz.feature_statistics("uniform_integers") @@ -130,7 +130,7 @@ def test_feature_statistics_not_passing_profile_type(): def test_feature_statistics_passing_profile_type(): target_profile = __generate_target_profile() reference_profile = __generate_reference_profile() - viz = NotebookProfileViewer() + viz = NotebookProfileVisualizer() viz.set_profiles(target_profile=target_profile, reference_profile=reference_profile) viz.feature_statistics("uniform_integers", "target") @@ -138,7 +138,7 @@ def test_feature_statistics_passing_profile_type(): def test_feature_statistics_passing_profile_type_and_prefered_height(): target_profile = __generate_target_profile() reference_profile = __generate_reference_profile() - viz = NotebookProfileViewer() + viz = NotebookProfileVisualizer() viz.set_profiles(target_profile=target_profile, reference_profile=reference_profile) viz.feature_statistics("uniform_integers", "target", "1000px") @@ -146,7 +146,7 @@ def test_feature_statistics_passing_profile_type_and_prefered_height(): def test_download_passing_all_arguments(tmpdir): target_profile = __generate_target_profile() reference_profile = __generate_reference_profile() - viz = NotebookProfileViewer() + viz = NotebookProfileVisualizer() viz.set_profiles(target_profile=target_profile, reference_profile=reference_profile) download = viz.download(viz.summary_drift_report(), tmpdir, html_file_name="foo") @@ -156,7 +156,7 @@ def test_download_passing_all_arguments(tmpdir): def test_constraints_report_without_preferred_height(): target_profile = __generate_target_profile() reference_profile = __generate_reference_profile() - viz = NotebookProfileViewer() + viz = NotebookProfileVisualizer() viz.set_profiles(target_profile=target_profile, reference_profile=reference_profile) dc = _get_sample_dataset_constraints() viz.constraints_report(dc) @@ -165,7 +165,7 @@ def test_constraints_report_without_preferred_height(): def test_constraints_report_with_preferred_height(): target_profile = __generate_target_profile() reference_profile = __generate_reference_profile() - viz = NotebookProfileViewer() + viz = NotebookProfileVisualizer() viz.set_profiles(target_profile=target_profile, reference_profile=reference_profile) dc = _get_sample_dataset_constraints() viz.constraints_report(dc, preferred_cell_height="1000px") @@ -174,7 +174,7 @@ def test_constraints_report_with_preferred_height(): def test_double_histogram_without_height(): target_profile = __generate_target_profile() reference_profile = __generate_reference_profile() - viz = NotebookProfileViewer() + viz = NotebookProfileVisualizer() viz.set_profiles(target_profile=target_profile, reference_profile=reference_profile) viz.double_histogram("uniform_integers") @@ -182,7 +182,7 @@ def test_double_histogram_without_height(): def test_double_histogram_with_height(): target_profile = __generate_target_profile() reference_profile = __generate_reference_profile() - viz = NotebookProfileViewer() + viz = NotebookProfileVisualizer() viz.set_profiles(target_profile=target_profile, reference_profile=reference_profile) viz.double_histogram("uniform_integers", "1000px") @@ -190,7 +190,7 @@ def test_double_histogram_with_height(): def test_distribution_chart_without_height(): target_profile = __generate_categorical_target_profile() reference_profile = __generate_categorical_reference_profile() - viz = NotebookProfileViewer() + viz = NotebookProfileVisualizer() viz.set_profiles(target_profile=target_profile, reference_profile=reference_profile) viz.distribution_chart("credit_card") @@ -198,7 +198,7 @@ def test_distribution_chart_without_height(): def test_distribution_chart_with_height(): target_profile = __generate_categorical_target_profile() reference_profile = __generate_categorical_reference_profile() - viz = NotebookProfileViewer() + viz = NotebookProfileVisualizer() viz.set_profiles(target_profile=target_profile, reference_profile=reference_profile) viz.distribution_chart("credit_card", "1000px") @@ -206,7 +206,7 @@ def test_distribution_chart_with_height(): def test_difference_distribution_chart_without_height(): target_profile = __generate_categorical_target_profile() reference_profile = __generate_categorical_reference_profile() - viz = NotebookProfileViewer() + viz = NotebookProfileVisualizer() viz.set_profiles(target_profile=target_profile, reference_profile=reference_profile) viz.difference_distribution_chart("credit_card") @@ -214,6 +214,6 @@ def test_difference_distribution_chart_without_height(): def test_difference_distribution_chart_with_height(): target_profile = __generate_categorical_target_profile() reference_profile = __generate_categorical_reference_profile() - viz = NotebookProfileViewer() + viz = NotebookProfileVisualizer() viz.set_profiles(target_profile=target_profile, reference_profile=reference_profile) viz.difference_distribution_chart("credit_card", "1000px") From d70c86a33acf14a79bbcabc4b16910f41016cd34 Mon Sep 17 00:00:00 2001 From: Felipe Adachi Date: Mon, 18 Apr 2022 18:12:04 -0300 Subject: [PATCH 2/2] undo change in notebook_tests.py --- test_notebooks/notebook_tests.py | 1 - 1 file changed, 1 deletion(-) diff --git a/test_notebooks/notebook_tests.py b/test_notebooks/notebook_tests.py index 2567bbe980..1e3870115a 100644 --- a/test_notebooks/notebook_tests.py +++ b/test_notebooks/notebook_tests.py @@ -9,7 +9,6 @@ skip_notebooks = [ "Guest Session Demo.ipynb", - "Auto Segmentation.ipynb", "RAPIDS GPU Integration Example.ipynb", "Analysis.ipynb", "ROV-whylogs.ipynb",