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Repository: GoogleChrome/CrUX
Branch: main
Commit: b62877a01a52
Files: 34
Total size: 1.0 MB
Directory structure:
gitextract_zxhgowfc/
├── .github/
│ └── ISSUE_TEMPLATE/
│ └── new-crux-metric-request.md
├── CONTRIBUTING.md
├── LICENSE
├── README.md
├── colab/
│ ├── crux-history-api.ipynb
│ └── navigation-types-and-lcp.ipynb
├── gs/
│ ├── README.md
│ ├── crux-api.gs
│ └── psi-api-v5.gs
├── js/
│ └── crux-api-util.js
├── sql/
│ ├── README.md
│ ├── cls-summary.sql
│ ├── core-web-vitals-compliance-rates.sql
│ ├── core-web-vitals.sql
│ ├── country-fast-fcp.sql
│ ├── fast-fcp-for-domain.sql
│ ├── global-connection-density.sql
│ ├── global-device-density.sql
│ ├── mastering-crux/
│ │ ├── 01-basic-cwv.sql
│ │ ├── 02-timeseries-cwv.sql
│ │ ├── 03-device-cwv.sql
│ │ └── 04-country-cwv.sql
│ ├── notification-permissions-origin-form-factor.sql
│ ├── notification-permissions.sql
│ ├── origins-for-domain.sql
│ ├── p75-fcp-timeseries.sql
│ ├── p75-fcp.sql
│ ├── p75-lcp-country.sql
│ ├── subregion-fast-fcp.sql
│ ├── test-my-site.sql
│ └── timeseries-fast-fcp.sql
└── utils/
├── countries.js
├── countries.json
└── countries.txt
================================================
FILE CONTENTS
================================================
================================================
FILE: .github/ISSUE_TEMPLATE/new-crux-metric-request.md
================================================
---
name: New CrUX metric request
about: Tell us about a new metric you'd like to see included in the report
title: ''
labels: enhancement
assignees: ''
---
**Proposal**
[What exactly is the metric you're proposing?]
**Use cases**
[How do you see this metric being useful in the dataset? Please list.]
**Measurement**
[How is the metric measured? Is it a web standard?]
**Schema**
[How will the data be represented in BigQuery? Sample schema below.]
```
"first_paint": {
"histogram": {
"bin": [
{"start": 0, "end": 100, "density": 0.003},
{"start": 100, "end": 200, "density": 0.014},
// …
]
}
}
```
================================================
FILE: CONTRIBUTING.md
================================================
# How to Contribute
We'd love to accept your patches and contributions to this project. There are
just a few small guidelines you need to follow.
## Contributor License Agreement
Contributions to this project must be accompanied by a Contributor License
Agreement. You (or your employer) retain the copyright to your contribution;
this simply gives us permission to use and redistribute your contributions as
part of the project. Head over to <https://cla.developers.google.com/> to see
your current agreements on file or to sign a new one.
You generally only need to submit a CLA once, so if you've already submitted one
(even if it was for a different project), you probably don't need to do it
again.
## Code reviews
All submissions, including submissions by project members, require review. We
use GitHub pull requests for this purpose. Consult
[GitHub Help](https://help.github.com/articles/about-pull-requests/) for more
information on using pull requests.
## Community Guidelines
This project follows [Google's Open Source Community
Guidelines](https://opensource.google.com/conduct/).
================================================
FILE: LICENSE
================================================
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================================================
FILE: README.md
================================================
# CrUX
CrUX is the [Chrome User Experience Report](https://developer.chrome.com/docs/crux/) from Google. It measures the user experience of the web and makes the raw histogram data available on BigQuery and through APIs.
This repository is a place for the web community to share queries, ideas, or issues. This is also a place to discover useful recipes for extracting insights from the CrUX dataset via [sql](./sql), [JavaScript to make API calls](./js), [AppScript](./gs), or [Colabs](./colab). If you have a recipe that you'd like to share, feel free to submit a pull request.
We also encourage CrUX users to use GitHub issues to [ask questions](https://github.com/GoogleChrome/CrUX/issues/new?labels=question), [suggest new features](https://github.com/GoogleChrome/CrUX/issues/new?labels=enhancement), or [file bugs](https://github.com/GoogleChrome/CrUX/issues/new?labels=bug). This process complements the [Google Group](https://groups.google.com/a/chromium.org/forum/#!forum/chrome-ux-report).
_This repository is not an officially supported Google product._
================================================
FILE: colab/crux-history-api.ipynb
================================================
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"height": 1000
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"text/html": [
"\n",
" For each metric with histograms and percentiles, we display two graphs:\n",
" <ul>\n",
" <li>The percentile graph shows the p75 values for the metric over time.\n",
" The shaded areas indicate good (light green),\n",
" needs improvement (light orange), and poor (light red).\n",
" <li>The tribin graph shows the percentages of page loads with\n",
" a good, needs improvement, or poor user experience over time.\n",
" </ul>\n",
" If the metric has labeled fractions, then we display these in a single\n",
" stacked bar chart.\n",
" In all cases, each point in time in the graph on the x axis\n",
" represents a 28 day collection period ending in that date.\n",
" "
]
},
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" vegaEmbed(outputDiv, spec, embedOpt)\n",
" .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
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4000.0)\", \"pct\": 0.30889892578125}, {\"last_date\": \"2023-12-09T00:00:00\", \"LCP\": \"needs improvement (2500.0 < LCP <= 4000.0)\", \"pct\": 0.304840087890625}, {\"last_date\": \"2023-12-16T00:00:00\", \"LCP\": \"needs improvement (2500.0 < LCP <= 4000.0)\", \"pct\": 0.304656982421875}, {\"last_date\": \"2023-12-23T00:00:00\", \"LCP\": \"needs improvement (2500.0 < LCP <= 4000.0)\", \"pct\": 0.307708740234375}, {\"last_date\": \"2023-12-30T00:00:00\", \"LCP\": \"needs improvement (2500.0 < LCP <= 4000.0)\", \"pct\": 0.310302734375}, {\"last_date\": \"2024-01-06T00:00:00\", \"LCP\": \"needs improvement (2500.0 < LCP <= 4000.0)\", \"pct\": 0.30938720703125}, {\"last_date\": \"2023-07-22T00:00:00\", \"LCP\": \"poor (LCP > 4000.0)\", \"pct\": 0.099884033203125}, {\"last_date\": \"2023-07-29T00:00:00\", \"LCP\": \"poor (LCP > 4000.0)\", \"pct\": 0.1051025390625}, {\"last_date\": \"2023-08-05T00:00:00\", \"LCP\": \"poor (LCP > 4000.0)\", \"pct\": 0.107177734375}, {\"last_date\": \"2023-08-12T00:00:00\", \"LCP\": \"poor (LCP > 4000.0)\", \"pct\": 0.099517822265625}, {\"last_date\": \"2023-08-19T00:00:00\", \"LCP\": \"poor (LCP > 4000.0)\", \"pct\": 0.08837890625}, {\"last_date\": \"2023-08-26T00:00:00\", \"LCP\": \"poor (LCP > 4000.0)\", \"pct\": 0.076202392578125}, {\"last_date\": \"2023-09-02T00:00:00\", \"LCP\": \"poor (LCP > 4000.0)\", \"pct\": 0.066680908203125}, {\"last_date\": \"2023-09-09T00:00:00\", \"LCP\": \"poor (LCP > 4000.0)\", \"pct\": 0.069091796875}, {\"last_date\": \"2023-09-16T00:00:00\", \"LCP\": \"poor (LCP > 4000.0)\", \"pct\": 0.070343017578125}, {\"last_date\": \"2023-09-23T00:00:00\", \"LCP\": \"poor (LCP > 4000.0)\", \"pct\": 0.066558837890625}, {\"last_date\": \"2023-09-30T00:00:00\", \"LCP\": \"poor (LCP > 4000.0)\", \"pct\": 0.06646728515625}, {\"last_date\": \"2023-10-07T00:00:00\", \"LCP\": \"poor (LCP > 4000.0)\", \"pct\": 0.064178466796875}, {\"last_date\": \"2023-10-14T00:00:00\", \"LCP\": \"poor (LCP > 4000.0)\", \"pct\": 0.08367919921875}, {\"last_date\": \"2023-10-21T00:00:00\", \"LCP\": \"poor (LCP > 4000.0)\", \"pct\": 0.1400146484375}, {\"last_date\": \"2023-10-28T00:00:00\", \"LCP\": \"poor (LCP > 4000.0)\", \"pct\": 0.193206787109375}, {\"last_date\": \"2023-11-04T00:00:00\", \"LCP\": \"poor (LCP > 4000.0)\", \"pct\": 0.2364501953125}, {\"last_date\": \"2023-11-11T00:00:00\", \"LCP\": \"poor (LCP > 4000.0)\", \"pct\": 0.2545166015625}, {\"last_date\": \"2023-11-18T00:00:00\", \"LCP\": \"poor (LCP > 4000.0)\", \"pct\": 0.26446533203125}, {\"last_date\": \"2023-11-25T00:00:00\", \"LCP\": \"poor (LCP > 4000.0)\", \"pct\": 0.28704833984375}, {\"last_date\": \"2023-12-02T00:00:00\", \"LCP\": \"poor (LCP > 4000.0)\", \"pct\": 0.317779541015625}, {\"last_date\": \"2023-12-09T00:00:00\", \"LCP\": \"poor (LCP > 4000.0)\", \"pct\": 0.33599853515625}, {\"last_date\": \"2023-12-16T00:00:00\", \"LCP\": \"poor (LCP > 4000.0)\", \"pct\": 0.332977294921875}, {\"last_date\": \"2023-12-23T00:00:00\", \"LCP\": \"poor (LCP > 4000.0)\", \"pct\": 0.32147216796875}, {\"last_date\": \"2023-12-30T00:00:00\", \"LCP\": \"poor (LCP > 4000.0)\", \"pct\": 0.31097412109375}, {\"last_date\": \"2024-01-06T00:00:00\", \"LCP\": \"poor (LCP > 4000.0)\", \"pct\": 0.29656982421875}]}}, {\"mode\": \"vega-lite\"});\n",
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"source": [
"#@markdown # CRUX History API\n",
"#@markdown Welcome to the CrUX History API. This Colab allows you to get time\n",
"#@markdown series data from the Chrome User Experience Report (CrUX)\n",
"#@markdown for specific origins and urls via the CrUX History API, and also get\n",
"#@markdown you started making your own API requests. You can easily look under\n",
"#@markdown the hood by clicking \"Show code\" at the end of this section.\n",
"#@markdown\n",
"#@markdown Please also see\n",
"#@markdown * [the reference documentation](https://developer.chrome.com/docs/crux/history-api/)\n",
"#@markdown * [our blog post](https://developer.chrome.com/blog/chrome-ux-report-history-api/)\n",
"#@markdown * [the canonical version of this Colab](https://colab.research.google.com/github/GoogleChrome/CrUX/blob/main/colab/crux-history-api.ipynb)\n",
"#@markdown * [the canonical source of this Colab](https://github.com/GoogleChrome/CrUX/blob/main/colab/crux-history-api.ipynb)\n",
"#@markdown * [our discussion forum for praise, complaints, and questions](https://groups.google.com/a/chromium.org/g/chrome-ux-report)\n",
"#@markdown\n",
"#@markdown ## API key\n",
"#@markdown To run this Colab, you'll need an API key. You can create one in the\n",
"#@markdown [Credentials page](https://console.developers.google.com/apis/credentials)\n",
"#@markdown and provision it for Chrome UX Report API usage. If you're making\n",
"#@markdown your own requests, this key needs to be included in every request\n",
"#@markdown with the `?key=` parameter, but in this Colab you can simply\n",
"#@markdown enter it as CRUX_KEY in the next line. After that, press the play\n",
"#@markdown button and the magic begins (it's OK to use the default Colab\n",
"#@markdown runtime).\n",
"CRUX_KEY = \"\" #@param {type: \"string\"}\n",
"\n",
"#@markdown Specify origin or url (not both).\n",
"ORIGIN = \"https://web.dev\" #@param {type: \"string\"}\n",
"URL = \"\" #@param {type: \"string\"}\n",
"#@markdown Specify a form factor (phone, desktop, tablet) or all.\n",
"FORM_FACTOR = \"ALL\" #@param [\"ALL\", \"PHONE\", \"DESKTOP\", \"TABLET\"]\n",
"#@markdown Specify the metrics; if left blank, we'll report all metrics.\n",
"METRICS = \"\" #@param {type:\"string\"}\n",
"\n",
"#@markdown What to include in the rendered output below?\n",
"EMIT_GRAPHS = True #@param {type:\"boolean\"}\n",
"EMIT_TABULAR_OUTPUT = False #@param {type:\"boolean\"}\n",
"EMIT_REQUEST_RESPONSE = False #@param {type:\"boolean\"}\n",
"\n",
"\n",
"import altair as alt\n",
"import requests\n",
"import pandas\n",
"import json\n",
"from typing import Any, Tuple, Dict, List\n",
"from IPython import display\n",
"from google.colab import data_table\n",
"\n",
"\n",
"def get_crux_api_response_from_form() -> Tuple[Dict[str, str], Dict[str, Any]]:\n",
" \"\"\"Based on the form data, makes a CrUX history request.\n",
"\n",
" The first return value is the request, the second is the parsed response.\n",
" \"\"\"\n",
" url = f'https://chromeuxreport.googleapis.com/v1/records:queryHistoryRecord?key={CRUX_KEY}'\n",
" json_request = {}\n",
" if URL:\n",
" json_request['url'] = URL\n",
" if ORIGIN:\n",
" json_request['origin'] = ORIGIN\n",
" if FORM_FACTOR != 'ALL':\n",
" json_request['form_factor'] = FORM_FACTOR\n",
" if METRICS:\n",
" json_request['metrics'] = METRICS\n",
" response = requests.post(url, json.dumps(json_request))\n",
" return json_request, response.json()\n",
"\n",
"\n",
"def short_name(metric: str) -> str:\n",
" \"\"\"A short name for a metric name.\n",
"\n",
" In CrUX API responses, metric names are provided in snake case, e.g.,\n",
" first_contentful_paint. This function computes the three or four letter\n",
" shortname, e.g., FCP.\n",
" \"\"\"\n",
" short = ''.join([s[0].upper()\n",
" for s in metric.split('_') if s != 'experimental'])\n",
" return short if short != 'ITNP' else 'INP'\n",
"\n",
"\n",
"def metrics_in(response: dict) -> List[str]:\n",
" \"\"\"Returns the metric names in a CrUX API history response.\"\"\"\n",
"\n",
" metrics = list(response['record']['metrics'].keys())\n",
" metrics.sort()\n",
" return metrics\n",
"\n",
"\n",
"def thresholds_by_metric(response: dict) -> Dict[str, Tuple[float, float]]:\n",
" \"\"\"The thresholds by metric name.\n",
"\n",
" Key in the returned dict is a metric name, e.g. 'first_contentful_paint'.\n",
" Value is a tuple of the low threshold, which separates 'good' from\n",
" 'needs improvement', and the high threshold, which separates\n",
" 'needs improvement' from 'poor'.\n",
" \"\"\"\n",
"\n",
" result = {}\n",
" for metric, data in response['record']['metrics'].items():\n",
" if 'histogramTimeseries' not in data: continue\n",
" result[metric] = (float(data['histogramTimeseries'][1]['start']),\n",
" float(data['histogramTimeseries'][1]['end']))\n",
" return result\n",
"\n",
"\n",
"def dataframe_for(metric, response) -> pandas.DataFrame:\n",
" \"\"\"Extracts the p75, histogram density, and fraction timeseries for a metric.\"\"\"\n",
"\n",
" timestamp = lambda e: pandas.Timestamp(e['year'], e['month'], e['day'])\n",
" cols = {\n",
" 'first_date': [timestamp(e['firstDate'])\n",
" for e in response['record']['collectionPeriods']],\n",
" 'last_date': [timestamp(e['lastDate'])\n",
" for e in response['record']['collectionPeriods']],\n",
" }\n",
" data = response['record']['metrics'][metric]\n",
" if 'fractionTimeseries' in data:\n",
" for (label, value) in data['fractionTimeseries'].items():\n",
" cols[label] = value['fractions']\n",
" if 'percentilesTimeseries' in data:\n",
" cols['p75'] = data['percentilesTimeseries']['p75s']\n",
" if 'histogramTimeseries' in data:\n",
" cols['good'] = data['histogramTimeseries'][0]['densities']\n",
" cols['needs improvement'] = data['histogramTimeseries'][1]['densities']\n",
" cols['poor'] = data['histogramTimeseries'][2]['densities']\n",
" return pandas.DataFrame(cols)\n",
"\n",
"\n",
"def url_normalization_details(response) -> str:\n",
" \"\"\"Summarizes the URL normalization made by the API.\"\"\"\n",
"\n",
" if 'urlNormalizationDetails' not in response: return ''\n",
" return 'URL was normalized from \"{originalUrl}\" to \"{normalizedUrl}\".'.format(\n",
" **response['urlNormalizationDetails'])\n",
"\n",
"\n",
"def display_header(label: str):\n",
" \"\"\"Displays a header in the output.\"\"\"\n",
" display.display(display.HTML(f'<h2>{label}</h2>'))\n",
"\n",
"\n",
"def make_p75_chart(stats: pandas.DataFrame, metric: str,\n",
" lo_threshold: float, hi_threshold: float) -> alt.Chart:\n",
" \"\"\"Creates a P75 chart, displaying p75 data points per collection period.\n",
"\n",
" The P75 data points are the metric value along the y axis, the last_date\n",
" of the collection time period is the x axis. Two horizontal\n",
" lines separate good_range from ni_range (needs improvement) and\n",
" from poor_range; these are rendered as areas in green / orange / red.\n",
" For graphs that don't have 'poor' P75 values, no red area is rendered.\n",
" \"\"\"\n",
" p75_stats = stats[['last_date', 'p75']].melt(\n",
" 'last_date', var_name='percentile', value_name=short_name(metric))\n",
" good_range = alt.Chart(\n",
" pandas.DataFrame({'y': [0], 'y2': [lo_threshold]})).mark_rect(\n",
" color='green', opacity=0.2).encode(\n",
" alt.Y('y', axis=alt.Axis(title=None)), y2='y2')\n",
" ni_range = alt.Chart(\n",
" pandas.DataFrame({'y': [lo_threshold], 'y2': [hi_threshold]})\n",
" ).mark_rect(color='orange', opacity=0.2).encode(\n",
" alt.Y('y', axis=alt.Axis(title=None)), y2='y2')\n",
" ranges = good_range + ni_range\n",
" max_p75 = float(pandas.to_numeric(stats['p75']).max())\n",
" if max_p75 > hi_threshold: # Are there poor p75 values?\n",
" poor_range = alt.Chart(\n",
" pandas.DataFrame({'y': [hi_threshold],\n",
" 'y2': [max_p75 * 1.1]})).mark_rect(\n",
" color='red', opacity=0.2).encode(\n",
" alt.Y('y',\n",
" axis=alt.Axis(title=None)), y2='y2')\n",
" ranges += poor_range\n",
" p75_chart = ranges + alt.Chart(p75_stats).mark_line().encode(\n",
" alt.X('last_date:T', axis=alt.Axis(title=None)),\n",
" y=short_name(metric)+':Q', color='percentile:N')\n",
" return p75_chart\n",
"\n",
"\n",
"def make_tribin_chart(stats: pandas.DataFrame, metric: str,\n",
" lo_threshold: float, hi_threshold : float) -> alt.Chart:\n",
" \"\"\"A Tribin chart shows histogram bin density values in a stacked bar chart.\n",
"\n",
" The y axis is the percentage of page loads that fall into a user experience\n",
" category (\"good\", \"needs improvement\", \"poor\"). The API returns\n",
" NaN densities for missing data, which we map to 0.0 here so that these\n",
" render as missing bars, since NaN can't be reasonably rendered on the y axis.\n",
" \"\"\"\n",
" good_label = f'good ({short_name(metric)} <= {lo_threshold})'\n",
" ni_label = ('needs improvement '\n",
" f'({lo_threshold} < {short_name(metric)} <= {hi_threshold})')\n",
" poor_label = f'poor ({short_name(metric)} > {hi_threshold})'\n",
" tribin_stats = stats[['last_date',\n",
" 'good',\n",
" 'needs improvement',\n",
" 'poor']].melt(\n",
" 'last_date', var_name=short_name(metric),\n",
" value_name='pct').replace(\n",
" {'good': good_label,\n",
" 'needs improvement': ni_label,\n",
" 'poor': poor_label,\n",
" 'NaN': 0.0})\n",
" tribin_chart = alt.Chart(tribin_stats).mark_bar().encode(\n",
" alt.X('last_date:T', axis=alt.Axis(title=None)),\n",
" alt.Y('sum(pct)', axis=alt.Axis(title=None, format='%')),\n",
" color=alt.Color(\n",
" short_name(metric), scale=alt.Scale(\n",
" domain=[poor_label, ni_label, good_label],\n",
" range=['red','orange', 'green'])),\n",
" order=alt.Order(short_name(metric),\n",
" sort='ascending')).configure_legend(\n",
" labelLimit=300)\n",
" return tribin_chart\n",
"\n",
"\n",
"def make_fractions_chart(stats: pandas.DataFrame, metric: str) -> alt.Chart:\n",
" \"\"\"A chart showing fraction timeseries in a stacked bar chart.\n",
"\n",
" The y axis is the fraction, adding up to 1.0. The API returns\n",
" NaN densities for missing data, which we map to 0.0 here so that these\n",
" render as missing bars, since NaN can't be reasonably rendered on the y axis.\n",
" \"\"\"\n",
" fraction_stats = stats[[e for e in stats.columns.values.tolist()\n",
" if e != 'first_date']].melt(\n",
" 'last_date', var_name=short_name(metric),\n",
" value_name='pct').replace(\n",
" {'NaN': 0.0})\n",
" tribin_chart = alt.Chart(fraction_stats).mark_bar().encode(\n",
" alt.X('last_date:T', axis=alt.Axis(title=None)),\n",
" alt.Y('sum(pct)', axis=alt.Axis(title=None, format='%')),\n",
" color=alt.Color(short_name(metric)),\n",
" order=alt.Order(short_name(metric),\n",
" sort='ascending')).configure_legend(labelLimit=300)\n",
" return tribin_chart\n",
"\n",
"\n",
"def display_metric_stats(metric: str, stats: pandas.DataFrame,\n",
" lo_threshold:float, hi_threshold:float):\n",
" \"\"\"For a specific metric, displays the requested stats (graphs, tabular).\"\"\"\n",
" name = short_name(metric)\n",
" display_header(f'{name} ({metric})')\n",
" stats = dataframe_for(metric, response)\n",
" web_dev_link = f'https://web.dev/{short_name(metric).lower()}/'\n",
" web_dev_link_text = f'web.dev/{short_name(metric).lower()}/'\n",
" display.display(display.HTML(\n",
" 'Learn more at '\n",
" f'<a href=\"{web_dev_link}\" target=\"_blank\">{web_dev_link_text}</a>.'))\n",
"\n",
" if EMIT_GRAPHS:\n",
" display.display(make_p75_chart(stats, metric, lo_threshold, hi_threshold))\n",
" display.display(make_tribin_chart(stats, metric, lo_threshold, hi_threshold))\n",
"\n",
" if EMIT_TABULAR_OUTPUT:\n",
" display.display(data_table.DataTable(stats.round(4),\n",
" min_width='100', include_index=False))\n",
"\n",
"\n",
"def display_fraction_metric_stats(metric: str, stats: pandas.DataFrame):\n",
" \"\"\"For a specific metric, displays the requested stats (graphs, tabular).\n",
"\n",
" This routine is specialized for fraction metrics, which don't have thresholds,\n",
" tribins (histograms), or p75 timeseries. Instead, these metrics have labeled\n",
" fractions for each timeseries entry, which add up to 1.0 (100%).\n",
" \"\"\"\n",
" display_header(f'{metric}')\n",
" stats = dataframe_for(metric, response)\n",
" if EMIT_GRAPHS:\n",
" display.display(make_fractions_chart(stats, metric))\n",
" if EMIT_TABULAR_OUTPUT:\n",
" display.display(data_table.DataTable(stats.round(4),\n",
" min_width='100', include_index=False))\n",
"\n",
"\n",
"request, response = get_crux_api_response_from_form()\n",
"if 'record' not in response:\n",
" display_header('No record found in response!')\n",
" EMIT_REQUEST_RESPONSE = True\n",
"else:\n",
" key = response['record']['key']\n",
" identifier = ('url <code>{url}</code>'.format(**key) if 'url' in key\n",
" else 'origin <code>{origin}</code>'.format(**key))\n",
" form_factor = ' on {formFactor}'.format(**key) if 'formFactor' in key else ''\n",
" display_header(f'Displaying CrUX data for {identifier}{form_factor}')\n",
" display.display(display.HTML(url_normalization_details(response)))\n",
" thresholds = thresholds_by_metric(response)\n",
"\n",
" if EMIT_GRAPHS:\n",
" display.display(display.HTML(\"\"\"\n",
" For each metric with histograms and percentiles, we display two graphs:\n",
" <ul>\n",
" <li>The percentile graph shows the p75 values for the metric over time.\n",
" The shaded areas indicate good (light green),\n",
" needs improvement (light orange), and poor (light red).\n",
" <li>The tribin graph shows the percentages of page loads with\n",
" a good, needs improvement, or poor user experience over time.\n",
" </ul>\n",
" If the metric has labeled fractions, then we display these in a single\n",
" stacked bar chart.\n",
" In all cases, each point in time in the graph on the x axis\n",
" represents a 28 day collection period ending in that date.\n",
" \"\"\"))\n",
"\n",
" for metric in metrics_in(response):\n",
" if metric in thresholds:\n",
" lo_threshold, hi_threshold = thresholds[metric]\n",
" display_metric_stats(metric, dataframe_for(metric, response),\n",
" lo_threshold, hi_threshold)\n",
" else:\n",
" display_fraction_metric_stats(metric, dataframe_for(metric, response))\n",
"\n",
"if EMIT_REQUEST_RESPONSE:\n",
" display_header('CrUX History API Request')\n",
" json_str = json.dumps(request)\n",
" display.display(display.HTML(f'<pre>{json_str}</pre>'))\n",
"\n",
" display_header('CrUX History API Response')\n",
" json_str = json.dumps(response, indent=2)\n",
" display.display(display.HTML(f'<pre>{json_str}</pre>'))"
]
}
],
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================================================
FILE: colab/navigation-types-and-lcp.ipynb
================================================
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gitextract_zxhgowfc/
├── .github/
│ └── ISSUE_TEMPLATE/
│ └── new-crux-metric-request.md
├── CONTRIBUTING.md
├── LICENSE
├── README.md
├── colab/
│ ├── crux-history-api.ipynb
│ └── navigation-types-and-lcp.ipynb
├── gs/
│ ├── README.md
│ ├── crux-api.gs
│ └── psi-api-v5.gs
├── js/
│ └── crux-api-util.js
├── sql/
│ ├── README.md
│ ├── cls-summary.sql
│ ├── core-web-vitals-compliance-rates.sql
│ ├── core-web-vitals.sql
│ ├── country-fast-fcp.sql
│ ├── fast-fcp-for-domain.sql
│ ├── global-connection-density.sql
│ ├── global-device-density.sql
│ ├── mastering-crux/
│ │ ├── 01-basic-cwv.sql
│ │ ├── 02-timeseries-cwv.sql
│ │ ├── 03-device-cwv.sql
│ │ └── 04-country-cwv.sql
│ ├── notification-permissions-origin-form-factor.sql
│ ├── notification-permissions.sql
│ ├── origins-for-domain.sql
│ ├── p75-fcp-timeseries.sql
│ ├── p75-fcp.sql
│ ├── p75-lcp-country.sql
│ ├── subregion-fast-fcp.sql
│ ├── test-my-site.sql
│ └── timeseries-fast-fcp.sql
└── utils/
├── countries.js
├── countries.json
└── countries.txt
Condensed preview — 34 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (1,134K chars).
[
{
"path": ".github/ISSUE_TEMPLATE/new-crux-metric-request.md",
"chars": 638,
"preview": "---\nname: New CrUX metric request\nabout: Tell us about a new metric you'd like to see included in the report\ntitle: ''\nl"
},
{
"path": "CONTRIBUTING.md",
"chars": 1100,
"preview": "# How to Contribute\n\nWe'd love to accept your patches and contributions to this project. There are\njust a few small guid"
},
{
"path": "LICENSE",
"chars": 11357,
"preview": "\n Apache License\n Version 2.0, January 2004\n "
},
{
"path": "README.md",
"chars": 1185,
"preview": "# CrUX\n\n![CrUX Logo](https://user-images.githubusercontent.com/1120896/55263154-74aa0a80-5246-11e9-96c3-5741f798f3aa.png"
},
{
"path": "colab/crux-history-api.ipynb",
"chars": 154254,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"pUbCOR"
},
{
"path": "colab/navigation-types-and-lcp.ipynb",
"chars": 807958,
"preview": "{\n \"nbformat\": 4,\n \"nbformat_minor\": 0,\n \"metadata\": {\n \"colab\": {\n \"provenance\": []\n },\n \"kernelspec\":"
},
{
"path": "gs/README.md",
"chars": 248,
"preview": "# Google Apps Script (.gs)\n\nResources for using Apps Script to extract CrUX insights from the PageSpeed Insights API.\n\nS"
},
{
"path": "gs/crux-api.gs",
"chars": 2585,
"preview": "// Copyright 2020 Google LLC.\n// SPDX-License-Identifier: Apache-2.0\n\nconst URLs = [\n 'https://web.dev/',\n 'https://we"
},
{
"path": "gs/psi-api-v5.gs",
"chars": 1843,
"preview": "// Copyright 2020 Google LLC.\n// SPDX-License-Identifier: Apache-2.0\n\n// Example of Google Apps Script code that connect"
},
{
"path": "js/crux-api-util.js",
"chars": 3292,
"preview": "// Copyright 2020 Google LLC.\n// SPDX-License-Identifier: Apache-2.0\n\nconst CrUXApiUtil = {};\n// Get your CrUX API key a"
},
{
"path": "sql/README.md",
"chars": 3017,
"preview": "# CrUX Cookbook\n\nRecipes for extracting insights from the [Chrome User Experience Report](https://developers.google.com/"
},
{
"path": "sql/cls-summary.sql",
"chars": 298,
"preview": "#standardSQL\n# This query only processes 117.5 MB because it uses the materialized dataset!\nSELECT\n p75_cls,\n small_cl"
},
{
"path": "sql/core-web-vitals-compliance-rates.sql",
"chars": 2758,
"preview": "#standardSQL\n# Calculate the percent of origins that comply with each Core Web Vital's \"good\" threshold for 75% or more "
},
{
"path": "sql/core-web-vitals.sql",
"chars": 731,
"preview": "# Query the Core Web Vitals for an origin.\n# This query only consumes 43 MB! :)\nSELECT\n # Largest Contentful Paint\n # "
},
{
"path": "sql/country-fast-fcp.sql",
"chars": 27036,
"preview": "#standardSQL\n# Create a `countries` alias that annotates each country's data with the corresponding country name and cou"
},
{
"path": "sql/fast-fcp-for-domain.sql",
"chars": 298,
"preview": "#standardSQL\n# Gets the % of fast FCP for a given domain.\nSELECT\n DISTINCT origin,\n SUM(fcp.density) AS pct_fast_fcp\nF"
},
{
"path": "sql/global-connection-density.sql",
"chars": 390,
"preview": "#standardSQL\n# Breaks down the global distribution of effective connection types.\n# eg: 90% 4G, 9% 3G, etc.\nSELECT\n effe"
},
{
"path": "sql/global-device-density.sql",
"chars": 377,
"preview": "#standardSQL\n# Breaks down the global distribution of form factors.\n# eg: 76% phone, 23% desktop, 1% tablet.\nSELECT\n for"
},
{
"path": "sql/mastering-crux/01-basic-cwv.sql",
"chars": 301,
"preview": "# Querying one month of Core Web Vitals stats for an origin.\nSELECT\n fast_lcp AS pct_good_lcp,\n p75_lcp,\n fast_inp AS"
},
{
"path": "sql/mastering-crux/02-timeseries-cwv.sql",
"chars": 319,
"preview": "# Timeseries of Core Web Vitals performance.\nSELECT\n yyyymm,\n fast_lcp AS pct_good_lcp,\n p75_lcp,\n fast_inp AS pct_g"
},
{
"path": "sql/mastering-crux/03-device-cwv.sql",
"chars": 478,
"preview": "# Core Web Vitals performance by device.\nSELECT\n yyyymm,\n device,\n fast_lcp / (fast_lcp + avg_lcp + slow_lcp) AS pct_"
},
{
"path": "sql/mastering-crux/04-country-cwv.sql",
"chars": 575,
"preview": "# Core Web Vitals performance by country.\nSELECT\n yyyymm,\n `chrome-ux-report`.experimental.GET_COUNTRY(country_code) A"
},
{
"path": "sql/notification-permissions-origin-form-factor.sql",
"chars": 1044,
"preview": "#standardSQL\n# Notification permission response rates for multiple origins and form factors.\nSELECT\n origin,\n form_fac"
},
{
"path": "sql/notification-permissions.sql",
"chars": 400,
"preview": "#standardSQL\n# Explore notification permission response rates.\nSELECT\n SUM(experimental.permission.notifications.accept"
},
{
"path": "sql/origins-for-domain.sql",
"chars": 279,
"preview": "#standardSQL\n# Gets all distinct origins for a given domain.\n# Example output: http://www.example.com, http://example.co"
},
{
"path": "sql/p75-fcp-timeseries.sql",
"chars": 831,
"preview": "#standardSQL\n# Approximates the 75th percentile FCP for origins over time.\n# Warning!! This query processes the entire d"
},
{
"path": "sql/p75-fcp.sql",
"chars": 501,
"preview": "#standardSQL\n# Approximates the 75th percentile FCP for a given origin and form factor.\nSELECT\n MIN(start) AS fcp\nFROM "
},
{
"path": "sql/p75-lcp-country.sql",
"chars": 368,
"preview": "#standardSQL\n# Get the 75th percentile LCP across all countries for a single month, origin, and device.\nSELECT\n country"
},
{
"path": "sql/subregion-fast-fcp.sql",
"chars": 38784,
"preview": "#standardSQL\n# Create a `countries` alias that decorates each dataset with geographic metadata.\n# Generated by utils/cou"
},
{
"path": "sql/test-my-site.sql",
"chars": 449,
"preview": "#standardSQL\n# Emulates the analysis on https://www.thinkwithgoogle.com/feature/testmysite\nSELECT\n `chrome-ux-report`.e"
},
{
"path": "sql/timeseries-fast-fcp.sql",
"chars": 332,
"preview": "#standardSQL\n# Gets a month-to-month sequence of % fast FCP for a given origin.\nSELECT\n _TABLE_SUFFIX AS yyyymm,\n SUM("
},
{
"path": "utils/countries.js",
"chars": 992,
"preview": "// The table to query for each country.\n// Feeling adventurous? Use '*' to query all monthly releases.\nvar CRUX_MONTH = "
},
{
"path": "utils/countries.json",
"chars": 23879,
"preview": "{\n \"af\": {\n \"name\": \"Afghanistan\",\n \"region\": \"Asia\",\n \"sub-region\": \"Southern Asia\"\n },\n \"ax\": {\n \"name\""
},
{
"path": "utils/countries.txt",
"chars": 2618,
"preview": "country_ad\ncountry_ae\ncountry_af\ncountry_ag\ncountry_ai\ncountry_al\ncountry_am\ncountry_ao\ncountry_ar\ncountry_as\ncountry_at"
}
]
About this extraction
This page contains the full source code of the GoogleChrome/CrUX GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 34 files (1.0 MB), approximately 588.6k tokens. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.