Gist a9239feb46c9a77aa7e57b052568f0a2

Gist cc6d3e2dfc8356dd8bb96b58c8c8ac7e

This example visualizes well-log data from a LAS file, displaying depth-based curves for porosity, gamma ray, caliper, and resistivity. The code uses D3.js to parse the JSON-structured LAS data, mapping each curve to a separate track with depth on the y-axis. It employs D3 scales and axes to render the line charts, with the well name and location shown as a title. The visualization highlights the raw curve values and their relative changes across the logged depth interval.

92% match

Gist c643e25774aea39556799cc517654d46

This example visualizes well log data from a Chevron oil well, showing depth-based curves for gamma ray, density porosity, neutron porosity, caliper, and deep resistivity. The code parses a JSON file containing LAS 2.0 format data and renders the measurements as multiple line charts using the Observable Plot library. The visualization displays the raw curve values against depth, with the top depth at 390 and bottom at 650 meters, sampled at 0.25-meter increments.

92% match

00-02-26-073-05W5-0.json well log converted from 00-02-26-073-05W5-0.las using wellio.js

This example visualizes a well log from the LAS file 00-02-26-073-05W5-0.las, converted to JSON using wellio.js. It displays depth-based curves for density porosity, neutron porosity, gamma ray, caliper, and deep resistivity, with depth on the vertical axis. The visualization uses the JSON structure to render each curve as a distinct line plot, showing how these subsurface measurements change over the logged interval from 350 to 590 meters. The data comes from the well "TRICENT ET AL MITSUE 02-26," recorded by Schlumberger in 1982, and the plot leverages the curve information block to map each measurement to its graphical representation.

86% match

A test LAS file of well log

This visualization displays a subset of well log data from a LAS file, plotting depth on the vertical axis against several measured curves, including gamma ray, caliper, neutron porosity, density porosity, and deep resistivity. The example uses the Python `lasio` library to parse the LAS file and Bokeh to render the curves as horizontally-oriented line plots. The depth series spans from 200 to roughly 375 meters at a 0.25-meter sampling interval, with a null value of -999.25 used for missing data. The plot shows the raw, unsmoothed measurements as they appear in the source file from JustinGosses' gist.

77% match

Gist 473f153bb8e97062f0f4696aebe7a22c

This example shows a time-series line chart of oceanographic measurements from the Hakai Quadra station, pulling data via a JSON request to ERDDAP. It plots a single variable against time using a date-parsed x-axis and linear y-axis. The code uses d3.json to load rows from the ERDDAP table, d3.utcParse for ISO timestamps, and d3.line with d3.scaleTime and d3.scaleLinear to render the path. Axes are drawn with d3.axisBottom and d3.axisLeft, with the line styled in steelblue.

MMbrownshoes

Gist 130517

This visualization displays a sparse, grid-based dataset from a gist by DylanFM, using a fixed-size coordinate system (values such as 1056, 64, 1152, and 4608) to plot points in a two-dimensional space. The rendering relies on D3’s `d3.forceSimulation` for positioning and likely `d3.geoPath` or a similar path generator to draw shapes, though the specific API is not explicitly shown. The data, provided as a plain text file, is read and mapped directly to screen coordinates without additional transformation, creating a minimal scatter-like plot. The example emphasizes raw data plotting with minimal abstraction, focusing on the direct conversion of numeric inputs into visual marks.

DDylanFM

Gist 81bb1fe48aaac31ecad321fb46af0847

The visualization maps discovered geospatial features onto a grid of cells, using color intensity to show the frequency of feature occurrences per cell. It highlights clusters of high-density areas, such as locations with counts of 23, 5, and 4, while most cells remain empty. The code uses JavaScript with a custom grid-rendering approach, likely employing canvas drawing or SVG elements, to display the distribution of features across the mapped region. Feature counts are stored in a two-dimensional array, with each cell’s value determining its visual encoding.

Gist 798b9a5ccb654ceebb5b29ce680c5707

This visualization presents a week of daily weather data for a single location, using a circular timeline to encode conditions such as temperature, humidity, wind, and cloud cover. Each day is a concentric ring, with hourly metrics plotted as colored arcs or bars. The code relies on D3.js for layout and rendering, specifically using `d3.forceSimulation` to position labels and `d3.scaleTime` for temporal axes. Data comes from a local JSON file containing Dark Sky API-style forecast fields, rendered in an SVG canvas.

MMbrownshoes