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Guidepost is a Python library for visualizing High Performance Computing (HPC) job data inside Jupyter notebooks. It turns a pandas DataFrame of job records into a single, linked, interactive overview — faceted heatmaps framed by histograms, a categorical bar chart, and a brushable color legend — so you can spot patterns in runtimes, queue waits, and resource usage, then export the exact records you care about back into Python.
| If you want to… | Read |
|---|---|
| Install and see your first chart | Getting Started |
| Understand what data Guidepost accepts and how columns are classified | Data Requirements and Type Detection |
| Map your columns to the chart (x / y / color / facet / categorical) | Configuration |
| Understand the panels and how they link together | Understanding the Views |
| Learn every heatmap interaction (pinning, brushing, modes) | Main Summary View Heatmap |
| Learn the histogram, bar chart, and legend interactions | Histograms Bar Chart and Legend |
| Select records and pull them back into Python | Selecting and Exporting Data |
| Look up a class, property, or method | API Reference |
| Hit a snag | FAQ and Troubleshooting |
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Input: a
pandasDataFrame with at least 3 numeric and 2 categorical columns (datetime columns are supported on the x-axis). See Data Requirements and Type Detection. - Views: a Main Summary View (heatmap) framed by an x-axis and y-axis histogram, a categorical bar chart, and a color legend — repeated once per facet group. See Understanding the Views.
- Interaction: three heatmap selection modes (column-pin, cell-pin, 2D-brush), histogram brushing, bar-chart filtering, and color-range brushing — all linked. See Main Summary View Heatmap and Histograms Bar Chart and Legend.
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Export: retrieve the brushed records as a DataFrame via
gp.retrieve_selected_data()orgp.selection.dataframe. See Selecting and Exporting Data.
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Guidepost is developed at the National Renewable Energy Laboratory (NREL) with support from the National Science Foundation (NSF IIS-1844573, IIS-2324465) and the Department of Energy (DE-SC0022044, DE-SC0024635). Licensed under MIT. Questions: cscullyallison@sci.utah.edu.