geostatspy
GSLIB-inspired geostatistics library for variogram analysis, kriging, and simulation. Use when Claude needs to: (1) Calculate experimental variograms, (2) Fit variogram models, (3) Perform simple/ordinary kriging, (4) Run sequential Gaussian simulation (SGSIM), (5) Apply normal score transforms, (6) Decluster spatial data, (7) Generate multiple realizations for uncertainty.
npx skills add majiayu000/claude-skill-registry --skill geostatspy --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# GeostatsPy - Geostatistical Analysis ## Quick Reference ```python import geostatspy.GSLIB as GSLIB import geostatspy.geostats as geostats import pandas as pd df = pd.read_csv('data.csv') df['npor'], tvpor, tnspor = geostats.nscore(df, 'porosity') # Transform lag, gamma, npairs = geostats.gamv(df, 'X', 'Y', 'npor', # Variogram tmin=-9999, tmax=9999, xlag=50, xltol=25, nlag=15, azm=0, atol=22.5, bandwh=9999, bandwd=9999) vario = GSLIB.make_variogram(nug=0.0, nst=1, it1=1, cc1=1.0, # Model azi1=0, hmaj1=300, hmin1=300) est, var = geostats.kb2d(df, 'X', 'Y', 'npor', ..., vario=vario) # Krige ``` ## Key Functions | Category | Functions | |----------|-----------| | Visualization | `locmap`, `pixelplt`, `hist` | | Variogram | `gamv`, `vmodel` | | Kriging | `kb2d`, `kb3d` | | Simulation | `sgsim`, `sisim` | | Transforms | `nscore`, `backtr` | | Declustering | `declus` | ## Common Operations ### 1. Normal Score Transform ```python df['npor'], tvpor, tnspor = geostats.nscore(df, 'porosity') original = geostats.backtr(nscore_data, tvpor, tnspor, zmin=0, zmax=0.3) ``` ### 2. Experimental Variogram ```python lag, gamma, npairs = geostats.gamv( df, 'X', 'Y', 'npor', tmin=-9999, tmax=9999, # Tr
- Quick Reference
- Key Functions
- Common Operations
- 1. Normal Score Transform
- 2. Experimental Variogram
- 3. Variogram Model
- 4. Kriging (kb2d)
- 5. Sequential Gaussian Simulation
- 6. Declustering
- Variogram Models
- Key Parameters
- When to Use vs Alternatives
- Common Workflows
- Variogram Analysis and Kriging Interpolation
What does the geostatspy skill do?
GSLIB-inspired geostatistics library for variogram analysis, kriging, and simulation. Use when Claude needs to: (1) Calculate experimental variograms, (2) Fit variogram models, (3) Perform simple/ordinary kriging, (4) Run sequential Gaussian simulation (SGSIM), (5) Apply normal score transforms, (6) Decluster spatial data, (7) Generate multiple realizations for uncertainty.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill geostatspy --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.