pastas
Groundwater time series analysis and modelling using transfer function noise models. Use when Claude needs to: (1) Analyze groundwater level time series, (2) Model well responses to precipitation/pumping, (3) Calibrate aquifer parameters from head data, (4) Forecast or hindcast groundwater levels, (5) Decompose hydrological signals into components, (6) Compare response functions, (7) Perform model diagnostics and uncertainty analysis.
npx skills add majiayu000/claude-skill-registry --skill pastas --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.
# Pastas - Groundwater Time Series Analysis ## Quick Reference ```python import pastas as ps import pandas as pd # Load data head = pd.read_csv('well.csv', index_col=0, parse_dates=True).squeeze() precip = pd.read_csv('precip.csv', index_col=0, parse_dates=True).squeeze() evap = pd.read_csv('evap.csv', index_col=0, parse_dates=True).squeeze() # Create model ml = ps.Model(head, name='Well_001') # Add recharge stress sm = ps.RechargeModel(precip, evap, rfunc=ps.Gamma(), name='recharge') ml.add_stressmodel(sm) # Solve and plot ml.solve() ml.plot() ``` ## Key Classes | Class | Purpose | |-------|---------| | `ps.Model` | Main model container | | `ps.StressModel` | Response to external stress (pumping, river) | | `ps.RechargeModel` | Recharge from precipitation minus evaporation | | `ps.Gamma` | Gamma distribution response function | | `ps.Exponential` | Simple exponential response function | ## Essential Operations ### Create and Solve Model ```python ml = ps.Model(head, name='well') ml.add_stressmodel(ps.RechargeModel(precip, evap, rfunc=ps.Gamma(), name='recharge')) ml.solve() ``` ### Add Pumping Well ```python pumping = pd.read_csv('pumping.csv', index_col=0, parse_dates=True).squee
- Quick Reference
- Key Classes
- Essential Operations
- Create and Solve Model
- Add Pumping Well
- Model Diagnostics
- Get Contributions
- Step and Impulse Response
- Export and Load
- Model Statistics
- Common Patterns
- Compare Response Functions
- Forecast Future Levels
- River or Custom Stress
What does the pastas skill do?
Groundwater time series analysis and modelling using transfer function noise models. Use when Claude needs to: (1) Analyze groundwater level time series, (2) Model well responses to precipitation/pumping, (3) Calibrate aquifer parameters from head data, (4) Forecast or hindcast groundwater levels, (5) Decompose hydrological signals into components, (6) Compare response functions, (7) Perform model diagnostics and uncertainty analysis.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill pastas --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.