算法艺术创作
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通过代码和可调参数创作生成艺术与交互式视觉效果。
striplog【设计与创意】Create, visualize, and analyze lithological and stratigraphic logs for well data. Use when the agent needs to: (1) Create lithology columns from depth intervals, (2) Parse geological descriptions into structured logs, (3) Visualize stratigraphic columns with patterns and colors, (4) Perform well-to-well correlations, (5) Extract statistics like net-t…
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from striplog import Striplog, Interval, Component
# Create from intervals
intervals = [
Interval(top=0, base=10, components=[Component({'lithology': 'sandstone'})]),
Interval(top=10, base=25, components=[Component({'lithology': 'shale'})]),
Interval(top=25, base=40, components=[Component({'lithology': 'limestone'})]),
]
strip = Striplog(intervals)
# Load from file
strip = Striplog.from_csv('lithology.csv') # Columns: top, base, lithology
# Access and display
print(strip)
strip.plot()
df = strip.to_dataframe()
| Class | Purpose |
|---|---|
Striplog | Main log container - holds intervals |
Interval | Depth interval with top, base, and components |
Component | Rock type definition with properties |
Lexicon | Rock type dictionary with synonyms |
Legend | Visualization styles (colors, patterns) |
# CSV format: top,base,lithology
strip = Striplog.from_csv('lithology.csv')
strip.plot()
from striplog import Striplog, Lexicon
description = """
0.0 - 5.5 m: Fine to medium sandstone
5.5 - 12.0 m: Grey shale with silt laminations
12.0 - 18.5 m: Massive limestone, fossiliferous
"""
strip = Striplog.from_description(description, lexicon=lexicon)
# Get interval at depth
interval = strip.read_at(z=15)
print(interval.primary.lithology)
# Crop to depth range
subset = strip.crop((10, 30))
# Unique lithologies
lithologies = strip.unique('lithology')
# Net-to-gross for specific lithology
ntg = strip.net_to_gross(pattern={'lithology': 'sandstone'})
print(f"Sandstone: {ntg * 100:.1f}%")
# Merge adjacent same-lithology intervals
merged = strip.merge_neighbours()
import matplotlib.pyplot as plt
wells = [Striplog.from_csv(f'well{i}.csv') for i in range(1, 4)]
fig, axes = plt.subplots(1, 3, figsize=(10, 8), sharey=True)
for ax, well, name in zip(axes, wells, ['Well 1', 'Well 2', 'Well 3']):
well.plot(ax=ax, legend=legend)
ax.set_title(name)
plt.tight_layout()
plt.savefig('correlation.png')
strip.to_csv('output.csv')
strip.to_las('output.las')
strip.to_json('output.json')
df = strip.to_dataframe()
| Pattern | Code | Typical Use |
|---|---|---|
| Dots | ... | Sandstone |
| Dashes | --- | Shale |
| Plus | +++ | Limestone |
| X | xxx | Dolomite |
| V | vvv | Volcanic |
| Tool | Best For |
|---|---|
| striplog | Structured lithology logs, interval-based data, NTG, correlation |
| welly | Well log curves (continuous data), multi-well projects |
| custom matplotlib | Simple one-off stratigraphic columns without interval logic |
Use striplog when you need to represent geological intervals (lithology, facies) as structured objects with querying, statistics, and correlation.
Use welly instead when working with continuous well log curves (GR, RHOB). Welly and striplog complement each other -- welly for curves, striplog for lithology intervals.
Use custom matplotlib instead for simple, one-off stratigraphic columns where you don't need interval querying or net-to-gross calculations.
- [ ] Prepare CSV with columns: top, base, lithology (optionally color)
- [ ] Load with `Striplog.from_csv('lithology.csv')`
- [ ] Define Legend with colors and hatch patterns for each lithology
- [ ] Verify intervals: check for gaps or overlaps
- [ ] Merge adjacent same-lithology intervals with `merge_neighbours()`
- [ ] Compute statistics: `net_to_gross()`, `unique()`
- [ ] Plot with legend and export to desired format
| Issue | Solution |
|---|---|
| Text not parsing | Define a Lexicon with synonyms |
| Wrong colors | Create custom Legend with Decor objects |
| Gaps in log | Check interval top/base values match |
| Plot looks wrong | Verify depth direction (increasing down) |