Python API Reference

Complete reference for the baselode Python package (v0.1.x).

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baselode.drill.data

Data loading and table normalisation helpers for drillhole datasets.

import baselode.drill.data as drill

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load_table

load_table(source, kind="csv", connection=None, query=None, table=None,
           column_map=None, source_column_map=None, **kwargs)

Low-level loader. Reads data from a CSV, Parquet, or SQL source and applies column standardisation.

Parameters

Parameter	Type	Default	Description
source	path / DataFrame	—	File path, pandas.DataFrame, or None (for SQL)
kind	"csv" | "parquet" | "sql"	"csv"	Source format
connection	SQLAlchemy engine, optional	None	Database connection for SQL sources
query	str, optional	None	SQL query string
table	str, optional	None	SQL table name (alternative to query)
column_map	dict, optional	None	Override the default column map
source_column_map	dict, optional	None	Extra raw→standard column overrides
**kwargs	—	—	Forwarded to pandas.read_csv / read_parquet

Returns: pandas.DataFrame

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standardize_columns

standardize_columns(df, column_map=None, source_column_map=None)

Rename DataFrame columns to the Baselode standard using the default column map (and optional overrides).

Parameters

Parameter	Type	Description
df	pandas.DataFrame	Input DataFrame
column_map	dict, optional	Override the default column map
source_column_map	dict, optional	Additional raw→standard overrides applied on top

Returns: pandas.DataFrame

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load_collars

load_collars(source, crs=None, source_column_map=None, keep_all=True, **kwargs)

Load drillhole collar data. Returns a geopandas.GeoDataFrame with point geometry built from lat/lon (preferred) or easting/northing.

Required columns (after mapping): hole_id, and either (latitude, longitude) or (easting, northing).

Returns: geopandas.GeoDataFrame

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load_surveys

load_surveys(source, source_column_map=None, keep_all=True, **kwargs)

Load directional survey data.

Required columns (after mapping): hole_id, depth, azimuth, dip

Returns: pandas.DataFrame sorted by hole_id, depth

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load_assays

load_assays(source, source_column_map=None, keep_all=True, **kwargs)

Load assay interval data. Computes a mid column as 0.5 * (from + to).

Required columns (after mapping): hole_id, from, to

Returns: pandas.DataFrame sorted by hole_id, from, to

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load_structures

load_structures(source, source_column_map=None, keep_all=True, **kwargs)

Load structural point measurement data (alpha/beta measurements).

Required columns (after mapping): hole_id, depth

Returns: pandas.DataFrame sorted by hole_id, depth

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load_geotechnical

load_geotechnical(source, source_column_map=None, keep_all=True, **kwargs)

Load geotechnical interval data (RQD, fracture count, weathering, etc.).

Required columns (after mapping): hole_id, from, to

Returns: pandas.DataFrame sorted by hole_id, from

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load_unified_dataset

load_unified_dataset(assays_source, structures_source,
                     source_column_map=None, **kwargs)

Load and merge assay intervals and structural data into one DataFrame. Recommended entry point for the 2D strip-log view.

• Assay rows: depth is set to the interval midpoint (mid).
• Structural rows: a synthetic ±0.05 m interval is added around depth.
• All rows are tagged with a _source column ('assay' | 'structural').

Returns: pandas.DataFrame sorted by hole_id, depth

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assemble_dataset

assemble_dataset(collars=None, surveys=None, assays=None,
                 structures=None, geotechnical=None, metadata=None)

Wrap pre-loaded DataFrames into a dataset dictionary.

Returns:

{
    "collars":       GeoDataFrame,
    "surveys":       DataFrame,
    "assays":        DataFrame,
    "structures":    DataFrame,
    "geotechnical":  DataFrame,
    "metadata":      dict
}

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join_assays_to_traces

join_assays_to_traces(assays, traces, on_cols=(HOLE_ID,))

Left-join 3D trace coordinates onto an assay DataFrame using hole_id.

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filter_by_project

filter_by_project(df, project_id=None)

Filter a DataFrame to a single project_id. Returns a copy of df unchanged if project_id is None.

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coerce_numeric

coerce_numeric(df, columns)

Convert listed columns to numeric dtype, coercing invalid values to NaN.

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baselode.drill.desurvey

Desurveying utilities — converts depth-based surveys into 3D spatial coordinates.

import baselode.drill.desurvey as desurvey

desurvey_holes

desurvey_holes(collars, surveys, step=1.0, method="minimum_curvature")

Desurvey all holes in collars using the matching rows in surveys.

Parameters

Parameter	Type	Default	Description
collars	GeoDataFrame	—	Collar table
surveys	DataFrame	—	Survey table
step	float	1.0	Output vertex spacing (metres)
method	str	"minimum_curvature"	"minimum_curvature", "tangential", or "balanced_tangential"

Returns: pandas.DataFrame with columns hole_id, md, easting, northing, elevation, azimuth, dip

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baselode.drill.view

Plotly-based strip-log visualisation helpers.

import baselode.drill.view as view

compute_interval_points

compute_interval_points(df, value_col, from_cols=(...), to_cols=(...), drop_null_values=True)

Convert assay rows into midpoint-based interval points suitable for Plotly error-bar plots.

Returns: pandas.DataFrame with columns z, val, from_val, to_val, err_plus, err_minus

plot_numeric_trace

plot_numeric_trace(interval_df, value_col, chart_type="markers+line",
                   color="#8b1e3f", intervals=True)

Plot a single numeric assay column as a Plotly figure.

chart_type options: "bar", "markers", "markers+line", "line"

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baselode.drill.view_3d

3D-ready payload generation.

import baselode.drill.view_3d as view3d

traces_as_segments

traces_as_segments(traces, color_by=None)

Convert a desurveyed trace DataFrame into a list of segment dicts ready for the JS Baselode3DScene.

intervals_as_tubes

intervals_as_tubes(intervals, radius=1.0, color_by=None)

Convert an assay/interval DataFrame into tube payload dicts for 3D rendering.

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baselode.map

Folium/Plotly map helpers.

import baselode.map as bmap

create_leaflet_map

create_leaflet_map(center=None, zoom_start=2)

Create a Folium Map with OpenStreetMap tiles.

map_collar_points

map_collar_points(collars, color_by=None)

Prepare collar points for 2D map plotting. Optionally attach a color_value column.

map_collars

map_collars(collars, color="#2563eb", radius=5, fill_opacity=0.7, tooltip_cols=None, ...)

Plot collar points on a Folium map. Returns the updated folium.Map.

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baselode.datamodel

Constants for the Baselode Open Data Model.

from baselode.datamodel import (
    HOLE_ID, LATITUDE, LONGITUDE, ELEVATION,
    AZIMUTH, DIP, FROM, TO, MID,
    PROJECT_ID, EASTING, NORTHING, CRS,
    DEPTH, ALPHA, BETA, COMMENTS
)