coords_from_df

coords_from_df(
    self ,
    dataframe: DataFrame ,
    units: dict[dict[str, Any], None] = None,
    extrapolate: bool = False,
    max_gap: float | None[float, None] = None,
)-> ‘Self’

Update non-dimensional coordinate of a patch using a dataframe.

Parameters

Parameter	Description
dataframe	Table with a column matching in title to one of patch.dims along with other coordinates to associate with dimension. Example one column matching distance axis and then latitude and longitude attached to the distances.
units	Dictionary mapping column name in dataframe to its units.
extrapolate	If True, extrapolate outside provided range in dataframe.
max_gap	If given, positions between two neighbouring rows further apart than this (in the dimension’s units) are NaN rather than interpolated, for tables that leave stretches unsurveyed.

Examples

import dascore as dc
import pandas as pd
# get example patch and create example dataframe
pa = dc.get_example_patch()
distance = pa.coords.get_array("distance")[::10]
df = pd.DataFrame(distance, columns=['distance'])
df['x'] = df['distance'] * 3 + 10
df['y'] = df['distance'] * 2.5 - 10
# attach dataframe to patch, interpolating when needed. This
# adds coordinates x and y which are associated with dimension distance.
patch_with_coords = pa.coords_from_df(df)
# Leave channels NaN where neighbouring rows are over 50 m apart.
patch_with_gaps = pa.coords_from_df(df.iloc[[0, 1, -2, -1]], max_gap=50)

Note

Exactly one of the column names in the dataframe must map to one of the patch.dims. This will either add new coordinates, or update existing ones if they already exist.
This function uses linear interpolation between the nearest two points to get values in patch coords that aren’t in the dataframe. Row order does not matter, but each value of the dimension column may appear only once.