pytuflow.NCMesh.curtain

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pytuflow.NCMesh.curtain#

NCMesh.curtain(locations, data_types, time)#

Extracts curtain data for the given locations and data types.

The locations can be a list of x, y tuple points, or a Well Known Text (WKT) line string. It can also be a dictionary of key, line-string pairs where the key is the name that will be used in the column name in the resulting DataFrame.

The locations argument can also be a single GIS file path e.g. Shapefile or GPKG (but any format supported by GDAL is also supported). GPKG’s should follow the TUFLOW convention if specifying the layer name within the database database.gpkg >> layer. If the GIS layer has a field called name, label, or ID then this will be used as the column name in the resulting DataFrame.

The resulting DataFrame will be made up of 3 columns- X, Y, value data. The X, Y values represent cells in the vertical plane, and should be treated as groups of 4 which denote the corners of a cell. The value represents the data value at that cell, which will be returned as a single number for scalar results and a tuple for vector results. Note, velocity will always be returned as vector (tuple) result.

The resulting DataFrame will use multi-index columns since the data is not guaranteed to have the same index. The level 1 index will be the label, and the level 2 index will be the data type. The X,Y offsets will always be the first two columns within the level 2 index.

Parameters:

  • locations (list[Point] | str | PathLike) – The location to extract the section data for.

  • data_types (str | list[str]) – The data types to extract the section data for.

  • time (TimeLike) – The time to extract the section data for.

Returns:

The section data.

Return type:

pd.DataFrame

Examples

Get the velocity (scalar) curtain data for a given line string defined as in a shapefile:

>>> mesh = ... # Assume mesh is a loaded Mesh result
>>> mesh.curtain('path/to/shapefile.shp', 'velocity', 1.5)
         Line_1
              x          y  velocity
0     53.431056  42.898541  0.009024
1     57.991636  42.898541  0.009024
2     57.991636  42.939461  0.009024
3     53.431056  42.939461  0.009024
4     57.991636  42.884058  0.010111
..          ...        ...       ...
199  257.097906  42.754025  0.049965
200  258.743717  42.759825  0.028459
201  263.876694  42.759825  0.028459
202  263.876694  42.875885  0.028459
203  258.743717  42.875885  0.028459
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