pytuflow.CATCHJson.curtain

pytuflow.CATCHJson.curtain#

CATCHJson.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. 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 or label 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:

>>> res = ... # Assume res is a loaded CATCHJson object
>>> res.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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