pytuflow.XMDF

pytuflow.XMDF#

class XMDF(fpath, twodm=None)#

Bases: Mesh

Class for handling XMDF output files.

The XMDF class will only load header information from the XMDF file on initialisation, this makes the class cheap to initialise. The class can be initialised and the methods times() and data_types() can be used without requiring QGIS libraries. However, extracting spatial data requires QGIS libraries to be available and QGIS to be initialised. The class will automatically load the 2dm file the first time a spatial method is called which can cause the first time a spatial method is called to be slow.

Parameters:

  • fpath (PathLike) – Path to the XMDF file, or XMDF SUP file.

  • twodm (PathLike, optional) – Path to the 2dm file. If not provided, the class will attempt to find the 2dm file with the same name as the XMDF file. If an XMDF SUP file is provided in the first argument, the 2dm argument is not used.

Examples

>>> from pytuflow import XMDF
>>> xmdf = XMDF('./path/to/xmdf')

Get all the data types available in the XMDF file:

>>> xmdf.data_types()
['bed level', 'depth', 'vector velocity', 'velocity', 'water level', 'time of peak h']

Get all the data types that have maximum values:

>>> xmdf.data_types('max')
['depth', 'vector velocity', 'velocity', 'water level']

Get all the available times in the XMDF file:

>>> xmdf.times()
[0.0, 0.016666666666666666, ..., 3.0]

Get the available times for the depth data type:

>>> xmdf.times('depth')
[0.0, 0.016666666666666666, ..., 3.0]

Get the water level time-series data for a given point defined as (x, y):

>>> xmdf.time_series((293250, 6178030), 'water level')
        time  pnt1/water level
0.000000               NaN
0.083333               NaN
0.166667               NaN
0.250000               NaN
0.333333               NaN
0.416667               NaN
0.500000               NaN
0.583333               NaN
0.666667         41.561204
0.750000         41.838923
...                    ...
2.666667         41.278006
2.750000         41.239387
2.833334         41.201996
2.916667         41.166462
3.000000         41.128152

Get velocity time-series using all the points within a shapefile:

>>> xmdf.time_series('path/to/shapefile.shp', 'vel')
    time  pnt1/velocity
0.000000            NaN
0.083333            NaN
0.166667            NaN
0.250000            NaN
0.333333            NaN
0.416667            NaN
0.500000            NaN
0.583333            NaN
0.666667       0.975577
0.750000       0.914921
...                 ...
2.666667       0.320217
2.750000       0.270925
2.833334       0.233793
2.916667       0.206761
3.000000       0.183721

Get the bed level and max water level data using a shapefile to define the location:

>>> xmdf.section('path/to/shapefile.shp', ['bed level', 'max h'], -1)
       Line_1                                 Line_2
       offset  bed level max water level      offset  bed level max water level
0    0.000000  43.646312             NaN    0.000000  43.112894             NaN
1    0.145704  43.645835             NaN    2.213407  43.088104             NaN
2    2.801012  43.647998             NaN    6.926959  43.035811             NaN
3    7.819650  43.643313             NaN   11.926842  42.987500             NaN
4   12.838279  43.634620             NaN   16.926803  42.950000             NaN
5   17.856980  43.626645             NaN   21.926729  42.916000             NaN
6   22.875678  43.615949             NaN   26.926655  42.888000             NaN
7   25.941652  43.611225             NaN   31.926952  42.865499             NaN
8   28.451249  43.603039             NaN   36.926835  42.846001             NaN
9   32.913571  43.591435             NaN   41.926869  42.829500             NaN
10  37.932185  43.578406             NaN   46.926830  42.812500             NaN
11  42.950809  43.569102             NaN   51.926756  42.795000       42.443355
12  47.969530  43.577088             NaN   56.926682  42.777190       42.443967
13  52.988495  43.666201             NaN   61.926643  42.760000       42.444545
14  58.007149  43.773129             NaN   66.926940  42.744000       42.445528
15  63.026036  43.897195             NaN   71.926823  42.730500       42.447615
16  68.044737  43.612406             NaN   76.926857  42.719000       42.449872
17  73.063420  42.849014       42.834780   81.926818  42.708500       42.452022
__init__(fpath, twodm=None)#
Parameters:

  • fpath (Path | str)

  • twodm (Path | str)

Methods

curtain

Extracts curtain data for the given locations and data types.

data_types

Return the available data types for the given filter.

profile

Extracts vertical profile data for the given locations and data types.

section

Extracts section data for the given locations and data types.

time_series

Extracts time-series data for the given locations and data types.

times

Returns a list of times for the given filter.

Attributes

ATTRIBUTE_TYPES

DOMAIN_TYPES

GEOMETRY_TYPES

ID_COLUMNS

name

The result name

reference_time

The reference time for the output
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