pytuflow.TPC.time_series

pytuflow.TPC.time_series#

TPC.time_series(locations, data_types, time_fmt='relative', *args, **kwargs)#

Returns a time series dataframe for the given location(s) and data type(s).

It’s possible to pass in a well known shorthand for the data type e.g. q for flow.

The location can be an ID or filter string, e.g. channel to extract the maximum values for all channels. An ID can be used alongside a filter string since there can be duplicate IDs across domains e.g. test/channel - where test is the name and channel is an additional filter. Note, the order does not matter, but it doesn’t work very well if your ID has a ‘/’ or has the same name as a filter string (e.g. calling a po line ‘line’).

The following filters are available for the TPC class:

Domain filters:

  • 1d

  • 2d (or po)

  • rl (or 0d)

Geometry filters:

  • node

  • channel

  • point - (for 2d and rl domains only - use node for 1d domain)

  • line - (for 2d and rl domains only - use channel for 1d domain)

  • polygon (or region)

Combine filters:

  • [filter1]/[filter2]/...: (use / to delim).

The returned column names will be in the format obj/data_type/location e.g. channel/flow/FC01.1_R. The data_type name in the column heading will be identical to the data type name passed into the function e.g. if h is used instead of water level, then the return will be node/h/FC01.1_R.1.

Parameters:

  • locations (str | list[str]) – The location to extract the time series data for. If None is passed in, all locations will be returned for the given data_types.

  • data_types (str | list[str]) – The data type to extract the time series data for. If None is passed in, all data types will be returned for the given locations.

  • time_fmt (str, optional) – The format for the time column. Options are ‘relative’ or ‘absolute’.

Returns:

The time series data.

Return type:

pd.DataFrame

Examples

Extracting flow for a given channel.

>>> res = TPC('path/to/file.tpc')
>>> res.time_series('ds1', 'q')
Time (h)   channel/q/ds1
0.000000           0.000
0.016667           0.000
...                  ...
2.983334           8.670
3.000000           8.391

Extracting all data types for a given location

>>> res.time_series('ds1', None)
Time (h)  channel/Flow/ds1  channel/Velocity/ds1
0.000000             0.000                 0.000
0.016667             0.000                 0.000
...                    ...                   ...
2.983334             8.670                 1.348
3.000000             8.391                 1.333

Extracting all flow results

>>> res.time_series(None, 'flow')
Time (h)  channel/flow/ds1  ...  channel/flow/FC_weir1
0.000000             0.000  ...                    0.0
0.016667             0.000  ...                    0.0
...                    ...  ...                    ...
2.983334             8.670  ...                    0.0
3.000000             8.391  ...                    0.0
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