Quick usage reference¶

Below you can find a quick guide outlining the usage of the tintX library.

The tintX user interface¶

The RunDirectory class serves as the main user interface to interact with the tracking algorithm. To be able to track cells tintX needs information on the datasets. This datasets are usually saved to netCDF or grib files or xarray Datasets. To make use of this interface an instance of the RunDirectory class has to be created. This can be done in multiple ways:

Using already opened Datasets¶

from tintx import RunDirectory

run_dir = RunDirectory(existing_xarray_dataset,
                       "variable_name",
                       x_coord="long_name",
                       y_coord="lat_name",
                       time_coord="time_name"
)

Using data saved to files¶

from tintx import RunDirectory

input_files = "/path/to/input_files/*.nc"
run_dir = RunDirectory.from_files(input_files,
                                  "variable_name"
                                  start="2020-01-01T00:00",
                                  end="2020-12-31T12:50"
)

Using previously tracked data¶

from tintx import RunDirectory
run_dir = RunDirectory.from_dataframe("output.hdf5)

Methods and properties¶

The following collection gives an overview of the usage of the created RunDirectory object which is referred as run_dir:

Applying the tracking algorithm¶

num_cells = run_dir.get_tracks(min_size=2, field_thresh=1)

See also

tintx.config

Accessing the cell tracks¶

Cell tracks are stored in a pandas.DataFrame

run_dir.tracks

Saving tracked cells to file¶

num_cells = run_dir.get_tracks(min_size=2, field_thresh=1)
run_dir.save_tracks("output.hdf5")

See also

tintx.RunDirectory.from_dataframe

Retrieving tuning parameters¶

from tintx import RunDirectory
run_dir = RunDirectory.from_dataframe("output.hdf5)
parameters = run_dir.get_parameters()

Accessing the data and metadata¶

xarray.Dataset holding the data that is tracked.

run_dir.data

xarray.DataArray holding the information of the longitude/latitude/time coordinates.

run_dir.lon
run_dir.lat
run_dir.time

Getting the first and last time step that is considered:

run_dir.start
run_dir.end

Getting the variable name of the field that is tracked:

run_dir.var_name

Visualising the tracked data¶

Plotting cell tracks:

ax = run.plot_trajectories(thresh=2, plot_style={"ms":25, "lw":1})

Creating an animation of the tracked cells:

anim = run.animate(vmax=3, fps=2, plot_style={"res": "10m", "lw":1})

See also

Module xarray: How to work with xarray datasets.
Module pandas: How to work with pandas DataFrames
Module cartopy: How to visualise geo spatial data with cartopy
Class matplotlib.animation.FuncAnimation: How to make use of the object created by FuncAnimation