ogstools.meshlib.mesh_series module#

class ogstools.meshlib.mesh_series.PVDDataItems[source]#

Bases: object

__init__(values)[source]#

__getitem__(index)[source]#

Return type:: ndarray

class ogstools.meshlib.mesh_series.MeshSeries[source]#

Bases: object

A wrapper around pyvista and meshio for reading of pvd and xdmf timeseries.

Initialize a MeshSeries object

param filepath:

Path to the PVD or XDMF file.

param time_unit:

Data unit of the timevalues.

param data_length_unit:

Length unit of the mesh data.

param output_length_unit:

Length unit in plots.

returns:

A MeshSeries object

__init__(filepath, time_unit='s', spatial_unit='m', spatial_output_unit='m')[source]#

Initialize a MeshSeries object

param filepath:

Path to the PVD or XDMF file.

param time_unit:

Data unit of the timevalues.

param data_length_unit:

Length unit of the mesh data.

param output_length_unit:

Length unit in plots.

returns:

A MeshSeries object

__getitem__(index)[source]#

Return type:: list[UnstructuredGrid]

data(variable_name)[source]#

Returns an DataItems object, that allows array indexing. To get “geometry”/”points” or “topology”/”cells” read the first time step and use pyvista functionality Selection example: ms = MeshSeries() temp = ms.data(“temperature”) time_step1_temps = temp[1,:] temps_at_some_points = temp[:,1:3] :param variable_name: Name the variable (e.g.”temperature”) :returns: Returns an objects that allows array indexing.

Return type:: DataItems

aggregate(variable, np_func, axis, mask=None)[source]#

Aggregate data of all timesteps using a specified function.

Parameters:

variable (Variable | str) – The mesh variable to be aggregated.
func – The aggregation function to apply.

Returns:

A numpy.ndarray of the same length as the timesteps if axis=0 or of the same length as the data if axis=1.

Return type:

ndarray

aggregate_over_time(variable, func)[source]#

Aggregate data over all timesteps using a specified function.

Parameters:

variable (Variable | str) – The mesh variable to be aggregated.
func (Literal['min', 'max', 'mean', 'median', 'sum', 'std', 'var']) – The aggregation function to apply.

Returns:

A mesh with aggregated data according to the given function.

Return type:

Mesh

clear()[source]#

closest_timestep(timevalue)[source]#

Return the corresponding timestep from a timevalue.

Return type:: int

closest_timevalue(timevalue)[source]#

Return the closest timevalue to a timevalue.

Return type:: float

ip_tesselated()[source]#

Create a new MeshSeries from integration point tessellation.

Return type:: MeshSeries

mesh(timestep, lazy_eval=True)[source]#

Selects mesh at given timestep all data function.

Return type:: Mesh

rawdata_file()[source]#

Checks, if working with the raw data is possible. For example, OGS Simulation results with XDMF support efficient raw data access via h5py

Returns:: The location of the file containing the raw data. If it does not support efficient read (e.g., no efficient slicing), it returns None.
Return type:: Path | None

read_interp(timevalue, lazy_eval=True)[source]#

Return the temporal interpolated mesh for a given timevalue.

Return type:: Mesh

property timesteps: range#: Return the timesteps of the timeseries data.

timevalues(time_unit=None)[source]#

Return the timevalues, optionally converted to another time unit.

Return type:: ndarray

values(data_name, selection=None)[source]#

Get the data in the MeshSeries for all timesteps.

Parameters:

data_name (str) – Name of the data in the MeshSeries.
selection (slice | ndarray | None) –
Can limit the data to be read. - Time is always the first dimension. - If None, it takes the selection that is defined in the xdmf file. - If a tuple or np.ndarray: see how h5py uses Numpy array indexing. - If a slice: see Python slice reference. - If a string: see example:

Example: "|0 0 0:1 1 1:1 190 3:97 190 3"

This represents the selection [(offset(0,0,0): step(1,1,1) : end(1,190,3) : of_data_with_size(97,190,30))].

Returns:

A numpy array of the requested data for all timesteps.

Return type:

ndarray

time_of_min(variable)[source]#

Returns a Mesh with the time of the variable minimum as data.

Return type:: Mesh

time_of_max(variable)[source]#

Returns a Mesh with the time of the variable maximum as data.

Return type:: Mesh

aggregate_over_domain(variable, func, mask=None)[source]#

Aggregate data over domain per timestep using a specified function.

Parameters:

variable (Variable | str) – The mesh variable to be aggregated.
func (Literal['min', 'max', 'mean', 'median', 'sum', 'std', 'var']) – The aggregation function to apply.
mask (ndarray | None) – A numpy array as a mask for the domain.

Returns:

A numpy array with aggregated data.

Return type:

ndarray

plot_domain_aggregate(variable, func, timesteps=None, time_unit='s', mask=None, ax=None, **kwargs)[source]#

Plot the transient aggregated data over the domain per timestep.

Parameters:

variable (Variable | str) – The mesh variable to be aggregated.
func (Literal['min', 'max', 'mean', 'median', 'sum', 'std', 'var']) – The aggregation function to apply.
timesteps (slice | None) – A slice to select the timesteps. Default: all.
time_unit (str | None) – Output unit of the timevalues.
mask (ndarray | None) – A numpy array as a mask for the domain.
ax (Axes | None) – matplotlib axis to use for plotting
kwargs (Any) – Keyword args passed to matplotlib’s plot function.

Returns:

A matplotlib Figure or None if plotting on existing axis.

Return type:

Figure | None

probe(points, data_name, interp_method='linear')[source]#

Probe the MeshSeries at observation points.

Parameters:

points (ndarray) – The observation points to sample at.
data_name (str) – Name of the data to sample.
interp_method (Literal['nearest', 'linear']) – Interpolation method, defaults to linear

Returns:

numpy array of interpolated data at observation points.

Return type:

ndarray

plot_probe(points, variable, variable_abscissa=None, labels=None, time_unit='s', interp_method='linear', colors=None, linestyles=None, ax=None, fill_between=False, **kwargs)[source]#

Plot the transient variable on the observation points in the MeshSeries.

param points:

The points to sample at.

param variable:

The variable to be sampled.

param labels:

The labels for each observation point.

param time_unit:

Output unit of the timevalues.

param interp_method:

Choose the interpolation method, defaults to linear for xdmf MeshSeries and probefilter for pvd MeshSeries.

param interp_backend:

Interpolation backend for PVD MeshSeries.

param kwargs:

Keyword arguments passed to matplotlib’s plot function.

returns:

A matplotlib Figure

Return type:: Figure | None

animate(variable, timesteps=None, mesh_func=lambda mesh: ..., plot_func=lambda *_: ..., **kwargs)[source]#

Create an animation for a variable with given timesteps.

Parameters:

variable (Variable) – the field to be visualized on all timesteps
timesteps (Sequence | None) – if sequence of int: the timesteps to animate if sequence of float: the timevalues to animate
mesh_func (Callable[[Mesh], Mesh]) – A function which expects to read a mesh and return a mesh. Useful, for slicing / clipping / thresholding the meshseries for animation.
plot_func (Callable[[Axes, float], None]) – A function which expects to read a matplotlib Axes and the time value of the current frame. Useful to customize the plot in the animation.

Keyword Arguments:

See ogstools.plot.contourf

Return type:

FuncAnimation

plot_time_slice(variable, points, y_axis='auto', interpolate=True, time_unit='s', time_logscale=False, fig=None, ax=None, cbar=True, **kwargs)[source]#

Parameters:

variable (Variable | str) – The variable to be visualized.
points (ndarray) – The points along which the data is sampled over time.
y_axis (Literal['x', 'y', 'z', 'dist', 'auto']) – The component of the sampling points which labels the y-axis. By default, if only one coordinate of the points is changing, this axis is taken, otherwise the distance along the line is taken.
interpolate (bool) – Smoothen the result be interpolation.
time_unit (str) – Time unit displayed on the x-axis.
time_logscale (bool) – Should log-scaling be applied to the time-axis?
fig (Figure | None) – matplotlib figure to use for plotting.
ax (Axes | None) – matplotlib axis to use for plotting.
cb_loc – Colorbar location. If None, omit colorbar.

Keyword Arguments:

cb_labelsize: colorbar labelsize
cb_loc: colorbar location (‘left’ or ‘right’)
cb_pad: colorbar padding
cmap: colormap
vmin: minimum value for colorbar
vmax: maximum value for colorbar
num_levels: number of levels for colorbar
figsize: figure size
dpi: resolution

Return type:

Figure