viscid.grid module

Grids contain fields and coordinates

class viscid.grid.Grid(*args, **kwargs)

Bases: viscid.tree.Node

Computational grid container

Grids contain fields and coordinates. Datasets recurse to grids using __getitem__ and get_field in order to find fields. Grids can also calculate fields by defining _get_* methods.

Attributes can be overridden globally to affect all data reads of a given grid type. Subclasses of Grid document their own special attributes. For example, one can change the default vector layout with:

viscid.grid.Grid.force_vector_layout = LAYOUT_INTERLACED

force_vecter_layout

force all vectors to be of a certain layout when they’re created (default: LAYOUT_DEFAULT)

Type:field.LAYOUT_*

longterm_field_caches

If True, then when a field is cached, it must be explicitly removed from memory with “clear_cache” or using the ‘with’ statemnt. If False, “shell copies” of fields are made so that memory is freed when the returned instance is garbage collected. Default: False

Type:bool

add_field(*fields)

Note: in XDMF reader, the grid will NOT have crds when adding fields, so any grid crd transforms won’t be set

clear_cache()

clear the cache on all child fields

crds

field_dict(fld_names=None, **kwargs)

fields as dict of {name: field}

field_names

fields = None

force_vector_layout = 'none'

geometry_info = None

get_crd_cc(axis, shaped=False)

returns a flat ndarray of coordinates along a given axis axis can be crd name as string, or index, as in x==2, y==1, z==2

get_crd_ec(axis, shaped=False)

returns a flat ndarray of coordinates along a given axis axis can be crd name as string, or index, as in x==2, y==1, z==2

get_crd_fc(axis, shaped=False)

returns a flat ndarray of coordinates along a given axis axis can be crd name as string, or index, as in x==2, y==1, z==2

get_crd_nc(axis, shaped=False)

returns a flat ndarray of coordinates along a given axis axis can be crd name as string, or index, as in x==2, y==1, z==2

get_crds_cc(axes=None, shaped=False)

returns all cell centered coords as a list of ndarrays, flat if shaped==False, or shaped if shaped==True

get_crds_ec(axes=None, shaped=False)

returns all edge centered coords as a list of ndarrays, flat if shaped==False, or shaped if shaped==True

get_crds_fc(axes=None, shaped=False)

returns all face centered coords as a list of ndarrays, flat if shaped==False, or shaped if shaped==True

get_crds_nc(axes=None, shaped=False)

returns all node centered coords as a list of ndarrays, flat if shaped==False, or shaped if shaped==True

get_field(fldname, time=None, force_longterm_caches=False, slc=Ellipsis)

get_grid(time=None)

get_time(*args, **kwargs)

get_times(*args, **kwargs)

iter_field_items(fld_names=None, **kwargs)

iterate over fields in a grid, if fld_names is given, it should be a list of field names to iterate over

iter_fields(fld_names=None, **kwargs)

iterate over fields in a grid, if fld_names is given, it should be a list of field names to iterate over

iter_times(*args, **kwargs)

longterm_field_caches = False

nr_times(*args, **kwargs)

static null_transform(something)

print_tree(depth=-1, prefix='')

remove_all_items()

set_crds(crds_object)

to_dataframe(fld_names=None, selection=Ellipsis, time_sel=slice(None, None, None), time_col='time', datetime_col='datetime')

Consolidate grid’s field data into pandas dataframe

Parameters:

• fld_names (sequence, None) – grab specific fields by name, or None to grab all fields
• selection (selection) – for selecting only parts of fields

Returns:

pandas.DataFrame

topology_info = None

xh_cc

xh_nc

xl_cc

xl_nc