jFluidHaver

class PlasmaCalcs.dimensions.fluids.jFluidHaver(*, jfluid=None, jfluids=None, **kw)

Bases: DimensionHaver

class which “has” a jFluidDimension. (jFluidDimension instance will be at self.jfluid_dim)

self.jfluid stores the current jfluid (possibly multiple). If None, use self.fluids instead.

self.jfluids stores “all possible jfluids” for the jFluidHaver.

Additionally, has various helpful methods for working with the FluidDimension,

e.g. current_n_fluid, iter_fluids, take_fluid.

See jFluidDimension.setup_haver for details.

(Some variables, like ‘nusj’ depend on multiple fluids; for those variables use fluid and jfluid.)

Methods

`__init_subclass__`(*[, dimension, dim_plural])	called when subclassing DimensionHaver; sets some useful attributes related to dimension.
`__setup_haver__`(dim_cls, **kw_super)	called by dim_cls.setup_haver(cls) when setting up cls so that it "has" dim_cls dimension.
`as_single_dimpoint`([values, dims])	return DimPoint with values for dims, but raise DimensionValueError if any value is_iterable_dim.
`assign_dim_coords`(array, *dims[, skip])	assign all dimensions in self as coords for array.
`check_pickle`([x])	checks that self (or, x, if provided) is pickleable, by pickling then unpickling.
`current_n_dimpoints`([dims])	return number of points represented by current values of dims.
`dim_values`([dims])	return dict of current values for dimensions in self.
`dims_apply`(funcname, *args_func[, dims])	apply funcname to each dimension in self, with args_func and kw_func.
`dims_get`(attr[, dims])	return dict of {dim: getattr(self.dimensions[dim], attr) for dim in dims}.
`enumerate_dimpoints`([dims, all])	iterate through values of dims, yielding (idx, DimPoint) pairs.
`get_behavior`([keys])	return value of self.behavior.
`get_first_dimpoint`([dims, enumerate])	return DimPoint taking the first value of each dim in self.dimensions.
`get_ncpu`()	returns ncpu, but if None, return multiprocessing.cpu_count() instead.
`getj`(var, *args__get[, jfluid])	returns self(var), but for jfluid instead of fluid.
`iter_dimpoints`([dims, all, restore, enumerate])	iterate through values of dims, returning DimPoints and setting dim values during iteration.
`load_across_dims`(loader, *args_loader[, ...])	return loader(...), iterating & joining across each dimension.
`load_across_dims_implied_by`(var, loader, ...)	return loader(...), iterating & joining across each dimension implied by var.
`maintaining_attrs`(attrs, *attrs_as_flags)	returns context manager which restores attrs of self to their original values, upon exit.
`pop_dim_keys`(kw)	return ({key: kw.pop(key) for key in self.dimensions if key in kw}, kw).
`set_attrs`(**attrs)	sets these attrs in self.
`set_pop_dim_attrs`(kw)	set self.{key} = kw.pop(key) for each key in self.dimensions if key in kw.
`using_attrs`([attrs_as_dict, _unset_sentinel])	returns context manager which sets attrs of obj upon entry; restores original values upon exit.
`using_first_dimpoint`([dims])	return context manager which sets dimensions to their first values (when called); restore original on exit.
`_call_loader0_at_dimpoint`(dimpoint, loader0, ...)	return self._call_loader_at_dimpoint(dimpoint, loader0, ...).
`_call_loader_at_dimpoint`(dimpoint, loader, ...)	with self.using(*dimpoint): return loader(args_loader, **kw_loader)
`_default_ncoarse`()	return default for ncoarse during self.load_across_dims.
`_default_ncpu`()	return default for ncpu during self.load_across_dims.
`_default_print_freq`()	returns default for print_freq.
`_default_timeout`()	return default for timeout during self.load_across_dims.
`_get_dimensions_dict`()	return dict of dimensions in self; {dimension name: Dimension object}
`_load_across_dims_postprocess`(arrs, dims, *, ...)	postprocessing results from load_across_dims, after all tasks have been completed.
`_load_across_dims_postprocess__arrs_to_nparr`(...)	returns single numpy array (with the data from arrs, which is an array of arrays).
`_load_across_dims_postprocess__broadcast_shapes`(...)	handles array broadcasting for _load_across_dims_postprocess.
`_repr_contents`()	return list of contents to go in repr of self.
`_special_dims_shifters`(dimnames, _shift_special)	return (dict for self.using(...), dict for result.isel(...)) to _shift_special_dims.

Attributes

`array_MBmax`	UNSET, None, or number
`assign_jfluid_along`	alias to self.jfluid_dim.assign_coord_along
`assign_jfluid_coord`	alias to self.jfluid_dim.assign_coord
`behavior`	dict of {attr: self.attr} for attr in self.behavior_attrs.
`behavior_attrs`	list of attrs in self which control behavior of self.
`cls_behavior_attrs`
`current_n_jfluid`	alias to self.jfluid_dim.current_n
`dimensions`	dict of dimensions in self; {dimension name: Dimension object}.
`dims`	return dict of current values for dimensions in self.
`enumerate_jfluid`	alias to self.jfluid_dim.enumerate
`enumerate_jfluids`	alias to self.jfluid_dim.enumerate_values
`iter_jfluid`	alias to self.jfluid_dim.iter
`iter_jfluids`	alias to self.jfluid_dim.iter_values
`iter_jfluids_partition`	alias to self.jfluid_dim.iter_partition
`jfluid`	alias to self.jfluid_dim.v
`jfluid_dim`	jfluid dimension for jFluidHaver.
`jfluid_is_iterable`	alias to self.jfluid_dim.is_iterable
`jfluid_list`	alias to self.jfluid_dim.list
`jfluid_type`	alias to self.jfluid_dim.get_type
`jfluids`	alias to self.jfluid_dim.values
`join_jfluids`	alias to self.jfluid_dim.join_along
`maintaining`	alias to maintaining_attrs
`ncoarse`	int
`ncpu`	None or int
`nondim_behavior_attrs`	list of attrs in self which control behavior of self, but which are NOT in self.dimensions.
`print_freq`	None, or number (possibly negative or 0)
`print_freq_explicit`	like self.print_freq, but converts UNSET to value based on self.verbose,
`take_jfluid`	alias to self.jfluid_dim.take
`take_jfluids`	alias to self.jfluid_dim.take_along
`timeout`	None or int
`using`	alias to using_attrs

classmethod __init_subclass__(*, dimension=None, dim_plural=None, **kw_super)

called when subclassing DimensionHaver; sets some useful attributes related to dimension.

dimension: str or None: name of dimension associated with this subclass.

if None, no particular dimension associated with this subclass.
dim_plural: str or None: plural form of dimension. if None, use str(dimension)+’s’.

Sets various attributes in cls:

cls._dimension = dimension,

cls._dim_plural = dim_plural,

cls._dim_types = dict of all {dimension name: Dimension subclass} from cls and cls.__bases__

(note - connecting self._dimension to this dict is not handled here;

it is handled by __setup_haver__ called by Dimension.setup_haver)

classmethod __setup_haver__(dim_cls, **kw_super): called by dim_cls.setup_haver(cls) when setting up cls so that it “has” dim_cls dimension.

_call_loader0_at_dimpoint(dimpoint, loader0, args_loader, *, nload, MBmax, **kw_loader): return self._call_loader_at_dimpoint(dimpoint, loader0, …).

Also does memory size check based on nload and MBmax.

intended for internal use only, inside load_across_dims.

arr0 is handled differently from other arrays in order to do some checks.

_call_loader_at_dimpoint(dimpoint, loader, args_loader, **kw_loader): with self.using(**dimpoint): return loader(*args_loader, **kw_loader)

_default_ncoarse(): return default for ncoarse during self.load_across_dims. returns DEFAULTS.LOADING_NCOARSE

_default_ncpu(): return default for ncpu during self.load_across_dims. returns DEFAULTS.LOADING_NCPU

_default_print_freq(): returns default for print_freq. Here, returns UNSET; i.e., infer from self.verbose.

(See self.print_freq_explicit to get the actual value of print_freq.)

_default_timeout(): return default for timeout during self.load_across_dims. returns DEFAULTS.LOADING_TIMEOUT

_get_dimensions_dict(): return dict of dimensions in self; {dimension name: Dimension object}

_load_across_dims_postprocess(arrs, dims, *, it_dimnames, assign_coords, isel=None): postprocessing results from load_across_dims, after all tasks have been completed.

i.e., after all arrays have been loaded, join them all together.

_load_across_dims_postprocess__arrs_to_nparr(arr0, arrs): returns single numpy array (with the data from arrs, which is an array of arrays).

_load_across_dims_postprocess__broadcast_shapes(arr0, arrs, it_dimnames)

handles array broadcasting for _load_across_dims_postprocess.

returns (arr0, arrs), but with all arrs having the same shape as arr0. Crash if not possible.

CAUTION: may edit arrs in-place.

(Won’t edit individual arrays’ data; just arrs which contains the arrays.)

NOTE: arr0 in result isn’t necessarily arrs.flat[0].: arr0 in result is the “model array”, used to model coords & dims on.

arr0 = first DataArray with size == max(size) across all arrays.

E.g. if array shapes are [(), (), (7,1), (1,3), (7,3), (7,3) ()],

then use arr0 = the first DataArray with shape (7,3).

if neither array with shape (7,3) is a DataArray (i.e., has coords…)

then crash instead (because we can’t properly assign coords to result.)

it_dimnames: list of names of iterable dimensions; sometimes included in error message.

_repr_contents(): return list of contents to go in repr of self.

_special_dims_shifters(dimnames, _shift_special): return (dict for self.using(…), dict for result.isel(…)) to _shift_special_dims.

See docs of load_across_dims for more details.

property array_MBmax: UNSET, None, or number

maximum result size allowed, in Megabytes.

will raise a MemorySizeError if result size would be larger than this.

UNSET –> use DEFAULTS.ARRAY_MBYTES_MAX (default: 1000 MB).

None –> no limit.

Assumes that each result (at each dimpoint) will be the same size.

as_single_dimpoint(values=None, *, dims=None, **values_as_kw)

return DimPoint with values for dims, but raise DimensionValueError if any value is_iterable_dim.

values: None or dict: values to use for the dimpoint.

values will be joined with **values_as_kw; provided any of either will be equivalent.

E.g. can use values={‘fluid’: ‘e’} or use fluid=’e’.

if any are provided –> use values corresponding to self.{dim}=values[dim] for dim in dims.

else –> use values of self.{dim} for dim in dims. (equivalent: self.dims_apply(‘_as_single’, dims=dims))
dims: None or iterable of strs appearing in self.dimensions.keys(): dimensions to include.

None –> infer dimensions from keys of values (and values_as_kw).

if no values were provided (values=None, and empty values_as_kw),

use all dimensions from self.dimensions.keys().

additional kwargs provide other {dim: value} items.

Examples:

self.as_single_dimpoint() –> DimPoint({dim: self.{dim} for dim in self.dimensions})
self.as_single_dimpoint({‘fluid’: ‘e’}) –> DimPoint({‘fluid’: ‘e’})
self.as_single_dimpoint(fluid=’e’) –> DimPoint({‘fluid’: ‘e’})
self.as_single_dimpoint({‘fluid’: ‘e’}, snap=0) –> DimPoint({‘fluid’: ‘e’, ‘snap’: 0})
self.as_single_dimpoint(dims=[‘fluid’, ‘snap’]) –> DimPoint({‘fluid’: self.fluid, ‘snap’: self.snap})

assign_dim_coords(array, *dims, skip=[])

assign all dimensions in self as coords for array. (self.assign_{dim}_coord(array))

Assumes array is an xarray and does not have any dimensions in self.

(array is not edited directly; returns result of assigning coords.)

dims: iterable of dimensions in self: assign only these dimensions as coords. (use all dimensions if len(dims)==0)
skip: iterable of dimensions in self: do not assign these dimensions as coords.

property assign_jfluid_along: alias to self.jfluid_dim.assign_coord_along

property assign_jfluid_coord: alias to self.jfluid_dim.assign_coord

property behavior: dict of {attr: self.attr} for attr in self.behavior_attrs. Note dims are separate;

dims go in behavior.dims. E.g. Behavior({‘units’:’si’,…}, dims={‘snap’:0,…}).

property behavior_attrs: list of attrs in self which control behavior of self.

Here, returns self.cls_behavior_attrs.

Subclasses could override if any behavior attrs are not known at the class-level,

e.g. if MySubclass’s list of behavior attrs varies between instances of MySubclass.

check_pickle(x=None): checks that self (or, x, if provided) is pickleable, by pickling then unpickling.

Returns result of unpickling. Useful for debugging.

current_n_dimpoints(dims=None)

return number of points represented by current values of dims.

dims: None or iterable of strs appearing in self.dimensions.keys(): dimensions to consider. None –> use all dimensions.

E.g. current_n_dimpoints(self, dims=[‘fluid’, ‘snap’]) –> number of (fluid, snap) points;

e.g. 3 fluids and 2 snaps –> 6 points.

Note, for classes using maindims, maindims are not included in the number of dimpoints.

Equivalent to len(list(self.iter_dimpoints(dims=dims, current=True)))

property current_n_jfluid: alias to self.jfluid_dim.current_n

dim_values(dims=None)

return dict of current values for dimensions in self.

dims: None or iterable: if provided, only include these dimensions.

Equivalent: DimRegion(self.dims_get(‘v’, dims=dims))

property dimensions: dict of dimensions in self; {dimension name: Dimension object}.

e.g. {‘fluid’: self.fluid_dim, ‘snap’: self.snap_dim, …}.

property dims: return dict of current values for dimensions in self. Equivalent: self.dim_values()

dims_apply(funcname, *args_func, dims=None, **kw_func)

apply funcname to each dimension in self, with args_func and kw_func.

dims: None or iterable of strs: if provided, only apply to these dimensions.

See also: dims_apply

enumerate_dimpoints(dims=None, *, all=False): iterate through values of dims, yielding (idx, DimPoint) pairs.

idx is a dict of {dim: i} such that DimPoint values are {dim: dims[i] for dim,i in idx.items()}.

Also, during iteration, set self.{dim} = value, as with self.iter_dim.

Equivalent to self.iter_dimpoints(dims=dims, all=all, enumerate=True)

property enumerate_jfluid: alias to self.jfluid_dim.enumerate

property enumerate_jfluids: alias to self.jfluid_dim.enumerate_values

get_behavior(keys=None)

return value of self.behavior.

keys: None or iterable: if provided, only include these attrs.

from nondim_behavior_attrs, or dims.

get_first_dimpoint(dims=None, *, enumerate=False)

return DimPoint taking the first value of each dim in self.dimensions.

dims: None or iterable of strs appearing in self.dimensions.keys(): dimensions to include. None –> use all dimensions.
enumerate: bool: whether to return (idx, DimPoint) instead of just DimPoint.

get_ncpu(): returns ncpu, but if None, return multiprocessing.cpu_count() instead.

(This is for convenience; using None will also work with any methods defined here.)

getj(var, *args__get, jfluid=UNSET, **kw__get)

returns self(var), but for jfluid instead of fluid.

jfluid: UNSET, None, or any jfluid specifier.: if provided, use this instead of self.jfluid.

Example:

m_s = self(‘m’)

with self.using(fluids=self.jfluids, fluid=self.jfluid):

m_j_0 = self(‘m’)

m_j_1 = self.getj(‘m’)

here, the values stored in the variables will be:

m_s = mass of self.fluid
m_j_0 = mass of self.jfluid. But, fluid dimension will be labeled ‘fluid’
m_j_1 = mass of self.jfluid. fluid dimension will be labeled ‘jfluid’.

additional args and kwargs are passed to self(var)

iter_dimpoints(dims=None, *, all=False, restore=True, enumerate=False)

iterate through values of dims, returning DimPoints and setting dim values during iteration.

DimPoints are dicts of {dim: value} for dim in dims, where not is_iterable_dim(value).

Also, during iteration, set self.{dim} = value, as with self.iter_dim.

dims: None or iterable of strs appearing in self.dimensions.keys(): dimensions to consider. None –> use all dimensions.
all: bool: whether to iterate through all possible values, or only the current values.

False –> iterate through current values (e.g., self.snap, self.fluid, …).

similar to itertools.product(self.iter_snap(), self.iter_fluid(), …)

True –> iterate through all possible values (e.g., self.snaps, self.fluid, …)

similar to itertools.product(self.iter_snaps(), self.iter_fluids(), …)

Equivalent to all=False if all dims are set to None, e.g. self.snap=None, …
restore: bool: whether to restore original dim values after iteration.
enumerate: bool, default False: whether to yield indices too, i.e. (idx, DimPoint) instead of just DimPoint.

idx would be a dict of {dim: i} such that DimPoint values are {dim: dims[i] for dim,i in idx.items()}.

property iter_jfluid: alias to self.jfluid_dim.iter

property iter_jfluids: alias to self.jfluid_dim.iter_values

property iter_jfluids_partition: alias to self.jfluid_dim.iter_partition

property jfluid: alias to self.jfluid_dim.v

property jfluid_dim: jfluid dimension for jFluidHaver.

jfluid_dim_cls: alias of jFluidDimension

property jfluid_is_iterable: alias to self.jfluid_dim.is_iterable

property jfluid_list: alias to self.jfluid_dim.list

property jfluid_type: alias to self.jfluid_dim.get_type

property jfluids: alias to self.jfluid_dim.values

property join_jfluids: alias to self.jfluid_dim.join_along

load_across_dims(loader, *args_loader, dims=[], assign_coords=None, loader0=None, _shift_special={}, **kw_loader)

return loader(…), iterating & joining across each dimension.

loader: callable of (*args_loader, **kw_loader) -> xarray.DataArray.: will call loader to get result values at each combination of dims values in self.

(loader will probably depend on dims values from self.)
dims: iterable of strs or Dimension objects: load across these Dimensions.

loads across the current values (when this method was called) of each dimension,

not necessarily “all” values. (e.g., self.snap, not self.snaps)

str values –> use self.dimensions[d] (where d is a str in dims).

len(dims)==0 –> just return loader(var, *args_loader, **kw_loader).

While loading, set dim.loading=True for each dim.
assign_coords: None or bool, default None: whether to dim.assign_coord for each result of loader, for each dimension.

None –> assign coord only if dim.name not already in array.coords.
loader0: None or callable: if provided, use loader0 to get the first array, then use loader for the rest.

Internally the first array’s .coords and .attrs are used to label the result;

however all other arrays do not need to be converted to xarray.
_shift_special: UNSET or dict of (dimstr: list of special values): workaround to encourage loader0 to be called on a “usual” case, not a special case.

if provided, and dimstr in dims, and d=self.dimensions[dimstr] has multiple values,

with special_value first, and at least one non-special value later, then

internally rearrange dim values order before loading,

then rearrange result back to original order (via indexing).

E.g. _shift_special=dict(snap=[INPUT_SNAP]) –> apply loader0 to the first non-INPUT_SNAP,

if there are any non-INPUT_SNAP snap values in snap, and ‘snap’ in dims.

— MULTIPROCESSING STRATEGY OPTIONS (from self) —

timeout: None or int: max duration, in seconds. Must be None or integer (due to limitations of signal.alarm method)

None –> no time limit.

Note: if time_limit is reached, will raise a TimeoutError and save the result so far.

(in this case, any not-yet-calculated values will each be RESULT_MISSING.)

# [TODO] make this happen, without making self un-picklable:

in case of crash, results so far can be found in self._latest_load_tasks.

Then possibly continued via:

results = self._latest_load_tasks(…, reset=False, skip_done=True)

result = self._load_across_dims_postprocess(results, dims, …)

# [TODO] if crashing and resuming is common, make that easier to do^

elf.timeout has not been set, use DEFAULTS.LOADING_TIMEOUT (default: None).
ncpu: None or int: max number of cpus to use for multiprocessing.

None –> use multiprocessing.cpu_count()

int –> use this value. if 0 or 1, do not use multiprocessing here.

Note: will actually use min(ncpu, number of calls to be made);

e.g. if ncpu=4 but len(arg_kw_tuples)=2, will only use 2 cpus.

elf.ncpu has not been set, use DEFAULTS.LOADING_NCPU (default: 1).
ncoarse: int: if >1, group tasks into groups of size ncoarse before performing them.

elf.ncoarse has not been set, use DEFAULTS.LOADING_NCOARSE (default: 1).
print_freq: None, or number (possibly negative or 0): >0 –> Minimum number of seconds between progress updates.

=0 –> print every progress update.

<0 –> never print progress updates.

None –> use DEFAULTS.PROGRESS_UPDATES_PRINT_FREQ

elf.print_freq has not been set, infer from self.verbose if it exists,

use DEFAULTS.PROGRESS_UPDATES_PRINT_FREQ (default: 2).

additional args & kwargs are passed as loader(*args_loader, **kw_loader).

load_across_dims_implied_by(var, loader, *args_loader, assign_coords=None, _min_split=1, **kw_loader)

return loader(…), iterating & joining across each dimension implied by var.

Equivalent to self.load_across_dims(loader, …, dims=self.match_var_loading_dims(var)).

var: str: variable which implies dims to load across, via self.match_var_loading_dims(var).
loader: callable of (*args_loader, **kw_loader) -> xarray.DataArray.: will call loader to get result values at each combination of dims values in self.

(loader will probably depend on dims values from self.)
assign_coords: None or bool, default None: whether to dim.assign_coord for each result of loader, for each dimension.

None –> assign coord only if dim.name not already in array.coords.
_min_split: int, default 1: if an implied dim has current_n() < min_split, don’t load across it.

1 –> no minimum.

additional args & kwargs are passed as loader(*args_loader, **kw_loader).

property maintaining: alias to maintaining_attrs

maintaining_attrs(*attrs, **attrs_as_flags): returns context manager which restores attrs of self to their original values, upon exit.

E.g. maintaining_attrs(obj, ‘attr1’, ‘attr2’, attr3=True, attr4=False)

–> will restore upon exit, original values of obj.attr1, attr2, and attr3, but not attr4.

property ncoarse: int

if >1, group tasks into groups of size ncoarse before performing them.

property ncpu

None or int

max number of cpus to use for multiprocessing.
None –> use multiprocessing.cpu_count()
int –> use this value. if 0 or 1, do not use multiprocessing here.

Note: will actually use min(ncpu, number of calls to be made);: e.g. if ncpu=4 but len(arg_kw_tuples)=2, will only use 2 cpus.

see also: self.get_ncpu() to read actual number of cpus when self.ncpu is None.

property nondim_behavior_attrs: list of attrs in self which control behavior of self, but which are NOT in self.dimensions.

pop_dim_keys(kw): return ({key: kw.pop(key) for key in self.dimensions if key in kw}, kw).

property print_freq: None, or number (possibly negative or 0)

>0 –> Minimum number of seconds between progress updates.

=0 –> print every progress update.

<0 –> never print progress updates.

None –> use DEFAULTS.PROGRESS_UPDATES_PRINT_FREQ

property print_freq_explicit: like self.print_freq, but converts UNSET to value based on self.verbose,

UNSET –> result depends on self.verbose:

False or <=0 –> -1

True or (>=1 and <5) –> None

>=5 –> 0 (i.e. print every progress update)

if self.verbose doesn’t exist –> None

if result would be None, instead give DEFAULTS.PROGRESS_UPDATES_PRINT_FREQ.

set_attrs(**attrs): sets these attrs in self.

set_pop_dim_attrs(kw): set self.{key} = kw.pop(key) for each key in self.dimensions if key in kw.

property take_jfluid: alias to self.jfluid_dim.take

property take_jfluids: alias to self.jfluid_dim.take_along

property timeout

None or int

max duration, in seconds. Must be None or integer (due to limitations of signal.alarm method)

None –> no time limit.

Note: if time_limit is reached, will raise a TimeoutError and save the result so far.: (in this case, any not-yet-calculated values will each be RESULT_MISSING.)

property using: alias to using_attrs

using_attrs(attrs_as_dict={}, _unset_sentinel=ATTR_UNSET, **attrs_and_values)

returns context manager which sets attrs of obj upon entry; restores original values upon exit.

_unset_sentinel: any value, default ATTR_UNSET: upon entry, delete any attrs with value _unset_sentinel (compared via ‘is’).

E.g. using_attrs(obj, _unset_sentinel=None, x=None) –> del obj.x upon entry.

using_first_dimpoint(dims=None)

return context manager which sets dimensions to their first values (when called); restore original on exit.

Useful for testing a single code at a single dimpoint without needing to set each dimension individually.

dims: None or iterable of strs appearing in self.dimensions.keys(): dimensions to include. None –> use all dimensions.