PlasmaCalcs.dimensions.main_dimensions.MainDimensionsHaver

class PlasmaCalcs.dimensions.main_dimensions.MainDimensionsHaver

Bases: DimensionHaver

All the dimensions which remain after indexing by all other DimensionHavers.

E.g., for simulation output from a 1024 x 512 x 512 simulation (in x, y, z),

for a given fluid, jfluid, component, and snapshot,

the array would be (1024, 512, 512).

This is probably the shape of (most) arrays as stored in memory.

use self.slices and self.slicing to control slicing behavior

Subclasses should implement:

maindims: listlike of strs

tells main dimensions. Will be accessed as instance.maindims.

Best to use a property if maindims depends on class.

get_maindims_coords: method

self.get_maindims_coords() should return dict of {dim: coords} for dims in self.maindims.

it should also slice coords appropriately, according to self.slices;

for this task, one helpful method might be self._apply_maindims_slices_to_dict().

__init__()

Methods

`__init__`()
`as_single_dimpoint`([values, dims])
`assign_dim_coords`(array, *dims[, skip])
`assign_maindims_coords`(array)
`check_pickle`([x])
`current_n_dimpoints`([dims])
`dim_values`([dims])
`dims_apply`(funcname, *args_func[, dims])
`dims_get`(attr[, dims])
`enumerate_dimpoints`([dims, all])
`get_behavior`([keys])
`get_first_dimpoint`([dims, enumerate])
`get_maindims_coords`()
`get_ncpu`()
`iter_dimpoints`([dims, all, restore, enumerate])
`load_across_dims`(loader, *args_loader[, ...])
`load_across_dims_implied_by`(var, loader, ...)
`load_maindims_var`(var, *args[, u, assign_labels])
`load_maindims_var_across_dims`(var[, dims, ...])
`maintaining_attrs`(attrs, *attrs_as_flags)
`pop_dim_keys`(kw)
`set_attrs`(**attrs)
`set_pop_dim_attrs`(kw)
`slice_maindims`(array, **kw_xarray_isel)
`slicestr`(*[, sep, keep_None])
`standardized_slices`()
`title_with_slices`(*[, sep, keep_None])
`using_attrs`([attrs_as_dict, _unset_sentinel])
`using_first_dimpoint`([dims])

Attributes

`array_MBmax`
`behavior`
`behavior_attrs`
`cls_behavior_attrs`
`dimensions`
`dims`
`maindims`
`maindims_full_shape`
`maindims_full_size`
`maindims_full_sizes`
`maindims_means`
`maindims_shape`
`maindims_size`
`maindims_sizes`
`maintaining`
`multi_slices`
`multi_slices_ikeep`
`multi_slices_ndim`
`ncoarse`
`ncpu`
`nondim_behavior_attrs`
`print_freq`
`print_freq_explicit`
`slices`
`slicing`
`timeout`
`using`

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.

assign_maindims_coords(array): assign maindims dims and coords, based on self.get_maindims_coords() with slicing=False.

array must have same shape as implied by maindims and coords.

if array is 0D, just return a 0D xr.DataArray.

returns an xarray with proper details for PlasmaCalcs.

This function creates a new xarray based on array, and maindims & coords are >0 dimensional.

This is not like assign_{dim}_coord functions, which assign 0D coord to an existing xarray.

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)))

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)

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_maindims_coords(): return dict of {dim: coords} for all dimensions in self.main_dims.

E.g., {‘x’: xcoords, ‘y’: ycoords, ‘z’: zcoords}, if main dimensions are x, y, z.

coords will each be sliced using the appropriate slices from self.slices.

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.)

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()}.

load_across_dims(loader, *args_loader, dims=[], assign_coords=None, loader0=None, **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.

— 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).

load_maindims_var(var, *args, u=None, assign_labels=True, **kw)

return var, formatted as an xarray with proper details for PlasmaCalcs.

loading var should give an array with self.maindims as dimensions.

Also does these steps:

1) assign maindims coords via self.assign_maindims_coords().
2) slice array via self.slices.
3) convert units, if u is not None
4) set result.attrs[‘units’] = self.units
5) if self.maindims_means: take mean of result, across all maindims.
6) use result = self._maindims_postprocess_callback(result), if possible.

u: None, value, or str: units factor for the result.

None –> don’t do any units conversions.

str –> multiply result by self.u(u)

value –> multiply result by u
assign_labels: bool: whether to assign_maindims_coords and self.record_units.

Recommend to always use True, unless using this function internally.

(e.g. for load_maindims_var_across_dims, only use the first time, for efficiency.)

IGNORED if self.maindims_means.

Note:

If load_direct(var) uses an override or gets from cache or self.setvars,

skip steps 1,2,3,4

([TODO] Might need to reconsider this behavior?)

Note:

If self.multi_slices are provided, load_maindims_var for each slice,
then combine results into an xarray.Dataset.
if assign_labels=False, combine results into a dict instead.

load_maindims_var_across_dims(var, dims=None, *, skip=[], u=None, **kw)

load maindims var across these dims. Use all dims from self.dimensions if dims is None.

Only loads across the current value of these dims (e.g., self.fluid, not self.fluids).

(Can set current value to multiple values e.g. self.component = (‘x’, ‘y’).)

u: None, value, or str: units factor for the result.

None –> don’t do any units conversions.

str –> multiply result by self.u(u)

value –> multiply result by u

property maindims_full_shape: self.maindims_shape when self.slices=None

property maindims_full_size: self.maindims_size when self.slices=None

property maindims_full_sizes: self.maindims_sizes when self.slices=None

property maindims_means: whether to immediately take means across maindims when loading arrays. (default False.)

True –> treat data across maindims as if it were the mean values, only.

Caution: this is different from taking means after doing calculations;

e.g., with maindims_means = True, ‘n*T’ –> mean(n)*mean(T), not mean(n*T).

property maindims_shape: tuple of (len(self.get_maindims_coords()[dim]) for dim in self.maindims).

Note, this should be sensitive to changes in self.slices. See also: self.maindims_full_shape.

property maindims_size: product of terms in self.maindims_shape.

Note, this should be sensitive to changes in self.slices. See also: self.maindims_full_size.

property maindims_sizes: dict of {dim: size of dim} for dim in self.maindims.

Note, this should be sensitive to changes in self.slices. See also: self.maindims_full_sizes.

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 multi_slices: dict of {key: slices dict}.

When getting any vars across maindims, make a Dataset by applying each of these, separately.

If len(multi_slices)>0 then ignore self.slices.

Can also provide special keys ‘ndim’ and/or ‘ikeep’ to create special slices:

Example: if self.maindims=[‘x’, ‘y’, ‘z’], then self.multi_slices = dict(ndim=2, ikeep=0)

is equivalent to: self.multi_slices = dict(x_y=dict(z=0), x_z=dict(y=0), y_z=dict(x=0))

Details:

ndim: None or int

None –> ignore, and do not create special slices.

int –> create special slices to keep this many dims after applying each slice.

Example: MultiSlices(ndim=2) is shorthand for

“MultiSlices with one slices for every possible combination of keeping 2 dims”.

Example: MultiSlices(ndim=2, dims=[‘x’, ‘y’, ‘z’], ikeep=0) is equivalent to:

MultiSlices(keep_x_y=dict(z=0), keep_y_z=dict(x=0), keep_x_z=dict(y=0))

Example: MultiSlices(ndim=1, dims=[‘x’, ‘y’, ‘z’], ikeep=0) is equivalent to:

MultiSlices(keep_x=dict(y=0, z=0), keep_y=dict(x=0, z=0), keep_z=dict(x=0, y=0))

ikeep: int or number between -1 < ikeep < 1

index to take when picking a single value for sliced dimensions for special slices.

Default is 0, e.g. when slicing x, keep x[0].

int –> when slicing dim, keep dim[ikeep]. E.g. 10 –> keep x[10]

non-int between -1 and 1 –> multiply by length of dim to get index.

see interprets_fractional_indexing for more details.

Can also set these as attributes of self.multi_slices to achieve the same effect.

E.g. self.multi_slices.ndim = 2

property multi_slices_ikeep: int or number between -1 < ikeep < 1

index to take when picking a single value for sliced dimensions for special slices.

Default is 0, e.g. when slicing x, keep x[0].

int –> when slicing dim, keep dim[ikeep]. E.g. 10 –> keep x[10]

non-int between -1 and 1 –> multiply by length of dim to get index.

see interprets_fractional_indexing for more details.

property multi_slices_ndim: None or int

None –> ignore, and do not create special slices.

int –> create special slices to keep this many dims after applying each slice.

Example: MultiSlices(ndim=2) is shorthand for

“MultiSlices with one slices for every possible combination of keeping 2 dims”.

Example: MultiSlices(ndim=2, dims=[‘x’, ‘y’, ‘z’], ikeep=0) is equivalent to:

MultiSlices(keep_x_y=dict(z=0), keep_y_z=dict(x=0), keep_x_z=dict(y=0))

Example: MultiSlices(ndim=1, dims=[‘x’, ‘y’, ‘z’], ikeep=0) is equivalent to:

MultiSlices(keep_x=dict(y=0, z=0), keep_y=dict(x=0, z=0), keep_z=dict(x=0, y=0))

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.

slice_maindims(array, **kw_xarray_isel): slice maindims of array using self.slices. See help(type(self).slices) for more details.

(if slices is an empty dict, return array, unchanged, without making a copy.)

Only slice dims which actually appear in array.

property slices: slices for maindims when loading arrays & during get_maindims_coords.

E.g. slices = dict(x=slice(0,50), y=7)

–> slice arrays along x & y, taking the first 50 x values, and only the 7th y value.

Notes:

- only applies slices along arrays which actually contain the related coordinates,

e.g. if z=10 appears in slice but loading an array with only x & y, won’t apply z=10 slice.

- supports fractional indexing, as per interprets_fractional_indexing.

Non-integer values between -1 and 1 can be used to infer to a fraction of the dimension length,

with negative values referring to a distance from the end, just like with integer indexing.

Example: dict(x=slice(-0.3, None, 0.01), y=0.8), where x and y each have length 1000

–> equivalent to dict(x=slice(-300, None, 10), y=800).

if self.slicing is False, self.slices will give an empty dict and cannot be set to any value!

however, the old value of self.slices will be remembered in case slicing is set to True later.

slicestr(*, sep=', ', keep_None=False): string representation of self.slices, for use in filenames, titles, etc.

comma-separated, alphabetized, ignoring slice(None).

Supports single-indexes (e.g. x=5), slices (e.g. y=slice(0, 4)),

and fractional indexing (e.g. z=slice(0, 0.5, 0.01)),

though fractional indexing will be converted to ints.

sep: str, separator between slices keep_None: bool, whether to keep slices with value None in the string.

property slicing: whether to slice maindims when loading arrays & during get_maindims_coords.

if False, self.slices will return an empty dict.

standardized_slices(): returns a copy of self.slices, but calling interprets_fractional_indexing on all slices,

using lengths from self.maindims_full_sizes.

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.)

title_with_slices(*, sep=', ', keep_None=False): return self.title with slicestr appended (after sep), if slicestr is not empty.

see self.slicestr() for more details.

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.