EppicSimInfoLoader

class PlasmaCalcs.hookups.eppic.eppic_sim_info.EppicSimInfoLoader

Bases: QuantityLoader

simulation info for EppicCalculator.

These are details about the simulation, not necessarily physics-based.

E.g., number of particles per simulation cell, number of MPI processors used, simulation dx.

(note that dx between cells in the output is actually dx_sim * nout_avg,

since nout_avg is used to average over a few cells in space before providing the output.)

Methods

__call__(var, *args[, name, item, verbose])	returns value of var from self.
attach_extra_coords(arr)	attach any self.extra_coords to array arr but only if it is an xarray.DataArray or xarray.Dataset
cls_help([qstr, only, tree, modules, ...])	prints str for help with quants.
cls_var_tree(var, *[, missing_ok])	return QuantTree of MatchedQuantity objects from matching var and all dependencies,
copy()	returns a deep copy of self.
direct_overrides_dynamic()	returns dict of {var: override} for all overrides of self which depend on behavior_attrs of self.
get__nit_since_prev_simple()	naive implementation of nit_since_prev.
get_behavior([keys])	return value of self.behavior.
get_cpu_per_each2ct()	conversion factors: cpu_per_each2ct * cpu_per_each == cpu_per_ct.
get_cpu_per_each2timestep()	conversion factors: cpu_per_each2timestep * cpu_per_each == cpu_per_timestep.
get_cpu_seconds_per_ct()	cpu seconds per cell per timestep, for each timer in timers.
get_cpu_seconds_per_each()	cpu seconds per (particle, per) cell, per timestep, as relevant to each timer in timers.
get_cpu_seconds_per_pct()	cpu seconds per particle, per cell, per timestep, for each timer in timers.
get_cpu_seconds_per_timestep()	cpu seconds per timestep, for each timer in timers.
get_ds_sim()	grid spacing (of simulation).
get_dt_sim()	time spacing (of simulation).
get_min_n_nodes_given_nsubdomains()	minimum number of nodes, given nsubdomains and tasks_per_node,
get_n_nodes()	number of nodes used to run this run.
get_n_processors()	number of MPI processors used to run this run.
get_ncells_sim()	number of gridcells from simulation (differs from output when nout_avg != 1).
get_ndim_space()	number of spatial dimensions in simulation.
get_nit_since_prev()	return number of timesteps since previous snapshot in self.
get_npcelld()	number of PIC particles per simulation cell.
get_npd()	number of PIC particles in each distribution.
get_npow2_processors()	largest power of 2 which evenly divides n_processors.
get_nptotcell()	number of particles per cell for all fluids combined, appropriately weighted considering subcycling.
get_nptotcelld()	number of PIC particles per simulation cell (total across all processors).
get_nptotd()	number of PIC particles (total across all processors).
get_nsubdomains()	number of subdomains.
get_set_or_cached(var)	returns var if found in self.setvars or self.cache, with compatible behavior_attrs.
get_slurm_options()	dataset of all slurm options from slurmfiles in self.dirname.
get_subcycle()	subcycling factor (for each fluid in self.fluid).
get_tasks_per_node()	number of processors per node.
get_time_requested()	time limit for this run.
get_time_requested_numeric(var, *[, _match])	time limit for this run, in hours, minutes, or seconds.
get_vars(vars, *args[, return_type, ...])	returns values of vars from self.
has_var(var)	return whether self can load var.
help([qstr, only, tree, modules, signature, ...])	prints str for help with quants.
help_call_options([search])	prints help for kw_call_options.
help_quants_str([qstr, only, tree, modules, ...])	returns str for help with quants.
help_str([qstr, only])	returns cls.help_quants_str(qstr=qstr, only=only, **kw).
kw_call_options(*[, sorted])	returns list of kwarg names which can be used to set attrs self during self.__call__.
load_direct(var, *args, **kw)	load var "directly", from some source which is not known by the main part of PlasmaCalcs.
load_fromfile(var, *args, **kw)	load var directly from a file.
maintaining_attrs(*attrs, **attrs_as_flags)	returns context manager which restores attrs of self to their original values, upon exit.
match_var(var, *[, check])	match var from cls.KNOWN_VARS or cls.KNOWN_PATTERNS, or raise FormulaMissingError.
match_var_loading_dims(var, **kw_loading_dims)	return dims for loading var across.
match_var_result_dims(var, **kw_result_dims)	return dims which result of cls(var) will vary across.
match_var_result_size(var, *[, maindims])	return size (number of elements) which self(var) will have.
match_var_tree([var])	return QuantTree of MatchedQuantity objects from matching var and all dependencies,
npd_for_fluid(fluid)	return the npd for this fluid.
quant_tree([var])	return QuantTree of MatchedQuantity objects from matching var and all dependencies,
set_var(var, value[, behavior_attrs, ...])	set var in self.
set_var_internal(var, value, behavior_attrs)	set var in self.
tree([var])	return QuantTree of MatchedQuantity objects from matching var and all dependencies,
unset_var(var[, behavior_attrs, missing_ok])	remove var from self.setvars (but only at values stored with relevant behavior).
unset_var_internal(var, behavior_attrs[, ...])	unset var from self.setvars.
using_at_call_depth(depth, **attrs_and_values)	context manager for setting attrs_and_values but only while call_depth == depth.
using_at_next_call_depth(**attrs_and_values)	context manager for setting attrs_and_values but only while call_depth == self.call_depth + 1
using_attrs([attrs_as_dict, _unset_sentinel])	returns context manager which sets attrs of obj upon entry; restores original values upon exit.
_apply_toplevel_scale_coords(arr)	apply self.toplevel_scale_coords to arr, if nonempty, else return arr unchanged.
_battrs_for_set_var_internal(behavior_attrs)	returns behavior_attrs which will be used by set_var_internal, given these inputs.
_battrs_for_unset_var_internal(behavior_attrs)	returns behavior_attrs which will be used by unset_var_internal, given these inputs.
_call_postprocess(result, *, var[, name, item])	postprocess result from self.__call__.
_call_postprocess_toplevel(result, *, var[, ...])	additional postprocessing for self.__call__ when call_depth=1.
_get_maybe_missing_var(var, *args[, ...])	return value of var, or None if FormulaMissingError and missing_vars 'ignore' or 'warn'.
_handle_typevar_nan(*[, errmsg])	crash with TypevarNanError if self.typevar_crash_if_nan, else return 'nan'.
_help_matches(qstr, k, v)	returns whether qstr matches k or v, and thus should be displayed during self.help(qstr).
_increment_call_depth()	context manager for incrementing call_depth.
_pop_kw_call_options(kw)	pop all self.kw_call_options() from kw, returning dict of popped options.
_provided_val(var[, _val, _known_vals])	returns the value of var, either from _known_vals or _val.

Attributes

KNOWN_PATTERNS
KNOWN_SETTERS
KNOWN_VARS
assign_behavior_attrs	whether to assign self.behavior values as attrs of result when calling self.
assign_behavior_attrs_max_call_depth	max call_depth at which to assign_behavior_attrs to result,
assign_behavior_attrs_skip_xr	whether to use include_xr=False if self.assign_behavior_attrs,
behavior	dict of {attr: self.attr} for attr in self.behavior_attrs.
behavior_attrs	list of attrs in self which control behavior of self.
call_depth	depth of the current call to self.
call_depth_manager	stores the value of call_depth, and helps to manage attrs dependent on call_depth value.
cls_behavior_attrs
direct_overrides	dict of {var: override} for all overrides of self which don't depend on behavior_attrs of self.
enable_fromfile	bool: whether self.load_fromfile is enabled during self.load_direct.
extra_coords	dict of {coord_name: coord_value} to attach to outputs of self(var).
get	alias to __call__
known_pattern
known_setter
known_var
maintaining	alias to maintaining_attrs
nondim_behavior_attrs	list of attrs in self which control behavior of self, but which are NOT in self.dimensions.
set	alias to set_var
setvar	alias to set_var
setvars	VarCache of vars set via self.set_var().
toplevel_scale_coords	dict of {coord_name: coord_scaling} to apply to top-level outputs of self(var).
typevar_crash_if_nan	bool.
unset	alias to unset_var
using	alias to using_attrs

__call__(var, *args, name=UNSET, item=False, verbose=UNSET, **kw)

returns value of var from self.

result is probably an xarray.DataArray, but not guaranteed.

var: str or iterable of strs.

Name of the var(s) to load. E.g. ‘n’ for number density, or [‘n’, ‘u’] for number density & velocity.

If multiple vars: returns an xarray.Dataset of all vars, via self.get_vars.

Determine how to load each var, as follows:

- (caching) if var in self.cache, with matching self.behavior_attrs, use value from cache.

[TODO] - caching not yet implemented. May allow for better efficiency.

- (setvars) if var in self.setvars, with matching self.behavior_attrs, use value from setvars.

[TODO] - improve set_var functionality.

set_var will allow user to apply PlasmaCalcs calculations to arbitrary values,

not just values from one of the hookups. Useful for testing & quick calculations.

- (KNOWN_VARS) if var in self.KNOWN_VARS,

use the corresponding function to get it.

- (KNOWN_PATTERNS) if var matches a pattern from self.KNOWN_PATTERNS,

use the corresponding function to get it.

- (direct) attempt to load var “directly”, via self.load_direct.

load_direct will almost always end up loading values directly from a file (e.g., “data”).

However, there is one more chance for it to get intercepted: via direct_overrides.

- (direct_overrides) if var in self.direct_overrides, attempts self.direct_overrides[var].

The idea is to use direct_overrides when requiring alternate instructions for

what would otherwise be a “base” var.

E.g., ‘n’ may be a “base” var, but if quasineutral then ‘ne’ is not saved in a file;

so, base_overrides[<key for density>] may tell how to get ‘ne’.

Note: for overrides which depend on current state, use direct_overrides_dynamic.

For overrides which are “always” implemented (not toggled by other things), use direct_overrides.

- (fromfile) load_direct uses self.load_fromfile whenever direct_overrides is not applicable.

Those are checked in the order listed.

If none of those work, raise FormulaMissingError.

name: UNSET, None, or str

try to set result.name = name.

UNSET –> use name = var.

item: bool

if True, convert result to single value (e.g., python float) via result.item().

This will cause crash if result is not a single value;

it will also cause all metadata stored in the result to be lost.

verbose: UNSET, bool, or int

set self.verbose during this call to self.

UNSET –> use self.verbose (unchanged)

kw may additionally contain any keys from self.kw_call_options().

if it does, pop those values, and temporarily set the corresponding attr.

E.g.: self(‘n’, units=’si’, fluid=1)

–> temporarily set units=’si’, fluid=1, while getting ‘n’.

See self.help_call_options() for more details.

[EFF] passes _match=re.fullmatch(pattern, var) to the getter function,

if the match is from KNOWN_PATTERNS (but not if it is from KNOWN_VARS).

_apply_toplevel_scale_coords(arr)

apply self.toplevel_scale_coords to arr, if nonempty, else return arr unchanged.

_battrs_for_set_var_internal(behavior_attrs, forall=[], *, ukey=None)

returns behavior_attrs which will be used by set_var_internal, given these inputs.

see help(self.set_var_internal) for details.

_battrs_for_unset_var_internal(behavior_attrs, forall=[], *, ukey=None)

returns behavior_attrs which will be used by unset_var_internal, given these inputs.

see help(self.unset_var_internal) for details.

_call_postprocess(result, *, var, name=UNSET, item=UNSET)

postprocess result from self.__call__. Called during self.__call__.

(self.call_depth inside here will tell depth of the current call; depth=1 for top level.)

result: any value, probably an xarray.DataArray

result from self.__call__, before postprocessing.

var, name, item: UNSET or value

passed directly from self.__call__.

The implementation here does the following (subclasses might override / add to this):

(1) if self.verbose >= 4, print a message about getting var.

(2) result = self.attach_extra_coords(result).

(3) if name was provided, set result.name = name, if possible.

(4) if self.assign_behavior_attrs at this call depth, do so now.

(5) if self.call_depth == 1, call self._call_postprocess_toplevel.

(6) if item, convert result to single value via result.item().

_call_postprocess_toplevel(result, *, var, name=UNSET, item=UNSET)

additional postprocessing for self.__call__ when call_depth=1.

called from self._call_postprocess, after doing other postprocessing, when call_depth=1.

result: any value, probably an xarray.DataArray

result from self.__call__, after other postprocessing (except item).

var, name, item: UNSET or value

passed directly from self.__call__.

Don’t need to handle these here because self._call_postprocess will handle it.

The implementation here does the following (subclasses might override / add to this):

(1) self._apply_toplevel_scale_coords (does nothing if self.toplevel_scale_coords is empty)

_get_maybe_missing_var(var, *args, missing_vars=UNSET, **kw)

return value of var, or None if FormulaMissingError and missing_vars ‘ignore’ or ‘warn’.

missing_vars: UNSET, ‘ignore’, ‘warn’, or ‘raise’

what to do if any var causes FormulaMissingError.

UNSET –> use self.missing_vars if it exists, else ‘raise’.

‘ignore’ –> return None.

‘warn’ –> return None, but also print a warning.

‘raise’ –> raise FormulaMissingError.

_handle_typevar_nan(*, errmsg='')

crash with TypevarNanError if self.typevar_crash_if_nan, else return ‘nan’.

if crashing, use error message:

errmsg + “nTo return ‘nan’ instead of crashing, set self.typevar_crash_if_nan=False.”

classmethod _help_matches(qstr, k, v)

returns whether qstr matches k or v, and thus should be displayed during self.help(qstr).

qstr: str

the str to match; from self.help(qstr)

k: varname

the varname to test for matches.

key from self.KNOWN_VARS.keys(), or key.str from self.KNOWN_PATTERNS.keys().

v: LoadableQuantity

the LoadableQuantity to test for matches.

value from self.KNOWN_VARS.values() or self.KNOWN_PATTERNS.values().

matches if any of these are true:

qstr == ‘’

qstr in k.split(‘_’) # size limitation and split(‘_’) because, e.g. during help(‘n’),

len(qstr)>=3 and qstr in k # want vars related to number density, not all vars with the letter ‘n’.

qstr in module.split(‘.’) (where, module == v.get_f_module(cls))

‘.’ in qstr and qstr in module

len(qstr)>=3 and qstr in value from module.split(‘.’)

len(qstr)>=3 and qstr in v.fname

re.fullmatch(k, qstr) # if k is a Pattern

otherwise, does not match.

_increment_call_depth()

context manager for incrementing call_depth.

use “with self._increment_call_depth():” inside of __call__, e.g.:

def __call__(self, *args, **kw):

with self._increment_call_depth():

# do stuff; possibly including calling self again.

Equivalent to self.call_depth_manager.increment()

_pop_kw_call_options(kw)

pop all self.kw_call_options() from kw, returning dict of popped options.

_provided_val(var, _val=None, _known_vals={})

returns the value of var, either from _known_vals or _val.

if _val provided, return it; if ‘_{var}’ in _known_vals, return it;

if both provided, crash with InputConflictError (unless they are the same object),

else, return None.

Can use this internally to avoid redundant recalculations. (See e.g. VectorArithmeticLoader)

property assign_behavior_attrs

whether to assign self.behavior values as attrs of result when calling self.

False –> don’t use self.behavior code architecture to assign attrs.

True –> equivalent to ‘nondefault’

‘nondefault’ –> self.behavior.assign_nondefault_attrs(result)

(for brevity, it does not assign behavior attrs with “default” value.)

‘all’ –> self.behavior.assign_attrs(result).

[EFF] only assigns attrs at call_depth >= self.assign_behavior_attrs_max_call_depth.

(default: only assigns attrs at call_depth=1, i.e. at top level.

property assign_behavior_attrs_max_call_depth

max call_depth at which to assign_behavior_attrs to result,

if self.assign_behavior_attrs indicates to assign behavior attrs.

default 1, i.e. only assign if at top level.

Use None to indicate “no max detph”.

property assign_behavior_attrs_skip_xr

whether to use include_xr=False if self.assign_behavior_attrs,

during self.behavior.assign_nondefault_attrs.

Use this if you want to assign behavior attrs EXCEPT array-valued behavior attrs.

attach_extra_coords(arr)

attach any self.extra_coords to array arr but only if it is an xarray.DataArray or xarray.Dataset

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.

property call_depth

depth of the current call to self. depth = number of calls to self from within self.

E.g., call_depth while calculating gyrofrequency:

# call_depth == 0, for any code run here (outside any call to self).

self(‘gyrof’)

# call_depth == 1, for any code run here (inside ‘gyrof’ call but not inside deeper calls).

q = self(‘q’)

# call_depth == 2, for code inside ‘q’ call.

mod_B = self(‘mod_B’)

# call_depth == 2, for code inside ‘mod_B’ call.

self(‘B’)

# call_depth == 3, for code inside ‘B’ call.

m = self(‘m’)

# call_depth == 2, for code inside ‘m’ call.

result = q * mod_B / m

Cannot be set directly; can only be manipulated via self.call_depth_manager.

property call_depth_manager

stores the value of call_depth, and helps to manage attrs dependent on call_depth value.

classmethod cls_help(qstr=None, only=None, *, tree=None, modules=False, signature=False, doc=True, dense=False, print=True, **kw)

prints str for help with quants. Fails for any quants which depend on present values of a cls instance.

qstr: None or str

None –> tells info about this class & how to use this function.

in particular, tells that quants are stored cls.KNOWN_VARS and cls.KNOWN_PATTERNS,

and describes behavior of calling help with a string.

str –> return str for help with all quants related to str.

use empty str to get help for all quants.

only: None or str

If provided, only get help for a subset of relevant quantities.

None –> get help with all quantities related to qstr.

‘VARS’ –> only get help with KNOWN_VARS.

‘PATTERNS’ –> only get help with KNOWN_PATTERNS.

‘TREE’ –> only get help with quantities in cls.cls_var_tree(str).

‘EXACT’ –> only get help for the KNOWN_VAR exactly matching qstr.

if provided when qstr is None, treat qstr as ‘’ instead.

tree: None or bool

How much help to give for quantities in cls.cls_var_tree(qstr).

False –> don’t even check cls.cls_var_tree(qstr).

True –> help for all quantities in cls.cls_var_tree.

None –> help for quantities in cls.cls_var_tree(qstr).flat_branches_until_vars()

i.e. patterns & vars in tree but ignore any nodes with LoadableVar ancestors.

e.g. qstr=’mean_mod_beta’ –> help with ‘mean_(.+)’, ‘mod_(.+)’, and ‘beta’,

but no help with dependencies of ‘beta’ (‘q’, ‘mod_B’, ‘m’).

modules: bool

Whether to include modules in result.

If True, result will be grouped into sections with modules written at top.

signature: signature: bool

whether to include line with signature in help string.

e.g. “help_str(f, *, module=True, signature=True, indent=None)”

doc: doc: bool

whether to include lines with docstring in help string.

e.g. “return str for help(f).” … and all the other docs in here.

dense: bool

Whether to reduce whitespace in result.

E.g. True –> no newlines between functions. False –> one newline between functions.

print: bool

whether to print the result. If False, return the result instead of printing.

classmethod cls_var_tree(var, *, missing_ok=False)

return QuantTree of MatchedQuantity objects from matching var and all dependencies,

using self.KNOWN_VARS and self.KNOWN_PATTERNS when searching for matches.

missing_ok: bool

whether to be lenient sometimes when missing details that would allow to fully determine deps.

see help(MatchedQuantity.dep_vars) for more details.

copy()

returns a deep copy of self.

[TODO] implement something less hacky than using the pickle module?

property direct_overrides

dict of {var: override} for all overrides of self which don’t depend on behavior_attrs of self.

For example, if user wants to set an override (or if setvars sets an override?), it will be here.

See also: self.direct_overrides_dynamic().

direct_overrides_dynamic()

returns dict of {var: override} for all overrides of self which depend on behavior_attrs of self.

property enable_fromfile

bool: whether self.load_fromfile is enabled during self.load_direct.

If False, raise QuantCalcError if load_direct can’t get value without load_fromfile().

property extra_coords

dict of {coord_name: coord_value} to attach to outputs of self(var).

Useful if planning to join the output of self(var) with output from a different QuantityLoader.

E.g. self.extra_coords={‘run’: ‘run 0’} and other.extra_coords={‘run’: ‘run 1’},

then xr.concat([self(‘n’), other(‘n’)], ‘run’) gives ‘n’ from self AND other.

(this is nice if self and other have same values for dims. Otherwise, might struggle.)

property get

alias to __call__

get__nit_since_prev_simple()

naive implementation of nit_since_prev. Makes fewer assumptions, but can be slow.

(e.g., took 0.8 seconds for 800 snaps. Compare to 0.02 seconds via nit_since_prev.)

nit_since_prev should dispatch to this method if any of the assumptions fail.

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_cpu_per_each2ct()

conversion factors: cpu_per_each2ct * cpu_per_each == cpu_per_ct.

Result is a dataset with timers as data vars.

These convert from (per particle per cell per timestep) to (per cell per timestep):

‘vadv time’, ‘xadv time’, ‘charge’, ‘output’.

(I.e., for these timers: result == nptotcell)

These do not convert at all:

‘collect’, ‘efield’

(I.e., for these timers: result == 1)

Other timers’ conversions are not included.

get_cpu_per_each2timestep()

conversion factors: cpu_per_each2timestep * cpu_per_each == cpu_per_timestep.

Result is a dataset with timers as data vars.

These convert from (per particle per cell per timestep) to (per timestep):

‘vadv time’, ‘xadv time’, ‘charge’, ‘output’.

(I.e., for these timers: result == nptotcell * ncells_sim)

These convert from (per cell per timestep) to (per timestep):

‘collect’, ‘efield’

(I.e. for these timers: result == ncells_sim)

Other timers’ conversions are not included.

get_cpu_seconds_per_ct()

cpu seconds per cell per timestep, for each timer in timers.

n_processors * timer2seconds * timers / (ncells_sim * nit_since_prev)

see also: cpu_seconds_per_timestep, cpu_seconds_per_pct

get_cpu_seconds_per_each()

cpu seconds per (particle, per) cell, per timestep, as relevant to each timer in timers.

cpu_seconds_per_pct for definitely scaling with nptotcell:

- vadv time

- xadv time

- charge

cpu_seconds_per_pct for timers probably scaling with nptotcell:

- output

cpu_seconds_per_ct for timers definitely NOT scaling with nptotcell:

- collect

- efield

EXCLUDE timers combining times above:

- Wall Clock

- Sys Clock

EXCLUDE timers not handled here:

- fluid

get_cpu_seconds_per_pct()

cpu seconds per particle, per cell, per timestep, for each timer in timers.

n_processors * timer2seconds * timers / (nptotcell * ncells_sim * nit_since_prev)

(note, nptotcell accounts for subcycling. See self.help(‘nptotcell’) for details.)

see also: cpu_seconds_per_timestep, cpu_seconds_per_ct

get_cpu_seconds_per_timestep()

cpu seconds per timestep, for each timer in timers.

n_processors * timer2seconds * timers / nit_since_prev

see also: cpu_seconds_per_ct, cpu_seconds_per_pct

get_ds_sim()

grid spacing (of simulation). vector(ds), e.g. [dx, dy, dz]. Depends on self.component.

ds_sim = (dx, dy, dz) from input deck (not divided by nout_avg)

get_dt_sim()

time spacing (of simulation). Time between iterations (not between snapshots)

get_min_n_nodes_given_nsubdomains()

minimum number of nodes, given nsubdomains and tasks_per_node,

to ensure n_processors % nsubdomains == 0,

where n_processors = n_nodes * tasks_per_node.

Equivalent: nsubdomains / gcd(nsubdomains, tasks_per_node)

get_n_nodes()

number of nodes used to run this run. From slurm file.

n_processors = n_nodes * tasks_per_node.

get_n_processors()

number of MPI processors used to run this run.

From logfile if possible, else from slurm files.

n_processors = n_nodes * tasks_per_node.

get_ncells_sim()

number of gridcells from simulation (differs from output when nout_avg != 1).

Scalar. E.g. Nx*Ny*Nz if 3D, Nx*Ny if 2D.

get_ndim_space()

number of spatial dimensions in simulation. 2 or 3.

get_nit_since_prev()

return number of timesteps since previous snapshot in self.

when determining previous snapshot, ignore any where snap.file_snap(self) is MISSING_SNAP.

return inf when no previous snap, e.g. at snap=0.

get_npcelld()

number of PIC particles per simulation cell.

get_npd()

number of PIC particles in each distribution.

This is equivalent to fluid[‘npd’] when it is provided,

otherwise determined by the appropriate alternative (npcelld, nptotd, or nptotcelld).

get_npow2_processors()

largest power of 2 which evenly divides n_processors.

E.g. if n_processors = 56 * 8 == 7 * 4 * 8 == 7 * 2^5, result would be 2^5.

get_nptotcell()

number of particles per cell for all fluids combined, appropriately weighted considering subcycling.

Equivalent: sum_fluids_(nptotcelld/subcycle)

get_nptotcelld()

number of PIC particles per simulation cell (total across all processors).

get_nptotd()

number of PIC particles (total across all processors).

get_nsubdomains()

number of subdomains. nsubdomains from the input deck.

Note: eppic runs require n_processors % nsubdomains == 0.

get_set_or_cached(var)

returns var if found in self.setvars or self.cache, with compatible behavior_attrs.

otherwise, raise CacheNotApplicableError.

if var is found in self.setvars and has relevant, but not matching behavior_attrs,

self.load_across_dims will be used to load the value.

get_slurm_options()

dataset of all slurm options from slurmfiles in self.dirname.

crash if multiple slurmfiles in self.dirname with conflicting options.

get_subcycle()

subcycling factor (for each fluid in self.fluid).

(If subcycle not provided for a distribution, assume it implies subcycle=1).

get_tasks_per_node()

number of processors per node. From slurm file.

n_processors = n_nodes * tasks_per_node.

get_time_requested()

time limit for this run. From slurm file. Result is a string, like hh:mm:ss.

For numeric results, see self(‘hours_requested’), minutes_requested, or seconds_requested.

get_time_requested_numeric(var, *, _match=None)

time limit for this run, in hours, minutes, or seconds. From slurm file.

Result is the total amount, e.g. ‘01:15:00’ –> mins_requested=75.

get_vars(vars, *args, return_type='dataset', missing_vars=UNSET, **kw)

returns values of vars from self.

result is probably an xarray.Dataset, but not guaranteed; also depends on return_type.

Equivalent to self(vars, *args, return_type=’dataset’, **kw).

(Actually, self(vars, …) will call self.get_vars(vars, …).)

vars: iterable of strs

Names of the vars to load. [‘n’, ‘u’] for number density & velocity.

if any of these vars returns a return_type object, expand its keys,

e.g. if ‘myDSvar’ returns dataset with ‘myvar1’, ‘myvar2’,

then [‘n’, ‘myDSvar’] gives dataset with ‘n’, ‘myvar1’, ‘myvar2’.

return_type: ‘dataset’ or ‘dict’

if ‘dataset’, return result as xarray.Dataset.

the data_var names will be the same as the var names.

if ‘dict’, return result as dict of {var: value}.

missing_vars: UNSET, ‘ignore’, ‘warn’, or ‘raise’

what to do if any vars cause FormulaMissingError.

UNSET –> use self.missing_vars if it exists, else ‘raise’.

‘ignore’ –> ignore missing vars, and don’t include them in the result.

‘warn’ –> ignore missing vars, but print a warning.

‘raise’ –> raise FormulaMissingError if any vars are missing.

additional args & kwargs are passed to self(…).

has_var(var)

return whether self can load var. True if self.match_var(var) is found, else False.

Subclasses might override, to include checks for whether var can be loaded from data.

[TODO] also check if var in self.cache or self.setvars.

help(qstr=None, only=None, *, tree=None, modules=False, signature=False, doc=True, dense=False, print=True)

prints str for help with quants.

qstr: None or str

None –> tells info about this class & how to use this function.

in particular, tells that quants are stored cls.KNOWN_VARS and cls.KNOWN_PATTERNS,

and describes behavior of calling help with a string.

str –> return str for help with all quants related to str.

use empty str to get help for all quants.

only: None or str

If provided, only get help for a subset of relevant quantities.

None –> get help with all quantities related to qstr.

‘VARS’ –> only get help with KNOWN_VARS.

‘PATTERNS’ –> only get help with KNOWN_PATTERNS.

‘TREE’ –> only get help with quantities in cls.cls_var_tree(str).

‘EXACT’ –> only get help for the KNOWN_VAR exactly matching qstr.

if provided when qstr is None, treat qstr as ‘’ instead.

tree: None or bool

How much help to give for quantities in cls.cls_var_tree(qstr).

False –> don’t even check cls.cls_var_tree(qstr).

True –> help for all quantities in cls.cls_var_tree.

None –> help for quantities in cls.cls_var_tree(qstr).flat_branches_until_vars()

i.e. patterns & vars in tree but ignore any nodes with LoadableVar ancestors.

e.g. qstr=’mean_mod_beta’ –> help with ‘mean_(.+)’, ‘mod_(.+)’, and ‘beta’,

but no help with dependencies of ‘beta’ (‘q’, ‘mod_B’, ‘m’).

modules: bool

Whether to include modules in result.

If True, result will be grouped into sections with modules written at top.

signature: signature: bool

whether to include line with signature in help string.

e.g. “help_str(f, *, module=True, signature=True, indent=None)”

doc: doc: bool

whether to include lines with docstring in help string.

e.g. “return str for help(f).” … and all the other docs in here.

dense: bool

Whether to reduce whitespace in result.

E.g. True –> no newlines between functions. False –> one newline between functions.

help_call_options(search=None)

prints help for kw_call_options.

if search is provided, only print help for keys containing search.

classmethod help_quants_str(qstr=None, only=None, *, tree=None, modules=True, signature=False, doc=True, dense=False, _instance=None)

returns str for help with quants.

qstr: None or str

None –> tells info about this class & how to use this function.

in particular, tells that quants are stored cls.KNOWN_VARS and cls.KNOWN_PATTERNS,

and describes behavior of calling help with a string.

str –> return str for help with all quants related to str.

use empty str to get help for all quants.

only: None or str

If provided, only get help for a subset of relevant quantities.

None –> get help with all quantities related to qstr.

‘VARS’ –> only get help with KNOWN_VARS.

‘PATTERNS’ –> only get help with KNOWN_PATTERNS.

‘TREE’ –> only get help with quantities in cls.cls_var_tree(str).

‘EXACT’ –> only get help for the KNOWN_VAR exactly matching qstr.

if provided when qstr is None, treat qstr as ‘’ instead.

tree: None or bool

How much help to give for quantities in cls.cls_var_tree(qstr).

False –> don’t even check cls.cls_var_tree(qstr).

True –> help for all quantities in cls.cls_var_tree.

None –> help for quantities in cls.cls_var_tree(qstr).flat_branches_until_vars()

i.e. patterns & vars in tree but ignore any nodes with LoadableVar ancestors.

e.g. qstr=’mean_mod_beta’ –> help with ‘mean_(.+)’, ‘mod_(.+)’, and ‘beta’,

but no help with dependencies of ‘beta’ (‘q’, ‘mod_B’, ‘m’).

modules: bool

Whether to include modules in result.

If True, result will be grouped into sections with modules written at top.

signature: signature: bool

whether to include line with signature in help string.

e.g. “help_str(f, *, module=True, signature=True, indent=None)”

doc: doc: bool

whether to include lines with docstring in help string.

e.g. “return str for help(f).” … and all the other docs in here.

dense: bool

Whether to reduce whitespace in result.

E.g. True –> no newlines between functions. False –> one newline between functions.

_instance: None or QuantityLoader instance

if provided, use _instance.match_var_tree() instead of cls.cls_var_tree().

classmethod help_str(qstr=None, only=None, **kw)

returns cls.help_quants_str(qstr=qstr, only=only, **kw).

cls.help() calls help_str.

subclasses might overwrite help_str, but probably won’t touch help_quants_str.

kw_call_options(*, sorted=True)

returns list of kwarg names which can be used to set attrs self during self.__call__.

(see self.__call__ for more details).

Here, returns list(self.behavior_attrs) + list(self._extra_kw_for_quantity_loader_call)

load_direct(var, *args, **kw)

load var “directly”, from some source which is not known by the main part of PlasmaCalcs.

Attempt the following, returning the first successful attempt:

- return self.direct_overrides[var](self, *args, **kw).

- return self.direct_overrides_dynamic()[var](self, *args, **kw).

- use self.load_fromfile.

return the result (probably a numpy array, but not guaranteed).

Examples:

load Bx directly from a file

load n for H+, using a different module which somehow gives nH+

(PlasmaCalcs doesn’t need to know where the value came from.)

if used an override, instead of loading from file,

set self._load_direct_used_override = var.

Otherwise, set it to None.

This might be used, e.g., to determine if the output came directly from a file or not.

load_fromfile(var, *args, **kw)

load var directly from a file. Other methods should usually use load_direct, instead.

the implementation here just raises LoadingNotImplementedError;

subclasses should implement this method in order to load any values from files.

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.

classmethod match_var(var, *, check=['KNOWN_VARS', 'KNOWN_PATTERNS'])

match var from cls.KNOWN_VARS or cls.KNOWN_PATTERNS, or raise FormulaMissingError.

returns result=MatchedQuantity(var, loadable, _match=_match) where:

loadable is the LoadableQuantity associated with this var,

_match is:

None, if var in cls.KNOWN_VARS;

re.fullmatch(pattern, var), if var matches any pattern in cls.KNOWN_PATTERNS.

if var matches multiple patterns, only the first matching pattern is used.

Uses MatchedVar if match from KNOWN_VARS, MatchedPattern if from KNOWN_PATTERNS.

(note that both MatchedVar and MatchedPattern subclass MatchedQuantity.)

check: str or list of str from [‘KNOWN_VARS’, ‘KNOWN_PATTERNS’]

where to check for matches. Default is to check KNOWN_VARS and KNOWN_PATTERNS.

E.g. to only check KNOWN_PATTERNS, use check=[‘KNOWN_PATTERNS’].

loadable and _match can be retrieved via result.loadable and result._match.

match_var_loading_dims(var, **kw_loading_dims)

return dims for loading var across.

Result will probably vary across these dims (but not guaranteed, if any dependency uses reduces_dims.)

These are all Dimension dims, not maindims. (E.g. ‘fluid’ and ‘snap’, but not ‘x’, ‘y’, ‘z’).

Equivalent: self.match_var_tree(var).loading_dims(**kw_loading_dims)

match_var_result_dims(var, **kw_result_dims)

return dims which result of cls(var) will vary across.

These are all Dimension dims, not maindims. (E.g. ‘fluid’ and ‘snap’, but not ‘x’, ‘y’, ‘z’).

Equivalent: cls.match_var_tree(var).result_dims(**kw_result_dims)

match_var_result_size(var, *, maindims=True, **kw_result_dims)

return size (number of elements) which self(var) will have.

(Efficient; doesn’t actually get self(var).)

Depends on current values of relevant dims. (E.g., self.fluid, not self.fluids)

maindims: bool

if True, include maindims_shape when calculating size.

match_var_tree(var=UNSET, **kw_quant_tree_from_quantity_loader)

return QuantTree of MatchedQuantity objects from matching var and all dependencies,

using self.KNOWN_VARS and self.KNOWN_PATTERNS when searching for matches.

var must be provided; var=UNSET will raise an error (helpful if tried calling this as a classmethod).

See also: type(self).cls_var_tree, for the classmethod version of this function.

Most of the time it is possible to get tree without any details from self,

but sometimes not. e.g. when getting collision frequencies, self.fluid affects deps.

additional kwargs will be passed to QuantTree.from_quantity_loader(…),

which passes kwargs from self.kw_call_options() into self.using(**kw) while getting deps.

matched_pattern_cls

alias of MatchedPattern

matched_var_cls

alias of MatchedVar

property nondim_behavior_attrs

list of attrs in self which control behavior of self, but which are NOT in self.dimensions.

npd_for_fluid(fluid)

return the npd for this fluid.

This is equivalent to fluid[‘npd’] when it is provided,

otherwise determined by the appropriate alternative (npcelld, nptotd, or nptotcelld).

This method is implemented for the calculator rather than the fluid,

because fluid doesn’t know the possibly-required global values (ncells and/or n_processors).

result will always be converted to int, since npd is an integer.

quant_tree(var=UNSET, **kw_quant_tree_from_quantity_loader)

return QuantTree of MatchedQuantity objects from matching var and all dependencies,

using self.KNOWN_VARS and self.KNOWN_PATTERNS when searching for matches.

var must be provided; var=UNSET will raise an error (helpful if tried calling this as a classmethod).

See also: type(self).cls_var_tree, for the classmethod version of this function.

Most of the time it is possible to get tree without any details from self,

but sometimes not. e.g. when getting collision frequencies, self.fluid affects deps.

additional kwargs will be passed to QuantTree.from_quantity_loader(…),

which passes kwargs from self.kw_call_options() into self.using(**kw) while getting deps.

quant_tree_cls

alias of QuantTree

property set

alias to set_var

set_var(var, value, behavior_attrs=None, forall=[], *, ukey=None, forced=False, **kw_using)

set var in self. When later doing self(var) to get var, return the set value,

but only if self.behavior is compatible with the relevant parts of self.behavior when var was set.

This function will use, if it exists:

self.KNOWN_SETTERS[var](self, value, behavior_attrs, forall=forall)

Otherwise, calls:

self.set_var_internal(var, value, self.behavior_attrs, forall=forall)

var: str

the var to set in self.

value: number, xarray, iterable or 1D array, array with shape matching self.maindims_shape.

the value to set var to.

number –> set var to this number.

xarray –> set var to this xarray.

[TODO](not yet implemented) iterable or 1D array –> set var to these values along dim=’testing’.

[TODO](not yet implemented) array with shape matching self.maindims_shape –> set var to this array.

behavior_attrs: None or list

tells which attrs from self control behavior of the set var.

The set var will only be retrieved when behavior_attrs of self are compatible.

E.g. set_var(‘n’, [‘fluid’, ‘snap’]) –> saves ‘n’ in cache with current fluid & snap.

Will only load ‘n’ if self.fluid and self.snap == cached fluid and snap for ‘n’.

if var in self.KNOWN_SETTERS, cannot provide behavior_attrs here.

else, use self.behavior_attrs if None.

forall: list of strings

if provided, tells which attrs of self do NOT control the behavior of the set var.

E.g. forall=[‘snap’] –> ‘snap’ will NOT be included in behavior_attrs.

(anything in behavior_attrs AND forall will be removed from the final behavior_attrs)

ukey: None or str

if provided, tells string to give to UnitsManager when converting value’s units.

When ukey is known, setting value in any unit system will enable to read it in all unit systems.

E.g. set_var(‘n’, 1e10, …, ukey=’n’, units=’si’)

–> self(‘n’, units=’raw’) == self(‘n’, units=’si’) * self.u(‘u’, ‘raw’, convert_from=’si’)

if not provided, value will be associated with current unit system;

attempted to read value in any other unit system will not used the cached value set here.

E.g. set_var(‘u’, 1e10, …, units=’si’) # ukey not provided

–> self(‘u’, units=’raw’) –> uses self’s other logic for getting ‘u’, not from setvars.

note: if provided, ‘units’ will be added to behavior_attrs if not already in there.

forced: bool, default True

handles the case where self.KNOWN_SETTERS[var] doesn’t exist. In that case…

True –> set var in self, anyway.

False –> crash; raise FormulaMissingError

additional kwargs, if provided, go to self.using(**kw) during the operation.

returns list of set quantities.

set_var_internal(var, value, behavior_attrs, forall=[], *, ukey=None)

set var in self. KNOWN_SETTERS functions may wish to use this method.

(KNOWN_SETTERS functions should NOT use self.set_var, to avoid recursion issue.)

This function has the internal logic for self.set_var;

set_var calls set_var_internal when self.KNOWN_SETTERS[var] not provided.

var: str

the var to set in self.

value: number, xarray, iterable or 1D array, array with shape matching self.maindims_shape.

the value to set var to. See help(self.set_var) for more info.

behavior_attrs: list of strings

the behavior attrs relevant to setting this var;

getting var only gives value when current behavior attrs values are compatible with the cached ones.

forall: list of strings

if provided, tells which behavior attrs do NOT control the behavior of the set var.

e.g. behavior_attrs=[‘snap’, ‘fluid’], forall=[‘snap’] –> use [‘fluid’], only.

ukey: None or str

if provided, tells string to give to UnitsManager when converting value’s units;

when ukey is provided, can retrieve value in any unit system (probably ‘si’ or ‘raw’).

when ukey not provided, if ‘units’ in used behavior attrs, can only retrieve value in that unit system.

property setvar

alias to set_var

property setvars

VarCache of vars set via self.set_var(). Returns these values when appropriate,

i.e. whenever self.behavior is compatible with the behavior in the cache.

To empty the cache, use self.setvars.clear() to empty the cache.

property toplevel_scale_coords

dict of {coord_name: coord_scaling} to apply to top-level outputs of self(var).

(Never applies to internal calls of self(var), only applies at self.call_depth==1.)

Useful if making plots and want to scale coords by some factor.

E.g., self.toplevel_scale_coords = {‘t’: 1000} to convert s to ms.

CAUTION: coord units labels will remain unaffected.

tree(var=UNSET, **kw_quant_tree_from_quantity_loader)

return QuantTree of MatchedQuantity objects from matching var and all dependencies,

using self.KNOWN_VARS and self.KNOWN_PATTERNS when searching for matches.

var must be provided; var=UNSET will raise an error (helpful if tried calling this as a classmethod).

See also: type(self).cls_var_tree, for the classmethod version of this function.

Most of the time it is possible to get tree without any details from self,

but sometimes not. e.g. when getting collision frequencies, self.fluid affects deps.

additional kwargs will be passed to QuantTree.from_quantity_loader(…),

which passes kwargs from self.kw_call_options() into self.using(**kw) while getting deps.

property typevar_crash_if_nan

bool. whether to crash methods if typevar output would be ‘nan’.

False –> return NaN when typevar gives ‘nan’, instead of crashing.

“typevar” here refers to any var used for checking which formula to use, from various options,

e.g. ‘ntype’ in MhdMultifluidLoader or ‘ionfrac_type’ in MhdIonizationLoader.

The relevant methods can check if self.typevar_crash_if_nan before returning a ‘nan’ result.

property unset

alias to unset_var

unset_var(var, behavior_attrs=[], *, missing_ok=True, **kw_using)

remove var from self.setvars (but only at values stored with relevant behavior).

[TODO] define rules for which vars unset which other vars…

e.g. for eppic right now, set_var(‘n’) sets ‘den’ but not ‘n’;

unset_var(‘n’) unsets nothing… but should probably alias to unset_var(‘den’).

behavior_attrs: list of strings

only remove cached values where self.behavior matches cached behavior for these attrs.

if empty, remove all cached values for var, regardless of associated behavior.

missing_ok: bool

whether it is okay for there to be zero matching cached values for var.

raise CacheNotApplicableError if missing_ok=False when there are no matching cached values.

additional kwargs, if provided, go to self.using(**kw) during the operation.

return list of CachedQuantity objects which were removed from self.setvars.

unset_var_internal(var, behavior_attrs, forall=[], *, ukey=None, missing_ok=True)

unset var from self.setvars.

KNOWN_SETTERS functions may wish to use this method, to unset dependent values.

E.g. if u depends on n, and n is changed, may wish to unset the value of u.

behavior_attrs: list of strings

the behavior attrs relevant to setting this var.

forall: list of strings

if provided, tells which behavior attrs to ignore when unsetting the var.

ukey: None or string

if provided, ignore ‘units’ behavior attr when unsetting the var

(due to assuming that ukey was provided when setting the var,

hence that the set var could be retrieved in any units system)

missing_ok: bool

whether it is okay for there to be zero matching cached values for var.

raise CacheNotApplicableError if missing_ok=False when there are no matching cached values.

return list of CachedQuantity objects which were removed from self.setvars.

property using

alias to using_attrs

using_at_call_depth(depth, **attrs_and_values)

context manager for setting attrs_and_values but only while call_depth == depth.

E.g.:

with self.using_at_call_depth(3, verbose=3):

self(‘sgyrof’)

# while self.call_depth == 3 inside of this ‘with’ block, uses self.verbose=3.

# but everywhere else, uses original value of verbose.

# assuming originally verbose=False (or unset), this example will print:

| | (call_depth=2) get var=’q’

| | (call_depth=2) get var=’mod_B’

| | (call_depth=2) get var=’m’

# compare this to simply using self.verbose=3, which would print:

| (call_depth=1) get var=’sgyrof’

| | (call_depth=2) get var=’q’

| | (call_depth=2) get var=’mod_B’

| | | (call_depth=3) get var=’B_dot_B’

| | | | (call_depth=4) get var=’B_xyz’

| | | | | (call_depth=5) get var=’B’

| | (call_depth=2) get var=’m’

Equivalent to self.call_depth_manager.using_obj_attrs_at(depth, **attrs_and_values)

using_at_next_call_depth(**attrs_and_values)

context manager for setting attrs_and_values but only while call_depth == self.call_depth + 1

Equivalent to self.using_at_call_depth(self.call_depth + 1, **attrs_and_values).

(Also equivalent to self.call_depth_manager.using_obj_attrs_at_next(**attrs_and_values).)

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.