neurots.generate.algorithms.common¶
Functionality used by multiple algorithms.
Functions
|
Check bif/term. |
|
Generate section data dictionary from arguments. |
Classes
|
Class to define the data for stop criteria based on the TMD method. |
- class neurots.generate.algorithms.common.TMDStop(bif_id, bif, term_id, term, ref)¶
Bases:
object
Class to define the data for stop criteria based on the TMD method.
- Parameters:
- child_length()¶
Return the child length.
Return the absolute difference between bifurcation and termination, which defines the length of the bar.
- expected_bifurcation_length()¶
Compute an estimate for the length of the branch if a bifurcation occurs.
That will happen at distance
bif
and therefore the expected length is:(bifurcation - current length)
. Ifbifurcation
isinf
, the expected length is zero.
- expected_maximum_length()¶
Return the expected length of the current section.
That is computed as the difference between reference value and the expected bifurcation value. If
bifurcation < termination
, then the reference value minus the termination will be computed instead. In real morphologies termination will be larger than bifurcation, unlessbifurcation
is set toinf
. So the expected length will be computed based on term only if the section will terminate before it bifurcates.
- expected_termination_length()¶
Compute an estimate for the length of the branch if a termination occurs.
That will happen at distance
term
and therefore the expected length is:(termination - current length)
. Iftermination
isinf
, the expected length is zero.
- printme()¶
Print all features.
- update_bif(bif_id, bif)¶
Set new values to bifurcation.
- update_term(term_id, term)¶
Set new values to termination.
- neurots.generate.algorithms.common.checks_bif_term(ref, bif, term, target_length)¶
Check bif/term.
- Parameters:
- Returns:
True
if:Ref < Bif < Term
unless Bif is infinite, thenRef < Term
.target_length >= (Bif - Ref)
.target_length >= (Term - Ref)
.
Otherwise returns
False
.- Return type:
- neurots.generate.algorithms.common.section_data(direction, first_point, stop_criteria, process_type)¶
Generate section data dictionary from arguments.