Module | Source File | Description |
---|---|---|
datastructs_hash_mod | hash.f90 | |
datastructs_histograms_mod | histograms.f90 | Module for weighted histograms with statistical operations. |
datastructs_kinds_mod | kinds.f90 | Module providing kind parameters and utility functions for numerical types. |
datastructs_lists_dynamical_list_mod | dynamical_list.f90 | This module implements a dynamical list, in which elements can be added or removed dynamically. It assumes that there is a maximum size, and elements can be added or removed up to that size. Example: |
datastructs_lists_fixed_list_mod | fixed_list.f90 | This module implements a fixed list, that has a fixed size and cannot be resized. It also has pointers, allowing to build a linked list structure. Example: |
datastructs_lists_mod | lists.f90 | General module for lists and related operations It imports the necessary modules for list and heap implementations |
datastructs_logger_mod | logger.f90 | Logging module for managing verbosity levels and output destinations. This module provides a simple logging system with support for different log levels (ERROR, WARNING, INFO, DEBUG), configurable verbosity, and output units. |
datastructs_maxheap_mod | maxheap.f90 | This module implements a maxheap structure, keeping track of the maximum value and its index. Example: |
datastructs_measures_mod | measures.f90 | This module implements a time step controller and an object to manage statistical measures |
datastructs_mod | datastructs_mod.f90 | Hash functions |
datastructs_samplers_base_mod | base.f90 | This module implements the basic structure for weighted samplers |
datastructs_samplers_btree_mod | btree.f90 | This modules implements a binary tree sampler It provides a way to sample elements based on their weights using a binary tree structure. |
datastructs_samplers_mod | samplers.f90 | Module to select an weighted sampler
The idea is that it contains the list of indexes up to a size n,
and each index has an weight
The sampling is done proportionally to the weights.
It depends on the |
datastructs_samplers_rejection_maxheap_composition_mod | rejection_maxheap_composition.f90 | This module implements a rejection composition sampler It is based on "St-Onge, G., Young, J. G., Hébert-Dufresne, L., & Dubé, L. J. (2019). Efficient sampling of spreading processes on complex networks using a composition and rejection algorithm. Computer physics communications, 240, 30-37" https://doi.org/10.1016/j.cpc.2019.02.008 |
datastructs_samplers_rejection_maxheap_mod | rejection_maxheap.f90 | |
datastructs_samplers_rejection_maxheap_two_classes_mod | rejection_maxheap_two_classes.f90 | This module implements rejection sampling with two classes We split the weights into two classes based on a threshold |
datastructs_samplers_rejection_mod | rejection.f90 | This module implements a rejection sampling |
datastructs_samplers_rejection_two_classes_mod | rejection_two_classes.f90 | This module implements rejection sampling with two classes We split the weights into two classes based on a threshold |