Back-jump probability and survival function #184
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* Finish the docstring. * Minor code refactoring. * Format code, comments and docstrings.
Removed calculations: * `prob_same_sym`: Probability that a compound is at time t0-dt in the same state as at time t0+dt. Added calculation: * `prob_b_as_b_con`: Probability that a compound is from time t0-dt until t0 *continuously* in the same state. * `prob_b_as_a_con`: Probability that a compound is at time t0-dt in the same state as directly after the state transition under the condition that it has *continuously* been in the same state as directly before the state transition from time t0-dt until t0. * `prob_a_as_a_con`: Probability that a compound is from time t0 until t0+dt *continuously* in the same state * `prob_a_as_b_con`: Probability that a compound is at time t0+dt in the same state as directly before the state transition under the condition that it has *continuously* been in the same state as directly after the state transition during the time t0+dt * `prob_back`: Probability that a compound returns at time t0+dt back to the same state as directly before the state transition * `prob_back_con`: Probability that a compound returns at time t0+dt back to the same state as directly before the state transition under the condition that it has *continuously* been in the same state as directly after the state transition from time t0 until t0+dt
Create a new function `mdtools.dtrj.back_jump_prob` that takes a discrete trajectory and calculates the probability to return back to the initial state at time t0+dt, given that a state transition has occurred at time t0.
Create a new function `mdtools.dtrj.back_jump_prob_discrete` that takes a discrete trajectory and calculates the probability to return back to the initial state at time t0+dt, given that a state transition has occurred at time t0 and given that the compound was in a specific state of another discrete trajectory at time t0.
Create a new script `scripts/discretization/back_jump_prob.py` that calculates the probability to return back to the initial state after a state transition as function of the time that has passed since the state transition.
Create a new script `scripts/discretization/back_jump_prob_discrete.py` that calculates the probability to return back to the initial state after a state transition as function of the time that has passed since the state transition given that the compound was in a specific state of another discrete trajectory at the time of the state transition.
Create a new function `mdtools.dtrj.surv_func` that takes a discrete trajectory and calculates the probability that a compound is still (or again) in the new state at time t0+dt given that a state transition has occurred at time t0.
Update the docstring of the function `mdtools.dtrj.remain_prob_discrete`
Create a new function `mdtools.dtrj.n_leaves_vs_time` that takes a discrete trajectory and calculates the total number of compounds that leave their state at time t0+dt given that they have entered the state at time t0. Additionally, it calculates the number of compounds that are at risk to leave the state at time t0+dt, i.e. the number of compounds that have continuously been in the state from time t0 to t0+dt.
Add the arguments `discard_neg_start` and `discard_all_neg` to the function `mdtools.dtrj.n_leaves_vs_time` that allow the user to discard negative states in the discrete trajectory in two different ways.
Create a new function `mdtools.dtrj.leave_prob` that takes a discrete trajectory and calculates the probability that a compound leaves its state at time t0+dt given that it has entered the state at time t0.
Create a new function `mdtools.dtrj.kaplan_meier` that estimates the state survival function using the Kaplan-Meier estimator.
Delete the function `mdtools.dtrj.surv_func`, because it is replaced by the more accurate Kaplan-Meier estimator of the survival function implemented in the the function `mdtools.dtrj.kaplan_meier`.
Create a new Function `mdtools.dtrj.n_leaves_vs_time_discrete` that takes a discrete trajectory and calculates the total number of compounds that leave their state at time t0+dt given that they have entered the state at time t0 and given that they were in a specific state of another discrete trajectory at time t0.
Create a new function that takes a discrete trajectory and calculates the probability that a compound leaves its state at time t0+dt given that it has entered the state at time t0 and given that it was in a specific state of another discrete trajectory at time t0.
Create a new function `mdtools.dtrj.kaplan_meier_discrete` that takes a discrete trajectory and calculates the probability that a compound is still in the new state at time t0+dt given that a state transition has occurred at time t0 and given that the compound was in a specific state of another discrete trajectory at time t0.
Add new arguments `discard_neg` and `discard_neg_btw` to the function `mdtools.dtrj.back_jump_prob` that allow the user to ignore negative states in two different ways.
Add new arguments `discard_neg` and `discard_neg_btw` to the function `mdtools.dtrj.back_jump_prob_discrete` that allow the user to ignore negative states in two different ways.
Add new command-line options `--discard-neg` and `--discard-neg-btw` to the script `scripts/discretization/back_jump_prob.py` that allow the user to discard negative states in different ways.
Add new command-line options `--discard-neg` and `--discard-neg-btw` to the script `scripts/discretization/back_jump_prob_discrete.py` that allow the user to discard negative states in different ways.
Check the format of the scripts `scripts/discretization/back_jump.py` and `scripts/discretization/back_jump_discrete.py` with black during the CI workflow.
Include the docstring of the scripts `scripts/discretization/back_jump.py` and `scripts/discretization/back_jump_discrete.py` in the official MDTools documentation.
Extend the "See Also" section of various scripts.
Calculate the transition information of the discrete trajectory (with `mdt.rti.dtrj_trans_info_str`) *after* the trajectory was corrected for intermittency.
Add the command-line option `--intermittency` to the script template `scripts/templates/script_template_dtrj.py`.
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Create a script `scripts/discretization/kaplan_meier.py` that calculates the state survival function using the Kaplan-Meier estimator.
Create a new script `scripts/discretization/kaplan_meier_discrete.py` that calculates the state survival function using the Kaplan-Meier estimator resolved with respect to the states in the second discrete trajectory.
Include the docstrings of the scripts `scripts/discretization/kaplan_meier.py` and `scripts/discretization/kaplan_meier_discrete.py` in the official MDTools documentation.
Check the format of the scripts `scripts/discretization/kaplan_meier.py` and `scripts/discretization/kaplan_meier_discrete.py` with black during the CI workflow.
Remove the script `scripts/discretization/state_probs_around_trans.py`, because its core functionality is now covered by `scripts/discretization/back_jump_prob.py` and `scripts/discretization/kaplan_meier.py`.
Add the following links to linkcheck's list of ignored links: * https://doi.org/10.1002/9781118033005 * https://doi.org/10.1080/00031305.2017.1356374 * https://doi.org/10.1080/01621459.1958.10501452
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Back-jump probability and survival function
Type of change
Proposed changes
Breaking changes
scripts/discretization/state_probs_around_trans.py, because its core functionality is now covered by the scriptsscripts/discretization/back_jump_prob.pyandscripts/discretization/kaplan_meier.py.Bug fixes
sripts/discretization/state_lifetime.pyandsripts/discretization/state_lifetime_discrete.py: Calculate the transition information of the discrete trajectory after the trajectory was corrected for intermittency.New features
New scripts
scripts/discretization/back_jump_prob.pyandscripts/discretization/back_jump_prob_discrete.pythat calculate the probability to return back to the initial state after a state transition as function of the time that has passed since the state transition.scripts/discretization/kaplan_meier.pyandscripts/discretization/kaplan_meier_discrete.pythat calculate the state survival function using the Kaplan-Meier estimator.New functions
mdtools.dtrj.back_jump_probandmdtools.dtrj.back_jump_prob_discretethat take a discrete trajectory and calculate the probability to return back to the initial state at time t0+dt, given that a state transition has occurred at time t0.mdtools.dtrj.kaplan_meierandmdtools.dtrj.kaplan_meier_discretethat estimate the state survival function using the Kaplan-Meier estimator.mdtools.dtrj.leave_probandmdtools.dtrj.leave_prob_discretethat take a discrete trajectory and calculate the probability that a compound leaves its state at time t0+dt given that it has entered the state at time t0.mdtools.dtrj.n_leaves_vs_timeandmdtools.dtrj.n_leaves_vs_time_discretethat take a discrete trajectory and calculate the total number of compounds that leave their state at time t0+dt given that they have entered the state at time t0.PR checklist