SMArt.md.ana package¶

Submodules¶

SMArt.md.ana.incl module¶

SMArt.md.ana.incl.CI(av1, av2, std1, std2)[source]¶: Crooks intersection

SMArt.md.ana.incl.Jarz(dGs, T=300)[source]¶: Jarzynski

SMArt.md.ana.incl.RMSD(coord1, coord2)[source]¶

class SMArt.md.ana.incl.Real(x)[source]¶

Bases: SMArt.incl.Defaults

fix()[source]¶

classmethod fix_float(x)[source]¶

classmethod set_tolerance(tolerance)[source]¶

SMArt.md.ana.incl.fix_float(x, tolerance=5)[source]¶: fixes float instances using round

SMArt.md.ana.incl.get_lifetime_trans(states_tser, N_states, state_offset=1, time_tser=None, include_first_state=True, include_last_state=True)[source]¶: count number of transitions and lifetimes of each of the states

SMArt.md.ana.incl.get_lset(lset)[source]¶

SMArt.md.ana.incl.normalize_RGB(*RGB, fac=1, col_type=<class 'tuple'>)[source]¶: normalize RGB list to 0-1

SMArt.md.ana.incl.read_data(f_path, skip_stride=None, **kwargs)[source]¶

SMArt.md.ana.incl.sim(dgdl)[source]¶: numerical integration using Simpson’s rule deprecated - use scipy.integrate.simpson instead

SMArt.md.ana.incl.sim_err(dgdl)[source]¶

SMArt.md.ana.incl.trap(dgdl, interval=False)[source]¶: numerical integration using trapezoidal rule deprecated - use scipy.integrate.trapezoid instead

SMArt.md.ana.incl.trap_err(dgdl)[source]¶

SMArt.md.ana.pert_FE module¶

class SMArt.md.ana.pert_FE.LPs_Map(l0, l1, LPs, LPs_pred)[source]¶

Bases: object

for_LPs_Es_nfr_mul(Es, nfr_mul, dEs=None)[source]¶

for_neigh_Es_nfr(Es, nfr, n_neigh=1)[source]¶

SMArt.md.ana.pert_FE.calc_OI_BAR(Es, nfr, Nbin=100)[source]¶

SMArt.md.ana.pert_FE.calc_dg_TI(exTI_data)[source]¶

SMArt.md.ana.pert_FE.calc_dg_bar(LPs_map, Es, nfr, T=300)[source]¶

SMArt.md.ana.pert_FE.calc_dg_exTI_lin(exTI_data, LPs_pred=None, flag_get_exTI_data_avg=True, fnc2integrate=<function simps>)[source]¶: calculates dG from exTI data using linear interpolation :param exTI_data: :param LPs_pred: :param flag_get_exTI_data_avg: :param fnc2call_left_right: (np.mean by default); it could also be min, max, lambda x:x[0] (return left) :return:

exTI_err excluding / including simulated LPs

SMArt.md.ana.pert_FE.calc_dg_exTI_mbar(mbar, dEs, LPs_pred, flag_calc_w=True, fnc2integrate=<function simps>)[source]¶

SMArt.md.ana.pert_FE.calc_dg_mbar(Es, nfr, T=300)[source]¶

SMArt.md.ana.pert_FE.calc_exTI_err(exTI_data, LPs_pred=None, flag_get_exTI_data_avg=True, fnc2call_left_right=<function mean>)[source]¶: calculates the difference between predictions from neighbouring points :param exTI_data: :param LPs_pred: :param flag_get_exTI_data_avg: :param fnc2call_left_right: (np.mean by default); it could also be min, max, lambda x:x[0] (return left) :return:

exTI_err excluding / including simulated LPs

SMArt.md.ana.pert_FE.calc_seg_props(LPs_map, Es, dEs, nfr_mul, si_skips=None, T=300, **kwargs)[source]¶: flag_calc_full_seg_props - include full segment in calc_props

SMArt.md.ana.pert_FE.check_fullseg_in_segs(seg, segs)[source]¶

SMArt.md.ana.pert_FE.check_overlapping_segs(seg1, seg2)[source]¶

SMArt.md.ana.pert_FE.combine_bar_dhdl(comb_data, bar_dhdl_data, sim_lp, flag_check_sim_lp=True, append_index=None)[source]¶: combines data into a dictionary bar_dhdl_data is a pandas dataframe with columns LPs_pred and rows values

SMArt.md.ana.pert_FE.combine_exTI(comb_data, exTI_data, sim_lp, flag_check_sim_lp=True)[source]¶

class SMArt.md.ana.pert_FE.dG_err_tols[source]¶

Bases: SMArt.incl.Defaults

classmethod get_default_dg_err_tols()[source]¶

SMArt.md.ana.pert_FE.get_LPs_pos(LPs_pred, LPs_list)[source]¶

SMArt.md.ana.pert_FE.get_LPs_times(new_LPs_weights, LPs_times, max_iter_LPs_t, max_iter_t_LP=1.0, max_t_LP=5.0, max_total_LPs_t=100.0, t_step=0.1, min_t_LP=0.5)[source]¶

SMArt.md.ana.pert_FE.get_full_dG_err_from_segs(seg_data_dG_err, segs2calc_dG, err_method={'full': ['mbar_err']})[source]¶: get the full dG from the segments :param seg_data_dG_err: output from update_LPs_times :param segs2calc_dG: output from update_LPs_times :param err_method: method used to calculate the error estimate of the segments, e.g. dict(full=[‘mbar_err’], BS={N_steps:[‘mbar’]}) :return: dG

SMArt.md.ana.pert_FE.get_full_dG_from_segs(seg_data_dG_err, segs2calc_dG, method='mbar')[source]¶: get the full dG from the segments :param seg_data_dG_err: output from update_LPs_times :param segs2calc_dG: output from update_LPs_times :param method: method used to calculate the dG of the segments, e.g. ‘mbar’ :return: dG

SMArt.md.ana.pert_FE.get_nfr_LPs(nfr)[source]¶

SMArt.md.ana.pert_FE.get_nfr_from_NFRs(NFRs, LPs, LPs_pred)[source]¶

SMArt.md.ana.pert_FE.get_nfr_from_nfr_mul(nfr_mul)[source]¶

SMArt.md.ana.pert_FE.get_nfr_mul_from_NFRs(NFRs, LPs, LPs_pred)[source]¶

SMArt.md.ana.pert_FE.get_segments2test(LPs, LPs_allowed, seg_width_slide=[0.3, 0.1, 0.2, 0.2], dl_merge_list=[0.2, 0.1, 0.05], **kwargs)[source]¶

Parameters

LPs –
seg_width_slide – parameters to generate segments to calculate properties
dl_merge_list – additional segments to take into account in addition ones defined by the LPs
flag_midpoints – add midpoints between LPs
kwargs – slide_win - in combination with dl_merge_list

Returns

SMArt.md.ana.pert_FE.get_skips(si_l, LPs=None, skip=1)[source]¶

SMArt.md.ana.pert_FE.prep_mbar_input(data_bar_sys, data_dhdl_sys=None, LPs=None, LPs_pred=None, skip=1, offset=0, data_frac=1.0, flag_bw=False, **kwargs)[source]¶

SMArt.md.ana.pert_FE.red_d_Es_nfr_mul(Es, dEs, nfr_mul, data_frac=1.0, offset=0, flag_bw=False, skips=1, flag_rnd_offset=False, flag_bs=False, seed=None, flag_reseed=False, **kwargs)[source]¶

SMArt.md.ana.pert_FE.reset_overlapping_segs(segs)[source]¶

SMArt.md.ana.pert_FE.seg_check_conv_calc_score(temp_seg, dG_err, tols, fnc_OI=<built-in function min>)[source]¶

SMArt.md.ana.pert_FE.si_data_bar_dhdl(data_bar_dhdl)[source]¶

SMArt.md.ana.pert_FE.si_skips_data_dEs(dEs, nfr_mul, skip=1)[source]¶

SMArt.md.ana.pert_FE.update_LPs_1(LPs, seg_score_flag, converged_segments, dl_min)[source]¶

SMArt.md.ana.pert_FE.update_LPs_2(LPs, seg_score_flag, converged_segments, dl_min)[source]¶

SMArt.md.ana.pert_FE.update_LPs_call(fnc2call, LPs, seg_score_flag, converged_segments, dl_min)[source]¶

SMArt.md.ana.pert_FE.update_LPs_times(data_bar_sys, data_dhdl_sys=None, T=300, **kwargs)[source]¶