iam-git / WellMet (public) (License: MIT) (since 2021-08-31) (hash sha1)
WellMet is pure Python framework for spatial structural reliability analysis. Or, more specifically, for "failure probability estimation and detection of failure surfaces by adaptive sequential decomposition of the design domain".

/samplebox.py (a0e0b4e593204ff6254f23a67652804db07800a6) (4284 bytes) (mode 100644) (type blob)

#!/usr/bin/env python
# coding: utf-8

"""
SampleBox = sample_R(f_model) + g_values
"""


import numpy as np


class SampleBox:
    """
    SampleBox = sample_R(f_model) + g_values
    
    .sampled_plan object
    .g_values
    .failsi
    
    Souřadnice primárně z prostoru modelu, ty co jsme rovnou
    posilali do g_modelu!
    """
    
        # nechtěl bys nazvy proměnných?
    def __new__(cls, sample_object, g_values=(), gm_signature=''):
        """
        Jedname tvrdě - není-li vstup konzistentní, 
        tak sbox vůbec nevytvaříme
        """
        g_values = np.atleast_1d(g_values)
        if len(sample_object) == len(g_values):
            sb = super(SampleBox, cls).__new__(cls)
            # nepotrebujeme žádné rozdělení, nic
            sb.sampled_plan = sample_object
            sb.g_values = g_values
            sb.gm_signature = gm_signature
            return sb
        else:
            raise ValueError("Sample and g_value hasn't the same length. Zkrátka, do sebe nepatří")
    
        
    def __str__(sb):
        return  '%s: %s at %s' %(sb.gm_signature, sb.g_values, sb.sampled_plan)
        
    def __repr__(sb):
        return  'SampleBox(%s, %s, %s)' %(repr(sb.sampled_plan), repr(sb.g_values), repr(sb.gm_signature))
        
    def __len__(sb):
        return len(sb.g_values)
        
        
    def __call__(sb):
        # я ваще хз
        # offer next sample?
        # do calculation?
        # add to this sample?
        # return new instance?
        # мар, сакра, кароно?
        
        # finally, we will offer sample to sample
        # like BlackBox does
        return sb.sampled_plan(1)
    
        
    def __getitem__(sb, slice):
        return SampleBox(sb.sampled_plan[slice], sb.g_values[slice], sb.gm_signature)
        
        
    def __getattr__(sb, attr):
        if attr == 'samplebox':
            return sb
        elif attr == 'failsi':
            # ~(g_values>0) to handle nan
            return ~(sb.g_values>0)
        elif attr == 'success_points':
            return np.argwhere(sb.g_values>0).flatten()
        elif attr == 'failure_points':
            return np.argwhere(~(sb.g_values>0)).flatten()
        elif attr == 'failure_samples':
            return sb[~(sb.g_values>0)]
        elif attr == 'success_samples':
            return sb[sb.g_values>0]
            
        # to je jistě k samplovi
        else:
            return getattr(sb.sampled_plan, attr)
        
        
    def add_sample(sb, input_sb):
        input_sb.consistency_check()
        
        # ты чьих будешь?
        # where are you from?
        # are you one of us?
        if sb.gm_signature == input_sb.gm_signature:
            # dá se tuhle kontrolu jednoduše napálit, ale to neřeším
            sb.sampled_plan.add_sample(input_sb.sampled_plan)
            sb.g_values = np.append(sb.g_values, input_sb.g_values)
            
            return sb.consistency_check()
            
            # je to pro případ prázdného sample_boxu
        elif sb.gm_signature == '':
            # dá se tuhle kontrolu jednoduše napálit, ale to neřeším
            sb.sampled_plan.add_sample(input_sb.sampled_plan)
            sb.g_values = np.append(sb.g_values, input_sb.g_values)
            sb.gm_signature = input_sb.gm_signature
            return sb.consistency_check()
        else:
            #raise ValueError("Merge sa nám nějak nepovedol")
            raise ValueError("gm_signatures are unequal. You are probably trying to merge data from different sources")
                
    def new_sample(sb, input_sb):
        """
        We want to create new SampleBox object with our distribution (f_model)
        but with data of input_sb (just like f_model.new_sample() does)
        """
        return SampleBox(sb.sampled_plan.new_sample(input_sb), input_sb.g_values, input_sb.gm_signature)
        
    def consistency_check(sb):
        if len(sb.sampled_plan)==len(sb.g_values):
            return True
        else:
            # уг тодӥськы чик мар кароно
            # ConsistencyError
            raise ValueError('SampleBox is in an inconsistent state and nobody knows what to do with it')
            


Mode Type Size Ref File
100644 blob 28117 0907e38499eeca10471c7d104d4b4db30b8b7084 IS_stat.py
100644 blob 6 0916b75b752887809bac2330f3de246c42c245cd __init__.py
100644 blob 73368 3d245b8568158ac63c80fa0847631776a140db0f blackbox.py
100644 blob 11243 10c424c2ce5e8cdd0da97a5aba74c54d1ca71e0d candybox.py
100644 blob 26958 5acc5a7b512691d13a511deee135fd90c86e55c8 convex_hull.py
100644 blob 84851 811ef4d818e55cbe2d0305da62fcc2e1cada359a dicebox.py
100644 blob 36930 a775d1114bc205bbd1da0a10879297283cca0d4c estimation.py
100644 blob 34394 3f0ab9294a9352a071de18553aa687c2a9e6917a f_models.py
100644 blob 31540 a577087003a885ca7499d1ee9451e703fa9d2d36 g_models.py
100644 blob 20552 d121d3a9f8fe544aaaf22f7524355e480b1e767f ghull.py
100644 blob 42820 1092b3b9f05b11d0c53b3aa63df2460ec355085d gl_plot.py
100644 blob 2718 5d721d117448dbb96c554ea8f0e4651ffe9ac457 gp_plot.py
100644 blob 29393 96162a5d181b8307507ba2f44bafe984aa939163 lukiskon.py
100644 blob 2004 6ea8dc8f50a656c48f786d5a00bd6398276c9741 misc.py
040000 tree - 0e9b1f6d2b2c5a92806b461940f0d2d4333bdc6c mplot
100644 blob 1450 4849f178b588e252b8c7f6a940d2d82ad35f6914 plot.py
100644 blob 2807 1feb1d43e90e027f35bbd0a6730ab18501cef63a plotly_plot.py
100644 blob 143853 ef70c013dbcb917af28451fac91dacba877aa029 qt_plot.py
100644 blob 8206 5981023118262109fca8309d9b313b521a25f88f reader.py
100644 blob 4284 a0e0b4e593204ff6254f23a67652804db07800a6 samplebox.py
100644 blob 6558 df0e88ea13c95cd1463a8ba1391e27766b95c3a5 sball.py
100644 blob 5553 bac994ae58f1df80c7f8b3f33955af5402f5a4f3 sball_old.py
100644 blob 2605 0034d2e3f14c056541888235e59127e8f28b131d schemes.py
100644 blob 21623 281aef80556b8d22842b8659f6f0b7dab0ad71af shapeshare.py
100644 blob 48537 10f90c5614e9a04f0cd9f78e75f0db4a6becb3e4 simplex.py
100644 blob 13090 2b9681eed730ecfadc6c61b234d2fb19db95d87d spring.py
100644 blob 10940 6965eabdb5599bb22773e7fef1178f9b2bb51efe stm_df.py
100644 blob 3433 3063a1b6a132cbb5440ab95f1b6af1f1ff4266ac testcases_2D.py
100644 blob 2465 d829bff1dd721bdb8bbbed9a53db73efac471dac welford.py
100644 blob 22048 4a6014ca5255aa96059ff9ed5a7e29df98d26ffc whitebox.py
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