
    Kik                     ^   S SK r S SKJr  S SKrS SKrS SKJr  S SKJ	r	J
r
  S SKJrJr  S SKJr  S SKJr  S SKJr   " S	 S
\	5      r " S S\
\	5      r " S S\	5      r " S S\	5      r " S S\	5      r " S S\	5      r " S S\	5      r " S S\	5      r " S S\	5      r " S S\	5      r\" SS9S 5       rS  r\" SS9S! 5       r \" SS9S" 5       r!\RD                  RG                  S#S$S%/5      S& 5       r$\" SS9S' 5       r%\" SS9S( 5       r&\" SS9S) 5       r'\" SS9S* 5       r(S+ r)S, r*S- r+g).    N)PrettyPrinter)config_context)BaseEstimatorTransformerMixin)SelectKBestchi2)LogisticRegressionCV)make_pipeline)_EstimatorPrettyPrinterc                   @    \ rS rSr              SS jrS rSrg)LogisticRegression   Nc                     Xl         X l        X0l        X@l        XPl        X`l        Xpl        Xl        Xl        Xl	        Xl
        Xl        Xl        Xl        g N)Cl1_ratiodualtolfit_interceptintercept_scalingclass_weightrandom_statesolvermax_itermulti_classverbose
warm_startn_jobs)selfr   r   r   r   r   r   r   r   r   r   r   r   r   r   s                  a/var/www/html/dynamic-report/venv/lib/python3.13/site-packages/sklearn/utils/tests/test_pprint.py__init__LogisticRegression.__init__   sO    "  	*!2(( &$    c                     U $ r    )r   Xys      r    fitLogisticRegression.fit1       r#   )r   r   r   r   r   r   r   r   r   r   r   r   r   r   )      ?r   F-C6?T   NNwarnd   r.   r   FN)__name__
__module____qualname____firstlineno__r!   r(   __static_attributes__r%   r#   r    r   r      s9     @r#   r   c                   (    \ rS rSrSS jrSS jrSrg)StandardScaler5   c                 (    X l         X0l        Xl        g r   )	with_meanwith_stdcopy)r   r;   r9   r:   s       r    r!   StandardScaler.__init__6   s    " 	r#   Nc                     U $ r   r%   r   r&   r;   s      r    	transformStandardScaler.transform;   r*   r#   )r;   r9   r:   )TTTr   )r0   r1   r2   r3   r!   r?   r4   r%   r#   r    r6   r6   5   s    
r#   r6   c                       \ rS rSrSS jrSrg)RFE?   Nc                 4    Xl         X l        X0l        X@l        g r   	estimatorn_features_to_selectstepr   )r   rF   rG   rH   r   s        r    r!   RFE.__init__@   s    "$8!	r#   rE   )Nr-   r   r0   r1   r2   r3   r!   r4   r%   r#   r    rB   rB   ?   s    r#   rB   c                   0    \ rS rSr         SS jrSrg)GridSearchCVG   Nc                     Xl         X l        X0l        X@l        XPl        X`l        Xpl        Xl        Xl        Xl	        Xl
        g r   )rF   
param_gridscoringr   iidrefitcvr   pre_dispatcherror_scorereturn_train_score)r   rF   rO   rP   r   rQ   rR   rS   r   rT   rU   rV   s               r    r!   GridSearchCV.__init__H   s>     #$
(&"4r#   )rS   rU   rF   rQ   r   rO   rT   rR   rV   rP   r   )	NNr.   Tr.   r   z2*n_jobszraise-deprecatingFrJ   r%   r#   r    rL   rL   G   s$    
 ' 5r#   rL   c                   T    \ rS rSrSSSSSSSSSSS	S
SSSS\R
                  4S jrSrg)CountVectorizerc   contentzutf-8strictNTz(?u)\b\w\w+\b)r-   r-   wordr+   r-   Fc                     Xl         X l        X0l        X@l        X`l        Xpl        Xl        XPl        Xl        Xl	        Xl
        Xl        Xl        Xl        Xl        UU l        UU l        g r   )inputencodingdecode_errorstrip_accentspreprocessor	tokenizeranalyzer	lowercasetoken_pattern
stop_wordsmax_dfmin_dfmax_featuresngram_range
vocabularybinarydtype)r   r_   r`   ra   rb   rf   rc   rd   rh   rg   rl   re   ri   rj   rk   rm   rn   ro   s                     r    r!   CountVectorizer.__init__d   sc    ( 
 (*(" "*$(&$
r#   )re   rn   ra   ro   r`   r_   rf   ri   rk   rj   rl   rc   rh   rb   rg   rd   rm   )r0   r1   r2   r3   npint64r!   r4   r%   r#   r    rY   rY   c   s@     &hh%$r#   rY   c                       \ rS rSrSS jrSrg)Pipeline   Nc                     Xl         X l        g r   )stepsmemory)r   rw   rx   s      r    r!   Pipeline.__init__   s    
r#   )rx   rw   r   rJ   r%   r#   r    rt   rt      s    r#   rt   c                   :    \ rS rSr              SS jrSrg)SVC   Nc                     X l         X0l        X@l        XPl        Xl        Xl        X`l        Xpl        Xl        Xl	        Xl
        Xl        Xl        Xl        g r   )kerneldegreegammacoef0r   r   	shrinkingprobability
cache_sizer   r   r   decision_function_shaper   )r   r   r~   r   r   r   r   r   r   r   r   r   r   r   r   s                  r    r!   SVC.__init__   sN    " 

"&$( '>$(r#   )r   r   r   r   r   r   r   r~   r   r   r   r   r   r   )r+   rbf   auto_deprecated        TFMbP?   NFovrNrJ   r%   r#   r    r{   r{      s3      %)r#   r{   c                   ,    \ rS rSr       SS jrSrg)PCA   Nc                 X    Xl         X l        X0l        X@l        XPl        X`l        Xpl        g r   )n_componentsr;   whiten
svd_solverr   iterated_powerr   )r   r   r;   r   r   r   r   r   s           r    r!   PCA.__init__   s*     )	$,(r#   )r;   r   r   r   r   r   r   )NTFautor   r   NrJ   r%   r#   r    r   r      s     )r#   r   c                   4    \ rS rSr           SS jrSrg)NMF   Nc                     Xl         X l        X0l        X@l        XPl        X`l        Xpl        Xl        Xl        Xl	        Xl
        g r   )r   initr   	beta_lossr   r   r   alphar   r   shuffle)r   r   r   r   r   r   r   r   r   r   r   r   s               r    r!   NMF.__init__   s=     )	" (
 r#   )r   r   r   r   r   r   r   r   r   r   r   )NNcd	frobeniusr,   r   Nr   r   r   FrJ   r%   r#   r    r   r      s*     r#   r   c                   <    \ rS rSr\R
                  SSSS4S jrSrg)SimpleImputer   meanNr   Tc                 @    Xl         X l        X0l        X@l        XPl        g r   )missing_valuesstrategy
fill_valuer   r;   )r   r   r   r   r   r;   s         r    r!   SimpleImputer.__init__   s     - $	r#   )r;   r   r   r   r   )r0   r1   r2   r3   rq   nanr!   r4   r%   r#   r    r   r      s     vvr#   r   Fprint_changed_onlyc                  R    [        5       n SnUSS  nU R                  5       U:X  d   eg )N!  
LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
                   intercept_scaling=1, l1_ratio=0, max_iter=100,
                   multi_class='warn', n_jobs=None, random_state=None,
                   solver='warn', tol=0.0001, verbose=0, warm_start=False)r-   )r   __repr__)lrexpecteds     r    
test_basicr      s5     
	BNH |H;;=H$$$r#   c                     [        SS9n SnU R                  5       U:X  d   e[        SSSSSS9n S	nUS
S  nU R                  5       U:X  d   e[        SS9nSnUR                  5       U:X  d   e[        [        S5      S9nSnUR                  5       U:X  d   e[	        [        [        R                  " SS
/5      SS95        g )NrZ   r   zLogisticRegression(C=99)g?Fi  T)r   r   r   r   r   zk
LogisticRegression(C=99, class_weight=0.4, fit_intercept=False, tol=1234,
                   verbose=True)r-   r   )r   zSimpleImputer(missing_values=0)NaNzSimpleImputer()g?)Csuse_legacy_attributes)r   r   r   floatreprr	   rq   array)r   r   imputers      r    test_changed_onlyr     s    	b	!B-H;;=H$$$ 

3et
B$H |H;;=H$$$1-G4H))) 5<8G$H))) 		3(!35	QRr#   c                  t    [        [        5       [        SS95      n SnUSS  nU R                  5       U:X  d   eg )Ni  r   a  
Pipeline(memory=None,
         steps=[('standardscaler',
                 StandardScaler(copy=True, with_mean=True, with_std=True)),
                ('logisticregression',
                 LogisticRegression(C=999, class_weight=None, dual=False,
                                    fit_intercept=True, intercept_scaling=1,
                                    l1_ratio=0, max_iter=100,
                                    multi_class='warn', n_jobs=None,
                                    random_state=None, solver='warn',
                                    tol=0.0001, verbose=0, warm_start=False))],
         transform_input=None, verbose=False)r-   )r
   r6   r   r   )pipeliner   s     r    test_pipeliner     sD     ^-/AC/HIH1H |H(***r#   c                      [        [        [        [        [        [        [        [        5       5      5      5      5      5      5      5      n SnUSS  nU R                  5       U:X  d   eg )Nat  
RFE(estimator=RFE(estimator=RFE(estimator=RFE(estimator=RFE(estimator=RFE(estimator=RFE(estimator=LogisticRegression(C=1.0,
                                                                                                                     class_weight=None,
                                                                                                                     dual=False,
                                                                                                                     fit_intercept=True,
                                                                                                                     intercept_scaling=1,
                                                                                                                     l1_ratio=0,
                                                                                                                     max_iter=100,
                                                                                                                     multi_class='warn',
                                                                                                                     n_jobs=None,
                                                                                                                     random_state=None,
                                                                                                                     solver='warn',
                                                                                                                     tol=0.0001,
                                                                                                                     verbose=0,
                                                                                                                     warm_start=False),
                                                                                        n_features_to_select=None,
                                                                                        step=1,
                                                                                        verbose=0),
                                                                          n_features_to_select=None,
                                                                          step=1,
                                                                          verbose=0),
                                                            n_features_to_select=None,
                                                            step=1, verbose=0),
                                              n_features_to_select=None, step=1,
                                              verbose=0),
                                n_features_to_select=None, step=1, verbose=0),
                  n_features_to_select=None, step=1, verbose=0),
    n_features_to_select=None, step=1, verbose=0)r-   )rB   r   r   )rfer   s     r    test_deeply_nestedr   3  sX     c#c#c#&8&:";<=>?@
AC5H: |H<<>X%%%r#   )r   r   )TzRFE(estimator=RFE(...)))FzERFE(estimator=RFE(...), n_features_to_select=None, step=1, verbose=0)c                     [        U S9   [        SS9n[        [        [        [        [        [        5       5      5      5      5      5      nUR	                  U5      U:X  d   e S S S 5        g ! , (       d  f       g = f)Nr   r-   )depth)r   r   rB   r   pformat)r   r   ppr   s       r    test_print_estimator_max_depthr   X  s[     
+=	>$1-#c#c"4"6789:;zz#(***	 
?	>	>s   AA,,
A:c                      S/SS// SQS.S// SQS./n [        [        5       U SS	9nS
nUSS  nUR                  5       U:X  d   eg )Nr   r   r,   r-   
   r/   i  )r~   r   r   linear)r~   r      )rS   a  
GridSearchCV(cv=5, error_score='raise-deprecating',
             estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
                           decision_function_shape='ovr', degree=3,
                           gamma='auto_deprecated', kernel='rbf', max_iter=-1,
                           probability=False, random_state=None, shrinking=True,
                           tol=0.001, verbose=False),
             iid='warn', n_jobs=None,
             param_grid=[{'C': [1, 10, 100, 1000], 'gamma': [0.001, 0.0001],
                          'kernel': ['rbf']},
                         {'C': [1, 10, 100, 1000], 'kernel': ['linear']}],
             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,
             scoring=None, verbose=0)r-   )rL   r{   r   )rO   gsr   s      r    test_gridsearchr   j  sa     7dD\8JK:$67J 
ceZA	.B)H |H;;=H$$$r#   c                  H   [        SSSS9n [        S[        5       4S[        5       4/5      n/ SQn/ SQn[        SS	9[	        5       /UUS
.[        [        5      /UUS./n[        USSUS9nSnUSS  nU R                  U5      n[        R                  " SSU5      nXv:X  d   eg )NTr-   )compactindentindent_at_name
reduce_dimclassify)         r      )r   )r   reduce_dim__n_componentsclassify__C)r   reduce_dim__kr   r   )rS   r   rO   a	  
GridSearchCV(cv=3, error_score='raise-deprecating',
             estimator=Pipeline(memory=None,
                                steps=[('reduce_dim',
                                        PCA(copy=True, iterated_power='auto',
                                            n_components=None,
                                            random_state=None,
                                            svd_solver='auto', tol=0.0,
                                            whiten=False)),
                                       ('classify',
                                        SVC(C=1.0, cache_size=200,
                                            class_weight=None, coef0=0.0,
                                            decision_function_shape='ovr',
                                            degree=3, gamma='auto_deprecated',
                                            kernel='rbf', max_iter=-1,
                                            probability=False,
                                            random_state=None, shrinking=True,
                                            tol=0.001, verbose=False))]),
             iid='warn', n_jobs=1,
             param_grid=[{'classify__C': [1, 10, 100, 1000],
                          'reduce_dim': [PCA(copy=True, iterated_power=7,
                                             n_components=None,
                                             random_state=None,
                                             svd_solver='auto', tol=0.0,
                                             whiten=False),
                                         NMF(alpha=0.0, beta_loss='frobenius',
                                             init=None, l1_ratio=0.0,
                                             max_iter=200, n_components=None,
                                             random_state=None, shuffle=False,
                                             solver='cd', tol=0.0001,
                                             verbose=0)],
                          'reduce_dim__n_components': [2, 4, 8]},
                         {'classify__C': [1, 10, 100, 1000],
                          'reduce_dim': [SelectKBest(k=10,
                                                     score_func=<function chi2 at some_address>)],
                          'reduce_dim__k': [2, 4, 8]}],
             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,
             scoring=None, verbose=0)zfunction chi2 at 0x.*>zfunction chi2 at some_address>)r   rt   r   r{   r   r   r   rL   r   resub)r   r   N_FEATURES_OPTIONS	C_OPTIONSrO   
gspipeliner   repr_s           r    test_gridsearch_pipeliner     s     
!a	MB,.SU0CDEH""I a0#%8(:$	
 't,-/$	
J h1Q:NJ%)HN |HJJz"EFF+-MuUEr#   c                  h   Sn [        SSSU S9n[        U 5       Vs0 s H  o"U_M     nn[        US9nSnUSS  nUR                  U5      U:X  d   e[        U S-   5       Vs0 s H  o"U_M     nn[        US9nSnUSS  nUR                  U5      U:X  d   eS[	        [        U 5      5      0n[        [        5       U5      nS	nUSS  nUR                  U5      U:X  d   eS[	        [        U S-   5      5      0n[        [        5       U5      nS
nUSS  nUR                  U5      U:X  d   eg s  snf s  snf )N   Tr-   )r   r   r   n_max_elements_to_show)rm   a  
CountVectorizer(analyzer='word', binary=False, decode_error='strict',
                dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',
                lowercase=True, max_df=1.0, max_features=None, min_df=1,
                ngram_range=(1, 1), preprocessor=None, stop_words=None,
                strip_accents=None, token_pattern='(?u)\\b\\w\\w+\\b',
                tokenizer=None,
                vocabulary={0: 0, 1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 6, 7: 7,
                            8: 8, 9: 9, 10: 10, 11: 11, 12: 12, 13: 13, 14: 14,
                            15: 15, 16: 16, 17: 17, 18: 18, 19: 19, 20: 20,
                            21: 21, 22: 22, 23: 23, 24: 24, 25: 25, 26: 26,
                            27: 27, 28: 28, 29: 29})a  
CountVectorizer(analyzer='word', binary=False, decode_error='strict',
                dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',
                lowercase=True, max_df=1.0, max_features=None, min_df=1,
                ngram_range=(1, 1), preprocessor=None, stop_words=None,
                strip_accents=None, token_pattern='(?u)\\b\\w\\w+\\b',
                tokenizer=None,
                vocabulary={0: 0, 1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 6, 7: 7,
                            8: 8, 9: 9, 10: 10, 11: 11, 12: 12, 13: 13, 14: 14,
                            15: 15, 16: 16, 17: 17, 18: 18, 19: 19, 20: 20,
                            21: 21, 22: 22, 23: 23, 24: 24, 25: 25, 26: 26,
                            27: 27, 28: 28, 29: 29, ...})r   a  
GridSearchCV(cv='warn', error_score='raise-deprecating',
             estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
                           decision_function_shape='ovr', degree=3,
                           gamma='auto_deprecated', kernel='rbf', max_iter=-1,
                           probability=False, random_state=None, shrinking=True,
                           tol=0.001, verbose=False),
             iid='warn', n_jobs=None,
             param_grid={'C': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,
                               15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,
                               27, 28, 29]},
             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,
             scoring=None, verbose=0)a  
GridSearchCV(cv='warn', error_score='raise-deprecating',
             estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
                           decision_function_shape='ovr', degree=3,
                           gamma='auto_deprecated', kernel='rbf', max_iter=-1,
                           probability=False, random_state=None, shrinking=True,
                           tol=0.001, verbose=False),
             iid='warn', n_jobs=None,
             param_grid={'C': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,
                               15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,
                               27, 28, 29, ...]},
             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,
             scoring=None, verbose=0))r   rangerY   r   listrL   r{   )r   r   irm   
vectorizerr   rO   r   s           r    test_n_max_elements_to_showr     s|   	 5	
B !&&< => =1Q$ =J> J7J8H |H::j!X--- !&&<q&@ AB A1Q$ AJB J7J=H |H::j!X--- tE"89:;J	ceZ	(B)H |H::b>X%%% tE"81"<=>?J	ceZ	(B)H |H::b>X%%%[ ?( Cs   D*!D/c                     [        5       n SnUSS  nU R                  SS9U:X  d   eSnUSS  nU R                  SS9U:X  d   eU R                  [        S5      S9n[        SR	                  UR                  5       5      5      nU R                  US9U:X  d   eS	U;  d   eS
nUSS  nU R                  US-
  S9U:X  d   eSnUSS  nU R                  US-
  S9U:X  d   eSnUSS  nU R                  US-
  S9U:X  d   eg )Nz
LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
                   in...
                   multi_class='warn', n_jobs=None, random_state=None,
                   solver='warn', tol=0.0001, verbose=0, warm_start=False)r-      )
N_CHAR_MAXzQ
Lo...
                   solver='warn', tol=0.0001, verbose=0, warm_start=False)r   inf z...a  
LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
                   intercept_scaling=1, l1_ratio=0,...00,
                   multi_class='warn', n_jobs=None, random_state=None,
                   solver='warn', tol=0.0001, verbose=0, warm_start=False)r   a   
LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
                   intercept_scaling=1, l1_ratio=0, max...r=100,
                   multi_class='warn', n_jobs=None, random_state=None,
                   solver='warn', tol=0.0001, verbose=0, warm_start=False)r   r   )r   r   r   lenjoinsplit)r   r   	full_repr
n_nonblanks       r    test_bruteforce_ellipsisr   #  sU   
 
	BNH |H;;#;&(222NH |H;;!;$000 uU|4IRWWY__./0J;;*;-:::	!!!
NH
 |H;;*r/;2h>>>NH
 |H;;*q.;1X===
NH
 |H;;*q.;1X===r#   c                  F    [        5       R                  [        5       5        g r   )r   pprintr   r%   r#   r    test_builtin_prettyprinterr   j  s    
 O-/0r#   c                       " S S[         5      n U " SSS S9nSnUR                  5       U:X  d   e[        SS9   S	nUR                  5       U:X  d   e S S S 5        g ! , (       d  f       g = f)
Nc                   <   ^  \ rS rSrSS jrSU 4S jjrS rSrU =r$ )'test_kwargs_in_init.<locals>.WithKWargsix  c                 N    Xl         X l        0 U l        U R                  " S0 UD6  g )Nr%   )ab_other_params
set_params)r   r   r  kwargss       r    r!   0test_kwargs_in_init.<locals>.WithKWargs.__init__{  s#    FF!#DOO%f%r#   c                 X   > [         TU ]  US9nUR                  U R                  5        U$ )N)deep)super
get_paramsupdater  )r   r  params	__class__s      r    r	  2test_kwargs_in_init.<locals>.WithKWargs.get_params  s,    W'T'2FMM$,,-Mr#   c                 l    UR                  5        H  u  p#[        XU5        X0R                  U'   M!     U $ r   )itemssetattrr  )r   r  keyvalues       r    r  2test_kwargs_in_init.<locals>.WithKWargs.set_params  s3    $lln
5)*/""3' - Kr#   )r  r   r  )
willchange	unchanged)T)	r0   r1   r2   r3   r!   r	  r  r4   __classcell__)r  s   @r    
WithKWargsr   x  s    	&	
	 	r#   r  	somethingabcd)r   cdz+WithKWargs(a='something', c='abcd', d=None)Fr   z:WithKWargs(a='something', b='unchanged', c='abcd', d=None))r   r   r   )r  estr   s      r    test_kwargs_in_initr  r  sd    ] ( {f
5C<H<<>X%%%	5	1O||~))) 
2	1	1s   A
A*c                    ^  " U4S jS[         [        5      mT" [        T" T" 5       5      T" 5       S5      5      n [        SS9   [	        U 5        TR
                  nS S S 5        STl        [        SS9   [	        U 5        TR
                  nS S S 5        WW:X  d   eg ! , (       d  f       NG= f! , (       d  f       N(= f)Nc                   D   >^  \ rS rSrSrSS jrUU 4S jrSS jrSrU =r	$ ):test_complexity_print_changed_only.<locals>.DummyEstimatori  r   c                     Xl         g r   rF   )r   rF   s     r    r!   Ctest_complexity_print_changed_only.<locals>.DummyEstimator.__init__  s    &Nr#   c                 J   > T=R                   S-  sl         [        TU ]	  5       $ )Nr-   )nb_times_repr_calledr  r   )r   DummyEstimatorr  s    r    r   Ctest_complexity_print_changed_only.<locals>.DummyEstimator.__repr__  s"    //14/7#%%r#   c                     U$ r   r%   r>   s      r    r?   Dtest_complexity_print_changed_only.<locals>.DummyEstimator.transform  s    Hr#   r"  r   )
r0   r1   r2   r3   r%  r!   r   r?   r4   r  )r  r&  s   @r    r&  r     s     	'	&	 	r#   r&  passthroughFr   r   T)r   r   r
   r   r   r%  )rF    nb_repr_print_changed_only_falsenb_repr_print_changed_only_truer&  s      @r    "test_complexity_print_changed_onlyr-    s    )=  n^%568H-XI 
5	1Y+9+N+N( 
2 +,N'	4	0Y*8*M*M' 
1 ,/NNNN 
2	1
 
1	0s   B5B/
B,/
B=),r   r   r   numpyrq   pytestsklearnr   sklearn.baser   r   sklearn.feature_selectionr   r   sklearn.linear_modelr	   sklearn.pipeliner
   sklearn.utils._pprintr   r   r6   rB   rL   rY   rt   r{   r   r   r   r   r   r   r   markparametrizer   r   r   r   r   r   r  r-  r%   r#   r    <module>r8     s   	     " 8 7 5 * 9" "J%} - 5= 58%m %P} )- )D)- )(- 8M   5)
% *
%S: 5)+ *+( 5)!& *!&H &)	
	+	+ 5)% *%4 5)? *?D 5)W& *W&t 5)C> *C>L1!*HOr#   