How to use set_simulator method in robotframework-ioslibrary

Best Python code snippet using robotframework-ioslibrary_python

dataloaders.py

Source:dataloaders.py

...74            filter_by_level=filter_by_level,75            num_to_keep=num_to_keep,76            genome_cache_size=genome_cache_size)77        if self.valid_loader:78            self.dataset.set_simulator(validation_distribution)79        else:80            self.dataset.set_simulator(training_distribution)81        super(RefSeqProkaryotaDataLoader,82              self).__init__(self.dataset, batch_size, shuffle,83                             validation_split, num_workers, drop_last)84    def enable_multithreading_if_possible(self):85        if self.dataset.genome_cache_is_full():86            try:87                if self.num_workers != self._num_workers:88                    self.num_workers = self._num_workers89                    self.logger.info(f'Enabling {self.num_workers} '90                                     'workers for data loading...')91            except AttributeError:92                pass93        else:94            self._num_workers = self.num_workers95            self.num_workers = 096    def step(self, epoch):97        super().step(epoch)98        self.enable_multithreading_if_possible()99        if not self.fixed_dataset:100            self.dataset.idx_offset = epoch * len(self.dataset)101            seed = epoch102            seed = seed * get_world_size() + get_global_rank()103            if self.valid_loader:104                seed = 2**32 - seed105                self.dataset.set_simulator(self.validation_distribution)106            else:107                self.dataset.set_simulator(self.training_distribution)108            self.dataset.simulator.set_seed(seed)109    def init_validation(self, other):110        super().init_validation(other)111        self.fixed_dataset = other.fixed_dataset112        self.valid_loader = True113        self.training_distribution = other.training_distribution114        self.validation_distribution = other.validation_distribution115class RefSeqProkaryotaBagsDataLoader(BaseDataLoader):116    """117    RefSeq DataLoader118    Any encoding or transformation should be done here119    and passed to the Dataset120    """121    def __init__(self,122                 target_format,123                 genome_dir,124                 taxonomy_dir,125                 total_bags,126                 bag_size,127                 batch_size,128                 fixed_dataset=False,129                 drop_last=False,130                 training_distribution='lognormal',131                 validation_distribution='lognormal',132                 reseed_every_n_bags=1,133                 accessions_file=None,134                 taxids_list=None,135                 error_model=None,136                 rmin=None,137                 rmax=None,138                 download=True,139                 shuffle=True,140                 validation_split=0.0,141                 num_workers=1,142                 noise=None,143                 filter_by_level=None,144                 num_to_keep=1,145                 genome_cache_size=1000,146                 single_read_target_vectors=False):147        self.logger = logging.getLogger(self.__class__.__name__)148        ncbi_email = 'your-email@domain.com'149        ncbi_api = None150        g2read = mytransforms.GenomeToNoisyRead(error_model,151                                                rmin,152                                                rmax,153                                                p=noise)154        self.rmin = rmin155        self.rmax = rmax156        self.noise = noise157        self.error_model = error_model158        self.fixed_dataset = fixed_dataset159        self.training_distribution = training_distribution160        self.validation_distribution = validation_distribution161        if self.error_model:162            self.rmin = g2read.rmin163            self.rmax = g2read.rmax164        trsfm_x = mytransforms.Compose([165            g2read,166            mytransforms.ToTensorWithView(dtype=torch.long, view=[1, -1])167        ])168        trsfm_y = mytransforms.Compose([mytransforms.ToTensorWithView()])169        self.valid_loader = False170        self.dataset = datasets.RefSeqProkaryotaBags(171            bag_size=bag_size,172            total_bags=total_bags,173            target_format=target_format,174            genome_dir=genome_dir,175            taxonomy_dir=taxonomy_dir,176            accessions_file=accessions_file,177            taxids_list=taxids_list,178            download=download,179            ncbi_email=ncbi_email,180            ncbi_api=ncbi_api,181            transform_x=trsfm_x,182            transform_y=trsfm_y,183            filter_by_level=filter_by_level,184            num_to_keep=num_to_keep,185            reseed_every_n_bags=reseed_every_n_bags,186            genome_cache_size=genome_cache_size,187            num_workers=num_workers,188            single_read_target_vectors=single_read_target_vectors)189        if self.valid_loader:190            self.dataset.set_simulator(validation_distribution)191        else:192            self.dataset.set_simulator(training_distribution)193        super(RefSeqProkaryotaBagsDataLoader,194              self).__init__(self.dataset, batch_size, shuffle,195                             validation_split, 0, drop_last)196    def step(self, epoch):197        super().step(epoch)198        if not self.fixed_dataset:199            self.dataset.idx_offset = epoch * len(self.dataset)200            if self.valid_loader:201                self.dataset.set_simulator(self.validation_distribution)202            else:203                self.dataset.set_simulator(self.training_distribution)204    def init_validation(self, other):205        super().init_validation(other)206        self.fixed_dataset = other.fixed_dataset207        self.valid_loader = True208        self.training_distribution = other.training_distribution209        self.validation_distribution = other.validation_distribution210class RefSeqProkaryotaLargeBagsDataLoader(BaseDataLoader):211    """212    RefSeq DataLoader213    Any encoding or transformation should be done here214    and passed to the Dataset215    """216    def __init__(217        self,218        # target_format,219        genome_dir,220        taxonomy_dir,221        total_bags,222        bag_size,223        mini_bag_size,224        batch_size,225        fixed_dataset=False,226        drop_last=False,227        training_distribution='lognormal',228        validation_distribution='lognormal',229        reseed_every_n_bags=1,230        accessions_file=None,231        taxids_list=None,232        error_model=None,233        rmin=None,234        rmax=None,235        download=True,236        shuffle=True,237        validation_split=0.0,238        num_workers=1,239        noise=None,240        filter_by_level=None,241        num_to_keep=1,242        genome_cache_size=1000,243        single_read_target_vectors=False):244        self.logger = logging.getLogger(self.__class__.__name__)245        ncbi_email = 'your-email@domain.com'246        ncbi_api = None247        g2read = mytransforms.GenomeToNoisyRead(error_model,248                                                rmin,249                                                rmax,250                                                p=noise)251        self.rmin = rmin252        self.rmax = rmax253        self.noise = noise254        self.error_model = error_model255        self.fixed_dataset = fixed_dataset256        self.training_distribution = training_distribution257        self.validation_distribution = validation_distribution258        if self.error_model:259            self.rmin = g2read.rmin260            self.rmax = g2read.rmax261        trsfm_x = mytransforms.Compose([262            g2read,263            mytransforms.ToTensorWithView(dtype=torch.long, view=[1, -1])264        ])265        trsfm_y = mytransforms.Compose([mytransforms.ToTensorWithView()])266        self.valid_loader = False267        self.dataset = datasets.RefSeqProkaryotaBags2(268            bag_size=bag_size,269            mini_bag_size=mini_bag_size,270            total_bags=total_bags,271            genome_dir=genome_dir,272            taxonomy_dir=taxonomy_dir,273            accessions_file=accessions_file,274            taxids_list=taxids_list,275            download=download,276            ncbi_email=ncbi_email,277            ncbi_api=ncbi_api,278            transform_x=trsfm_x,279            transform_y=trsfm_y,280            filter_by_level=filter_by_level,281            num_to_keep=num_to_keep,282            reseed_every_n_bags=reseed_every_n_bags,283            genome_cache_size=genome_cache_size,284            num_workers=num_workers)285        if self.valid_loader:286            self.dataset.set_simulator(validation_distribution)287        else:288            self.dataset.set_simulator(training_distribution)289        super().__init__(self.dataset,290                         batch_size,291                         shuffle,292                         validation_split,293                         0,294                         drop_last,295                         collate_fn=nothing_collate_fn)296    def step(self, epoch):297        super().step(epoch)298        if not self.fixed_dataset:299            self.dataset.idx_offset = epoch * len(self.dataset)300            if self.valid_loader:301                self.dataset.set_simulator(self.validation_distribution)302            else:303                self.dataset.set_simulator(self.training_distribution)304    def init_validation(self, other):305        super().init_validation(other)306        self.fixed_dataset = other.fixed_dataset307        self.valid_loader = True308        self.training_distribution = other.training_distribution309        self.validation_distribution = other.validation_distribution310class RefSeqProkaryotaKmerDataLoader(BaseDataLoader):311    """312    RefSeq DataLoader313    Any encoding or transformation should be done here314    and passed to the Dataset315    """316    def __init__(self,317                 genome_dir,318                 taxonomy_dir,319                 total_samples,320                 batch_size,321                 kmer_vocab_file,322                 fixed_dataset=False,323                 drop_last=False,324                 training_distribution='uniform',325                 validation_distribution='lognormal',326                 accessions_file=None,327                 taxids_list=None,328                 error_model=None,329                 rmin=None,330                 rmax=None,331                 kmer_processing_method='hash',332                 hash_bits=16,333                 lsh=True,334                 lsh_k=11,335                 alternative_lsh=False,336                 download=True,337                 shuffle=True,338                 validation_split=0.0,339                 num_workers=1,340                 noise=None,341                 forward_reads_only=False,342                 filter_by_level=None,343                 num_to_keep=1,344                 genome_cache_size=1000):345        # if kmer_vocab_file is provided:346        #    kmers are mapped to numbers according to numbers347        # else:348        #    if kmer_processing_method is 'hash':349        #       use normal hash or lsh according to hash_bits, lsh, lsh_k, alternative_lsh350        #    if kmer_processing_method is 'count':351        #       use kmer counter to count subkmers of size lsh_k352        assert kmer_processing_method in ['hash', 'count']353        self.logger = logging.getLogger(self.__class__.__name__)354        ncbi_email = 'your-email@domain.com'355        ncbi_api = None356        g2read = mytransforms.GenomeToNoisyKmerRead(357            kmer_vocab_file,358            error_model,359            rmin,360            rmax,361            p=noise,362            forward_reads_only=forward_reads_only,363            hash_bits=hash_bits,364            lsh=lsh,365            lsh_k=lsh_k,366            alternative_lsh=alternative_lsh,367            kmer_processing_method=kmer_processing_method)368        self.lsh_k = lsh_k369        if kmer_processing_method == 'hash':370            totensor = mytransforms.ToTensorWithView(dtype=torch.long,371                                                     view=[-1])372        else:373            totensor = mytransforms.ToTensorWithView(dtype=torch.float)374        self.rmin = rmin375        self.rmax = rmax376        self.noise = noise377        self.error_model = error_model378        self.fixed_dataset = fixed_dataset379        self.training_distribution = training_distribution380        self.validation_distribution = validation_distribution381        try:382            self.vocab_size = g2read.vocab_size383        except AttributeError:384            self.vocab_size = None385        if self.error_model:386            self.rmin = g2read.rmin387            self.rmax = g2read.rmax388        trsfm_x = mytransforms.Compose([g2read, totensor])389        trsfm_y = mytransforms.Compose([mytransforms.ToTensorWithView()])390        self.valid_loader = False391        self.dataset = datasets.RefSeqProkaryota(392            genome_dir,393            taxonomy_dir,394            total_samples,395            accessions_file,396            taxids_list,397            download=download,398            ncbi_email=ncbi_email,399            ncbi_api=ncbi_api,400            transform_x=trsfm_x,401            transform_y=trsfm_y,402            filter_by_level=filter_by_level,403            num_to_keep=num_to_keep,404            genome_cache_size=genome_cache_size)405        if self.valid_loader:406            self.dataset.set_simulator(validation_distribution)407        else:408            self.dataset.set_simulator(training_distribution)409        super().__init__(self.dataset, batch_size, shuffle, validation_split,410                         num_workers, drop_last)411    def enable_multithreading_if_possible(self):412        if self.dataset.genome_cache_is_full():413            try:414                if self.num_workers != self._num_workers:415                    self.num_workers = self._num_workers416                    self.logger.info(f'Enabling {self.num_workers} '417                                     'workers for data loading...')418            except AttributeError:419                pass420        else:421            self._num_workers = self.num_workers422            self.num_workers = 0423    def step(self, epoch):424        super().step(epoch)425        self.enable_multithreading_if_possible()426        if not self.fixed_dataset:427            self.dataset.idx_offset = epoch * len(self.dataset)428            seed = epoch429            seed = seed * get_world_size() + get_global_rank()430            if self.valid_loader:431                seed = 2**32 - seed432                self.dataset.set_simulator(self.validation_distribution)433            else:434                self.dataset.set_simulator(self.training_distribution)435            self.dataset.simulator.set_seed(seed)436    def init_validation(self, other):437        super().init_validation(other)438        self.fixed_dataset = other.fixed_dataset439        self.valid_loader = True440        self.training_distribution = other.training_distribution441        self.validation_distribution = other.validation_distribution442class RefSeqProkaryotaKmerBagsDataLoader(BaseDataLoader):443    """444    RefSeq DataLoader445    Any encoding or transformation should be done here446    and passed to the Dataset447    """448    def __init__(self,449                 target_format,450                 genome_dir,451                 taxonomy_dir,452                 total_bags,453                 bag_size,454                 batch_size,455                 kmer_vocab_file,456                 fixed_dataset=False,457                 drop_last=False,458                 training_distribution='lognormal',459                 validation_distribution='lognormal',460                 reseed_every_n_bags=1,461                 accessions_file=None,462                 taxids_list=None,463                 error_model=None,464                 rmin=None,465                 rmax=None,466                 kmer_processing_method='hash',467                 hash_bits=16,468                 lsh=True,469                 lsh_k=11,470                 alternative_lsh=False,471                 download=True,472                 shuffle=True,473                 validation_split=0.0,474                 num_workers=1,475                 noise=None,476                 forward_reads_only=False,477                 filter_by_level=None,478                 num_to_keep=1,479                 genome_cache_size=1000,480                 single_read_target_vectors=False):481        self.logger = logging.getLogger(self.__class__.__name__)482        ncbi_email = 'your-email@domain.com'483        ncbi_api = None484        g2read = mytransforms.GenomeToNoisyKmerRead(485            kmer_vocab_file,486            error_model,487            rmin,488            rmax,489            p=noise,490            forward_reads_only=forward_reads_only,491            hash_bits=hash_bits,492            lsh=lsh,493            lsh_k=lsh_k,494            alternative_lsh=alternative_lsh,495            kmer_processing_method=kmer_processing_method)496        self.lsh_k = lsh_k497        if kmer_processing_method == 'hash':498            totensor = mytransforms.ToTensorWithView(dtype=torch.long,499                                                     view=[-1])500        else:501            totensor = mytransforms.ToTensorWithView(dtype=torch.float)502        self.rmin = rmin503        self.rmax = rmax504        self.noise = noise505        self.error_model = error_model506        self.fixed_dataset = fixed_dataset507        self.training_distribution = training_distribution508        self.validation_distribution = validation_distribution509        try:510            self.vocab_size = g2read.vocab_size511        except AttributeError:512            self.vocab_size = None513        if self.error_model:514            self.rmin = g2read.rmin515            self.rmax = g2read.rmax516        trsfm_x = mytransforms.Compose([g2read, totensor])517        trsfm_y = mytransforms.Compose([mytransforms.ToTensorWithView()])518        self.valid_loader = False519        self.dataset = datasets.RefSeqProkaryotaBags(520            bag_size=bag_size,521            total_bags=total_bags,522            target_format=target_format,523            genome_dir=genome_dir,524            taxonomy_dir=taxonomy_dir,525            accessions_file=accessions_file,526            taxids_list=taxids_list,527            download=download,528            ncbi_email=ncbi_email,529            ncbi_api=ncbi_api,530            transform_x=trsfm_x,531            transform_y=trsfm_y,532            filter_by_level=filter_by_level,533            num_to_keep=num_to_keep,534            reseed_every_n_bags=reseed_every_n_bags,535            genome_cache_size=genome_cache_size,536            num_workers=num_workers,537            single_read_target_vectors=single_read_target_vectors)538        if self.valid_loader:539            self.dataset.set_simulator(validation_distribution)540        else:541            self.dataset.set_simulator(training_distribution)542        super().__init__(self.dataset, batch_size, shuffle, validation_split,543                         0, drop_last)544    def step(self, epoch):545        super().step(epoch)546        if not self.fixed_dataset:547            self.dataset.idx_offset = epoch * len(self.dataset)548            if self.valid_loader:549                self.dataset.set_simulator(self.validation_distribution)550            else:551                self.dataset.set_simulator(self.training_distribution)552    def init_validation(self, other):553        super().init_validation(other)554        self.fixed_dataset = other.fixed_dataset555        self.valid_loader = True556        self.training_distribution = other.training_distribution557        self.validation_distribution = other.validation_distribution558if __name__ == '__main__':559    # Testing560    # rspdl = RefSeqProkaryotaDataLoader(561    #     ("/home/ageorgiou/eth/spring2019/"562    #      "thesis/data/refseq_prokaryota/genomes"),563    #     ("/home/ageorgiou/eth/spring2019/"564    #      "thesis/data/refseq_prokaryota/taxonomy"),565    #     100,...

test_sre.py

Source:test_sre.py

...40plt.show()41from sklearn.pipeline import Pipeline42clf = Pipeline([('normalize', StandardScaler()), ('classifier', MLPClassifier(alpha=1, max_iter=1000))])43lfi = emceeSRE()44lfi.set_simulator(simulator)45lfi.learn_logL_with_classifier(clf, mins, maxs, Nsamples=10000, test_size=0.1)46lfi.set_obs(y_obs)47from skorch import NeuralNetClassifier48import torch49from torch import nn50from torch.nn import functional as F51class ClassifierModule(nn.Module):52    def __init__(53            self,54            num_units=50,55            nonlin=F.relu,56            dropout=0.5,57    ):58        super(ClassifierModule, self).__init__()59        self.num_units = num_units60        self.nonlin = nonlin61        self.dropout = dropout62        self.dense0 = nn.Linear(y_obs.shape[1]+2, num_units)63        self.nonlin = nonlin64        self.dropout = nn.Dropout(dropout)65        self.dense1 = nn.Linear(num_units, 20)66        self.output = nn.Linear(20, 2)67    def forward(self, X, **kwargs):68        X = self.nonlin(self.dense0(X))69        X = self.dropout(X)70        X = F.relu(self.dense1(X))71        X = F.softmax(self.output(X), dim=-1)72        return X73model = NeuralNetClassifier(74    ClassifierModule,75    max_epochs=1000,76    lr=0.1,77    # optimizer=torch.optim.SGD,78    # criterion=nn.BCELoss,79    iterator_train__shuffle=True,80)81lfi = emceeSRE()82lfi.set_simulator(simulator)83lfi.learn_logL_with_classifier(model, mins, maxs, Nsamples=10000, test_size=0.1)84lfi.set_obs(y_obs)85from sklearn.neighbors import KNeighborsClassifier, RadiusNeighborsClassifier86clf = KNeighborsClassifier(n_neighbors=50)87# clf = Pipeline([('normalize', StandardScaler()), ('classifier', KNeighborsClassifier(n_neighbors=30) )]) 88lfi = emceeSRE()89lfi.set_simulator(simulator)90lfi.learn_logL_with_classifier(clf, mins, maxs, Nsamples=10000, test_size=0.1)91lfi.set_obs(y_obs)92def logL(theta):93	lp = log_prior(theta)94	if not np.isfinite(lp): return lp 95	return lp+lfi.logL_estimator(theta)96pos = mins+(maxs-mins) * np.random.uniform(0,1,size=(64, len(mins)))97nwalkers, ndim = pos.shape98n_samples = 100099sampler = emcee.EnsembleSampler(nwalkers, ndim, logL)100sampler.run_mcmc(pos, n_samples, progress=True);101flat_samples = sampler.get_chain(discard=0, flat=True) 102from sklearn import svm103clf = svm.SVC(kernel='linear', probability=True)104lfi = emceeSRE()105lfi.set_simulator(simulator)106lfi.learn_logL_with_classifier(clf, mins, maxs, Nsamples=10000, test_size=0.1)107lfi.set_obs(y_obs)108from sklearn.ensemble import AdaBoostClassifier109clf = AdaBoostClassifier(n_estimators=100, random_state=0)110lfi = emceeSRE()111lfi.set_simulator(simulator)112lfi.learn_logL_with_classifier(clf, mins, maxs, Nsamples=10000, test_size=0.1)113lfi.set_obs(y_obs)114c = ChainConsumer()115labels = [ke for ke in bounds.keys()]116theta_true = [line.true_slope, line.true_intercept]117c.add_chain(flat_samples, parameters=labels)118fig = c.plotter.plot(figsize="column", truth=theta_true)119fig.set_size_inches(3 + fig.get_size_inches())  # Resize fig for doco. You don't need this.120plt.show()121ni = 50122mesh = np.array([[i,j] for i in np.linspace(mins[0],maxs[0],ni) for j in np.linspace(mins[1],maxs[1],ni)])123logLmesh = np.array([logL(m) for m in tqdm(mesh)])124plt.pcolor(mesh[:,0].reshape(ni,ni),mesh[:,1].reshape(ni,ni),logLmesh.reshape(ni,ni))125############## https://elfi.readthedocs.io/en/latest/quickstart.html126import elfi 127import scipy.stats as ss128mu = elfi.Prior('uniform', -2, 4)129sigma = elfi.Prior('uniform', 1, 4)130def simulator(theta, batch_size=1, random_state=None):131	if type(theta)==list: theta = np.array(theta)132	if theta.ndim>1:133		out = [simulator(tt, batch_size=batch_size, random_state=random_state) for tt in theta]134		return np.array(out).squeeze()135	mu, sigma = theta136	mu, sigma = np.atleast_1d(mu, sigma)137	return ss.norm.rvs(mu[:, None], sigma[:, None], size=(batch_size, 30), random_state=random_state)138def mean(y):139    return np.mean(y, axis=1)140def var(y):141    return np.var(y, axis=1)142# Set the generating parameters that we will try to infer143mean0 = 1144std0 = 3145# Generate some data (using a fixed seed here)146np.random.seed(20170525)147y_obs = simulator([mean0, std0])148print(y_obs)149def log_prior(theta):150	if -2<=theta[0]<=4 and 1<=theta[1]<=4: 151		return 0152	return -np.inf153def logL(theta):154	lp = log_prior(theta)155	if not np.isfinite(lp): return lp 156	return lp+lfi.logL_estimator(theta)157mins = np.array([-2, 1])158maxs = np.array([ 4, 4])159from sklearn.ensemble import RandomForestClassifier160clf = RandomForestClassifier(n_estimators=100, random_state=0, verbose=1, n_jobs=4)161lfi = emceeSRE()162lfi.set_simulator(simulator)163lfi.learn_logL_with_classifier(clf, mins, maxs, Nsamples=50000, test_size=0.1)164lfi.set_obs(y_obs)165ni = 50166mesh = np.array([[i,j] for i in np.linspace(mins[0],maxs[0],ni) for j in np.linspace(mins[1],maxs[1],ni)])167logLmesh = np.array([logL(m) for m in tqdm(mesh)])168plt.pcolor(mesh[:,0].reshape(ni,ni),mesh[:,1].reshape(ni,ni),logLmesh.reshape(ni,ni))169from skorch import NeuralNetClassifier170import torch171from torch import nn172from torch.nn import functional as F173class ClassifierModule(nn.Module):174    def __init__(175            self,176            num_units=50,177            nonlin=F.relu,178            dropout=0.5,179    ):180        super(ClassifierModule, self).__init__()181        self.num_units = num_units182        self.nonlin = nonlin183        self.dropout = dropout184        self.dense0 = nn.Linear(y_obs.shape[1]+2, num_units)185        self.nonlin = nonlin186        self.dropout = nn.Dropout(dropout)187        self.dense1 = nn.Linear(num_units, 20)188        self.output = nn.Linear(20, 2)189    def forward(self, X, **kwargs):190        X = self.nonlin(self.dense0(X))191        X = self.dropout(X)192        X = F.relu(self.dense1(X))193        X = F.sigmoid(self.output(X)) #F.softmax(self.output(X), dim=-1)194        return X195model = NeuralNetClassifier(196    ClassifierModule,197    max_epochs=1000,198    lr=0.01,199    # optimizer=torch.optim.SGD,200    criterion=nn.CrossEntropyLoss, #criterion=nn.NLLLoss, # criterion=nn.BCELoss,201    # iterator_train__shuffle=True,202)203model.fit(X,y)204lfi = emceeSRE()205lfi.set_simulator(simulator)206lfi.learn_logL_with_classifier(model, mins, maxs, Nsamples=10000, test_size=0.1)...

test_simulators.py

Source:test_simulators.py

...15    @pytest.mark.timeout(300)16    @pytest.mark.parametrize("simulator_class", simulator_class_set)17    @pytest.mark.parametrize("m", mSet)18    def test_dc_simulation(self, simulator_class, m):19        simulator = self.set_simulator(simulator_class, m)20        simulator.isGravity = True21        actuatorVoltages = [-1500]22        simulator.actuator_dc_drive([actuatorVoltages], saving=False)23        self.simulator_testing(simulator)24    @pytest.mark.timeout(300)25    @pytest.mark.parametrize("simulator_class", simulator_class_set)26    @pytest.mark.parametrize("m", mSet)27    def test_ac_simulation(self, simulator_class, m):28        simulator = self.set_simulator(simulator_class, m)29        drivenFrequency = 1.030        actuatorLength = 10.031        arg = [m, drivenFrequency, actuatorLength]32        simulator.drive(arg, saving=False)33        self.simulator_testing(simulator)34    @pytest.mark.timeout(300)35    @pytest.mark.parametrize("simulator_class", simulator_class_set)36    @pytest.mark.parametrize("m", mSet)37    def test_data_recorder(self, simulator_class, m):38        simulator = self.set_simulator(simulator_class, m)39        io_buf = io.BytesIO()40        simulator.outfilename = io_buf41        simulator.reset()42        drivenFrequency = 1.043        actuatorLength = 10.044        arg = [m, drivenFrequency, actuatorLength]45        simulator.drive(arg, saving=True)46        io_buf.seek(0)47        file = io_buf.read()48        recorder = RobotShapeRecorderFromData(data_filename=io.BytesIO(file), record_intervals_in_steps=1, saving=False)49        recorder.reset()50        recorder.run()51class TestInchwormRobotSimulator(BaseSimulatorTest):52    simulator_class_set = [Inchworm, InchwormBackward, FullFrictionalInchworm, InchwormCrawl, FrictionalFrogMotionV2]53    mSet = [1, 3]54    @pytest.mark.timeout(300)55    @pytest.mark.parametrize("simulator_class", simulator_class_set)56    @pytest.mark.parametrize("m", mSet)57    def test_simulation(self, simulator_class, m):58        simulator = self.set_simulator(simulator_class, m)59        period = 1.060        simulator.drive([period], saving=False)61        self.simulator_testing(simulator)62    @pytest.mark.timeout(300)63    @pytest.mark.parametrize("simulator_class", simulator_class_set)64    @pytest.mark.parametrize("m", mSet)65    def test_data_recorder(self, simulator_class, m):66        simulator = self.set_simulator(simulator_class, m)67        io_buf = io.BytesIO()68        simulator.outfilename = io_buf69        simulator.reset()70        period = 1.071        simulator.drive([period], saving=True)72        io_buf.seek(0)73        file = io_buf.read()74        recorder = RobotShapeRecorderFromData(data_filename=io.BytesIO(file), record_intervals_in_steps=1, saving=False)75        recorder.reset()76        recorder.run()77        self.recorder_testing(recorder)78    @pytest.mark.timeout(300)79    @pytest.mark.parametrize("simulator_class", simulator_class_set)80    @pytest.mark.parametrize("m", mSet)81    def test_save_and_load_state(self, simulator_class, m):82        simulator = self.set_simulator(simulator_class, m)83        simulator.reset()84        period = 1.085        simulator.drive([period], saving=False, closing=False)86        state_file = simulator.save_simulator_state()87        state_file_name = state_file.name88        simulator.close()89        simulator.reset()90        simulator.load_simulator_state(state_file_name)91        os.remove(state_file_name)92        simulator.drive([period], saving=False, closing=True)93    @pytest.mark.timeout(300)94    @pytest.mark.parametrize("simulator_class", simulator_class_set)95    @pytest.mark.parametrize("m", mSet)96    def test_simulator_copy(self, simulator_class, m):97        simulator = self.set_simulator(simulator_class, m)98        simulator.reset()99        state_file = simulator.save_simulator_state()100        state_file_name = state_file.name101        simulator.copy(state_file_name)102class TestInchwormRobotScanner(BaseSimulatorTest):103    scanner_class_set = [InchwormPeriodScan,104                         InchwormCrawlPeriodScan,105                         InchwormReversedPeriodScan,106                         InchwormBackwardPeriodScan,107                         FullInchwormPeriodScan,108                         FullFrictionalInchwormPeriodScan,109                         FrictionalFrogMotionV2PeriodScan,110                         FullFrictionalInchwormPeriodDampingFrictionScan,111                         SingleActuatorCantileverDCResponseVoltageScan,112                         SingleActuatorCantileverACResponse,113                         InchwormDCResponseVoltageScan]114    mSet = [1, 3]115    @pytest.mark.timeout(300)116    @pytest.mark.parametrize("scanner_class", scanner_class_set)117    @pytest.mark.parametrize("m", mSet)118    def test_scaning(self, scanner_class, m):119        scanner = self.set_simulator(scanner_class, m)120        scanner.scan(saving=False)121class TestRobotShapeRecorder(BaseSimulatorTest):122    simulator_name_set = ['forward',123                          'backward',124                          'crawl',125                          'full frictional inchworm']126    @pytest.mark.timeout(300)127    @pytest.mark.parametrize("simulator_name", simulator_name_set)128    def test_recording(self, simulator_name):129        recorder = self.set_recorder(simulator_name)130        recorder.run()...

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