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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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