Python numpy.array_str() Examples
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Example #1
Source File: query_request.py From ibeis with Apache License 2.0 | 6 votes |
def set_external_qaid_mask(qreq_, masked_qaid_list): r""" Args: qaid_list (list): CommandLine: python -m ibeis.algo.hots.query_request --test-set_external_qaid_mask Example: >>> # ENABLE_DOCTEST >>> from ibeis.algo.hots.query_request import * # NOQA >>> import ibeis >>> ibs = ibeis.opendb(db='testdb1') >>> qaid_list = [1, 2, 3, 4, 5] >>> daid_list = [1, 2, 3, 4, 5] >>> qreq_ = ibs.new_query_request(qaid_list, daid_list) >>> masked_qaid_list = [2, 4, 5] >>> qreq_.set_external_qaid_mask(masked_qaid_list) >>> result = np.array_str(qreq_.qaids) >>> print(result) [1 3] """ qreq_.set_internal_masked_qaids(masked_qaid_list) # --- Internal Annotation ID Masks ----
Example #2
Source File: mnist_inference.py From uai-sdk with Apache License 2.0 | 6 votes |
def execute(self, data, batch_size): sess = self.output['sess'] x = self.output['x'] y_ = self.output['y_'] imgs = [] for i in range(batch_size): im = Image.open(data[i]).resize((28, 28)).convert('L') im = np.array(im) im = im.reshape(784) im = im.astype(np.float32) im = np.multiply(im, 1.0 / 255.0) imgs.append(im) imgs = np.array(imgs) predict_values = sess.run(y_, feed_dict={x: imgs}) print(predict_values) ret = [] for val in predict_values: ret_val = np.array_str(np.argmax(val)) + '\n' ret.append(ret_val) return ret
Example #3
Source File: arrayprint.py From Mastering-Elasticsearch-7.0 with MIT License | 6 votes |
def _array_str_implementation( a, max_line_width=None, precision=None, suppress_small=None, array2string=array2string): """Internal version of array_str() that allows overriding array2string.""" if (_format_options['legacy'] == '1.13' and a.shape == () and not a.dtype.names): return str(a.item()) # the str of 0d arrays is a special case: It should appear like a scalar, # so floats are not truncated by `precision`, and strings are not wrapped # in quotes. So we return the str of the scalar value. if a.shape == (): # obtain a scalar and call str on it, avoiding problems for subclasses # for which indexing with () returns a 0d instead of a scalar by using # ndarray's getindex. Also guard against recursive 0d object arrays. return _guarded_str(np.ndarray.__getitem__(a, ())) return array2string(a, max_line_width, precision, suppress_small, ' ', "")
Example #4
Source File: dataset.py From ektelo with Apache License 2.0 | 6 votes |
def __init__(self, hist, reduce_to_domain_shape=None, dist=None): """ Any instances with equal key() values should have equal hash() values domain_shape will be result of regular grid partition """ if isinstance(reduce_to_domain_shape, int): # allow for integers in 1D, instead of shape tuples reduce_to_domain_shape = (reduce_to_domain_shape, ) if dist is not None: self._dist_str = numpy.array_str(numpy.array(dist)) else: self._dist_str = '' self._hist = hist self._reduce_to_domain_shape = reduce_to_domain_shape if hist.shape != reduce_to_domain_shape else None self._dist = dist self._payload = None self._compiled = False
Example #5
Source File: classDefinitions.py From pyMHT with BSD 3-Clause "New" or "Revised" License | 6 votes |
def predictAisMeasurements(self, scanTime, aisMeasurements): import pymht.models.pv as model import pymht.utils.kalman as kalman assert len(aisMeasurements) > 0 aisPredictions = AisMessageList(scanTime) scanTimeString = datetime.datetime.fromtimestamp(scanTime).strftime("%H:%M:%S.%f") for measurement in aisMeasurements: aisTimeString = datetime.datetime.fromtimestamp(measurement.time).strftime("%H:%M:%S.%f") log.debug("Predicting AIS (" + str(measurement.mmsi) + ") from " + aisTimeString + " to " + scanTimeString) dT = scanTime - measurement.time assert dT >= 0 state = measurement.state A = model.Phi(dT) Q = model.Q(dT) x_bar, P_bar = kalman.predict(A, Q, np.array(state, ndmin=2), np.array(measurement.covariance, ndmin=3)) aisPredictions.measurements.append( AIS_prediction(model.C_RADAR.dot(x_bar[0]), model.C_RADAR.dot(P_bar[0]).dot(model.C_RADAR.T), measurement.mmsi)) log.debug(np.array_str(state) + "=>" + np.array_str(x_bar[0])) aisPredictions.aisMessages.append(measurement) assert len(aisPredictions.measurements) == len(aisMeasurements) return aisPredictions
Example #6
Source File: model_based_rl.py From me-trpo with MIT License | 6 votes |
def log_dictionary(mode_order, validation_costs, min_validation_costs, logger, first_n=5): for mode in mode_order: if mode in validation_costs: costs = validation_costs[mode] if hasattr(costs, '__iter__'): assert 'estimated' in mode msg = np.array_str(costs[:first_n], max_line_width=50, precision=2) logger.info('\t%.5s_validation_cost:\t%s' % (mode, msg)) logger.info('\t\tavg=%.2f, increase_ratio=%.2f' % ( np.mean(costs), np.mean(costs > min_validation_costs[mode]) )) logger.info('\t\tmode=%.2f, std=%.2f, min=%.2f, max=%.2f' % (np.median(costs), np.std(costs), np.min(costs), np.max(costs))) else: logger.info('\t%.5s_validation_cost:\t%.3f' % (mode, costs))
Example #7
Source File: formatting.py From cupy with MIT License | 6 votes |
def array_str(arr, max_line_width=None, precision=None, suppress_small=None): """Returns the string representation of the content of an array. Args: arr (array_like): Input array. It should be able to feed to :func:`cupy.asnumpy`. max_line_width (int): The maximum number of line lengths. precision (int): Floating point precision. It uses the current printing precision of NumPy. suppress_small (bool): If ``True``, very small number are printed as zeros. .. seealso:: :func:`numpy.array_str` """ return numpy.array_str(cupy.asnumpy(arr), max_line_width, precision, suppress_small)
Example #8
Source File: mnist_inference.py From uai-sdk with Apache License 2.0 | 6 votes |
def execute(self, data, batch_size): BATCH = namedtuple('BATCH', ['data', 'label']) self.model.bind(data_shapes=[('data', (batch_size, 1, 28, 28))], label_shapes=[('softmax_label', (batch_size, 10))], for_training=False) self.model.set_params(self.arg_params, self.aux_params) ret = [] for i in range(batch_size): im = Image.open(data[i]).resize((28, 28)) im = np.array(im) / 255.0 im = im.reshape(-1, 1, 28, 28) self.model.forward(BATCH([mx.nd.array(im)], None)) predict_values = self.model.get_outputs()[0].asnumpy() val = predict_values[0] ret_val = np.array_str(np.argmax(val)) + '\n' ret.append(ret_val) return ret
Example #9
Source File: variable.py From chainer with MIT License | 6 votes |
def variable_str(var): """Return the string representation of a variable. Args: var (~chainer.Variable): Input Variable. .. seealso:: numpy.array_str """ arr = _cpu._to_cpu(var.array) if var.name: prefix = 'variable ' + var.name else: prefix = 'variable' if arr is None: lst = 'None' else: lst = numpy.array2string(arr, None, None, None, ' ', prefix + '(') return '%s(%s)' % (prefix, lst)
Example #10
Source File: local_frame.py From pytim with GNU General Public License v3.0 | 6 votes |
def _(): """ additional tests here we generate a paraboloid (x^2+y^2) and a hyperbolic paraboloid (x^2-y^2) to check that the curvature code gives the right answers for the Gaussian (4, -4) and mean (2, 0) curvatures >>> import pytim >>> x,y=np.mgrid[-5:5,-5:5.]/2. >>> p = np.asarray(list(zip(x.flatten(),y.flatten()))) >>> z1 = p[:,0]**2+p[:,1]**2 >>> z2 = p[:,0]**2-p[:,1]**2 >>> >>> for z in [z1, z2]: ... pp = np.asarray(list(zip(x.flatten()+5,y.flatten()+5,z))) ... curv = pytim.observables.Curvature(cutoff=1.,warning=False).compute(pp) ... val = (curv[np.logical_and(p[:,0]==0,p[:,1]==0)]) ... # add and subtract 1e3 to be sure to have -0 -> 0 ... print(np.array_str((val+1e3)-1e3, precision=2, suppress_small=True)) [[4. 2.]] [[-4. 0.]] """ #
Example #11
Source File: arrayprint.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 6 votes |
def _array_str_implementation( a, max_line_width=None, precision=None, suppress_small=None, array2string=array2string): """Internal version of array_str() that allows overriding array2string.""" if (_format_options['legacy'] == '1.13' and a.shape == () and not a.dtype.names): return str(a.item()) # the str of 0d arrays is a special case: It should appear like a scalar, # so floats are not truncated by `precision`, and strings are not wrapped # in quotes. So we return the str of the scalar value. if a.shape == (): # obtain a scalar and call str on it, avoiding problems for subclasses # for which indexing with () returns a 0d instead of a scalar by using # ndarray's getindex. Also guard against recursive 0d object arrays. return _guarded_str(np.ndarray.__getitem__(a, ())) return array2string(a, max_line_width, precision, suppress_small, ' ', "")
Example #12
Source File: arrayprint.py From recruit with Apache License 2.0 | 6 votes |
def _array_str_implementation( a, max_line_width=None, precision=None, suppress_small=None, array2string=array2string): """Internal version of array_str() that allows overriding array2string.""" if (_format_options['legacy'] == '1.13' and a.shape == () and not a.dtype.names): return str(a.item()) # the str of 0d arrays is a special case: It should appear like a scalar, # so floats are not truncated by `precision`, and strings are not wrapped # in quotes. So we return the str of the scalar value. if a.shape == (): # obtain a scalar and call str on it, avoiding problems for subclasses # for which indexing with () returns a 0d instead of a scalar by using # ndarray's getindex. Also guard against recursive 0d object arrays. return _guarded_str(np.ndarray.__getitem__(a, ())) return array2string(a, max_line_width, precision, suppress_small, ' ', "")
Example #13
Source File: trajectory.py From FCGF with MIT License | 5 votes |
def __str__(self): return 'metadata : ' + ' '.join(map(str, self.metadata)) + '\n' + \ "pose : " + "\n" + np.array_str(self.pose)
Example #14
Source File: imdb.py From mx-maskrcnn with Apache License 2.0 | 5 votes |
def append_flipped_images(self, roidb): """ append flipped images to an roidb flip boxes coordinates, images will be actually flipped when loading into network :param roidb: [image_index]['boxes', 'gt_classes', 'gt_overlaps', 'flipped'] :return: roidb: [image_index]['boxes', 'gt_classes', 'gt_overlaps', 'flipped'] """ print 'append flipped images to roidb' assert self.num_images == len(roidb) for i in range(self.num_images): roi_rec = roidb[i] boxes = roi_rec['boxes'].copy() oldx1 = boxes[:, 0].copy() oldx2 = boxes[:, 2].copy() boxes[:, 0] = roi_rec['width'] - oldx2 - 1 boxes[:, 2] = roi_rec['width'] - oldx1 - 1 assert (boxes[:, 2] >= boxes[:, 0]).all(),\ 'img_name %s, width %d\n' % (roi_rec['image'], roi_rec['width']) + \ np.array_str(roi_rec['boxes'], precision=3, suppress_small=True) entry = {'image': roi_rec['image'], 'height': roi_rec['height'], 'width': roi_rec['width'], 'boxes': boxes, 'gt_classes': roidb[i]['gt_classes'], 'gt_overlaps': roidb[i]['gt_overlaps'], 'max_classes': roidb[i]['max_classes'], 'max_overlaps': roidb[i]['max_overlaps'], 'flipped': True} roidb.append(entry) self.image_set_index *= 2 return roidb
Example #15
Source File: callback.py From ST-MetaNet with MIT License | 5 votes |
def finish(self, metrics): output_str = '' if metrics is not None: for metric in metrics: result = metric.get_value() for k, v in result.items(): v = np.array_str(v.asnumpy()) output_str += '\t' + k + ': ' + v self.end = time.time() print('%s\tEpoch[%d]\tTime:%.2fs%s' % (self.title, self.epoch, self.end-self.start, output_str)) self.reset()
Example #16
Source File: callback.py From ST-MetaNet with MIT License | 5 votes |
def log_metrics(self, nbatch, metrics): if nbatch % self.frequent == 0: output_str = '' if metrics is not None: for metric in metrics: result = metric.get_value() for k, v in result.items(): v = np.array_str(v.asnumpy()) output_str += '\t' + k + ': ' + v time_spent = time.time() - self.tic self.tic = time.time() speed = self.frequent / time_spent print('%s\tEpoch[%d]\tBatch[%d]\tTime spent:%.2fs\tSpeed: %.2fbatch/s%s' % (self.title, self.epoch, nbatch, time_spent, speed, output_str))
Example #17
Source File: test_remainder.py From discrete_sieve with Apache License 2.0 | 5 votes |
def test_invertibility(): xs = np.random.randint(0, 5, 100) ys = xs / 2 + np.random.randint(0, 2, 100) g = re.Remainder(xs, ys, k_max=8) zs = g.transform(xs, ys) print 'mi, h', g.mi, g.h print zip(xs, ys, zs) print np.array_str(g.pz_xy, precision=2, suppress_small=True) predict_xs = g.predict(ys, zs) print zip(predict_xs, xs) assert np.all(xs == predict_xs), predict_xs assert g.h < 0.01, "%0.5f" % g.h assert g.mi < 0.1, "%0.5f" % g.mi
Example #18
Source File: numeric.py From Splunking-Crime with GNU Affero General Public License v3.0 | 5 votes |
def array_str(a, max_line_width=None, precision=None, suppress_small=None): """ Return a string representation of the data in an array. The data in the array is returned as a single string. This function is similar to `array_repr`, the difference being that `array_repr` also returns information on the kind of array and its data type. Parameters ---------- a : ndarray Input array. max_line_width : int, optional Inserts newlines if text is longer than `max_line_width`. The default is, indirectly, 75. precision : int, optional Floating point precision. Default is the current printing precision (usually 8), which can be altered using `set_printoptions`. suppress_small : bool, optional Represent numbers "very close" to zero as zero; default is False. Very close is defined by precision: if the precision is 8, e.g., numbers smaller (in absolute value) than 5e-9 are represented as zero. See Also -------- array2string, array_repr, set_printoptions Examples -------- >>> np.array_str(np.arange(3)) '[0 1 2]' """ return array2string(a, max_line_width, precision, suppress_small, ' ', "", str)
Example #19
Source File: forecast.py From anticipy with BSD 3-Clause "New" or "Revised" License | 5 votes |
def _get_df_fit_model(source, model, weights, actuals_x_range, freq, is_fit, cost, aic_c, params, status): # Generate a metadata dataframe for the output of fit_model() if params is None: params = np.array([]) df_result = ( pd.DataFrame( columns=[ 'source', 'model', 'weights', 'actuals_x_range', 'freq', 'is_fit', 'cost', 'aic_c', 'params_str', 'status', 'source_long', 'params'], data=[ [ source, model, weights, actuals_x_range, freq, is_fit, cost, aic_c, np.array_str( params, precision=1), status, '{}:{}:{}:{}'.format( source, weights, freq, actuals_x_range), params]])) return df_result
Example #20
Source File: mnist_inference_json.py From uai-sdk with Apache License 2.0 | 5 votes |
def execute(self, data, batch_size): sess = self.output['sess'] x = self.output['x'] y_ = self.output['y_'] ids = [] imgs = [] for i in range(batch_size): json_input = json.load(data[i]) data_id = json_input['appid'] img_data = json_input['img'].decode('base64') im = Image.open(StringIO.StringIO(img_data)).resize((28, 28)).convert('L') im = np.array(im) im = im.reshape(784) im = im.astype(np.float32) im = np.multiply(im, 1.0 / 255.0) imgs.append(im) ids.append(data_id) imgs = np.array(imgs) print(imgs.shape) predict = sess.run(y_, feed_dict={x: imgs}) ret = [] for i in range(batch_size): ret_val = np.array_str(np.argmax(predict[i])) ret_item = json.dumps({ids[i]: ret_val}) ret.append(ret_item) return ret
Example #21
Source File: helpers.py From iffse with MIT License | 5 votes |
def np_to_string(n): """ Converts a one dimensional numpy array into a string format to be stored in db """ # Squeeze out dims n = np.squeeze(n) return np.array_str(n)[1:-1]
Example #22
Source File: test_regression.py From pySINDy with MIT License | 5 votes |
def test_array_str_64bit(self): # Ticket #501 s = np.array([1, np.nan], dtype=np.float64) with np.errstate(all='raise'): np.array_str(s) # Should succeed
Example #23
Source File: test_formatting.py From cupy with MIT License | 5 votes |
def test_array_str(self): a = testing.shaped_arange((2, 3, 4), cupy) b = testing.shaped_arange((2, 3, 4), numpy) self.assertEqual(cupy.array_str(a), numpy.array_str(b))
Example #24
Source File: distanceratioexperiment.py From aurum-datadiscovery with MIT License | 5 votes |
def __vector_to_string(self, vector): """ Returns string representation of vector. """ return numpy.array_str(vector)
Example #25
Source File: recallprecisionexperiment.py From aurum-datadiscovery with MIT License | 5 votes |
def __vector_to_string(self, vector): """ Returns string representation of vector. """ return numpy.array_str(numpy.round(unitvec(vector), decimals=3))
Example #26
Source File: quaternion.py From qiskit-terra with Apache License 2.0 | 5 votes |
def __str__(self): return np.array_str(self.data)
Example #27
Source File: quaternion.py From qiskit-terra with Apache License 2.0 | 5 votes |
def __repr__(self): return np.array_str(self.data)
Example #28
Source File: two_qubit_decompose.py From qiskit-terra with Apache License 2.0 | 5 votes |
def __repr__(self): # FIXME: this is worth making prettier since it's very useful for debugging return ("{}\n{}\nUd({}, {}, {})\n{}\n{}\n".format( np.array_str(self.K1l), np.array_str(self.K1r), self.a, self.b, self.c, np.array_str(self.K2l), np.array_str(self.K2r)))
Example #29
Source File: iqp.py From qiskit-terra with Apache License 2.0 | 5 votes |
def __init__(self, interactions: Union[List, np.array]) -> None: """Create IQP circuit. Args: interactions: input n-by-n symmetric matrix. Raises: CircuitError: if the inputs is not as symmetric matrix. """ num_qubits = len(interactions) interactions = np.array(interactions) if not np.allclose(interactions, interactions.transpose()): raise CircuitError("The interactions matrix is not symmetric") a_str = np.array_str(interactions) a_str.replace('\n', ';') name = "iqp:" + a_str.replace('\n', ';') inner = QuantumCircuit(num_qubits, name=name) super().__init__(num_qubits, name=name) inner.h(range(num_qubits)) for i in range(num_qubits): for j in range(i+1, num_qubits): if interactions[i][j] % 4 != 0: inner.cu1(interactions[i][j] * np.pi / 2, i, j) for i in range(num_qubits): if interactions[i][i] % 8 != 0: inner.u1(interactions[i][i] * np.pi / 8, i) inner.h(range(num_qubits)) all_qubits = self.qubits self.append(inner, all_qubits)
Example #30
Source File: test_regression.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
def test_array_str_64bit(self): # Ticket #501 s = np.array([1, np.nan], dtype=np.float64) with np.errstate(all='raise'): np.array_str(s) # Should succeed