Python moves.zip函数代码示例

    def __to_dp_matrix_mt(self, value_matrix):
        from concurrent import futures

        col_data_map = {}

        try:
            with futures.ProcessPoolExecutor(self.max_workers) as executor:
                future_list = [
                    executor.submit(
                        _to_dp_list_helper,
                        self,
                        col_idx,
                        values,
                        self.__get_col_type_hint(col_idx),
                        self.strip_str_value,
                    )
                    for col_idx, values in enumerate(zip(*value_matrix))
                ]

                for future in futures.as_completed(future_list):
                    col_idx, value_dp_list = future.result()
                    col_data_map[col_idx] = value_dp_list
        finally:
            logger.debug("shutdown ProcessPoolExecutor: workers={}".format(self.max_workers))
            executor.shutdown()

        return list(zip(*[col_data_map[col_idx] for col_idx in sorted(col_data_map)]))

def reverse_points_if_backwards(xy, xy_next):
    """
    This function aligns xy_next so that it is in the same direction as xy.
    Nothing occurs if they are already aligned

    inputs:
    xy, xy_next - list of tuples [(x1, y1), (x2, y2) ...]
    xy and xy_next are seperated by one timestep.
    the function returns the reversed spine and a flag to see it was reversed
    """

    x, y = zip(*xy)
    xnext, ynext = zip(*xy_next)
    xnext_rev = xnext[::-1]
    ynext_rev = ynext[::-1]

    distance_original = 0.
    distance_rev = 0.
    for k in range(len(x)):
        distance_original += ((x[k] - xnext[k]) ** 2 + (y[k] - ynext[k]) ** 2)
        distance_rev += (x[k] - xnext_rev[k]) ** 2 + (y[k] - ynext_rev[k]) ** 2
        if (distance_original > distance_rev):
            #print "reversed", index, distance_rev, distance_original
            newxy = list(zip(xnext_rev, ynext_rev))
            return (newxy, True)
        else:
            #print "ok", index
            return (xy_next, False)

    def _write_atoms(self, atoms):
        self.f.write('\n')
        self.f.write('Atoms\n')
        self.f.write('\n')

        try:
            charges = atoms.charges
        except (NoDataError, AttributeError):
            has_charges = False
        else:
            has_charges = True

        indices = atoms.indices + 1
        types = atoms.types.astype(np.int32)

        if self.convert_units:
            coordinates = self.convert_pos_to_native(atoms.positions, inplace=False)

        if has_charges:
            for index, atype, charge, coords in zip(indices, types, charges,
                    coordinates):
                self.f.write('{i:d} 0 {t:d} {c:f} {x:f} {y:f} {z:f}\n'.format(
                             i=index, t=atype, c=charge, x=coords[0],
                             y=coords[1], z=coords[2]))
        else:
            for index, atype, coords in zip(indices, types, coordinates):
                self.f.write('{i:d} 0 {t:d} {x:f} {y:f} {z:f}\n'.format(
                             i=index, t=atype, x=coords[0], y=coords[1],
                             z=coords[2]))

def save_weights_to_hdf5_group(f, layers):
  from tensorflow.python.keras._impl.keras import __version__ as keras_version  # pylint: disable=g-import-not-at-top

  save_attributes_to_hdf5_group(
      f, 'layer_names', [layer.name.encode('utf8') for layer in layers])
  f.attrs['backend'] = K.backend().encode('utf8')
  f.attrs['keras_version'] = str(keras_version).encode('utf8')

  for layer in layers:
    g = f.create_group(layer.name)
    symbolic_weights = layer.weights
    weight_values = K.batch_get_value(symbolic_weights)
    weight_names = []
    for i, (w, val) in enumerate(zip(symbolic_weights, weight_values)):
      if hasattr(w, 'name') and w.name:
        name = str(w.name)
      else:
        name = 'param_' + str(i)
      weight_names.append(name.encode('utf8'))
    save_attributes_to_hdf5_group(g, 'weight_names', weight_names)
    for name, val in zip(weight_names, weight_values):
      param_dset = g.create_dataset(name, val.shape, dtype=val.dtype)
      if not val.shape:
        # scalar
        param_dset[()] = val
      else:
        param_dset[:] = val

    def staged_predict_proba(self, X, vote_function=None):
        """
        Predict probabilities on each stage. To get unbiased predictions, you can pass training dataset
        (with same order of events) and vote_function=None.

        :param X: pandas.DataFrame of shape [n_samples, n_features]
        :param vote_function: function to combine prediction of folds' estimators.
            If None then self.vote_function is used.
        :type vote_function: None or function

        :return: iterator for numpy.array of shape [n_samples, n_classes] with probabilities
        """
        if vote_function is not None:
            print('Using voting KFold prediction')
            X = self._get_train_features(X)
            iterators = [estimator.staged_predict_proba(X) for estimator in self.estimators]
            for fold_prob in zip(*iterators):
                probabilities = numpy.array(fold_prob)
                yield vote_function(probabilities)
        else:
            print('Default prediction')
            X = self._get_train_features(X)
            folds_column = self._get_folds_column(len(X))
            iterators = [self.estimators[fold].staged_predict_proba(X.iloc[folds_column == fold, :])
                         for fold in range(self.n_folds)]
            for fold_prob in zip(*iterators):
                probabilities = numpy.zeros(shape=(len(X), 2))
                for fold in range(self.n_folds):
                    probabilities[folds_column == fold] = fold_prob[fold]
                yield probabilities

    def _reduce(results, dataset_out, data_name, dtype, shuffle, rng):
        if len(results) > 0 and (len(data_name) != len(results[0]) or
                                 len(dtype) != len(results[0])):
            raise ValueError('Returned [{}] results but only given [{}] name and'
                             ' [{}] dtype'.format(
                                 len(results[0]), len(data_name), len(dtype)))

        final = [[] for i in range(len(results[0]))]
        for res in results:
            for i, j in zip(res, final):
                j.append(i)
        final = [np.vstack(i)
                 if isinstance(i[0], np.ndarray)
                 else np.asarray(reduce(lambda x, y: x + y, i))
                 for i in final]
        # shufle features
        if shuffle > 2:
            permutation = rng.permutation(final[0].shape[0])
            final = [i[permutation] for i in final]
        # save to dataset
        for i, name, dt in zip(final, data_name, dtype):
            shape = i.shape
            dt = np.dtype(dt)
            x = dataset_out.get_data(name, dtype=dt, shape=shape, value=i)
            x.flush()
        return None

def make_factor_text(factor, name):
    collapse_uniform = True
    if collapse_uniform and ut.almost_allsame(factor.values):
        # Reduce uniform text
        ftext = name + ':\nuniform(%.3f)' % (factor.values[0],)
    else:
        values = factor.values
        try:
            rowstrs = ['p(%s)=%.3f' % (','.join(n), v,)
                       for n, v in zip(zip(*factor.statenames), values)]
        except Exception:
            rowstrs = ['p(%s)=%.3f' % (','.join(n), v,)
                       for n, v in zip(factor._row_labels(False), values)]
        idxs = ut.list_argmaxima(values)
        for idx in idxs:
            rowstrs[idx] += '*'
        thresh = 4
        always_sort = True
        if len(rowstrs) > thresh:
            sortx = factor.values.argsort()[::-1]
            rowstrs = ut.take(rowstrs, sortx[0:(thresh - 1)])
            rowstrs += ['... %d more' % ((len(values) - len(rowstrs)),)]
        elif always_sort:
            sortx = factor.values.argsort()[::-1]
            rowstrs = ut.take(rowstrs, sortx)
        ftext = name + ': \n' + '\n'.join(rowstrs)
    return ftext

 def append(self, *arrays):
   if self.read_only:
     raise RuntimeError("This Data is set in read-only mode")
   accepted_arrays = []
   add_size = 0
   # ====== check if shape[1:] matching ====== #
   for a, d in zip(arrays, self._data):
     if hasattr(a, 'shape'):
       if a.shape[1:] == d.shape[1:]:
         accepted_arrays.append(a)
         add_size += a.shape[0]
     else:
       accepted_arrays.append(None)
   # ====== resize ====== #
   old_size = self.__len__()
   # special case, Mmap is init with temporary size = 1 (all zeros),
   # NOTE: risky to calculate sum of big array here
   if old_size == 1 and \
   sum(np.sum(np.abs(d[:])) for d in self._data) == 0.:
     old_size = 0
   # resize and append data
   self.resize(old_size + add_size) # resize only once will be faster
   # ====== update values ====== #
   for a, d in zip(accepted_arrays, self._data):
     if a is not None:
       d[old_size:old_size + a.shape[0]] = a
   return self

    def test_format_1_converter(self):
        filename = os.path.join(self.tempdir, 'svhn_format_1.hdf5')
        parser = argparse.ArgumentParser()
        subparsers = parser.add_subparsers()
        subparser = subparsers.add_parser('svhn')
        svhn.fill_subparser(subparser)
        subparser.set_defaults(directory=self.tempdir, output_file=filename)
        args = parser.parse_args(['svhn', '1'])
        args_dict = vars(args)
        func = args_dict.pop('func')
        func(**args_dict)
        h5file = h5py.File(filename, mode='r')

        expected_features = sum((self.f1_mock[split]['image']
                                 for split in ('train', 'test', 'extra')), [])
        for val, truth in zip(h5file['features'][...], expected_features):
            assert_equal(val, truth.transpose(2, 0, 1).flatten())

        expected_labels = sum((self.f1_mock[split]['label']
                               for split in ('train', 'test', 'extra')), [])
        for val, truth in zip(h5file['bbox_labels'][...], expected_labels):
            truth[truth == 10] = 0
            assert_equal(val, truth)

        expected_lefts = sum((self.f1_mock[split]['left']
                              for split in ('train', 'test', 'extra')), [])
        for val, truth in zip(h5file['bbox_lefts'][...], expected_lefts):
            assert_equal(val, truth)

    def write_card(self, size=8, is_double=False):
        msg = '\n$' + '-' * 80
        msg += '\n$ %s Matrix %s\n' % ('DMI', self.name)
        list_fields = ['DMI', self.name, 0, self.form, self.tin,
                       self.tout, None, self.nRows, self.nCols]
        if size == 8:
            msg += print_card_8(list_fields)
        #elif is_double:
            #msg += print_card_double(list_fields)
        else:
            msg += print_card_16(list_fields)
        #msg += self.print_card(list_fields,size=16,isD=False)

        if self.is_complex():
            for (gci, gcj, reali, imagi) in zip(self.GCi, self.GCj, self.Real, self.Complex):
                list_fields = ['DMI', self.name, gcj, gci, reali, imagi]
                if size == 8:
                    msg += print_card_8(list_fields)
                elif is_double:
                    msg += print_card_double(list_fields)
                else:
                    msg += print_card_16(list_fields)
        else:
            for (gci, gcj, reali) in zip(self.GCi, self.GCj, self.Real):
                list_fields = ['DMI', self.name, gcj, gci, reali]
                if size == 8:
                    msg += print_card_8(list_fields)
                elif is_double:
                    msg += print_card_double(list_fields)
                else:
                    msg += print_card_16(list_fields)
        return msg

    def _write_sort1_as_sort2(self, f, page_num, page_stamp, header, words):
        element = self.element
        element_type = self.element_data_type
        times = self._times

        node_id = 0  ## TODO: fix the node id
        for inode, (eid, etypei) in enumerate(zip(element, element_type)):
            t1 = self.data[:, inode, 0].ravel()
            t2 = self.data[:, inode, 1].ravel()
            t3 = self.data[:, inode, 2].ravel()
            r1 = self.data[:, inode, 3].ravel()
            r2 = self.data[:, inode, 4].ravel()
            r3 = self.data[:, inode, 5].ravel()

            header[1] = ' POINT-ID = %10i\n' % node_id
            f.write(''.join(header + words))
            for dt, t1i, t2i, t3i, r1i, r2i, r3i in zip(times, t1, t2, t3, r1, r2, r3):
                vals = [t1i, t2i, t3i, r1i, r2i, r3i]
                vals2 = write_floats_13e(vals)
                (dx, dy, dz, rx, ry, rz) = vals2
                f.write('%14s %6s     %-13s  %-13s  %-13s  %-13s  %-13s  %s\n' % (
                    write_float_12E(dt), etypei, dx, dy, dz, rx, ry, rz))
            f.write(page_stamp % page_num)
            page_num += 1
        return page_num

def detect_gid_list(ibs, gid_list, tree_path_list, downsample=True, **kwargs):
    """
    Args:
        gid_list (list of int): the list of IBEIS image_rowids that need detection
        tree_path_list (list of str): the list of trees to load for detection
        downsample (bool, optional): a flag to indicate if the original image
            sizes should be used; defaults to True

            True:  ibs.get_image_detectpaths() is used
            False: ibs.get_image_paths() is used

    Kwargs (optional): refer to the PyRF documentation for configuration settings

    Yields:
        results (list of dict)
    """
    # Get new gpaths if downsampling
    if downsample:
        gpath_list = ibs.get_image_detectpaths(gid_list)
        neww_list = [vt.open_image_size(gpath)[0] for gpath in gpath_list]
        oldw_list = [oldw for (oldw, oldh) in ibs.get_image_sizes(gid_list)]
        downsample_list = [oldw / neww for oldw, neww in zip(oldw_list, neww_list)]
    else:
        gpath_list = ibs.get_image_paths(gid_list)
        downsample_list = [None] * len(gpath_list)
    # Run detection
    results_iter = detect(ibs, gpath_list, tree_path_list, **kwargs)
    # Upscale the results
    for gid, downsample, (gpath, result_list) in zip(gid_list, downsample_list, results_iter):
        # Upscale the results back up to the original image size
        if downsample is not None and downsample != 1.0:
            for result in result_list:
                for key in ["centerx", "centery", "xtl", "ytl", "width", "height"]:
                    result[key] = int(result[key] * downsample)
        yield gid, gpath, result_list

def reorder(outcomes, pmf, sample_space, index=None):
    """
    Helper function to reorder outcomes and pmf to match sample_space.

    """
    try:
        order = [(sample_space.index(outcome), i)
                 for i, outcome in enumerate(outcomes)]
    except ValueError:
        # Let's identify which outcomes were not in the sample space.
        bad = []
        for outcome in outcomes:
            try:
                sample_space.index(outcome)
            except ValueError:
                bad.append(outcome)
        if len(bad) == 1:
            single = True
        else:
            single = False
        raise InvalidOutcome(bad, single=single)

    order.sort()
    _, order = zip(*order)

    if index is None:
        index = dict(zip(outcomes, range(len(outcomes))))

    outcomes = [outcomes[i] for i in order]
    pmf = [pmf[i] for i in order]
    new_index = dict(zip(outcomes, range(len(outcomes))))
    return outcomes, pmf, new_index

def pr_dict(dbpr):
    d = {dk: getattr(dbpr, sk) for sk, dk in zip(SRC_PR_KEYS, PR_KEYS)}
    dd = {dk: getattr(dbpr, sk) for sk, dk in zip(('name',), ('Label',))}

    d.update(dd)

    return d

def setup_plan(plan):
  """Sets up a TensorFlow Fold plan for MNIST.

  The inputs are 28 x 28 images represented as 784-dimensional float32
  vectors (scaled to [0, 1] and categorical digit labels in [0, 9].

  The training loss is softmax cross-entropy. There is only one
  metric, accuracy. In inference mode, the output is a class label.

  Dropout is applied before every layer (including on the inputs).

  Args:
    plan: A TensorFlow Fold plan to set up.
  """
  # Convert the input NumPy array into a tensor.
  model_block = td.Vector(INPUT_LENGTH)

  # Create a placeholder for dropout, if we are in train mode.
  keep_prob = (tf.placeholder_with_default(1.0, [], name='keep_prob')
               if plan.mode == plan.mode_keys.TRAIN else None)

  # Add the fully connected hidden layers.
  for _ in xrange(FLAGS.num_layers):
    model_block >>= td.FC(FLAGS.num_units, input_keep_prob=keep_prob)

  # Add the linear output layer.
  model_block >>= td.FC(NUM_LABELS, activation=None, input_keep_prob=keep_prob)

  if plan.mode == plan.mode_keys.INFER:
    # In inference mode, we run the model directly on images.
    plan.compiler = td.Compiler.create(model_block)
    logits, = plan.compiler.output_tensors
  else:
    # In training/eval mode, we run the model on (image, label) pairs.
    plan.compiler = td.Compiler.create(
        td.Record((model_block, td.Scalar(tf.int64))))
    logits, y_ = plan.compiler.output_tensors

  y = tf.argmax(logits, 1)  # create the predicted output tensor

  datasets = tf.contrib.learn.datasets.mnist.load_mnist(FLAGS.logdir_base)
  if plan.mode == plan.mode_keys.INFER:
    plan.examples = datasets.test.images
    plan.outputs = [y]
  else:
    # Create loss and accuracy tensors, and add them to the plan.
    loss = tf.nn.sparse_softmax_cross_entropy_with_logits(
        logits=logits, labels=y_)
    plan.losses['cross_entropy'] = loss
    accuracy = tf.reduce_mean(tf.cast(tf.equal(y, y_), tf.float32))
    plan.metrics['accuracy'] = accuracy
    if plan.mode == plan.mode_keys.TRAIN:
      plan.examples = zip(datasets.train.images, datasets.train.labels)
      plan.dev_examples = zip(datasets.validation.images,
                              datasets.validation.labels)
      # Turn dropout on for training, off for validation.
      plan.train_feeds[keep_prob] = FLAGS.keep_prob
    else:
      assert plan.mode == plan.mode_keys.EVAL
      plan.examples = zip(datasets.test.images, datasets.test.labels)