Python numpy.searchsorted方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import searchsorted [as 別名]
def _interp_ebv(self, datum, dist):
        """
        Calculate samples of E(B-V) at an arbitrary distance (in kpc) for one
        test coordinate.
        """
        dm = 5. * (np.log10(dist) + 2.)
        idx_ceil = np.searchsorted(datum['DM_bin_edges'], dm)
        if idx_ceil == 0:
            dist_0 = 10.**(datum['DM_bin_edges'][0]/5. - 2.)
            return dist/dist_0 * datum['samples'][:,0]
        elif idx_ceil == len(datum['DM_bin_edges']):
            return datum['samples'][:,-1]
        else:
            dm_ceil = datum['DM_bin_edges'][idx_ceil]
            dm_floor = datum['DM_bin_edges'][idx_ceil-1]
            a = (dm_ceil - dm) / (dm_ceil - dm_floor)
            return (
                (1.-a) * datum['samples'][:,idx_ceil]
                +    a * datum['samples'][:,idx_ceil-1]
            ) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import searchsorted [as 別名]
def get_real_index(self, relativeIndex):
        """
        Compute real index of the given relativeIndex considering
        already collected indexes.

        :Parameters:
            #. relativeIndex (int): Atom relative index to already collected
               indexes.

        :Parameters:
            #. index (int): Atom real index.
        """
        ### THIS IS NOT TESTED YET.
        indexes = np.array( sorted(self.indexes) )
        shift   = np.searchsorted(a=indexes, v=relativeIndex, side='left')
        index   = relativeIndex+shift
        for idx in indexes[shift:]:
            if idx > index:
                break
            index += 1
        return index 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import searchsorted [as 別名]
def collect(self, index, dataDict, check=True):
        """
        Collect atom given its index.

        :Parameters:
            #. index (int): The atom index to collect.
            #. dataDict (dict): The atom data dict to collect.
            #. check (boolean):  Whether to check dataDict keys before
               collecting. If set to False, user promises that collected
               data is a dictionary and contains the needed keys.
        """
        assert not self.is_collected(index), LOGGER.error("attempting to collect and already collected atom of index '%i'"%index)
        # add data
        if check:
            assert isinstance(dataDict, dict), LOGGER.error("dataDict must be a dictionary of data where keys are dataKeys")
            assert tuple(sorted(dataDict)) == self.__dataKeys, LOGGER.error("dataDict keys don't match promised dataKeys")
        self.__collectedData[index] = dataDict
        # set indexes sorted array
        idx = np.searchsorted(a=self.__indexesSortedArray, v=index, side='left')
        self.__indexesSortedArray = np.insert(self.__indexesSortedArray, idx, index)
        # set state
        self.__state = str(uuid.uuid1()) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import searchsorted [as 別名]
def release(self, index):
        """
        Release atom from list of collected atoms and return its
        collected data.

        :Parameters:
            #. index (int): The atom index to release.

        :Returns:
            #. dataDict (dict): The released atom collected data.
        """
        if not self.is_collected(index):
            LOGGER.warn("Attempting to release atom %i that is not collected."%index)
            return
        index = self.__collectedData.pop(index)
        # set indexes sorted array
        idx = np.searchsorted(a=self.__indexesSortedArray, v=index, side='left')
        self.__indexesSortedArray = np.insert(self.__indexesSortedArray, idx, index)
        # set state
        self.__state = str(uuid.uuid1())
        # return
        return index 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import searchsorted [as 別名]
def move(self, coordinates):
        """
        Move coordinates.

        :Parameters:
            #. coordinates (np.ndarray): The coordinates on which to apply
               the transformation.

        :Returns:
            #. coordinates (np.ndarray): The new coordinates after applying
               the transformation.
        """
        if self.__randomize:
            index = INT_TYPE( np.searchsorted(self.__selectionScheme, generate_random_float()) )
            moveGenerator = self.__collection[ index ]
        else:
            moveGenerator = self.__collection[self.__step]
            self.__step   = (self.__step+1)%len(self.__collection)
        # perform the move
        return moveGenerator.move(coordinates) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import searchsorted [as 別名]
def sample_categorical(prob, rng):
    """Sample from independent categorical distributions

    Each batch is an independent categorical distribution.

    Parameters
    ----------
    prob : numpy.ndarray
      Probability of the categorical distribution. Shape --> (batch_num, category_num)
    rng : numpy.random.RandomState

    Returns
    -------
    ret : numpy.ndarray
      Sampling result. Shape --> (batch_num,)
    """
    ret = numpy.empty(prob.shape[0], dtype=numpy.float32)
    for ind in range(prob.shape[0]):
        ret[ind] = numpy.searchsorted(numpy.cumsum(prob[ind]), rng.rand()).clip(min=0.0,
                                                                                max=prob.shape[
                                                                                        1] - 0.5)
    return ret 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import searchsorted [as 別名]
def sample_doc(self, current_idx, sentence_weighted=True):
        # Uses the current iteration counter to ensure we don't sample the same doc twice
        if sentence_weighted:
            # With sentence weighting, we sample docs proportionally to their sentence length
            if self.doc_cumsum is None or len(self.doc_cumsum) != len(self.doc_lengths):
                self._precalculate_doc_weights()
            rand_start = self.doc_cumsum[current_idx]
            rand_end = rand_start + self.cumsum_max - self.doc_lengths[current_idx]
            sentence_index = randrange(rand_start, rand_end) % self.cumsum_max
            sampled_doc_index = np.searchsorted(self.doc_cumsum, sentence_index, side='right')
        else:
            # If we don't use sentence weighting, then every doc has an equal chance to be chosen
            sampled_doc_index = (current_idx + randrange(1, len(self.doc_lengths))) % len(self.doc_lengths)
        assert sampled_doc_index != current_idx
        if self.reduce_memory:
            return self.document_shelf[str(sampled_doc_index)]
        else:
            return self.documents[sampled_doc_index] 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import searchsorted [as 別名]
def residual_resample(weights):
    n = len(weights)
    indices = np.zeros(n, np.uint32)
    # take int(N*w) copies of each weight
    num_copies = (n * weights).astype(np.uint32)
    k = 0
    for i in range(n):
        for _ in range(num_copies[i]):  # make n copies
            indices[k] = i
            k += 1
    # use multinormial resample on the residual to fill up the rest.
    residual = weights - num_copies  # get fractional part
    residual /= np.sum(residual)
    cumsum = np.cumsum(residual)
    cumsum[-1] = 1
    indices[k:n] = np.searchsorted(cumsum, np.random.uniform(0, 1, n - k))
    return indices 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import searchsorted [as 別名]
def select(self, population: Population, n: int = 1) -> Sequence[Individual]:
        """Return `n` individuals from the population.

        Parameters
        ----------
        population
            A Population of Individuals.
        n : int
            The number of parents to select from the population. Default is 1.

        Returns
        -------
        Sequence[Individual]
            The selected Individuals.

        """
        super().select(population, n)
        population_total_errors = np.array([i.total_error for i in population])
        sum_of_total_errors = np.sum(population_total_errors)
        probabilities = 1.0 - (population_total_errors / sum_of_total_errors)
        selected_ndxs = np.searchsorted(np.cumsum(probabilities), random(n))
        return [population[ndx] for ndx in selected_ndxs] 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import searchsorted [as 別名]
def __init__(self, ts, granularity, start=None):
        # NOTE(sileht): The whole class assumes ts is ordered and don't have
        # duplicate timestamps, it uses numpy.unique that sorted list, but
        # we always assume the orderd to be the same as the input.
        self.granularity = granularity
        self.can_derive = isinstance(granularity, numpy.timedelta64)
        self.start = start
        if start is None:
            self._ts = ts
            self._ts_for_derive = ts
        else:
            self._ts = ts[numpy.searchsorted(ts['timestamps'], start):]
            if self.can_derive:
                start_derive = start - granularity
                self._ts_for_derive = ts[
                    numpy.searchsorted(ts['timestamps'], start_derive):
                ]
        if self.can_derive:
            self.indexes = round_timestamp(self._ts['timestamps'], granularity)
        elif calendar.GROUPINGS.get(granularity):
            self.indexes = calendar.GROUPINGS.get(granularity)(
                self._ts['timestamps'])
        self.tstamps, self.counts = numpy.unique(self.indexes,
                                                 return_counts=True) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import searchsorted [as 別名]
def __getitem__(self, key):
        if isinstance(key, numpy.datetime64):
            idx = numpy.searchsorted(self.timestamps, key)
            if self.timestamps[idx] == key:
                return self[idx]
            raise KeyError(key)
        if isinstance(key, slice):
            if isinstance(key.start, numpy.datetime64):
                start = numpy.searchsorted(self.timestamps, key.start)
            else:
                start = key.start
            if isinstance(key.stop, numpy.datetime64):
                stop = numpy.searchsorted(self.timestamps, key.stop)
            else:
                stop = key.stop
            key = slice(start, stop, key.step)
        return self.ts[key] 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import searchsorted [as 別名]
def set_values(self, values, before_truncate_callback=None):
        """Set the timestamps and values in this timeseries.

        :param values: A sorted timeseries array.
        :param before_truncate_callback: A callback function to call before
                                         truncating the BoundTimeSerie to its
                                         maximum size.
        :return: None of the return value of before_truncate_callback
        """
        if self.block_size is not None and len(self.ts) != 0:
            index = numpy.searchsorted(values['timestamps'],
                                       self.first_block_timestamp())
            values = values[index:]
        super(BoundTimeSerie, self).set_values(values)
        if before_truncate_callback:
            return_value = before_truncate_callback(self)
        else:
            return_value = None
        self._truncate()
        return return_value 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import searchsorted [as 別名]
def truncate(self, oldest_point=None):
        """Truncate the time series up to oldest_point excluded.

        :param oldest_point: Oldest point to keep from, this excluded.
                             Default is the aggregation timespan.
        :type oldest_point: numpy.datetime64 or numpy.timedelta64
        :return: The oldest point that could have been kept.
        """
        last = self.last
        if last is None:
            return
        if oldest_point is None:
            oldest_point = self.aggregation.timespan
            if oldest_point is None:
                return
        if isinstance(oldest_point, numpy.timedelta64):
            oldest_point = last - oldest_point
        index = numpy.searchsorted(self.ts['timestamps'], oldest_point,
                                   side='right')
        self.ts = self.ts[index:]
        return oldest_point 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import searchsorted [as 別名]
def branch(configs, weights):
    """
    Perform branching on a set of walkers  by stochastic reconfiguration

    Walkers are resampled with probability proportional to the weights, and the new weights are all set to be equal to the average weight.
    
    Args:
      configs: (nconfig,nelec,3) walker coordinates

      weights: (nconfig,) walker weights

    Returns:
      configs: resampled walker configurations

      weights: (nconfig,) all weights are equal to average weight
    """
    nconfig = configs.configs.shape[0]
    wtot = np.sum(weights)
    probability = np.cumsum(weights / wtot)
    base = np.random.rand()
    newinds = np.searchsorted(probability, (base + np.arange(nconfig) / nconfig) % 1.0)
    configs.resample(newinds)
    weights.fill(wtot / nconfig)
    return configs, weights 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import searchsorted [as 別名]
def encode_edges(edges, nodes):
    """Encode data with dictionary

    Args:
        edges (np.ndarray): np array of the form [node1, node2].
        nodes (np.array): list of unique nodes

    Returns:
        np.ndarray: relabeled edges

    Examples:
        >>> import numpy as np
        >>> edges = np.array([['A', 'B'], ['A', 'C']])
        >>> nodes = np.array(['C', 'B', 'A'])
        >>> print(encode_edges(edges, nodes))
        [[2 1]
         [2 0]]
    """
    sidx = nodes.argsort()
    relabeled_edges = sidx[np.searchsorted(nodes, edges, sorter=sidx)]
    return relabeled_edges