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Python bottleneck.nanmin方法代码示例

本文整理汇总了Python中bottleneck.nanmin方法的典型用法代码示例。如果您正苦于以下问题:Python bottleneck.nanmin方法的具体用法?Python bottleneck.nanmin怎么用?Python bottleneck.nanmin使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在bottleneck的用法示例。


在下文中一共展示了bottleneck.nanmin方法的5个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。

示例1: quickMinMax

# 需要导入模块: import bottleneck [as 别名]
# 或者: from bottleneck import nanmin [as 别名]
def quickMinMax(self, data):
        """
        Estimate the min/max values of *data* by subsampling.
        Returns [(min, max), ...] with one item per channel
        """
        while data.size > 1e6:
            ax = np.argmax(data.shape)
            sl = [slice(None)] * data.ndim
            sl[ax] = slice(None, None, 2)
            data = data[sl]
            
        cax = self.axes['c']
        if cax is None:
            return [(float(nanmin(data)), float(nanmax(data)))]
        else:
            return [(float(nanmin(data.take(i, axis=cax))), 
                     float(nanmax(data.take(i, axis=cax)))) for i in range(data.shape[-1])] 
开发者ID:SrikanthVelpuri,项目名称:tf-pose,代码行数:19,代码来源:ImageView.py

示例2: reduce_to_array

# 需要导入模块: import bottleneck [as 别名]
# 或者: from bottleneck import nanmin [as 别名]
def reduce_to_array(self, reduce_func_nb, *args, **kwargs):
        """See `vectorbt.tseries.nb.reduce_to_array_nb`.

        `**kwargs` will be passed to `vectorbt.tseries.common.TSArrayWrapper.wrap_reduced`.

        Example:
            ```python-repl
            >>> min_max_nb = njit(lambda col, a: np.array([np.nanmin(a), np.nanmax(a)]))
            >>> print(df.vbt.tseries.reduce_to_array(min_max_nb, index=['min', 'max']))
                   a    b    c
            min  1.0  1.0  1.0
            max  5.0  5.0  3.0
            ```"""
        checks.assert_numba_func(reduce_func_nb)

        result = nb.reduce_to_array_nb(self.to_2d_array(), reduce_func_nb, *args)
        return self.wrap_reduced(result, **kwargs) 
开发者ID:polakowo,项目名称:vectorbt,代码行数:19,代码来源:accessors.py

示例3: min

# 需要导入模块: import bottleneck [as 别名]
# 或者: from bottleneck import nanmin [as 别名]
def min(self, **kwargs):
        """Return min of non-NaN elements."""
        return self.wrap_reduced(nanmin(self.to_2d_array(), axis=0), **kwargs) 
开发者ID:polakowo,项目名称:vectorbt,代码行数:5,代码来源:accessors.py

示例4: quickMinMax

# 需要导入模块: import bottleneck [as 别名]
# 或者: from bottleneck import nanmin [as 别名]
def quickMinMax(self, data):
        """
        Estimate the min/max values of *data* by subsampling.
        """
        while data.size > 1e6:
            ax = np.argmax(data.shape)
            sl = [slice(None)] * data.ndim
            sl[ax] = slice(None, None, 2)
            data = data[sl]
        return nanmin(data), nanmax(data) 
开发者ID:AOtools,项目名称:soapy,代码行数:12,代码来源:ImageView.py

示例5: _phase2

# 需要导入模块: import bottleneck [as 别名]
# 或者: from bottleneck import nanmin [as 别名]
def _phase2(self):
        """
        Execute phase 2 of the SP region. This phase is used to compute the
        active columns.
        
        Note - This should only be called after phase 1 has been called and
        after the inhibition radius and neighborhood have been updated.
        """
        
        # Shift the outputs
        self.y[:, 1:] = self.y[:, :-1]
        self.y[:, 0] = 0
        
        # Calculate k
        #   - For a column to be active its overlap must be at least as large
        #     as the overlap of the k-th largest column in its neighborhood.
        k = self._get_num_cols()
        
        if self.global_inhibition:
            # The neighborhood is all columns, thus the set of active columns
            # is simply columns that have an overlap >= the k-th largest in the
            # entire region
            
            # Compute the winning column indexes
            ix = np.argpartition(-self.overlap[:, 0], k - 1)[:k]
            
            # Set the active columns
            self.y[ix, 0] = self.overlap[ix, 0] > 0
        else:
            # The neighborhood is bounded by the inhibition radius, therefore
            # each column's neighborhood must be considered
            
            for i in xrange(self.ncolumns):
                # Get the neighbors
                ix = np.where(self.neighbors[i])[0]
                
                # Compute the minimum top overlap
                if ix.shape[0] <= k:
                    # Desired number of candidates is at or below the desired
                    # activity level, so find the overall min
                    m = max(bn.nanmin(self.overlap[ix, 0]), 1)
                else:
                    # Desired number of candidates is above the desired
                    # activity level, so find the k-th largest
                    m = max(-np.partition(-self.overlap[ix, 0], k - 1)[k - 1],
                        1)
                
                # Set the column activity
                if self.overlap[i, 0] >= m: self.y[i, 0] = True 
开发者ID:tehtechguy,项目名称:mHTM,代码行数:51,代码来源:region.py


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