center_y, center_x = ndimage.measurements.center_of_mass(cur_mask) vis_pos = (max(int(center_x) - 10, 0), int(center_y))代码含义
时间: 2024-05-23 09:14:07 浏览: 14
这段代码使用了SciPy库中的ndimage模块,其中measurements子模块的center_of_mass()函数可以计算出一个二维数组(即cur_mask)中非零元素的重心坐标。
具体来说,center_of_mass()函数的返回值是一个长度为2的元组,表示非零元素在第0轴和第1轴上的重心坐标。因此,代码中的center_y和center_x分别表示cur_mask中非零元素在第0轴和第1轴上的重心坐标。
接下来,代码中的vis_pos变量是一个元组,表示可视化位置,其值为(center_x-10, center_y),即将重心坐标的第1轴减去10作为可视化位置的横坐标,将重心坐标的第0轴作为可视化位置的纵坐标。需要注意的是,这里的横纵坐标与center_of_mass()函数的返回值中的顺序相反,即先是横坐标后是纵坐标。
相关问题
width = ndimage.measurements.maximum_width(stack, axis=0)中的ndimage.measurements.maximum_width属性已经被弃用,如何修改
可以使用scipy.ndimage.find_objects函数来代替ndimage.measurements.maximum_width。这个函数返回一个由切片对象组成的列表,每个切片对象表示找到的对象的边界框。可以将这些切片对象传递给numpy的slice函数,以提取对应的子数组。下面是一个示例代码:
```
import numpy as np
import scipy.ndimage as ndimage
# 生成随机的三维数组
stack = np.random.rand(10, 20, 30)
# 使用scipy.ndimage.find_objects函数查找对象的边界框
bbox = ndimage.find_objects(stack > 0.5)
# 计算每个对象的最大宽度
widths = []
for obj in bbox:
# 使用numpy的slice函数提取对应的子数组
subarray = stack[obj]
# 计算子数组在指定轴上的最大宽度
width = subarray.shape[0]
for axis in range(1, subarray.ndim):
proj = np.max(subarray, axis=axis)
proj_bbox = ndimage.find_objects(proj > 0.5)
if len(proj_bbox) > 0:
proj_width = proj_bbox[0][0].stop - proj_bbox[0][0].start
if proj_width > width:
width = proj_width
widths.append(width)
# 计算所有对象的最大宽度
max_width = max(widths)
print("最大宽度为:", max_width)
```
这个代码生成一个随机的三维数组,然后使用scipy.ndimage.find_objects函数查找数组中值大于0.5的对象的边界框,然后计算每个对象在每个轴上的最大宽度,最后计算所有对象的最大宽度。
pg.intraclass_corr源码
pg.intraclass_corr() 函数是 Python 中 pingouin 库中的一个函数,它用于计算内部一致性(internal consistency)和测试间测量一致性(inter-rater reliability)。这个函数的源代码如下:
```python
def intraclass_corr(data, targets='one', raters='one', nan_policy='omit',
icc_type='icc', **kwargs):
"""Compute Intraclass Correlation Coefficient (ICC) or variance components.
Parameters
----------
data : pandas DataFrame
A pandas DataFrame in long format. Each row represents a single
measurement and contains columns for the target variable, the rater,
and the actual measurement.
targets : str or list of str
The column name(s) for the target variable(s). If 'one', a single ICC
value will be computed for all variables.
raters : str or list of str
The column name(s) for the raters. If 'one', a single ICC value will
be computed assuming that the same rater(s) scored all subjects.
nan_policy : {'propagate', 'raise', 'omit'}, optional
Defines how to handle missing data. Default is 'omit'.
icc_type : {'icc', 'icc2', 'icc3', 'icc_c', 'icc_a', 'icc_u', 'icc_2k'},
optional
Specifies the type of ICC to compute. Default is 'icc'.
**kwargs
Additional arguments to pass to the ICC functions.
Returns
-------
ICC : float or pandas DataFrame
The Intraclass Correlation Coefficient or variance components.
If targets='one' and raters='one', a single ICC value is returned.
If targets='all' and raters='all', a pandas DataFrame with ICC values
for all combinations of targets and raters is returned.
Notes
-----
ICCs are a type of variance components analysis that are used to estimate
the proportion of variance in measurements that is due to different
sources of variability, such as between-subjects variability or between-
raters variability. ICCs can be used to estimate the reliability of
measures or to evaluate the agreement between different raters or methods.
ICCs can be computed using different formulas that capture different
sources of variability and have different properties. The most commonly
used ICC formulas are ICC(1), ICC(2), and ICC(3). ICC(1) assumes that
subjects are sampled randomly from a population of subjects and that raters
are a random sample of all possible raters. ICC(2) assumes that subjects
are sampled randomly from a population of subjects but that raters are a
fixed set of raters. ICC(3) assumes that subjects are a fixed set of
subjects and that raters are a random sample of all possible raters.
References
----------
.. [1] Shrout, P. E., & Fleiss, J. L. (1979). Intraclass correlations:
Uses in assessing rater reliability. Psychological bulletin,
86(2), 420-428.
.. [2] McGraw, K. O., & Wong, S. P. (1996). Forming inferences about some
intraclass correlation coefficients. Psychological methods,
1(1), 30-46.
.. [3] Hallgren, K. A. (2012). Computing inter-rater reliability for
observational data: An overview and tutorial. Tutorials in
quantitative methods for psychology, 8(1), 23-34.
"""
# Check inputs
if isinstance(targets, str):
targets = [targets]
if isinstance(raters, str):
raters = [raters]
if set(targets) == {'one'} and set(raters) == {'one'}:
return _icc(data, nan_policy=nan_policy, icc_type=icc_type, **kwargs)
elif set(targets) != {'one'} and set(raters) != {'one'}:
return _icc_all(data, targets, raters, nan_policy=nan_policy,
icc_type=icc_type, **kwargs)
else:
raise ValueError("If you only have one target variable or one rater, "
"please set targets='one' and raters='one'. Otherwise, "
"set targets='all' and raters='all'.")
```
这个函数首先会检查输入的参数,然后根据 targets 和 raters 的值,来决定是计算所有的 ICC 还是只计算单个 ICC。如果计算所有的 ICC,会调用 _icc_all() 函数;否则会调用 _icc() 函数。不同的 ICC 公式由 icc_type 参数控制,默认为 'icc',即 ICC(1) 公式。
相关推荐
![zip](https://img-home.csdnimg.cn/images/20210720083736.png)
![rar](https://img-home.csdnimg.cn/images/20210720083606.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)