会务通信的iCCS控制服务器
时间: 2023-05-16 11:06:45 浏览: 51
iCCS控制服务器是一种用于会务通信的服务器,它可以控制和管理会议中的各种通信设备,包括音频、视频、数据等。iCCS控制服务器可以提高会议的效率和质量,使会议更加顺畅和高效。关于iCCS控制服务器的具体实现和技术细节,我需要更多的信息才能回答您的问题。
相关问题
瑞萨RH850F1KM的输入捕获例程
瑞萨RH850F1KM是一款32位的汽车微控制器,它支持多种外设和功能。以下是一个简单的输入捕获例程,展示如何使用RH850F1KM的输入捕获功能:
```c
#include "rh850f1km.h"
void input_capture_init()
{
// 选择输入捕获功能的引脚
// 例如,选择P0_0作为输入捕获引脚
MPC.P00PFS.BYTE = 0x09; // PFS0_9: INPFCAP0
// 配置输入捕获模式
ICU.ICCR[0].BIT.ICCS = 0x01; // 使用捕获计数器CH0
ICU.ICMR[0].BIT.ICPSEL = 0x01; // 选择输入捕获模式1 (上升沿触发)
// 配置输入捕获时钟源
ICU.ICOCR[0].BIT.ICODIV = 0x00; // 不分频
ICU.ICOCR[0].BIT.ICOSEL = 0x00; // 选择内部高精度时钟源
// 启用输入捕获中断
ICU.IER[1].BIT.IEN_ICIE0 = 1; // CH0 捕获中断使能
// 清除输入捕获中断标志
ICU.IR[1].BIT.IR_ICIF0 = 0; // CH0 捕获中断标志
// 使能输入捕获功能
ICU.ICCR[0].BIT.ICEN = 1; // CH0 捕获功能使能
}
// 输入捕获中断处理函数
void input_capture_isr()
{
uint16_t capture_value = ICU.ICCPW[0].WORD; // 获取捕获计数器的值
// 在这里处理输入捕获事件
}
int main()
{
input_capture_init();
while(1)
{
// 主循环中进行其他操作
}
return 0;
}
```
以上代码是一个简单的输入捕获例程,它配置了RH850F1KM的输入捕获模块,并使用中断方式处理输入捕获事件。你可以根据自己的需求进行适当的修改和扩展。
请注意,以上代码只是一个示例,实际使用时需要根据具体的硬件和需求进行适当的配置和调整。确保查阅RH850F1KM的技术手册和参考资料以获取更详细的信息和指导。
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。
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