def getPureSample(raw_data,start,end,sr=22050): ''' Takes a numpy array and spilts its using start and end args raw_data=numpy array of audio sample start=time end=time sr=sampling_rate mode=mono/stereo ''' max_ind = len(raw_data) start_ind = min(int(start * sr), max_ind) end_ind = min(int(end * sr), max_ind) return raw_data[start_ind: end_ind] 将上述python代码翻译成matlab代码
时间: 2024-04-12 08:30:25 浏览: 8
以下是将上述Python代码翻译为MATLAB代码的示例:
```matlab
function pureSample = getPureSample(raw_data, start, ending, sr)
% Takes a vector and splits it using start and ending args
% raw_data: vector of audio sample
% start: time
% ending: time
% sr: sampling rate
max_ind = length(raw_data);
start_ind = min(floor(start * sr), max_ind);
end_ind = min(floor(ending * sr), max_ind);
pureSample = raw_data(start_ind:end_ind);
end
```
请注意,这只是一个示例翻译,并假设你已经定义了一个名为`getPureSample`的函数,并在调用时传入了相应的参数。你可能需要根据你的实际情况进行一些调整和修改。确保在MATLAB中传递正确的参数和调用方式。
相关问题
data = data.iloc[:, 3:] data[data == 'NR'] = 0 raw_data = data.to_numpy()
这段代码的作用是对数据进行处理和转换。让我逐行解释给你:
- `data = data.iloc[:, 3:]`:这行代码使用`iloc`函数对`data`进行切片操作,将所有行保留,但是从第3列开始(索引为3)的所有列被保留。这样做的目的是去掉前面的列,因为它们可能是不相关的特征或标签。
- `data[data == 'NR'] = 0`:这行代码使用条件语句将数据中所有等于'NR'的元素替换为0。这通常是为了处理缺失值或特殊标记符号。
- `raw_data = data.to_numpy()`:这行代码将处理后的数据转换为NumPy数组的形式,并将其赋值给`raw_data`变量。`to_numpy()`是pandas DataFrame对象的方法,用于将数据转换为NumPy数组。
请注意,这些代码假设数据已经被正确加载到`data`变量中,并且该变量是一个pandas DataFrame对象。
mport numpy as np def rts_smooth(data, window_size, smooth_factor): assert window_size % 2 == 1, "Window size must be odd" assert 0 <= smooth_factor <= 1, "Smooth factor must be between 0 and 1" half_window = (window_size - 1) // 2 data_length = len(data) smoothed_data = np.zeros(data_length) for i in range(half_window, data_length - half_window): window = data[i - half_window:i + half_window + 1] median = np.median(window) deviation = np.abs(window - median) threshold = smooth_factor * np.median(deviation) if np.abs(data[i] - median) > threshold: smoothed_data[i] = median else: smoothed_data[i] = data[i] # 处理首尾值 smoothed_data[:half_window] = data[:half_window] smoothed_data[data_length - half_window:] = data[data_length - half_window:] return smoothed_data 使用示例 data = [10, 15, 20, 12, 18, 22, 25, 16, 14, 23] window_size = 3 smooth_factor = 0.6 smoothed_data = rts_smooth(data, window_size, smooth_factor) print(smoothed_data)这段代码如果输入数据有很多维度怎么改
如果输入数据有多个维度,可以使用`np.apply_along_axis`函数来应用平滑函数到每个维度上。下面是修改后的代码示例:
```python
import numpy as np
def rts_smooth(data, window_size, smooth_factor):
assert window_size % 2 == 1, "Window size must be odd"
assert 0 <= smooth_factor <= 1, "Smooth factor must be between 0 and 1"
half_window = (window_size - 1) // 2
data_length = len(data)
smoothed_data = np.zeros_like(data)
def smooth_func(x):
for i in range(half_window, data_length - half_window):
window = x[i - half_window:i + half_window + 1]
median = np.median(window)
deviation = np.abs(window - median)
threshold = smooth_factor * np.median(deviation)
if np.abs(x[i] - median) > threshold:
smoothed_data[i] = median
else:
smoothed_data[i] = x[i]
smoothed_data[:half_window] = x[:half_window]
smoothed_data[data_length - half_window:] = x[data_length - half_window:]
return smoothed_data
smoothed_data = np.apply_along_axis(smooth_func, axis=0, arr=data)
return smoothed_data
# 使用示例
data = np.array([[10, 15, 20, 12, 18, 22, 25, 16, 14, 23],
[5, 8, 10, 12, 9, 7, 15, 20, 18, 17]])
window_size = 3
smooth_factor = 0.6
smoothed_data = rts_smooth(data, window_size, smooth_factor)
print(smoothed_data)
```
这样,`rts_smooth`函数将会对输入数据的每个维度进行平滑处理。输出结果将保持与输入数据相同的维度。