def updateStack(self, blocks, indexes, overlapping=0, block_size=64): """ Update the self.stack with the divided volumes. @blocks (list) The divided volumes. @indexes (list) The list containing index numbers corresponding to the blocks. @overlapping (integer) A overlapping pixel size. Should be the same value used in the block_separator function. @block_size (integer) A block size determining the divided volume size. Should be the same value used in the block_separator function. """ self.checkStack() for block, index in zip(blocks, indexes): self.stack[index[0]:index[0]+block_size, index[1]:index[1]+block_size, index[2]:index[2]+block_size] = block[overlapping:-overlapping, overlapping:-overlapping, overlapping:-overlapping] return请完整详细解释每一行代码的意思
时间: 2023-06-04 10:03:39 浏览: 69
这段代码是一个函数,名为updateStack,其功能是将分块后的体素更新至当前的空间中。它包含了四个参数:
1. blocks:分块后的体素,即被分成若干小块的三维立方体体素数据。
2. indexes:代表每个小块在原来的大数据空间中的位置和大小。
3. overlapping:代表每个小块在更新时需要进行重叠的像素大小(因为分块后的小块之间可能存在重叠)。
4. block_size:代表每个小块的大小。
函数的执行过程如下:
- 调用checkStack()函数,检查当前的空间是否已经被初始化。
- 遍历每个小块和其对应的index,对于每个小块,根据对应的index在空间中定位并更新其像素值。
- 最后返回更新后的空间。
具体来说,第4行代码用zip函数将blocks和indexes的对应小块和index打包成元素为元组的列表,以进行并列遍历。第6行代码使用numpy库的数组切片操作[],从当前的空间self.stack上取出对应小块部分,并用等号=将其赋值为对应小块的像素值。切片操作[起始位置:终止位置]代表了numpy数组的一个视图,即将数组分隔成指定位置两侧的两部分,而不进行复制操作。切片操作中的起始位置和终止位置是左闭右开的区间表示法,代表从第一个位置开始到第二个位置结束,但不包括第二个位置。
因此,这段代码的作用是根据传入的分块后的体素和位置信息,更新当前的空间。
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def updateStack(self, blocks, indexes, overlapping=0, block_size=64): """ Update the self.stack with the divided volumes. @blocks (list) The divided volumes. @indexes (list) The list containing index numbers corresponding to the blocks. @overlapping (integer) A overlapping pixel size. Should be the same value used in the block_separator function. @block_size (integer) A block size determining the divided volume size. Should be the same value used in the block_separator function. """ self.checkStack() for block, index in zip(blocks, indexes): self.stack[index[0]:index[0]+block_size, index[1]:index[1]+block_size, index[2]:index[2]+block_size] = block[overlapping:-overlapping, overlapping:-overlapping, overlapping:-overlapping] return请详细解释每一行代码的意思
这段代码是一个函数,函数名为"updateStack",用来更新另一个变量"self.stack",将输入的被划分成若干个块的体积(即"blocks")按照对应的索引(即"indexes")拼接在一起,并返回更新后的"self.stack"。
具体每一行代码的含义如下:
1. def updateStack(self, blocks, indexes, overlapping=0, block_size=64):
这一行是函数的定义,其中第一个参数"self"表示该函数是属于某个类(应该是一个Python类)里的方法,其余三个参数分别是用来传入该函数需要的被划分成若干个块的体积、对应的索引,并且可以指定重叠像素大小和块大小的默认值是64。
2. self.checkStack()
这一行调用类中的"checkStack"方法,用来检查"self.stack"是否存在,如果不存在则创建一个空数组。
3. for block, index in zip(blocks, indexes):
这一行使用Python中的"zip"函数将"blocks"和"indexes"中的元素一一对应起来,并依次传递给"block"和"index"两个变量。
4. self.stack[index[0]:index[0] block_size, index[1]:index[1] block_size, index[2]:index[2] block_size] = block[overlapping:-overlapping, overlapping:-overlapping, overlapping:-overlapping]
这一行是该函数的主要逻辑,用来将"block"按照"index"对应的位置拼接起来。具体说明如下:
- [index[0]:index[0]+block_size, index[1]:index[1]+block_size, index[2]:index[2]+block_size] 表示对应的位置范围,用来指定拼接后的体积大小。
- "= block[overlapping:-overlapping, overlapping:-overlapping, overlapping:-overlapping]" 表示拼接后赋值给该位置。这里使用了Python中的切片操作,去掉每个块的边缘"overlapping"个像素,避免拼接时出现重复部分。
5. return
这一行表示该函数的返回值为拼接完成后的更新后的"self.stack"。
def filterNormalization(self, block_size=64, all_at_once = False): """ Normalize signal intensity. @block_size (integer) A block size determining the divided volume size. This argument is passed to the block_separator function. @all_at_once (bool) A flag determining all-at-onec processing. This argument is passed to the block_separator function. """ print("Intensity normalization") if self.peak_air == None: raise Exception('Call the calculateNormalizationParam in ahead.') maxid = [self.peak_air, self.peak_soil] maxid = [i-self.hist_x[0] for i in maxid] plt.figure() plt.plot(self.hist_x, self.hist_y) plt.plot(self.hist_x[maxid], self.hist_y[maxid],'ro') plt.xlabel('intensity') plt.ylabel('count') plt.pause(.01) i_block = self.block_separator(overlapping = 1, block_size = block_size, all_at_once = all_at_once) for blocks, indexes in i_block: blocks = tqdm_multiprocessing(functools.partial(normalizeIntensity, peak_air=self.peak_air, peak_soil=self.peak_soil), blocks) self.updateStack(blocks, indexes, overlapping = 1, block_size = block_size) return请完整详细解释每一行的代码意思
def filterNormalization(self, block_size=64, all_at_once=False):
"""
Normalize signal intensity.
@block_size (integer): A block size determining the divided volume size.
This argument is passed to the block_separator function.
@all_at_once (bool): A flag determining all-at-once processing.
This argument is passed to the block_separator function.
"""
# 打印字符串
print("Intensity normalization")
# 如果没有设置峰值,抛出异常
if self.peak_air == None:
raise Exception('Call the calculateNormalizationParam in ahead.')
# 设置峰值
maxid = [self.peak_air, self.peak_soil]
maxid = [i-self.hist_x[0] for i in maxid]
# 绘图
plt.figure()
plt.plot(self.hist_x, self.hist_y)
plt.plot(self.hist_x[maxid], self.hist_y[maxid],'ro')
plt.xlabel('intensity')
plt.ylabel('count')
plt.pause(.01)
# 将数据分块处理
i_block = self.block_separator(overlapping=1, block_size=block_size, all_at_once=all_at_once)
# 对分块数据进行处理
for blocks, indexes in i_block:
# 对分块数据进行处理
blocks = tqdm_multiprocessing(functools.partial(normalizeIntensity, peak_air=self.peak_air, peak_soil=self.peak_soil), blocks)
# 更新数据
self.updateStack(blocks, indexes, overlapping=1, block_size=block_size)
# 返回结果
return