slice

Within brackets, python's slice shorthand auto-generates tuples of slice objects: class Foo(object): def __getitem__(self, key): print key Foo()[1::, 2:20:5] This prints (slice(1, None, None), slice(2, 20, 5)) . As far as I can tell, however, this shorthand doesn't work outside brackets. Is there any way to use slice shorthand in other contexts? I could define a dummy object that simply returns whatever it is passed to __getitem__ -- that would at least give me a way to generate slice tuples using the shorthand syntax. Is there a more pythonic way? NumPy has an s_ object that will do this for

I have a multidimensional array a: a = np.random.uniform(1,10,(2,4,2,3,10,10)) For dimensions 4-6, I have 3 lists which contain the indexes for slicing that dimension of array 'a' dim4 = [0,2] dim5 = [3,5,9] dim6 = [1,2,7,8] How do I slice out array 'a' such that i get: b = a[0,:,0,dim4,dim5,dim6] So b should be an array with shape (4,2,3,4), and containing elements from the corresponding dimensions of a. When I try the code above, I get an error saying that different shapes can't be broadcast together for axis 4-6, but if I were to do: b = a[0,:,0:2,0:3,0:4] then it does work, even though the

我已用随机生产函数取模拟5张图片各有8个box的坐标值，而后验证batch_slice（）函数的意义。由于inputs_slice = [x[i] for x in inputs] output_slice = graph_fn(*inputs_slice)代码一时蒙蔽，故而对其深入理解，如下:代码如下： import tensorflow as tfimport randomimport numpy as npsess=tf.Session()input=np.array([random.randint(0,150) for i in range(5*8*4)]).reshape((5,8,4))# print('show input=',input)ax=np.array([random.randint(0,7) for i in range(5*6)]).reshape((5,6))inputs=[input,ax]print('true_inputs=',inputs)def batch_slice(inputs, graph_fn, batch_size, names=None): """Splits inputs into slices and feeds each slice to a copy of the given computation graph and

X264编码库libx264实现真正的视频编解码，该编解码算法是基于块的混合编码技术，即帧内/帧间预测，然后对预测值变换、量化，最后熵编码所得。 编码帧的类型分为I帧（x264_type_i）、P帧（x264_type_p）、B帧（x264_type_b），在H264中叫做图像片Slice。 X264把整帧图像看作一个Slice，片中有slice_type_i、slice_type_p、slice_type_b之分。 I帧只有slice_type_i，P帧有slice_type_i、slice_type_p，B帧三种片都有。 X264的H264视频编码过程可以分为三个步骤：首先根据规则判定当前帧的编码类型，如果是B帧，要缓冲存放、获取；然后对待编码图像进行帧内预测、帧间预测、整数DCT变换、量化和熵编码；最后把压缩的H264数据进行NAL层打包输出。 X264编码器有关的重要结构体： x264_image_t：实际参与编码的编码帧图像信息。 typedef struct { int i_csp; //图像空间颜色 int i_plane; //图像平面数目 int i_stride[4]; //每个图像平面的跨度，也就是每一行数据的字节数 uint8_t *plane[4]; //每个图像平面存放数据的起始地址，plane[0]是Y平面，plane[1]是U平面，plane[2

here is my problem: I have 2 arrays. One is character array and represents a sliding window. Characters gets shifted from the beginning and pushed at the end. I would like to use a second array to store references to array slices that 'follow' the characters as they move along. Example: my @char_array = ('h','e','l','l','o','w','o','r','l','d'); my $char_arr_ref=[@char_array[1..$#char_array]]; print @$char_arr_ref, "\n"; # slice contains 'elloworld'; shift(@char_array); push(@char_array), 'x'; print @$char_arr_ref, "\n"; # slice still contains 'elloworld', not 'lloworldx' as I need; IN other

I can see from the pandas documentation that you can go: df.loc[['a','b','c'],:] For time series, why can you not go: x = df.loc[['2005-10-27 14:30':'2005-10-27 15:15', '2006-04-14 14:40':'2006-04-14 15:20', '2008-01-25 14:30':'2008-01-25 15:30'],:] I get a syntax error. Can you not do multiple sliced ranges on a time series? Is there a workaround? While a DataFrame index will accept a list of column indexes, it will not accept a list of row slice objects. This should do what you want, it loops through your desired ranges compiling a new DataFrame. import numpy as np import pandas as pd # let