fancy-index | A responsive Apache index page | Static Site Generator library
kandi X-RAY | fancy-index Summary
kandi X-RAY | fancy-index Summary
A responsive Apache index page. I was tired of seeing the ugly apache-generated index page, so I decided to do something about it. Inspired by Seti UI and atom file-icons, this project adds an .htaccess file which tells apache to use a table, among other things, instead of .
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QUESTION
Please help understand the design decision or rational of the Numpy indexing with tuple (i, j) into a ndarray.
BackgroundWhen the index is a single tuple (4, 2), then (i=row, j=column).
...ANSWER
Answered 2020-Dec-21 at 08:33It's quite easy to understand how it works (and the motivation behind this design decision).
Numpy stores its ndarrays as contiguous blocks of memory. Each element is stored in a sequential manner every n bytes after the previous.
(images referenced from this excellent SO post)
So if your 3D array looks like this -
Then in memory its stores as -
When retrieving an element (or a block of elements), NumPy calculates how many strides (bytes) it needs to traverse to get the next element in that direction/axis. So, for the above example, for axis=2 it has to traverse 8 bytes (depending on the datatype) but for axis=1 it has to traverse 8*4 bytes, and axis=0 it needs 8*8 bytes.
With this in mind, let's look at what you are trying to do.
QUESTION
I have a http server (apache HTTPD v2.4) where directory listing is enabled. I configured it to use the 'fancy-index' from https://github.com/Vestride/fancy-index, and works great. However, its searching function didn't give what I was looking for.
I'd like to configure the webserver such that if I goto http://mywebserver.com/someDir/*?list, it will return a list of all the files in someDir in a "file, filesize" format.
Or, if I goto http://mywebserver.com/someDir/essay2020*.txt?list , I'll get a custom list of all essay2020*.txt files. Or some form of a simple regex: say "essay2020[0-2]*.txt".
I can write a php/perl/python script that can read a directory and return the 'file, filesize' list. But how do I configure the .htaccess to call this script? Do I have to muck around with 'RewriteRule'? or something special?
As an added note, I'm not limited to modifying .htaccess only. If I can do all of this by modifying the httpd.conf, I'm open to that suggestion too.
Thanks.
...ANSWER
Answered 2020-May-01 at 19:43The answer in Comments by CBroe did it. By just modifying the apache2.conf file to add:
QUESTION
Please consider the following code:
...ANSWER
Answered 2019-Jun-28 at 12:10You can simply use arr as an indexing array for mapping:
QUESTION
ND4J INDArray slicing is achieved through one of the overloaded get() methods as answered in java - Get an arbitrary slice of a Nd4j array - Stack Overflow. As an INDArray takes a continuous block of native memory, does slicing using get() make a copy of the original memory (especially row slicing, in which it is possible create a new INDArray with the same backing memory)?
I have found another INDArray method subArray(). Does this one make any difference?
I am asking this because I am trying to create a DatasetIterator that can directly extract data from INDArrays, and I want to eliminate possible overhead. There is too much abstraction in the source code and I couldn't find the implementation myself.
A similar question about NumPy is asked in python - Numpy: views vs copy by slicing - Stack Overflow, and the answer can be found in Indexing — NumPy v1.16 Manual:
...The rule of thumb here can be: in the context of lvalue indexing (i.e. the indices are placed in the left hand side value of an assignment), no view or copy of the array is created (because there is no need to). However, with regular values, the above rules for creating views does apply.
ANSWER
Answered 2019-May-13 at 09:16The short answer is: no it is using reference when possible. To make a copy the .dup() function can be called.
To quote https://deeplearning4j.org/docs/latest/nd4j-overview
Views: When Two or More NDArrays Refer to the Same Data
A key concept in ND4J is the fact that two NDArrays can actually point to the same underlying data in memory. Usually, we have one NDArray referring to some subset of another array, and this only occurs for certain operations (such as INDArray.get(), INDArray.transpose(), INDArray.getRow() etc. This is a powerful concept, and one that is worth understanding.
There are two primary motivations for this:
There are considerable performance benefits, most notably in avoiding copying arrays We gain a lot of power in terms of how we can perform operations on our NDArrays Consider a simple operation like a matrix transpose on a large (10,000 x 10,000) matrix. Using views, we can perform this matrix transpose in constant time without performing any copies (i.e., O(1) in big O notation), avoiding the considerable cost copying all of the array elements. Of course, sometimes we do want to make a copy - at which point we can use the INDArray.dup() to get a copy. For example, to get a copy of a transposed matrix, use INDArray out = myMatrix.transpose().dup(). After this dup() call, there will be no link between the original array myMatrix and the array out (thus, changes to one will not impact the other).
QUESTION
I'm trying to apply an addition operator to an array where I want repeated indices to indicate repeated addition operations. From a Python Data Science Book (https://jakevdp.github.io/PythonDataScienceHandbook/02.07-fancy-indexing.html), it seems that this is possible using np.add.at(original matrix, indices, thing to add), but I can't figure out how to specify the indices to operate on columns, not rows.
e.g. Dummy Example
...ANSWER
Answered 2018-Nov-12 at 06:59In [12]: A = np.arange(12).reshape(4,3)
In [13]: np.add.at(A, (slice(None), [0,0]), 1)
In [14]: A
Out[14]:
array([[ 2, 1, 2],
[ 5, 4, 5],
[ 8, 7, 8],
[11, 10, 11]])
QUESTION
Fancy Indexing vs Views in Numpy
In an answer to this equation: is is explained that different idioms will produce different results.
Using the idiom where fancy indexing is to chose the values and said values are set to a new value in the same line means that the values in the original object will be changed in place.
However the final example below:
https://scipy-cookbook.readthedocs.io/items/ViewsVsCopies.html
"A final exercise"
The example appears to use the same idiom:
a[x, :][:, y] = 100
but it still produces a different result depending on whether x is a slice or a fancy index (see below):
...ANSWER
Answered 2018-Oct-24 at 16:54Python evaluates each set of [] separately. a[x, :][:, y] = 100 is 2 operations.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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Install fancy-index
Clone or download the files.
Add them to your Sites directory. The structure should be /Users/USERNAME/Sites/fancy-index.
Copy the .htaccess file up one directory to /Users/USERNAME/Sites/.htaccess.
Update your DocumentRoot in /etc/apache2/users/USERNAME.conf to point to your Sites. This will also cause localhost to point to Sites and you won't have to use the ~USERNAME to access it.
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