sped | Sistema para geração do SPED
kandi X-RAY | sped Summary
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Top functions reviewed by kandi - BETA
- Returns a string representation of the campo
- Return a string representation of the campha
- Gets a string representation of this proposition
- Convert to string
- Create a string representation of the campera
- Create a string representation of this campera
- Determines string representation of the campo
- Convert the proposition to a string
- Determines the string representation of the campo
- Define a string
- Return a string representation of the campo
- Return a string representation of the proposition
- Serialize to string
- Create a string representation of this proposition
- Return a string representation of this proposition
- Returns a string representation of the campera
- Return a string representation of the campera
- Create a string representation of the campo
- Convert this proposition to a string
- Returns a string representation of this proposition
sped Key Features
sped Examples and Code Snippets
Community Discussions
Trending Discussions on sped
QUESTION
Physical Background
I'm working on a function that calculates some metrics for each vertical profile in an up to four dimensional temperature field (time, longitude, latitude, pressure as height measure). I have a working function that takes the pressure and temperature at a single location and returns the metrics (tropopause information). I want to wrap it with a function that applies it to every vertical profile in the data passed.
Technical Description of the Problem
I want my function to apply another function to every 1D array corresponding to the last dimension in my N-dimensional array, where N <= 4. So I need an efficient loop over all dimensions but the last one without knowing the number of dimensions beforehand.
Why I Open a New Question
I am aware of several questions (e.g., iterating over some dimensions of a ndarray, Iterating over the last dimensions of a numpy array, Iterating over 3D numpy using one dimension as iterator remaining dimensions in the loop, Iterating over a numpy matrix with unknown dimension) asking how to iterate over a specific dimension or how to iterate over an array with unknown dimensions. The combination of these two problems is new as far as I know. Using numpy.nditer for example I haven't found out how to exclude just the last dimension regardless of the number of dimensions left.
EDIT
I tried to do a minimal, reproducible example:
...ANSWER
Answered 2021-Jun-07 at 11:09I've used @hpaulj 's reshape approach several times. It means the loop can iterate the whole array by 1d slices.
Simplified the function and data to have something to test.
QUESTION
So I'm making a Conway's Game of Life in Python 3 and I have this function called updateboard
that gives birth and kills cells based on their neighbor count (from 0 to 8) stored in self.neighbors
. The function looks like this:
ANSWER
Answered 2021-May-29 at 05:38Note that this behaviour might change depending on the Python interpreter or CPU architecture you are using.
In general, x86 CPUs has a special bit that is checked whether a value is zero after arithmetic operation, called Zero-Flag. This is used when you want equality checks, e.g.:
if x == 3
In assembly, it would be:
QUESTION
I want to download and extract 100 tar.gz files that are each 1GB in size. Currently, I've sped it up with multithreading and by avoiding disk IO via in-memory byte streams, but can anyone show me how to make this faster (just for curiosity's sake)?
...ANSWER
Answered 2021-May-11 at 07:53Your computation is likely IO bound. Compression is generally a slow task, especially the gzip algorithm (new algorithms can be much faster). From the provided information, the average reading speed is about 70 Mo/s. This means that the storage throughput is at least roughly 140 Mo/s. It looks like totally normal and expected. This is especially true if you use a HDD or a slow SSD.
Besides this, it seems you iterate over the files twice due to the selection of members
. Keep in mind that tar gz files are a big block of files packed together and then compressed with gzip. To iterate over the filenames the tar file need to be already partially decompressed. This may not be a problem regarding the implementation of tarfile
(possible caching). If the size of all the discarded files is small, it may be better to simply decompress the whole archive in a raw and then remove the files to discard. Moreover, if you have a lot a memory and the size of all discarded files is not small, you can decompress the files in an in-memory virtual storage device first in order to write the discarded files. This can be natively done on Linux systems.
QUESTION
I have a table called videos. It's got more columns than this but it should be enough for this example.
...ANSWER
Answered 2021-May-10 at 15:09The query you show needs the following index:
QUESTION
Suppose I have a list of (greyscale) pixels, e.g.
...ANSWER
Answered 2021-Apr-23 at 03:21I'd start by sorting the arrays using np.lexsort
:
QUESTION
I'm trying to query BigQuery use the BigQuery API with the Python client library.
However, for some reason, my query seems to "hang" for about 150 seconds when calling the BigQuery API, i.e., at the following line (see below for full code sample):
results = client.query(query)
Note: it doesn't matter what the actual query is. Therefore, in my sample code below, I'm just putting SELECT 1
as a query.
Interestingly, there is only a delay for the first query - all subsequent queries are as fast as expected.
I've checked the query time in the Query History for BQ, and it confirms that all of the queries take less than a second. So it's definitely not the actual query that's taking so long, but something else.
I'm guessing that this may somehow related to the authentication, but I'm not sure why that would be or if I'm doing anything wrong - or how it can be sped up, most importantly.
Any hints are greatly appreciated.
...ANSWER
Answered 2021-Apr-21 at 13:18Ok, so after two days of trying to find a solution, the exact same script, which took 160 seconds yesterday, is now running in about 4 seconds. It would seem that there was something wrong on Google's side of things.
QUESTION
There are a lot of questions here about matching points in polygons efficiently (examples: Here and Here). The primary variables of interest in these are high number of points N, and number of polygon vertices V. These are all good and useful, but I am looking at a high number of points N and polygons G. This also means that my output will be different (I've primarily seen output consisting of the points that fall inside a polygon, but here I'd like to know the polygon attached to a point).
I have a shapefile with a large number of polygons (hundreds of thousands). Polygons can touch, but there is little to no overlap between them (any overlap of interiors would be a result of error - think census block groups). I also have a csv with points (millions), and I would like to categorize those points by which polygon the point falls in, if any. Some may not fall into a polygon (continuing with my example, think points over the ocean). Below I set up a toy example to look at the issue.
Setup:
...ANSWER
Answered 2021-Apr-20 at 14:45It sounds like you could avoid iterating through all polygons by using the nearest STRtree algorithm, as written in the documentation (along with note above about recovering indices of the polygons) - and checking if the point sits within the nearest polygon. I.e. something like
QUESTION
I was wondering whether there was a way to speed up this code:
...ANSWER
Answered 2021-Apr-20 at 12:22You didn't provide any sample data or expected outputs, so it is hard to answer this question.
Theoretically, you should be able to groupby and then use transform, which will assign the group value to each row in the group. If you are more comfortable using agg, you can calculate the group value and then join the original dataframe and the aggregates on 'OrigCodeNew'.
QUESTION
I am trying to increase the speed of an aerodynamics function in Python.
Function Set: ...ANSWER
Answered 2021-Mar-23 at 03:51First of all, Numba can perform parallel computations resulting in a faster code if you manually request it using mainly parallel=True
and prange
. This is useful for big arrays (but not for small ones).
Moreover, your computation is mainly memory bound. Thus, you should avoid creating big arrays when they are not reused multiple times, or more generally when they cannot be recomputed on the fly (in a relatively cheap way). This is the case for r_0
for example.
In addition, memory access pattern matters: vectorization is more efficient when accesses are contiguous in memory and the cache/RAM is use more efficiently. Consequently, arr[0, :, :] = 0
should be faster then arr[:, :, 0] = 0
. Similarly, arr[:, :, 0] = arr[:, :, 1] = 0
should be mush slower than arr[:, :, 0:2] = 0
since the former performs to noncontinuous memory passes while the latter performs only one more contiguous memory pass. Sometimes, it can be beneficial to transpose your data so that the following calculations are much faster.
Moreover, Numpy tends to create many temporary arrays that are costly to allocate. This is a huge problem when the input arrays are small. The Numba jit can avoid that in most cases.
Finally, regarding your computation, it may be a good idea to use GPUs for big arrays (definitively not for small ones). You can give a look to cupy or clpy to do that quite easily.
Here is an optimized implementation working on the CPU:
QUESTION
Problem:
I have a fixture that takes about 5 minutes to instantiate. This fixture relies on a fixture from another package that I cannot touch. The time of the fixture can be drastically sped up depending on the state of a different (must faster instantiating) fixture. For example, this is the psuedo code of what I am looking to do:
...ANSWER
Answered 2021-Apr-01 at 12:15You can use the “factory as fixture” pattern:
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