sped | Sistema para geração do SPED

 by   wmixvideo Java Version: Current License: Apache-2.0

kandi X-RAY | sped Summary

kandi X-RAY | sped Summary

sped is a Java library. sped has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

Sistema para geração do SPED Fiscal:.

            kandi-support Support

              sped has a low active ecosystem.
              It has 27 star(s) with 18 fork(s). There are 10 watchers for this library.
              It had no major release in the last 6 months.
              There are 1 open issues and 1 have been closed. On average issues are closed in 68 days. There are 2 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of sped is current.

            kandi-Quality Quality

              sped has 0 bugs and 0 code smells.

            kandi-Security Security

              sped has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              sped code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              sped is licensed under the Apache-2.0 License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              sped releases are not available. You will need to build from source code and install.
              Build file is available. You can build the component from source.
              sped saves you 2854 person hours of effort in developing the same functionality from scratch.
              It has 6170 lines of code, 842 functions and 150 files.
              It has low code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed sped and discovered the below as its top functions. This is intended to give you an instant insight into sped implemented functionality, and help decide if they suit your requirements.
            • 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
            Get all kandi verified functions for this library.

            sped Key Features

            No Key Features are available at this moment for sped.

            sped Examples and Code Snippets

            No Code Snippets are available at this moment for sped.

            Community Discussions


            Numpy iteration over all dimensions but the last one with unknown number of dimensions
            Asked 2021-Jun-07 at 11:09

            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.


            I tried to do a minimal, reproducible example:



            Answered 2021-Jun-07 at 11:09

            I'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.

            Source https://stackoverflow.com/questions/67857864


            why is "if x:" significantly faster than "if x>0"? Conway's game of life
            Asked 2021-May-29 at 05:43

            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:



            Answered 2021-May-29 at 05:38

            Note 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:

            Source https://stackoverflow.com/questions/67747896


            Downloading & extracting in parallel, maximizing performance?
            Asked 2021-May-11 at 07:53

            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)?



            Answered 2021-May-11 at 07:53

            Your 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.

            Source https://stackoverflow.com/questions/67478659


            Even with indices this query is relatively slow. How to speed it up?
            Asked 2021-May-10 at 15:09

            I have a table called videos. It's got more columns than this but it should be enough for this example.



            Answered 2021-May-10 at 15:09

            The query you show needs the following index:

            Source https://stackoverflow.com/questions/67472523


            Efficiently amalgamate duplicate pixels (by summing) from sparse representation using Python/numpy
            Asked 2021-Apr-23 at 03:21

            Suppose I have a list of (greyscale) pixels, e.g.



            Answered 2021-Apr-23 at 03:21

            I'd start by sorting the arrays using np.lexsort:

            Source https://stackoverflow.com/questions/67191456


            Why does BigQuery API call take so long?
            Asked 2021-Apr-21 at 13:18

            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.



            Answered 2021-Apr-21 at 13:18

            Ok, 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.

            Source https://stackoverflow.com/questions/67168019


            Efficiently match point to geometry (point in poly) for large collections of both points and polygons
            Asked 2021-Apr-20 at 17:38

            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.




            Answered 2021-Apr-20 at 14:45

            It 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

            Source https://stackoverflow.com/questions/67170145


            How to speed up for loop subsetting a DataFrame by a given value in a column and applying a formula in Python
            Asked 2021-Apr-20 at 13:05

            I was wondering whether there was a way to speed up this code:



            Answered 2021-Apr-20 at 12:22

            You 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'.

            Source https://stackoverflow.com/questions/67177129


            Can I speed up this aerodynamics calculation with Numba, vectorization, or multiprocessing?
            Asked 2021-Apr-19 at 20:44

            I am trying to increase the speed of an aerodynamics function in Python.

            Function Set: ...


            Answered 2021-Mar-23 at 03:51

            First 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:

            Source https://stackoverflow.com/questions/66750661


            How to conditionally skip instantiation of fixture in pytest?
            Asked 2021-Apr-02 at 23:13


            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:



            Answered 2021-Apr-01 at 12:15

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


            No vulnerabilities reported

            Install sped

            You can download it from GitHub.
            You can use sped like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the sped component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .


            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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