spencer-tcd | Data export script for the Spencer ST3 Transcranial Doppler | CSV Processing library
kandi X-RAY | spencer-tcd Summary
kandi X-RAY | spencer-tcd Summary
This script exports data via USB serial from the Spencer ST3 PMD150 Trans-Cranial Doppler machine and saves it in CSV format. The script is also capable of sending the collected data to an endpoint using HTTP. Tested on python version > 3.7.x.
Support
Quality
Security
License
Reuse
Top functions reviewed by kandi - BETA
- Handle incoming packets
- Builds a numeric object from a given header
- Decode a single message
- Builds an event object
- Builds a wave object
- Build the error response
- Receive data from the socket
- Return True if the connection is currently open
- Post to serial connection
- Load fake data
- Read packets from the serial port
- Checks if the checksum is valid
- Send data to remote host
- Makes a PUT request
- Connect to the serial port
- Check if the serial port is open
- Starts the scheduler
- Stop the controller
- Start the controller
spencer-tcd Key Features
spencer-tcd Examples and Code Snippets
Community Discussions
Trending Discussions on CSV Processing
QUESTION
I am currently working on a spring based API which has to transform csv data and to expose them as json. it has to read big CSV files which will contain more than 500 columns and 2.5 millions lines each. I am not guaranteed to have the same header between files (each file can have a completly different header than another), so I have no way to create a dedicated class which would provide mapping with the CSV headers. Currently the api controller is calling a csv service which reads the CSV data using a BufferReader.
The code works fine on my local machine but it is very slow : it takes about 20 seconds to process 450 columns and 40 000 lines. To improve speed processing, I tried to implement multithreading with Callable(s) but I am not familiar with that kind of concept, so the implementation might be wrong.
Other than that the api is running out of heap memory when running on the server, I know that a solution would be to enhance the amount of available memory but I suspect that the replace() and split() operations on strings made in the Callable(s) are responsible for consuming a large amout of heap memory.
So I actually have several questions :
#1. How could I improve the speed of the CSV reading ?
#2. Is the multithread implementation with Callable correct ?
#3. How could I reduce the amount of heap memory used in the process ?
#4. Do you know of a different approach to split at comas and replace the double quotes in each CSV line ? Would StringBuilder be of any healp here ? What about StringTokenizer ?
Here below the CSV method
...ANSWER
Answered 2022-Jan-29 at 02:56I don't think that splitting this work onto multiple threads is going to provide much improvement, and may in fact make the problem worse by consuming even more memory. The main problem is using too much heap memory, and the performance problem is likely to be due to excessive garbage collection when the remaining available heap is very small (but it's best to measure and profile to determine the exact cause of performance problems).
The memory consumption would be less from the replace and split operations, and more from the fact that the entire contents of the file need to be read into memory in this approach. Each line may not consume much memory, but multiplied by millions of lines, it all adds up.
If you have enough memory available on the machine to assign a heap size large enough to hold the entire contents, that will be the simplest solution, as it won't require changing the code.
Otherwise, the best way to deal with large amounts of data in a bounded amount of memory is to use a streaming approach. This means that each line of the file is processed and then passed directly to the output, without collecting all of the lines in memory in between. This will require changing the method signature to use a return type other than List. Assuming you are using Java 8 or later, the Stream API can be very helpful. You could rewrite the method like this:
QUESTION
I have a JSON that I want to insert into BQ. The column data type is STRING. Here is the sample JSON value.
...ANSWER
Answered 2021-Jun-02 at 06:55I think there is an issue with how you escape the double quotes.
I could reproduce the issue you describe, and fixed it by escaping the double quotes with " instead of a backslash \:
QUESTION
I'm sorry if this has been asked before. It probably has, but I just have not been able to find it. On with the question:
I often have loops which are initialized with certain conditions that affect or (de)activate certain behaviors inside them, but do not drastically change the general loop logic. These conditions do not change through the loop's operation, but have to be checked every iteration anyways. Is there a way to optimized said loop in a pythonic way to avoid doing the same check every single time? I understand this would be a compiler's job in any compiled language, but there ain't no compiler here.
Now, for a specific example, imagine I have a function that parses a CSV file with a format somewhat like this, where you do not know in advance the columns that will be present on it:
...ANSWER
Answered 2021-Apr-23 at 11:36Your code seems right to me, performance-wise.
You are doing your checks at the beginning of the loop:
QUESTION
I am attempting to create an program to scrape xml files. I'm experimenting with go because of it's goroutines. I have several thousand files, so some type of multiprocessing is almost a necessity...
I got a program to successfully run, and convert xml to csv(as a test, not quite the end result), on a test set of files, but when run with the full set of files, it gives this:
...ANSWER
Answered 2021-Apr-21 at 15:25I apologize for not including the correct error. as the comments pointed out i was doing something dumb and creating a routine for every file. Thanks to JimB for correcting me, and torek for providing a solution and this link. https://gobyexample.com/worker-pools
QUESTION
I am processing CSV and using the following code to process a single line.
...ANSWER
Answered 2020-Jul-31 at 21:54The fastest way to do something is to not do it at all.
If you can ensure that your source string s will outlive the use of the returned vector, you could replace your std::vector with std::vector which would point to the beginning of each substring. You then replace your identified delimiters with zeroes.
[EDIT] I have not moved up to C++17, so no string_view for me :)
NOTE: typical CSV is different from what you imply; it doesn't use escape for the comma, but surrounds entries with comma in it with double quotes. But I assume you know your data.
Implementation:
QUESTION
I am using regex for CSV processing where data can be in Quotes, or no quotes. But if there is just a comma at the starting column, it skips it.
Here is the regex I am using:
(?:,"|^")(""|[\w\W]*?)(?=",|"$)|(?:,(?!")|^(?!"))([^,]*?|)(?=$|,)
Now the example data I am using is:
,"data",moredata,"Data"
Which should have 4 matches ["","data","moredata","Data"], but it always skips the first comma. It is fine if there is quotes on the first column, or it is not blank, but if it is empty with no quotes, it ignores it.
Here is a sample code I am using for testing purposes, it is written in Dart:
...ANSWER
Answered 2020-May-11 at 22:02Investigating your expression
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install spencer-tcd
You can use spencer-tcd like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
Support
Reuse Trending Solutions
Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items
Find more librariesStay Updated
Subscribe to our newsletter for trending solutions and developer bootcamps
Share this Page
