lisc | Literature Scanner : Automated collection | Data Mining library

 by   lisc-tools Python Version: 0.3.0rc1 License: Apache-2.0

kandi X-RAY | lisc Summary

kandi X-RAY | lisc Summary

lisc is a Python library typically used in Data Processing, Data Mining applications. lisc has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can install using 'pip install lisc' or download it from GitHub, PyPI.

Literature Scanner: Automated collection & analyses of the scientific literature.
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            kandi-support Support

              lisc has a low active ecosystem.
              It has 76 star(s) with 10 fork(s). There are 5 watchers for this library.
              There were 2 major release(s) in the last 12 months.
              There are 1 open issues and 26 have been closed. On average issues are closed in 238 days. There are 1 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of lisc is 0.3.0rc1

            kandi-Quality Quality

              lisc has 0 bugs and 0 code smells.

            kandi-Security Security

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

            kandi-License License

              lisc 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

              lisc releases are available to install and integrate.
              Deployable package is available in PyPI.
              Build file is available. You can build the component from source.
              lisc saves you 904 person hours of effort in developing the same functionality from scratch.
              It has 2066 lines of code, 256 functions and 87 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed lisc and discovered the below as its top functions. This is intended to give you an instant insight into lisc implemented functionality, and help decide if they suit your requirements.
            • Collect counts for a collection of counts
            • Get information about the given label
            • Get the count of the page
            • Build the URL for the given util
            • Join two strings together
            • Collect a list of words
            • Check the results data
            • Save the configuration to a directory
            • Save the term to a json file
            • Load a custom object
            • Loads the term
            • Get a list of all files in a folder
            • Compute counts for each term in the corpus
            • Collect counts for counts and labels
            • Compute the score
            • Compute normalization
            • Run a single collection of words
            • Collect a list of words to collect
            • Plot a density matrix
            • Compute the normalization
            • Plot a counts matrix
            • Plot a cluster map
            • Build the url for a given util
            • Save object to pickle
            • Import a module
            • Add a list of terms
            • Load data from a JSON file
            • Get all files in a folder
            • Collect citation data
            • Adds one or more terms
            • Check if data is available
            • Create a SCDB file structure
            Get all kandi verified functions for this library.

            lisc Key Features

            No Key Features are available at this moment for lisc.

            lisc Examples and Code Snippets

            No Code Snippets are available at this moment for lisc.

            Community Discussions

            QUESTION

            Image Segmentation and Masking
            Asked 2021-Apr-27 at 15:29

            Need assistance with the simple task. I’m playing around with the LISC dataset that contains hematological images taken from peripheral blood and segmentation masks of manual ground truth for these graphical samples. The task is the following:

            1. Segment isolated leukocytes by removing/cropping irrelevant background elements using the segmentation masks given in the dataset. Try this on one sample only.
            2. Once accomplished, go through the whole folder, and segment/crop the rest of the samples.

            Results should be like this (these were obtained via a combination of Mask R-CNN, GrabCut, and OpenCV — but not suitable for the current project I’m working on):

            Here is the code that I’ve got so far (from jupyter notebook):

            ...

            ANSWER

            Answered 2021-Apr-27 at 15:29

            The change in colors is the result of the specified heatmap (viridis instead of binary) as noted above in comments.

            The output image has different coloration than the input image because OpenCV uses BGR rather than RGB for colors, so it's likely your red and blue channels are swapped. If you read an image with OpenCV and plot with Matplotlib or vice versa. There are two easy solutions:

            1.) Both read and plot images with OpenCV. You can replace plt.imshow(im_orig) with:

            Source https://stackoverflow.com/questions/67271730

            QUESTION

            Poor C performance with both pthread and printf
            Asked 2020-Oct-31 at 11:59

            I'm testing a c code for linux with large arrays to measure thread performance, the application scales very well when threads are increased until max cores (8 for Intel 4770), but this is only for the pure math part of my code.

            If I add the printf part for resulted arrays then the times becomes too large, from few seconds to several minutes even if redirected to a file, when printf those arrays should add just a few seconds.

            The code:

            (gcc 7.5.0-Ubuntu 18.04)

            without printf loop:

            gcc -O3 -m64 exp_multi.c -pthread -lm

            with printf loop:

            gcc -DPRINT_ARRAY -O3 -m64 exp_multi.c -pthread -lm

            ...

            ANSWER

            Answered 2020-Oct-15 at 21:27

            I don't think this has much to with pthread because your code only appears to call printf after the threads are joined. Instead, the poor performance is likely due to cache misses by needing to read from the xv and yv arrays in every iteration of the print loop.

            Source https://stackoverflow.com/questions/64376657

            QUESTION

            c# how to align 2 dimensional array columns with string
            Asked 2019-Nov-22 at 22:21

            My question is how can I align columns that the word's gonna be one beneath another. I've done some simple translator and I want them to be aligned. I managed to align them by using \t but I think there might be some other way to this. This is my Code:

            ...

            ANSWER

            Answered 2019-Nov-22 at 12:33

            If you want each column to start at the same position you will need to calculate the length of each string in each column and find the longest string. After that append i.e. spaces to each string so that each one in a row has the same length. Then you can start the following column based on the max length of the previous column so you would have something like this:

            Source https://stackoverflow.com/questions/58993806

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

            Vulnerabilities

            No vulnerabilities reported

            Install lisc

            You can install using 'pip install lisc' or download it from GitHub, PyPI.
            You can use lisc 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

            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 .
            Find more information at:

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            Install
          • PyPI

            pip install lisc

          • CLONE
          • HTTPS

            https://github.com/lisc-tools/lisc.git

          • CLI

            gh repo clone lisc-tools/lisc

          • sshUrl

            git@github.com:lisc-tools/lisc.git

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