spectrum | The No Hassle JavaScript Colorpicker | Frontend Framework library

 by   bgrins JavaScript Version: 1.6.0 License: MIT

kandi X-RAY | spectrum Summary

kandi X-RAY | spectrum Summary

spectrum is a JavaScript library typically used in User Interface, Frontend Framework, React, NPM applications. spectrum has no bugs, it has a Permissive License and it has medium support. However spectrum has 1 vulnerabilities. You can download it from GitHub, Maven.

The No Hassle JavaScript Colorpicker
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            kandi-support Support

              spectrum has a medium active ecosystem.
              It has 2289 star(s) with 599 fork(s). There are 93 watchers for this library.
              OutlinedDot
              It had no major release in the last 12 months.
              There are 172 open issues and 237 have been closed. On average issues are closed in 120 days. There are 58 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of spectrum is 1.6.0

            kandi-Quality Quality

              spectrum has 0 bugs and 0 code smells.

            kandi-Security Security

              spectrum has 1 vulnerability issues reported (0 critical, 1 high, 0 medium, 0 low).
              spectrum code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              spectrum is licensed under the MIT License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              spectrum releases are available to install and integrate.
              Deployable package is available in Maven.
              Installation instructions are not available. Examples and code snippets are available.

            Top functions reviewed by kandi - BETA

            kandi has reviewed spectrum and discovered the below as its top functions. This is intended to give you an instant insight into spectrum implemented functionality, and help decide if they suit your requirements.
            • Starts the element .
            • Initializes and updates DOM elements .
            • The default fms in an element .
            • Play animation .
            • Creates a promise that is resolved with the given handler .
            • Called when a request is completed
            • Searches for a single selector .
            • Creates a new matcher matcher .
            • workaround for AJAX requests
            • Gets an internal data reference .
            Get all kandi verified functions for this library.

            spectrum Key Features

            No Key Features are available at this moment for spectrum.

            spectrum Examples and Code Snippets

            Return the trace of the spectrum .
            pythondot img1Lines of Code : 33dot img1License : Non-SPDX (Apache License 2.0)
            copy iconCopy
            def _trace(self):
                # The diagonal of the [[nested] block] circulant operator is the mean of
                # the spectrum.
                # Proof:  For the [0,...,0] element, this follows from the IDFT formula.
                # Then the result follows since all diagonal elements   
            Assert that the matrix is a Hermitian spectrum .
            pythondot img2Lines of Code : 21dot img2License : Non-SPDX (Apache License 2.0)
            copy iconCopy
            def assert_hermitian_spectrum(self, name="assert_hermitian_spectrum"):
                """Returns an `Op` that asserts this operator has Hermitian spectrum.
            
                This operator corresponds to a real-valued matrix if and only if its
                spectrum is Hermitian.
            
                 
            The shape of the spectrum .
            pythondot img3Lines of Code : 18dot img3License : Non-SPDX (Apache License 2.0)
            copy iconCopy
            def _shape(self):
                s_shape = self._spectrum.shape
                # Suppose spectrum.shape = [a, b, c, d]
                # block_depth = 2
                # Then:
                #   batch_shape = [a, b]
                #   N = c*d
                # and we want to return
                #   [a, b, c*d, c*d]
                batch_shape = s_s  
            How to Calculate and Publish Arrays in SwiftUI
            Lines of Code : 28dot img4License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            class ArrayGenerator: ObservableObject {
                @Published var spectrum = [Float](repeating: 0.0, count: 1000)
            
                func run() {
                    DispatchQueue.main.async { [self] in
                        for bin in 0 ..< 1000 {
                            spectrum[bin]
            Findpeaks in Spectrum Matalb
            Lines of Code : 46dot img5License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            idx = F >= 250; % 250 Hz
            
            [pks,loc] = findpeaks(ES(idx),F(idx));
            
            % Let's create a signal with fs = 2500 Hz
            % This is just to create an example, don't worry about these lines
            fs = 2500;
            f0
            How can I get exactly the same results in OpenCV as with ImageMagick's "convert -fft"?
            Lines of Code : 43dot img6License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            convert lena.png -colorspace gray -fft -delete 1 -evaluate log 10000 lena_fft_spec10000.png
            
            Gray = 0.298839*R+0.586811*G+0.114350*B
            
            spectrum = np.log10(10000*mag+1)/np.log10(10000+1)
            
            How can I get exactly the same results in OpenCV as with ImageMagick's "convert -fft"?
            Lines of Code : 36dot img7License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            import numpy as np
            import cv2
            
            # read input as grayscale
            # opencv dft only works on grayscale
            img = cv2.imread('lena.png', 0)
            
            # convert image to floats and do dft saving as complex output
            dft = cv2.dft(np.float32(img), flags = cv2.DFT_COM
            Octave using 'for' statement to show two animations simultaneously
            Lines of Code : 184dot img8License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            axes ("position", [0.5, 0.67, 0.2, 0.17]); 
            
            h = figure;
            ax1 = axes('position',[.1,.5,.4,.4]);
            ax2 = axes('position',[.5,.1,.4,.4]);
            
            v = -10; 
            wmin = 0; dw=0.1; Wmax = 6*pi();
            w = [wmin:dw:Wmax];
            t0 = 0; dt = 0.01;
            How to use grep to find a specific string of numbers and move that to a new test file
            Lines of Code : 7dot img9License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            grep -hioP "(Average spectrum energy: *| *= )\K\S*" *
            
            awk '
            /Average spectrum energy/ { printf "%.8f    ", $4 }
            //         { printf "%.16f\n", $3 }
            ' * >newdata.txt
            
            copy iconCopy
            /**
            * @NL80211_BSS_BEACON_IES: binary attribute containing the raw
            * information
            */
            
            Сapabilities Information: 0x0431
                        .... .... .... ...1 = ESS capabilities: Transmitter is an AP
                        .... .... ..

            Community Discussions

            QUESTION

            Pandas - Read in CSV with variable row length
            Asked 2022-Mar-13 at 07:42

            There are questions that deal with this issue in:

            Read CSV into a dataFrame with varying row lengths using Pandas

            How to read a no header csv with variable length csv using pandas

            However changes to Pandas seem to be deprecating those solutions. If I run this:

            ...

            ANSWER

            Answered 2022-Mar-13 at 06:37

            Here is one simple way to do it with the help of csv and itertools modules from Python standard library:

            Source https://stackoverflow.com/questions/71388166

            QUESTION

            Result from audio FFT function makes it near impossible to inspect low/mid frequencies
            Asked 2022-Feb-17 at 17:32

            I am trying to build a graphical audio spectrum analyzer on Linux. I run an FFT function on each buffer of PCM samples/frames fed to the audio hardware so I can see which frequencies are the most prevalent in the audio output. Everything works, except the results from the FFT function only allocate a few array elements (bins) to the lower and mid frequencies. I understand that audio is logarithmic, and the FFT works with linear data. But with so little allocation to low/mid frequencies, I'm not sure how I can separate things cleanly to show the frequency distribution graphically. I have tried with window sizes of 256 up to 1024 bytes, and while the larger windows give more resolution in the low/mid range, it's still not that much. I am also applying a Hann function to each chunk of data to smooth out the window boundaries.

            For example, I test using a mono audio file that plays tones at 120, 440, 1000, 5000, 15000 and 20000 Hz. These should be somewhat evenly distributed throughout the spectrum when interpreting them logarithmically. However, since FFTW works linearly, with a 256 element or 1024 element array only about 10% of the return array actually holds values up to about 5 kHz. The remainder of the array from FFTW contains frequencies above 10-15 kHz.

            Here's roughly the result I'm after:

            But this is what I'm actually getting:

            Again, I understand this is probably working as designed, but I still need a way to get more resolution in the bottom and mids so I can separate the frequencies better.

            What can I do to make this work?

            ...

            ANSWER

            Answered 2022-Feb-17 at 11:22

            What you are seeing is indeed the expected outcome of an FFT (Fourier Transform). The logarithmic f-axis that you're expecting is achieved by the Constant-Q transform.

            Now, the implementation of the Constant-Q transform is non-trivial. The Fourier Transform has become popular precisely because there is a fast implementation (the FFT). In practice, the constant-Q transform is often implemented by using an FFT, and combining multiple high-frequency bins. This discards resolution in the higher bins; it doesn't give you more resolution in the lower bins.

            To get more frequency resolution in the lower bins of the FFT, just use a longer window. But if you also want to keep the time resolution, you'll have to use a hop size that's smaller than the window size. In other words, your FFT windows will overlap.

            Source https://stackoverflow.com/questions/71155035

            QUESTION

            mapping colors to amplitude
            Asked 2022-Feb-14 at 16:47

            I am working on a music visualizer and I am hoping for the colors on the spectrum to gradually change from green to red based on the amplitude. Here are the instructions given:

            Change the colour of each bar such that it gradually changes from green to red based on the amplitude value [2 marks]. For example

            An amplitude value of 0 the colour values are R:0, G:255 and B:0. An amplitude value of 127 colour values are R:127, G:127 and B:0 An amplitude value of 255 colour values are R:255, G:0 and B: 0

            Here is my code:

            ...

            ANSWER

            Answered 2022-Feb-14 at 16:47

            what you have there are specific points in a function, but you've defined those points for entire ranges. What you should do is write a function that outputs these values in a smooth fashion.

            So let's look at your first number, which I assume is the R (red) value. if(spectrum[i] > 200) red = 255; if(spectrum[i] > 100 && spectrum[i] < 200) red = 127; else red = 0; What if instead of outputting single values, you made a function to map the amplitude directly to a Red value? To start with, make it really simple:

            Source https://stackoverflow.com/questions/71115211

            QUESTION

            Output of fft.fft() for magnitude and phase (angle) not corresponding the the values set up
            Asked 2022-Jan-18 at 21:18

            I set up a sine wave of a certain amplitude, frequency and phase, and tried recovering the amplitude and phase:

            ...

            ANSWER

            Answered 2022-Jan-18 at 21:18
            • You need to normalize the fft by 1/N with one of the two following changes (I used the 2nd one):
              S = np.fft.fft(s) --> S = 1/N*np.fft.fft(s)
              magnitude = np.abs(S[index[0]]) --> magnitude = 1/N*np.abs(S[index[0]])
            • Don't use index, = np.where(np.isclose(frequency, f0, atol=1/(T*N))), the fft is not exact and the highest magnitude may not be at f0, use np.argmax(np.abs(S)) instead which will give you the peak of the signal which will be very close to f0
            • np.angle messes up (I think its one of those pi,pi/2 arctan offset things) just do it manually with np.arctan(np.real(x)/np.imag(x))
            • use more points (I made N higher) and make T smaller for higher accuracy
            • since a DFT (discrete fourier transform) is double sided and has peak signals in both the negative and positive frequencies, the peak in the positive side will only be half the actual magnitude. For an fft you need to multiply every frequency by two except for f=0 to acount for this. I multiplied by 2 in magnitude = np.abs(S[index])*2/N

            Source https://stackoverflow.com/questions/70759395

            QUESTION

            Amazon S3 cp fails with (AccessDenied) when calling the GetObjectTagging operation
            Asked 2022-Jan-17 at 15:02

            When I try to copy data from public bucket into my own it fails with below error

            ...

            ANSWER

            Answered 2021-Aug-17 at 22:01

            It's not your fault. It is due to the Bucket Policy on the source bucket. It is not permitting the GetObjectTagging API call.

            The awssampledbuswest2 bucket has been setup to permit access from Amazon Redshift as per examples in the AWS documentation. Such access does not attempt to retrieve object tags.

            However, when copying between buckets, the AWS CLI aws s3 cp command attempts to make a complete copy of the object including object tags. This causes it to fail.

            The copy to your local file system worked successfully because the AWS CLI does not attempt to get tags when copying to a destination outside of S3 because normal operating systems do not have the concept of tags on files.

            To avoid this problem, you can use the aws s3api copy-object command to copy the file between buckets, which simply does a copy without attempting to copy the tags:

            Source https://stackoverflow.com/questions/68824370

            QUESTION

            Matplotlib share x-axis between imshow and plot
            Asked 2022-Jan-04 at 21:52

            I am trying to plot two imshow and one plot above each other sharing their x-axis. The figure layout is set up using gridspec. Here is a MWE:

            ...

            ANSWER

            Answered 2022-Jan-04 at 19:44

            Constrained_layout was specifically designed with this case in mind. It will work with your gridspec solution above, but more idiomatically:

            Source https://stackoverflow.com/questions/70583705

            QUESTION

            numpy vectorizing a function slows it down?
            Asked 2022-Jan-01 at 10:42

            I'm writing a program in which I'm trying to see how well a given redshift gets a set of lines detected in an spectrum to match up to an atomic line database. The closer the redshift gets the lines to overlap, the lower the "score" and the higher the chance that the redshift is correct.

            I do this by looping over a range of possible redshifts, calculating the score for each. Within that outer loop, I was looping within each line in the set of detected lines to calculate its sub_score, and summing that inner loop to get the overall score.

            I tried to vectorize the inner loop with numpy, but surprisingly it actually slowed down the execution. In the example given, the nested for loop takes ~2.6 seconds on my laptop to execute, while the single for loop with numpy on the inside takes ~5.3 seconds.

            Why would vectorizing the inner loop slow things down? Is there a better way to do this that I'm missing?

            ...

            ANSWER

            Answered 2022-Jan-01 at 10:42

            Numpy codes generally creates many temporary arrays. This is the case for your function find_nearest_line for example. Working on all the items of det_lines simultaneously would results in the creation of many relatively big arrays (1000 * 10_000 * 8 = 76 MiB per array). The thing is big array often do not fit in CPU caches. If so, the array needs to be stored in RAM with a much lower throughput and much higher latency. Moreover, allocating/freeing bigger array takes more time and results often in more page faults (due to the actual implementation of most default standard allocators). It is sometimes faster to use big array because the overhead of the CPython interpreter is huge but both strategies are inefficient in practice.

            The thing is that the algorithm is not efficient. Indeed, you can sort the array and use a binary search to find the closest value much more efficiently. np.searchsorted does most of the work but it only returns the index of the closest value greater (or equal) than the target value. Thus, there is some additional operation to do to get the closest value (possibly greater or lesser than the target value). Note that this algorithm do not generate huge array thanks to the binary search.

            Source https://stackoverflow.com/questions/70545476

            QUESTION

            Setting offset and dodging in a circular hierarchical dendrogram
            Asked 2021-Nov-06 at 19:12

            I have been trying to create a dendrogram with hierarchical edge bundling using the ggraph package, and have run into 2 major issues.

            Questions and code are revised slightly for clarity

            • Firstly, the graph seems to always start from arbitrary points on the circle, while the default should be 90 degrees (12 o'clock). Setting different values for the offset argument, which should be passed to [layout_tbl_graph_dendrogram()]2 also has no effect. Vertices 6 and 41, connected by the red and yellow edges, should be the first and last vertices and fall near 90 degrees.
            • The second issue is that I wish for overlapping edges (those connecting the same vertices) to be very slightly offset, but usual ggplot2 functions such as position_dodge() shift the entire edge. I don't want any shifts where edges are connected to the vertices, but without position_dodge() or similar functions (or with position_dodge(width=0)) the red edge completely covers the yellow, as they share the same two vertices (6 and 41).

            Here's a reproducible example:

            ...

            ANSWER

            Answered 2021-Nov-06 at 19:12

            For the first problem, I just ended up taking the layout file using create_layout(), modifying the locations of the vertices manually (doing some basic trigonometry) before sending the file to ggraph(). For the second problem, I just found all overlapping edges and split the connections dataframe into dataframes of non-overlapping edges, then passed them to geom_conn_bundle() functions separately and with differing tension values. It would be excellent if someone could come up with a better answer to the first problem though! Here's the repository.

            Source https://stackoverflow.com/questions/69822695

            QUESTION

            how to fill the values in numpy to create a Spectrum
            Asked 2021-Nov-02 at 17:27

            I have done the following code but do not understand properly what is going on there. Can anyone explain how to fill colors in Numpy?

            Also I want to set in values in a way from 1 to 0 to give spectrum an intensity. E.g-: 0 means low intensity, 1 means high intensity

            ...

            ANSWER

            Answered 2021-Oct-30 at 10:41

            First of all: The results here when I tried the code is different then what you displayed in the question.

            Color Monochromatic

            Let's say we have a gray scaled picture. Each pixel would have a value of integers between [0, 255]. Sometimes these values can be floats between [0, 1].

            Here 0 is black and 255 is white. The vales between (0, 255) are grays. Towards 0 it gets more gray, towards 255 its less gray.

            Polychromatic

            (I'm not sure about the term Polychromatic) Colored pixels are not so different then gray scaled ones. The only different is colored pixels storing 3 different values between [0, 255] for each Red, Green and Blue values.

            see: https://www.researchgate.net/figure/The-additive-model-of-RGB-Red-green-and-blue-are-the-primary-stimuli-for-human-colour_fig2_328189604

            Now let's see what what the image you are creating is like:

            Creation:

            You are crating a matrix of zeros with shape of: 256, 256 * 6, 3, which is: 256, 1536, 3.

            R values

            Then with the first line you are replacing the first column with something else:

            Source https://stackoverflow.com/questions/69778351

            QUESTION

            LDFLAGS not read in Makevars.win when building an Rcpp package
            Asked 2021-Oct-30 at 00:21

            Short and sweet:

            I'm writing an Rcpp package that uses zlib and sqlite.

            In the following Makevars.win file, I set Compiler flags and try to set some targets.

            ...

            ANSWER

            Answered 2021-Oct-30 at 00:21

            There are a lot of things going on there we need to decompose.

            First off, you managed to have SHLIB use your enumerated list of object files. Good! I recently had to the same and I used a OBJECTS list. I think you may get lucky if you stick the -fstack-protector into PKG_LIBS because the PKG_* variables are there for your expand on the defaults use (in the hidden Makefile controlled by R). Whereas ... LDFLAGS may just get ignored.

            Otherwise, I would recommend to sample among the 4000+ CRAN packages with compiled code. Some will set similar things, the search with the 'CRAN' "org" at GitHub is crude but better than nuttin'. Good luck!

            Edit: You could look at my (more complicated still) Makevars.win for RInside. I just grep'ed among all the repos I have here and I don't have a current example of anybody setting -fSOMETHING on Windows.

            Edit 2: I do actually have a better example for your. Each and every RcppArmadillo package uses

            Source https://stackoverflow.com/questions/69773745

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

            Vulnerabilities

            No vulnerabilities reported

            Install spectrum

            You can download it from GitHub, Maven.

            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 .
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            https://github.com/bgrins/spectrum.git

          • CLI

            gh repo clone bgrins/spectrum

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            git@github.com:bgrins/spectrum.git

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