I have an audio signal in wav format. I have to plot its spectrogram using Winger-Vill distribution. I have tried other libraries and other options to plot the spectrogarm like librosa, scipy and matplotlib spec but to compare the differences between these methods, I should use Winger-Vill distribution.
I would appreciate your answers in advance.
Related
I'm trying to use kernel density estimator to obtain the distribution of my data. Using the seaborn, I can simply call sns.kdeplot(temp, shade=True) and that will plot the kde or the distribution of my data. However, with seaborn, I cannot obtain scores for new data points. However, if I'm using the sklearn library, I can simply call kde.score_samples(data). Therefore, how can I achieve the same thing with seaborn? Or, is there a way I can return the kde obtained from seaborn?
Any help is much appreciated!
After looking through the documentation https://github.com/mwaskom/seaborn/blob/a9577e705023873de7c7bbf3e9b6ae0dc1880b51/seaborn/distributions.py#L2641, the bandwidth of the kernel is calculated as: bw = stats.gaussian_kde(a).scotts_factor() * a.std(ddof=1) Consequently, I've used the bw with kde from sklearn as: kde = KernelDensity(kernel='gaussian', bandwidth=bw).fit(temp.reshape(-1, 1)).
Thus, now I'm able to call: kde.score_samples(x_axis.reshape(-1, 1)).
I use Octave v 4.4.1 and it has been observed that I get different looking plots while plotting using Octave-CLI and Octave-GUI. I had configured the graphics_toolkit to be Gnuplot.
Plot using Octave - CLI :
Plot using Octave - GUI :
I am unsure why such a difference exists between the plots. Is there a way by which I can get the behaviour of Octave-GUI plot on Octave-CLI? It will be well appreciated if someone can say about the reason why the differences happen to be so.
Can any one provide an example , or advice as to what would be the best approach for using gnu-plot or something similar from within a Haskell GUI?
the Chart package and the plot package both use cairo as a 2D renderer which can be used with GTK.
The plot-gtk package provides a custom widget to display plots in GTK. plot can plot functions or data series which are Data.Vector.Storable from the vector package and thus are compatible with hmatrix and friends.
There is an example in the plot package in Graphics.Rendering.Plot
I want to draw the following implicit function with gnuplot
x**2+y**2+(z-1)**3-2
I know that maple or matlab can to this very simple but I want to use gnuplot.
Up to know I have no idea so I can't provide a starting point.
sorry
Here the result plotted with maple
According to the Gnuplot FAQ, this is not directly possible. There is a workaround for 2D-functions, but I don't see how this method can be applied to 3D graphs. I'd recommend solving the equation in Octave or some similar program and outputting the solutions to a file, which you can then feed into GnuPlot.
I'm looking for some tool/library that is able to draw simple 2d geometries from text file or programatically. I already found List of interactive geometry software but that not quite what I'm looking for. I would prefer something more similar in usage to graphviz or gnuplot. I already wrote some scripts for gnuplot but this tool has been designed for different purposes. Required functionality:
support for different kind of 2D geometries: points, segments, lines, circles, polygons
simple input type format maybe similar to postgis Well Known Text
support for objects additional data like tags and colors definition
output in common image format or some kind of interactive GUI (with zoom in/out and select object)
configurable grid
autoscale or draw in defined area
I will use it for testing geometry algorithms and don't want to reinvent the wheel.
Matplotlib. I'm not familiar with all the aspects of this Python library but I've heard it is pretty good.
To quote their introduction,
matplotlib is a python 2D plotting
library which produces publication
quality figures in a variety of
hardcopy formats and interactive
environments across platforms.
matplotlib can be used in python
scripts, the python and ipython shell
(ala MATLAB®* or Mathematica®†), web
application servers, and six graphical
user interface toolkits.
matplotlib tries to make easy things
easy and hard things possible. You can
generate plots, histograms, power
spectra, bar charts, errorcharts,
scatterplots, etc, with just a few
lines of code. For a sampling, see the
screenshots, thumbnail gallery, and
examples directory
(source: sourceforge.net)
>
For example, using "ipython -pylab" to
provide an interactive environment, to
generate 10,000 gaussian random
numbers and plot a histogram with 100
bins, you simply need to type
x = randn(10000)
hist(x, 100)
For the power user, you have full
control of line styles, font
properties, axes properties, etc, via
an object oriented interface or via a
set of functions familiar to MATLAB
users. The pylab mode provides all of
the pyplot plotting functions listed
below, as well as non-plotting
functions from numpy and
matplotlib.mlab.
Maybe dia, with it's SVG output option is what you're looking for? It can be scripted in Python.