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Couldn't plot audio file using power spectral density
Hi... I'm trying to plot an audio file using power spectral density (PSD) and couldn't get an proper out put, so can any one please help me with a proper code
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When running:
mc.run_model(tmy_data)
which air mass model is used?
https://pvpmc.sandia.gov/modeling-steps/1-weather-design-inputs/irradiance-and-insolation-2/air-mass/
how can I change to other air mass model?
Moreover, where can I find that information (what mathematical models are created in python and how to change it, to run the: mc.run_model(tmy_data).
The ModelChain documentation states:
airmass_model (str, default 'kastenyoung1989') – Passed to location.get_airmass.
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Is there a simple way or a package for creating binned scatterplots in python?
I have a scatterplot. I am fitting a local polynomial regression to the data using the package "localreg". I get multiple lines as output. I am searching for a 1 line output. In order to get this I want to used a binned scatterplot. Is there no easy way to do this ?
You can first bin your arrays, do your fitting and then create your plot.
https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.binned_statistic.html
There is the hexabin plot in Matplotlib, if this suits your purpose. Here is an example. Here another example using seaborn.
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I'm preparing my dataset to be preprocessed before training with CNN model but i couldn't generate data from this type of file which contain several signals.
I recommend using the gdflib library. It'll allow you to process your .gdf files by organizing your data into nodes for further processing.
It would also help if you could please provide a minimal reproducible example of what you have tried.
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How can I generate random data in google maps like format i.e. 29.299332, 52.892959?
Do you prefer any package for this purpose?
If you just want a pair of random numbers between 0 and 90 degrees, why not just use the random package?
import random
print([random.random()*90, random.random()*90]) #[34.050498339418986, 5.622759330528135]
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How would I use the dataset at http://oceancolor.gsfc.nasa.gov/DOCS/DistFromCoast/ to efficiently determine the distance of a given coordinate (lat,lng) to the nearest coastline?
It's quite a large file. Is there a library that can help with processing this kind of data?