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Project Jupyter (ˈdʒuːpɪtər) is a project to develop open-source software, open standards, and services for interactive computing across multiple programming languages. It was spun off from IPython in 2014 by Fernando Pérez and Brian Granger. Project Jupyter's name is a reference to the three core programming languages supported by Jupyter, which are Julia, Python and R. Its name and logo are an homage to Galileo's discovery of the moons of Jupiter, as documented in notebooks attributed to Galileo. Project Jupyter has developed and supported the interactive computing products Jupyter Notebook, JupyterHub, and JupyterLab. Jupyter is financially sponsored by NumFOCUS. The first version of Notebooks for IPython was released in 2011 by a team including Fernando Pérez, Brian Granger, and Min Ragan-Kelley. In 2014, Pérez announced a spin-off project from IPython called Project Jupyter. IPython continues to exist as a Python shell and a kernel for Jupyter, while the notebook and other language-agnostic parts of IPython moved under the Jupyter name. Jupyter supports execution environments (called "kernels") in several dozen languages, including Julia, R, Haskell, Ruby, and Python (via the IPython kernel). In 2015, about 200,000 Jupyter notebooks were available on GitHub. By 2018, about 2.5 million were available. In January 2021, nearly 10 million were available, including notebooks about the first observation of gravitational waves and about the 2019 discovery of a supermassive black hole. Major cloud computing providers have adopted the Jupyter Notebook or derivative tools as a frontend interface for cloud users. Examples include Amazon SageMaker Notebooks, Google's Colaboratory, and Microsoft's Azure Notebook. Visual Studio Code supports local development of Jupyter notebooks. As of July 2022, the Jupyter extension for VS Code has been downloaded over 40 million times, making it the second-most popular extension in the VS Code Marketplace.
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References [2]Paolo Prandoni, [3]LCAV, EPFL
The Parks-McClellan (PM) algorithm is a clever application of advanced
polynomial fitting techniques to the problem of FIR filter design. In
this notebook we will explore the key ideas behind the method by
considering the design of a simple Type-I lowpass filter.
NOTE: This notebook uses interactive widgets. If the interactive
controls do not work or show up, please fo [2]Paolo Prandoni, [3]LCAV, EPFL
In this notebook we will use the adaptive LMS filter to estimate the
impulse response of a reverberating room.
A typical use case is handsfree telephony: since the received signal,
amplified and played by the speaker, is picked up by the microphone, we
need to eliminate it from the signal that is transmitted back in order
to avoid a feedback loop. The signal picked up by the microph [2]Paolo Prandoni, [3]LCAV, EPFL
In this notebook we will investigate the effect of phase on the
perceptual quality of a sound. It is often said that the human ear is
commercial-grade audio equipment takes place in the magnitude domain
only.
In [1]:
%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import IPython
from scipy.io import wavfile
In [2]:
plt.rcParams["figure.fig