Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.
DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.
SciPy (pronounced 'saɪpaɪ "sigh pie") is a free and open-source Python library used for scientific computing and technical computing. SciPy contains modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and , ODE solvers and other tasks common in science and engineering. SciPy is also a family of conferences for users and developers of these tools: SciPy (in the United States), EuroSciPy (in Europe) and SciPy.in (in India). Enthought originated the SciPy conference in the United States and continues to sponsor many of the international conferences as well as host the SciPy website. The SciPy library is currently distributed under the BSD license, and its development is sponsored and supported by an open community of developers. It is also supported by NumFOCUS, a community foundation for supporting reproducible and accessible science. The SciPy package is at the core of Python's scientific computing capabilities. Available sub-packages include: cluster: hierarchical clustering, vector quantization, K-means constants: physical constants and conversion factors fft: Discrete Fourier Transform algorithms fftpack: Legacy interface for Discrete Fourier Transforms integrate: numerical integration routines interpolate: interpolation tools io: data input and output linalg: linear algebra routines misc: miscellaneous utilities (e.g. example images) ndimage: various functions for multi-dimensional ODR: orthogonal distance regression classes and algorithms optimize: optimization algorithms including linear programming signal: signal processing tools sparse: sparse matrices and related algorithms spatial: algorithms for spatial structures such as k-d trees, nearest neighbors, Convex hulls, etc. special: special functions stats: statistical functions weave: tool for writing C/C++ code as Python multiline strings (now deprecated in favor of Cython) The basic data structure used by SciPy is a multidimensional array provided by the NumPy module.
[2]Paolo Prandoni, [3]LCAV, EPFL
In this notebook we will design and use filters to extract specific
audio frequencies from a music file.
In [1]:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import IPython
import scipy.signal as sp
from scipy.io import wavfile
infringment...)
In [2]:
IPython.display.Audio(s, rate=SF)
Out[2]:
Your browser does not support the audio element.
[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 implement a simple fractional resampler that
uses local Lagrange interpolation. The resampler can be used to perform
any rational sampling rate change (but beware of aliasing when
downsampling!)
In [1]:
import numpy as np
import IPython
from scipy.io import wavfile
Local Lagrange interpolation[4]¶
To perform resampling we will need to compute subsample values and, in