package scipy

Get an attribute of this module as a Py.Object.t. This is useful to pass a Python function to another function.

Get an 8-bit grayscale bit-depth, 512 x 512 derived image for easy use in demos

The image is derived from accent-to-the-top.jpg at http://www.public-domain-image.com/people-public-domain-images-pictures/

Parameters ---------- None

Returns ------- ascent : ndarray convenient image to use for testing and demonstration

Examples -------- >>> import scipy.misc >>> ascent = scipy.misc.ascent() >>> ascent.shape (512, 512) >>> ascent.max() 255

>>> import matplotlib.pyplot as plt >>> plt.gray() >>> plt.imshow(ascent) >>> plt.show()

Return weights for an Np-point central derivative.

Assumes equally-spaced function points.

If weights are in the vector w, then derivative is w0 * f(x-ho*dx) + ... + w-1 * f(x+h0*dx)

Parameters ---------- Np : int Number of points for the central derivative. ndiv : int, optional Number of divisions. Default is 1.

Notes ----- Can be inaccurate for large number of points.

Find the n-th derivative of a function at a point.

Given a function, use a central difference formula with spacing `dx` to compute the `n`-th derivative at `x0`.

Parameters ---------- func : function Input function. x0 : float The point at which `n`-th derivative is found. dx : float, optional Spacing. n : int, optional Order of the derivative. Default is 1. args : tuple, optional Arguments order : int, optional Number of points to use, must be odd.

Notes ----- Decreasing the step size too small can result in round-off error.

Examples -------- >>> from scipy.misc import derivative >>> def f(x): ... return x**3 + x**2 >>> derivative(f, 1.0, dx=1e-6) 4.9999999999217337

Load an electrocardiogram as an example for a one-dimensional signal.

The returned signal is a 5 minute long electrocardiogram (ECG), a medical recording of the heart's electrical activity, sampled at 360 Hz.

Returns ------- ecg : ndarray The electrocardiogram in millivolt (mV) sampled at 360 Hz.

Notes ----- The provided signal is an excerpt (19:35 to 24:35) from the `record 208`_ (lead MLII) provided by the MIT-BIH Arrhythmia Database 1_ on PhysioNet 2_. The excerpt includes noise induced artifacts, typical heartbeats as well as pathological changes.

.. _record 208: https://physionet.org/physiobank/database/html/mitdbdir/records.htm#208

.. versionadded:: 1.1.0

References ---------- .. 1 Moody GB, Mark RG. The impact of the MIT-BIH Arrhythmia Database. IEEE Eng in Med and Biol 20(3):45-50 (May-June 2001). (PMID: 11446209); :doi:`10.13026/C2F305` .. 2 Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation 101(23):e215-e220; :doi:`10.1161/01.CIR.101.23.e215`

Examples -------- >>> from scipy.misc import electrocardiogram >>> ecg = electrocardiogram() >>> ecg array(-0.245, -0.215, -0.185, ..., -0.405, -0.395, -0.385) >>> ecg.shape, ecg.mean(), ecg.std() ((108000,), -0.16510875, 0.5992473991177294)

As stated the signal features several areas with a different morphology. E.g. the first few seconds show the electrical activity of a heart in normal sinus rhythm as seen below.

>>> import matplotlib.pyplot as plt >>> fs = 360 >>> time = np.arange(ecg.size) / fs >>> plt.plot(time, ecg) >>> plt.xlabel('time in s') >>> plt.ylabel('ECG in mV') >>> plt.xlim(9, 10.2) >>> plt.ylim(-1, 1.5) >>> plt.show()

After second 16 however, the first premature ventricular contractions, also called extrasystoles, appear. These have a different morphology compared to typical heartbeats. The difference can easily be observed in the following plot.

>>> plt.plot(time, ecg) >>> plt.xlabel('time in s') >>> plt.ylabel('ECG in mV') >>> plt.xlim(46.5, 50) >>> plt.ylim(-2, 1.5) >>> plt.show()

At several points large artifacts disturb the recording, e.g.:

>>> plt.plot(time, ecg) >>> plt.xlabel('time in s') >>> plt.ylabel('ECG in mV') >>> plt.xlim(207, 215) >>> plt.ylim(-2, 3.5) >>> plt.show()

Finally, examining the power spectrum reveals that most of the biosignal is made up of lower frequencies. At 60 Hz the noise induced by the mains electricity can be clearly observed.

>>> from scipy.signal import welch >>> f, Pxx = welch(ecg, fs=fs, nperseg=2048, scaling='spectrum') >>> plt.semilogy(f, Pxx) >>> plt.xlabel('Frequency in Hz') >>> plt.ylabel('Power spectrum of the ECG in mV**2') >>> plt.xlim(f[0, -1]) >>> plt.show()

Get a 1024 x 768, color image of a raccoon face.

raccoon-procyon-lotor.jpg at http://www.public-domain-image.com

Parameters ---------- gray : bool, optional If True return 8-bit grey-scale image, otherwise return a color image

Returns ------- face : ndarray image of a racoon face

Examples -------- >>> import scipy.misc >>> face = scipy.misc.face() >>> face.shape (768, 1024, 3) >>> face.max() 255 >>> face.dtype dtype('uint8')

>>> import matplotlib.pyplot as plt >>> plt.gray() >>> plt.imshow(face) >>> plt.show()