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Misc

Doccer

Module Scipy.​Misc.​Doccer wraps Python module scipy.misc.doccer.

docformat

function docformat
val docformat :
  ?kwds:(string * Py.Object.t) list ->
  Py.Object.t list ->
  Py.Object.t

docformat is deprecated! scipy.misc.docformat is deprecated in Scipy 1.3.0

extend_notes_in_docstring

function extend_notes_in_docstring
val extend_notes_in_docstring :
  ?kwds:(string * Py.Object.t) list ->
  Py.Object.t list ->
  Py.Object.t

extend_notes_in_docstring is deprecated! scipy.misc.extend_notes_in_docstring is deprecated in SciPy 1.3.0

filldoc

function filldoc
val filldoc :
  ?kwds:(string * Py.Object.t) list ->
  Py.Object.t list ->
  Py.Object.t

filldoc is deprecated! scipy.misc.filldoc is deprecated in SciPy 1.3.0

indentcount_lines

function indentcount_lines
val indentcount_lines :
  ?kwds:(string * Py.Object.t) list ->
  Py.Object.t list ->
  Py.Object.t

indentcount_lines is deprecated! scipy.misc.indentcount_lines is deprecated in SciPy 1.3.0

inherit_docstring_from

function inherit_docstring_from
val inherit_docstring_from :
  ?kwds:(string * Py.Object.t) list ->
  Py.Object.t list ->
  Py.Object.t

inherit_docstring_from is deprecated! scipy.misc.inherit_docstring_from is deprecated in SciPy 1.3.0

replace_notes_in_docstring

function replace_notes_in_docstring
val replace_notes_in_docstring :
  ?kwds:(string * Py.Object.t) list ->
  Py.Object.t list ->
  Py.Object.t

replace_notes_in_docstring is deprecated! scipy.misc.replace_notes_in_docstring is deprecated in SciPy 1.3.0

unindent_dict

function unindent_dict
val unindent_dict :
  ?kwds:(string * Py.Object.t) list ->
  Py.Object.t list ->
  Py.Object.t

unindent_dict is deprecated! scipy.misc.unindent_dict is deprecated in SciPy 1.3.0

unindent_string

function unindent_string
val unindent_string :
  ?kwds:(string * Py.Object.t) list ->
  Py.Object.t list ->
  Py.Object.t

unindent_string is deprecated! scipy.misc.unindent_string is deprecated in SciPy 1.3.0

ascent

function ascent
val ascent :
  unit ->
  [`ArrayLike|`Ndarray|`Object] Np.Obj.t

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()

central_diff_weights

function central_diff_weights
val central_diff_weights :
  ?ndiv:int ->
  np:int ->
  unit ->
  [`ArrayLike|`Ndarray|`Object] Np.Obj.t

Return weights for an Np-point central derivative.

Assumes equally-spaced function points.

If weights are in the vector w, then derivative is w[0] * f(x-hodx) + ... + w[-1] * f(x+h0dx)

Parameters

  • Np : int Number of points for the central derivative.

  • ndiv : int, optional Number of divisions. Default is 1.

Returns

  • w : ndarray Weights for an Np-point central derivative. Its size is Np.

Notes

Can be inaccurate for a large number of points.

Examples

We can calculate a derivative value of a function.

>>> from scipy.misc import central_diff_weights
>>> def f(x):
...     return 2 * x**2 + 3
>>> x = 3.0 # derivative point
>>> h = 0.1 # differential step
>>> Np = 3 # point number for central derivative
>>> weights = central_diff_weights(Np) # weights for first derivative
>>> vals = [f(x + (i - Np/2) * h) for i in range(Np)]
>>> sum(w * v for (w, v) in zip(weights, vals))/h
11.79999999999998

This value is close to the analytical solution: f'(x) = 4x, so f'(3) = 12

References

.. [1] https://en.wikipedia.org/wiki/Finite_difference

derivative

function derivative
val derivative :
  ?dx:float ->
  ?n:int ->
  ?args:Py.Object.t ->
  ?order:int ->
  func:Py.Object.t ->
  x0:float ->
  unit ->
  Py.Object.t

Find the nth derivative of a function at a point.

Given a function, use a central difference formula with spacing dx to compute the nth derivative at x0.

Parameters

  • func : function Input function.

  • x0 : float The point at which the nth 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

electrocardiogram

function electrocardiogram
val electrocardiogram :
  unit ->
  [`ArrayLike|`Ndarray|`Object] Np.Obj.t

Load an electrocardiogram as an example for a 1-D 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()

face

function face
val face :
  ?gray:bool ->
  unit ->
  [`ArrayLike|`Ndarray|`Object] Np.Obj.t

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()