Dj,dZddlmZddlmZmZddlmZmZm Z gdZ edddZ ed dd Z ed d Z ed ddZeddZy)zW Functions which are common and require SciPy Base and Level 1 SciPy (special, linalg) ) _deprecated)_central_diff_weights _derivative)array frombufferload)central_diff_weights derivativeascentfaceelectrocardiogramzscipy.misc.central_diff_weights is deprecated in SciPy v1.10.0; and will be completely removed in SciPy v1.12.0. You may consider using findiff: https://github.com/maroba/findiff or numdifftools: https://github.com/pbrod/numdifftools)msgct||S)a< 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-ho*dx) + ... + w[-1] * f(x+h0*dx) .. deprecated:: 1.10.0 `central_diff_weights` has been deprecated from `scipy.misc.central_diff_weights` in SciPy 1.10.0 and it will be completely removed in SciPy 1.12.0. You may consider using findiff: https://github.com/maroba/findiff or numdifftools: https://github.com/pbrod/numdifftools 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 )r)Npndivs VC:\Crop_Prediction\Backend\crop-ai-system\venv\Lib\site-packages\scipy/misc/_common.pyr r sz !T **zscipy.misc.derivative is deprecated in SciPy v1.10.0; and will be completely removed in SciPy v1.12.0. You may consider using findiff: https://github.com/maroba/findiff or numdifftools: https://github.com/pbrod/numdifftoolsc"t||||||S)aq 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`. .. deprecated:: 1.10.0 `derivative` has been deprecated from `scipy.misc.derivative` in SciPy 1.10.0 and it will be completely removed in SciPy 1.12.0. You may consider using findiff: https://github.com/maroba/findiff or numdifftools: https://github.com/pbrod/numdifftools 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 )r)funcx0dxnargsorders rr r Os^ tRQe 44rzscipy.misc.ascent has been deprecated in SciPy v1.10.0; and will be completely removed in SciPy v1.12.0. Dataset methods have moved into the scipy.datasets module. Use scipy.datasets.ascent instead.cddl}ddl}|jj|jj t d}t |d5}t|j|}ddd|S#1swYSxYw)a 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/ .. deprecated:: 1.10.0 `ascent` has been deprecated from `scipy.misc.ascent` in SciPy 1.10.0 and it will be completely removed in SciPy 1.12.0. Dataset methods have moved into the `scipy.datasets` module. Use `scipy.datasets.ascent` instead. 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() rNz ascent.datrb) pickleospathjoindirname__file__openrr)rrfnamefr s rr r sdR GGLL2< @E eT 'av{{1~&' M' Ms A33A=zscipy.misc.face has been deprecated in SciPy v1.10.0; and will be completely removed in SciPy v1.12.0. Dataset methods have moved into the scipy.datasets module. Use scipy.datasets.face instead.cddl}ddl}t|jj |jj t dd5}|j}ddd|j}t|d}d|_ |dur>d |dddddfzd |ddddd fzzd |ddddd fzzjd}|S#1swYrxYw)a Get a 1024 x 768, color image of a raccoon face. raccoon-procyon-lotor.jpg at http://www.public-domain-image.com .. deprecated:: 1.10.0 `face` has been deprecated from `scipy.misc.face` in SciPy 1.10.0 and it will be completely removed in SciPy 1.12.0. Dataset methods have moved into the `scipy.datasets` module. Use `scipy.datasets.face` instead. Parameters ---------- gray : bool, optional If True return 8-bit grey-scale image, otherwise return a color image Returns ------- face : ndarray image of a raccoon 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() rNzface.datruint8)dtype)iTgzG?gQ?gQ?) bz2rr#rr r!r"read decompressrshapeastype)grayr-rr%rawdatadatar s rr r sV bggll277??84jA4 HA&&( >>' "D d' *DDJ t|tAaE{"a!e$%a!e$%&,fWo  Ks CCzscipy.misc.electrocardiogram has been deprecated in SciPy v1.10.0; and will be completely removed in SciPy v1.12.0. Dataset methods have moved into the scipy.datasets module. Use scipy.datasets.electrocardiogram instead.cddl}|jj|jjtd}t |5}|dj t}ddddz dz }|S#1swYxYw)a 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. .. deprecated:: 1.10.0 `electrocardiogram` has been deprecated from `scipy.misc.electrocardiogram` in SciPy 1.10.0 and it will be completely removed in SciPy 1.12.0. Dataset methods have moved into the `scipy.datasets` module. Use `scipy.datasets.electrocardiogram` instead. 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 numpy as np >>> 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() rNzecg.datecgr)gi@)rrr r!r"rr1int)r file_pathfiler6s rr r spN RWW__X6 BI i&D5k  %& : C J &&s A44A=N)r+)g?r+r*)F)__doc__scipy._lib.deprecationrscipy._lib._finite_differencesrrnumpyrrr__all__r r r r r r:rrr@s /M))  GH 8+ H 8+v GH *5 H *5Z >?* ?*Z <=2 =2j IJ h J hr