[;jddlZddlmZmZmZddlmZmZddlm Z  ddl Z e je jddeeZ e fd Ze d d Ze d d Ze d ddZy#e$rdZ dZ Y?wxYw)N)array frombufferload)registry registry_urls)xp_capabilitiesz scipy-datazhttps://github.com/scipy/)pathbase_urlrurlsc| tdtjddtjdj i}|j ||S)NzsMissing optional dependency 'pooch' required for scipy.datasets module. Please use pip or conda to install 'pooch'.z User-AgentzSciPy scipy)headers) downloader) ImportErrorpoochHTTPDownloadersysmodules __version__fetch) dataset_name data_fetcherrs aG:\00. PROJECTS\API\Inventory\templateJSON\kerjaOCR\Lib\site-packages\scipy/datasets/_fetchers.py fetch_datarsc67 7%% G(<(H(H'IJKJ   lz  BBT) out_of_scopecddl}td}t|d5}t|j |}ddd|S#1swYSxYw)aX Get an 8-bit grayscale bit-depth, 512 x 512 derived image for easy use in demos. The image is derived from https://pixnio.com/people/accent-to-the-top Parameters ---------- None Returns ------- ascent : ndarray convenient image to use for testing and demonstration Examples -------- >>> import scipy.datasets >>> ascent = scipy.datasets.ascent() >>> ascent.shape (512, 512) >>> ascent.max() np.uint8(255) >>> import matplotlib.pyplot as plt >>> plt.gray() >>> plt.imshow(ascent) >>> plt.show() rNz ascent.datrb)pickleropenrr)r fnamefascents rr$r$*sGB | $E eT av{{1~&  M  Ms AA ctd}t|5}|djt}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. 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.datasets import electrocardiogram >>> ecg = electrocardiogram() >>> ecg array([-0.245, -0.215, -0.185, ..., -0.405, -0.395, -0.385], shape=(108000,)) >>> 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() zecg.datecgNgi@)rrastypeint)r"filer&s relectrocardiogramr+WsNx y !E e5k  %  : C J s AA cdddl}td}t|d5}|j}ddd|j }t |dj d}|dur>d |dddddfzd |ddddd fzzd |ddddd fzzjd}|S#1swYzxYw)a Get a 1024 x 768, color image of a raccoon face. The image is derived from https://pixnio.com/fauna-animals/raccoons/raccoon-procyon-lotor 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.datasets >>> face = scipy.datasets.face() >>> face.shape (768, 1024, 3) >>> face.max() np.uint8(255) >>> import matplotlib.pyplot as plt >>> plt.gray() >>> plt.imshow(face) >>> plt.show() rNzface.datruint8)dtype)ir'TgzG?gQ?rgQ?)bz2rr!read decompressrreshaper()grayr1r"r#rawdata face_datafaces rr8r8sB z "E eT a&&( w'I iw / 7 7 GD t|tAq!G}$td1a7m';;tAq!G}$%&,fWo K  s B&&B/)F)rnumpyrrr _registryrrscipy._lib._array_apir rcreateos_cacherrrr$r+r8rrr?s )).1  5<<U^^L ) -  L+7 Cd#)$)Xd#`$`Fd#)$)e ELsA<< BB