from numpy import array, frombuffer, load from ._registry import registry, registry_urls try: import pooch except ImportError: pooch = None data_fetcher = None else: data_fetcher = pooch.create( # Use the default cache folder for the operating system # Pooch uses appdirs (https://github.com/ActiveState/appdirs) to # select an appropriate directory for the cache on each platform. path=pooch.os_cache("scipy-data"), # The remote data is on Github # base_url is a required param, even though we override this # using individual urls in the registry. base_url="https://github.com/scipy/", registry=registry, urls=registry_urls ) def fetch_data(dataset_name, data_fetcher=data_fetcher): if data_fetcher is None: raise ImportError("Missing optional dependency 'pooch' required " "for scipy.datasets module. Please use pip or " "conda to install 'pooch'.") # The "fetch" method returns the full path to the downloaded data file. return data_fetcher.fetch(dataset_name) def ascent(): """ 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() """ import pickle # The file will be downloaded automatically the first time this is run, # returning the path to the downloaded file. Afterwards, Pooch finds # it in the local cache and doesn't repeat the download. fname = fetch_data("ascent.dat") # Now we just need to load it with our standard Python tools. with open(fname, 'rb') as f: ascent = array(pickle.load(f)) return ascent def electrocardiogram(): """ 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() """ fname = fetch_data("ecg.dat") with load(fname) as file: ecg = file["ecg"].astype(int) # np.uint16 -> int # Convert raw output of ADC to mV: (ecg - adc_zero) / adc_gain ecg = (ecg - 1024) / 200.0 return ecg def face(gray=False): """ 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() """ import bz2 fname = fetch_data("face.dat") with open(fname, 'rb') as f: rawdata = f.read() face_data = bz2.decompress(rawdata) face = frombuffer(face_data, dtype='uint8') face.shape = (768, 1024, 3) if gray is True: face = (0.21 * face[:, :, 0] + 0.71 * face[:, :, 1] + 0.07 * face[:, :, 2]).astype('uint8') return face