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Editing: _fetchers.cpython-311.pyc
� d�cg � � � d dl mZmZmZ ddlmZmZ d dlZ ej ej d� � dee�� � Z n# e $ r dZdZ Y nw xY we fd�Z d � Z d � Zd d �ZdS )� )�array� frombuffer�load� )�registry� registry_urlsNz scipy-datazhttps://github.com/scipy/)�path�base_urlr �urlsc �N � |�t d� � �|� | � � S )NzsMissing optional dependency 'pooch' required for scipy.datasets module. Please use pip or conda to install 'pooch'.)� ImportError�fetch)� dataset_name� data_fetchers �:/usr/lib/python3/dist-packages/scipy/datasets/_fetchers.py� fetch_datar s5 � ���� 6� 7� 7� 7� � � �l� +� +�+� c � � ddl } t d� � }t |d� � 5 }t | � |� � � � }ddd� � n # 1 swxY w Y |S )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/ 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() 255 >>> import matplotlib.pyplot as plt >>> plt.gray() >>> plt.imshow(ascent) >>> plt.show() r Nz ascent.dat�rb)�pickler �openr r )r �fname�f�ascents r r r " s� � �@ �M�M�M� �|� $� $�E� �e�T� � � '�a��v�{�{�1�~�~�&�&��'� '� '� '� '� '� '� '� '� '� '���� '� '� '� '� �Ms �#A�A�Ac �� � t d� � } t | � � 5 }|d � t � � }ddd� � n # 1 swxY w Y |dz dz }|S )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]) >>> 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.dat�ecgN� g i@)r r �astype�int)r �filer s r �electrocardiogramr! N s� � �v �y� !� !�E� �e��� &���5�k� � ��%�%��&� &� &� &� &� &� &� &� &� &� &���� &� &� &� &� ��:�� �C� �Js �!A � A�AFc � � ddl }t d� � }t |d� � 5 }|� � � }ddd� � n # 1 swxY w Y |� |� � }t |d�� � }d|_ | du rKd |dd�dd�df z d |dd�dd�d f z z d |dd�dd�d f z z � d� � }|S )a 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.datasets >>> face = scipy.datasets.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() r Nzface.datr �uint8)�dtype)i r � Tg�z�G��?g� ��Q��?r g�Q� ��?� )�bz2r r �read� decompressr �shaper )�grayr' r r �rawdata� face_data�faces r r. r. � s' � �B �J�J�J� �z� "� "�E� �e�T� � � �a��&�&�(�(��� � � � � � � � � � ���� � � � ����w�'�'�I� �i�w� /� /� /�D� �D�J� �t�|�|��t�A�A�A�q�q�q�!�G�}�$�t�d�1�1�1�a�a�a��7�m�';�;��t�A�A�A�q�q�q�!�G�}�$�%�&,�f�W�o�o� � �Ks �A�A � A )F)�numpyr r r � _registryr r �pooch�create�os_cacher r r r r! r. � r r �<module>r5 s �� )� )� )� )� )� )� )� )� )� )� .� .� .� .� .� .� .� .���L�L�L� �5�<� �U�^�L� )� )� -�� � � � �L�L�� � � � � �E��L�L�L�����& +7� ,� ,� ,� ,�)� )� )�X`� `� `�F+� +� +� +� +� +s �; � A�A
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