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Editing: _common.cpython-311.pyc
� d�cp+ � � � d Z ddlmZ ddlmZmZ ddlmZmZm Z g d�Z ed�� � dd �� � Z ed �� � dd�� � Z ed�� � d� � � Z ed�� � dd�� � Z ed�� � d� � � ZdS )zW Functions which are common and require SciPy Base and Level 1 SciPy (special, linalg) � )� _deprecated)�_central_diff_weights� _derivative)�array� frombuffer�load)�central_diff_weights� derivative�ascent�face�electrocardiogramz�scipy.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)�msg� c �" � t | |� � 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 )�Np�ndivs �4/usr/lib/python3/dist-packages/scipy/misc/_common.pyr r s � �z !��T� *� *�*� z�scipy.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/numdifftools� �?� � c �* � 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 )�func�x0�dx�n�args�orders r r r O s � �^ �t�R��Q��e� 4� 4�4r z�scipy.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.c � � ddl } ddl}|j � |j � 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/ .. 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() r Nz ascent.dat�rb) �pickle�os�path�join�dirname�__file__�openr r )r! r" �fname�fr s r r r � s� � �R �M�M�M� �I�I�I� �G�L�L��� � ��2�2�<� @� @�E� �e�T� � � '�a��v�{�{�1�~�~�&�&��'� '� '� '� '� '� '� '� '� '� '���� '� '� '� '� �Ms �#B � B�Bz�scipy.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.Fc �� � ddl }ddl}t |j � |j � t � � d� � 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 .. 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 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() r Nzface.datr �uint8)�dtype)i � r Tg�z�G��?g� ��Q��?r g�Q� ��?� ) �bz2r" r' r# r$ r% r& �read� decompressr �shape�astype)�grayr/ r" r) �rawdata�datar s r r r � s? � �V �J�J�J� �I�I�I� �b�g�l�l�2�7�?�?�8�4�4�j�A�A�4� H� H� �A��&�&�(�(��� � � � � � � � � � ���� � � � � �>�>�'� "� "�D� �d�'� *� *� *�D� �D�J� �t�|�|��t�A�A�A�a�a�a��E�{�"�T�D����1�1�1�Q��K�%7�7�$��a�a�a����!�e��:L�L�T�T�U\�]�]�� �Ks �A0�0A4�7A4z�scipy.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.c � � ddl } | j � | j � 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. .. 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() r Nzecg.dat�ecgr- g i@)r" r# r$ r% r& r r3 �int)r" � file_path�filer8 s r r r � s� � �N �I�I�I��� � �R�W�_�_�X� 6� 6� �B�B�I� �i� � � &�D��5�k� � ��%�%��&� &� &� &� &� &� &� &� &� &� &���� &� &� &� &� ��:�� �C� �Js � !A9�9A=� A=N)r )r r r r )F)�__doc__�scipy._lib.deprecationr � scipy._lib._finite_differencesr r �numpyr r r �__all__r r r r r r r r �<module>rA s� ��� � /� .� .� .� .� .� M� M� M� M� M� M� M� M� )� )� )� )� )� )� )� )� )� )� � � �� �� G� H� H� H� 8+� 8+� 8+� H� H� 8+�v �� G� H� H� H� *5� *5� *5� H� H� *5�Z �� >� ?� ?� ?�*� *� ?� ?�*�Z �� <� =� =� =�0� 0� 0� =� =�0�f �� I� J� J� J� h� h� J� J� h� h� hr
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