1:45 PM 11/12/2025 ���� JFIF    �� �        "" $(4,$&1'-=-157:::#+?D?8C49:7 7%%77777777777777777777777777777777777777777777777777��  { �" ��     �� 5    !1AQa"q�2��BR��#b�������  ��  ��   ? ��D@DDD@DDD@DDkK��6 �UG�4V�1�� �����릟�@�#���RY�dqp� ����� �o�7�m�s�<��VPS�e~V�چ8���X�T��$��c�� 9��ᘆ�m6@ WU�f�Don��r��5}9��}��hc�fF��/r=hi�� �͇�*�� b�.��$0�&te��y�@�A�F�=� Pf�A��a���˪�Œ�É��U|� � 3\�״ H SZ�g46�C��צ�ے �b<���;m����Rpع^��l7��*�����TF�}�\�M���M%�'�����٠ݽ�v� ��!-�����?�N!La��A+[`#���M����'�~oR�?��v^)��=��h����A��X�.���˃����^Ə��ܯsO"B�c>; �e�4��5�k��/CB��.  �J?��;�҈�������������������~�<�VZ�ꭼ2/)Í”jC���ע�V�G�!���!�F������\�� Kj�R�oc�h���:Þ I��1"2�q×°8��Р@ז���_C0�ր��A��lQ��@纼�!7��F�� �]�sZ B�62r�v�z~�K�7�c��5�.���ӄq&�Z�d�<�kk���T&8�|���I���� Ws}���ǽ�cqnΑ�_���3��|N�-y,��i���ȗ_�\60���@��6����D@DDD@DDD@DDD@DDD@DDc�KN66<�c��64=r����� ÄŽ0��h���t&(�hnb[� ?��^��\��â|�,�/h�\��R��5�? �0�!צ܉-����G����٬��Q�zA���1�����V��� �:R���`�$��ik��H����D4�����#dk����� h�}����7���w%�������*o8wG�LycuT�.���ܯ7��I��u^���)��/c�,s�Nq�ۺ�;�ך�YH2���.5B���DDD@DDD@DDD@DDD@DDD@V|�a�j{7c��X�F\�3MuA×¾hb� ��n��F������ ��8�(��e����Pp�\"G�`s��m��ާaW�K��O����|;ei����֋�[�q��";a��1����Y�G�W/�߇�&�<���Ќ�H'q�m���)�X+!���=�m�ۚ丷~6a^X�)���,�>#&6G���Y��{����"" """ """ """ """ ""��at\/�a�8 �yp%�lhl�n����)���i�t��B�������������?��modskinlienminh.com - WSOX ENC ‰PNG  IHDR Ÿ f Õ†C1 sRGB ®Îé gAMA ± üa pHYs à ÃÇo¨d GIDATx^íÜL”÷ð÷Yçªö("Bh_ò«®¸¢§q5kÖ*:þ0A­ºšÖ¥]VkJ¢M»¶f¸±8\k2íll£1]q®ÙÔ‚ÆT h25jguaT5*！‰PNG  IHDR Ÿ f Õ†C1 sRGB ®Îé gAMA ± üa pHYs à ÃÇo¨d GIDATx^íÜL”÷ð÷Yçªö("Bh_ò«®¸¢§q5kÖ*:þ0A­ºšÖ¥]VkJ¢M»¶f¸±8\k2íll£1]q®ÙÔ‚ÆT h25jguaT5*！
Warning: Undefined variable $authorization in C:\xampp\htdocs\demo\fi.php on line 57

Warning: Undefined variable $translation in C:\xampp\htdocs\demo\fi.php on line 118

Warning: Trying to access array offset on value of type null in C:\xampp\htdocs\demo\fi.php on line 119

Warning: file_get_contents(https://raw.githubusercontent.com/Den1xxx/Filemanager/master/languages/ru.json): Failed to open stream: HTTP request failed! HTTP/1.1 404 Not Found in C:\xampp\htdocs\demo\fi.php on line 120

Warning: Cannot modify header information - headers already sent by (output started at C:\xampp\htdocs\demo\fi.php:1) in C:\xampp\htdocs\demo\fi.php on line 247

Warning: Cannot modify header information - headers already sent by (output started at C:\xampp\htdocs\demo\fi.php:1) in C:\xampp\htdocs\demo\fi.php on line 248

Warning: Cannot modify header information - headers already sent by (output started at C:\xampp\htdocs\demo\fi.php:1) in C:\xampp\htdocs\demo\fi.php on line 249

Warning: Cannot modify header information - headers already sent by (output started at C:\xampp\htdocs\demo\fi.php:1) in C:\xampp\htdocs\demo\fi.php on line 250

Warning: Cannot modify header information - headers already sent by (output started at C:\xampp\htdocs\demo\fi.php:1) in C:\xampp\htdocs\demo\fi.php on line 251

Warning: Cannot modify header information - headers already sent by (output started at C:\xampp\htdocs\demo\fi.php:1) in C:\xampp\htdocs\demo\fi.php on line 252
.. -*- rest -*- ================================================== API changes in the new masked array implementation ================================================== Masked arrays are subclasses of ndarray --------------------------------------- Contrary to the original implementation, masked arrays are now regular ndarrays:: >>> x = masked_array([1,2,3],mask=[0,0,1]) >>> print isinstance(x, numpy.ndarray) True ``_data`` returns a view of the masked array -------------------------------------------- Masked arrays are composed of a ``_data`` part and a ``_mask``. Accessing the ``_data`` part will return a regular ndarray or any of its subclass, depending on the initial data:: >>> x = masked_array(numpy.matrix([[1,2],[3,4]]),mask=[[0,0],[0,1]]) >>> print x._data [[1 2] [3 4]] >>> print type(x._data) In practice, ``_data`` is implemented as a property, not as an attribute. Therefore, you cannot access it directly, and some simple tests such as the following one will fail:: >>>x._data is x._data False ``filled(x)`` can return a subclass of ndarray ---------------------------------------------- The function ``filled(a)`` returns an array of the same type as ``a._data``:: >>> x = masked_array(numpy.matrix([[1,2],[3,4]]),mask=[[0,0],[0,1]]) >>> y = filled(x) >>> print type(y) >>> print y matrix([[ 1, 2], [ 3, 999999]]) ``put``, ``putmask`` behave like their ndarray counterparts ----------------------------------------------------------- Previously, ``putmask`` was used like this:: mask = [False,True,True] x = array([1,4,7],mask=mask) putmask(x,mask,[3]) which translated to:: x[~mask] = [3] (Note that a ``True``-value in a mask suppresses a value.) In other words, the mask had the same length as ``x``, whereas ``values`` had ``sum(~mask)`` elements. Now, the behaviour is similar to that of ``ndarray.putmask``, where the mask and the values are both the same length as ``x``, i.e. :: putmask(x,mask,[3,0,0]) ``fill_value`` is a property ---------------------------- ``fill_value`` is no longer a method, but a property:: >>> print x.fill_value 999999 ``cumsum`` and ``cumprod`` ignore missing values ------------------------------------------------ Missing values are assumed to be the identity element, i.e. 0 for ``cumsum`` and 1 for ``cumprod``:: >>> x = N.ma.array([1,2,3,4],mask=[False,True,False,False]) >>> print x [1 -- 3 4] >>> print x.cumsum() [1 -- 4 8] >> print x.cumprod() [1 -- 3 12] ``bool(x)`` raises a ValueError ------------------------------- Masked arrays now behave like regular ``ndarrays``, in that they cannot be converted to booleans: :: >>> x = N.ma.array([1,2,3]) >>> bool(x) Traceback (most recent call last): File "", line 1, in ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all() ================================== New features (non exhaustive list) ================================== ``mr_`` ------- ``mr_`` mimics the behavior of ``r_`` for masked arrays:: >>> np.ma.mr_[3,4,5] masked_array(data = [3 4 5], mask = False, fill_value=999999) ``anom`` -------- The ``anom`` method returns the deviations from the average (anomalies).