So given three vectors x, y, and z, construct 3x3D arrays (instead of 2x2D arrays) which can be used as coordinates. This is a ridiculous question but I couldn't find anything yet. Getting started with NP on MXNet numpy.meshgrid¶ numpy.meshgrid (*xi, copy=True, sparse=False, indexing='xy') [source] ¶ Return coordinate matrices from coordinate vectors. Moreover, it allows the programmers to alter the number of elements that would be structured across a particular dimension. :octocat: Numpy for handling n-dim array, algebraic operation, plotting, statistics, and NumPy array functions :rocket: - npshub/numpy Numpy reshape() can create multidimensional arrays and derive other mathematical statistics. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. En outre, plusieurs éléments d'une matrice de diffusion peuvent faire référence à un seul emplacement de mémoire. newshape int or tuple of ints. Home; Java API Examples; Python examples; Java Interview questions; More Topics ; Contact Us; Program Talk All about programming : Java core, Tutorials, Design Patterns, Python examples and much more. Learn how to use python api numpy.meshgrid. If so, you could use numpy.ndindex. Guide. An instance of numpy.lib.index_tricks.nd_grid which returns an dense (or fleshed out) mesh-grid when indexed, so that each returned argument has the same shape. Numpy (as of 1.8 I think) now supports higher that 2D generation of position grids with meshgrid.One important addition which really helped me is the ability to chose the indexing order (either xy or ij for Cartesian or matrix indexing respectively), which I verified with the following example:. : bool, facultatif. j'étudie "Python Machine Learning" DE Sebastian Raschka, et il l'utilise pour tracer les frontières de décision. sparse=False, copy=False Voici une liste de commandes de base pour commencer à travailler avec Matplotlib et Numpy. (N1, N2, N3,...Nn) Dans le cas 2-D avec des entrées de longueur M et N, les sorties sont de forme (N, M) pour l'indexation «xy» et (M, N) pour l'indexation «ij». This function is used to create a rectangular grid out of two given one-dimensional arrays representing the Cartesian indexing or Matrix indexing. x1, x2,…, xn : bool, facultatif. Now let's say we defined the function in a slightly different way (maybe we didn't have plotting in mind). The following are 30 code examples for showing how to use numpy.mgrid().These examples are extracted from open source projects. NumPyの関数であるnp.meshgridはmatplotlibでグラフを描画する際、格子点を作りたいときや組み合わせを生成したいときに便利な機能です。本記事では、np.meshgridの使い方について解説しました。 reshape (-1, 1), g [1]. Spécifiquement avec meshgrid dans numpy 1.7: np.vstack(np.meshgrid(x_p,y_p,z_p)).reshape(3,-1).T Cela fonctionne bien pour moi, même avec de grandes grilles. avec les dimensions tel que Ni = len(xi) indexing: parmi (xy, ij), défaut : xy. >>> x = np. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. meshgridjuga mendukung urutan terbalik dari dimensi serta representasi hasil yang jarang. Please note, however, that while we’re trying to be as close to NumPy as possible, some features are not implemented yet. Créez des tableaux de coordonnées ND pour les évaluations vectorisées de champs scalaires / vectoriels ND sur des grilles ND, à l'aide des tableaux de coordonnées unidimensionnels x1, x2,…, xn. It is derived from the merger of two earlier modules named Numeric and Numarray.The actual work is done by calls to routines written in the Fortran and C languages. NumPy dispose d’un grand nombre de fonctions mathématiques qui peuvent être appliquées directement à un tableau. copie forme de tableaux si indexant = 'xy' avec les éléments de The np reshape() method is used for giving new shape to an array without changing its elements. NumPy est utilisé pour effectuer des calculs sur de gros volumes de données. Tag: python,arrays,numpy,matrix. x1 Learn how to use python api numpy.reshape. Order: Default is C which is an essential row style. (N2, N1, N3,...Nn) Ni=len(xi) Ce tutoriel est le premier d'une série de tutoriels qui vous guideront depuis les bases jusqu'au sommet de la science des données. Now let's say we defined the function in a slightly different way (maybe we didn't have plotting in mind). 一、meshgrid函数 meshgrid函数通常使用在数据的矢量化上。它适用于生成网格型数据,可以接受两个一维数组生成两个二维矩阵,对应两个数组中所有的(x,y)对。 示例展示: 由上面的示例展示可以看出,meshgrid的作用是: 根据传入的两个一维数组参数生成两个数组元素的列表。 This is curated list of numpy array functions and examples I’ve built for myself. The axes of numpy arrays are identified by integer indices; for a 2-D array, thus, there is axis=0 and axis=1. The syntax is numpy.reshape(a, newShape, order='C') Here, a: Array that you want to reshape . Reshaping means changing the shape of an array. 1 view. Make N-D coordinate arrays for vectorized evaluations of N-D scalar/vector fields over N-D grids, given one-dimensional coordinate arrays x1, x2,…, xn. Make N-D coordinate arrays for vectorized evaluations of N-D scalar/vector fields over N-D grids, given one-dimensional coordinate arrays x1, x2,…, xn. An instance of numpy.lib.index_tricks.nd_grid which returns an dense (or fleshed out) mesh-grid when indexed, so that each returned argument has the same shape. meshgrid - numpy reshape . That is, we can reshape the data to any dimension using the reshape() function. def _draw_slice(f, x, y=None, scale=True, shift=False): """plot a slice of a 2D function 'f' in 1D x is an array used to set up the axis y is a fixed value for the 2nd axis if scale is provided, scale the intensity as 'z = log(4*z*scale+1)+2' if shift is provided, shift the intensity as 'z = z+shift' (useful for … Je veux créer la liste des points qui correspondent à une grille. Renvoie les matrices de coordonnées des vecteurs de coordonnées. Make N-D coordinate arrays for vectorized evaluations of N-D scalar/vector fields over N-D grids, given … pi / 2, np. Créez des tableaux de coordonnées ND pour les évaluations vectorisées de champs scalaires / vectoriels ND sur des grilles ND, à l'aide des tableaux de coordonnées unidimensionnels x1, x2,…, xn. : array_like. By reshaping we can add or remove dimensions or change number of elements in each dimension. In the 2-D case with inputs of length M and N, the outputs are of shape (N, M) for ‘xy’ indexing and (M, N) for ‘ij’ indexing. Modifié dans la version 1.9: les cas , renvoyer import numpy as np x = np.arange(1,10) y = np.arange(1,10) Let’s plot it to see how it looks like. indexation append (g [0]. Exampe of Reshape numpy.genfromtxt(StringIO.StringIO('12\t3.4\n56\t7.8\n')): lit le flux et le transforme en array 2d. reshape() returns the view Note that both reshape() method of numppy.ndarray and numpy.reshape() function return a view instead of a copy whenever possible. 0 votes . Indexation cartésienne ('xy', par défaut) ou matricielle ('ij') de la sortie. Array reshape not mapping correctly to numpy meshgrid. Donner la chaîne 'ij' renvoie une grille maillée avec une indexation matricielle, tandis que 'xy' renvoie une grille maillée avec une indexation cartésienne. Unfortunately, this does not meet the requirements of the OP since the integral assumption (starting with 0) is not met. Visit the post for more. Meshgrid is a useful feature of NumPy when creating a grid of co-ordinates. ndindex (2, 2)). x1, x2, x3, … : 1-D ndarray représentant les coordonnées de la grille. import matplotlib.pyplot as plt plt.scatter(x,y) plt.savefig("scatter-plot.png") Il crée une instance de Ndarray avec valeurs uniformément espacéeset retourne la référence. meshgrid - numpy reshape . meshgrid ([0, 1],[0, 1]) np. From the coordinate vectors, the meshgrid() function returns the coordinate matrices. The example in the question is not completely clear - either extra commas are missing or extra brakets. sin (x) >>> y array([-1., 0., 1.]) Reshape From 1-D to 2-D. Array to be reshaped. Je sais que ça crée une sorte de grille de coordonnées pour tracer, mais je ne vois pas l'avantage direct de ça. The numpy.meshgrid function is used to create a rectangular grid out of two given one-dimensional arrays representing the Cartesian indexing or Matrix indexing. Dans ce cas, la fonction est appliquée à chacun des éléments du tableau. Show Source D2L Book GitHub Table Of Contents. meshgrid Ainsi, avec trois vecteurs x, y et z, construisez des tableaux 3x3D (au lieu de tableaux 2x2D) qui peuvent être utilisés comme coordonnées. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. numpy.reshape() Python’s numpy module provides a function reshape() to change the shape of an array, numpy.reshape(a, newshape, order='C') Parameters: a: Array to be reshaped, it can be a numpy array of any shape or a list or list of lists. La valeur par défaut est True. Higher dimensions: print list (np. Chapter 3  Numerical calculations with NumPy. Exampe of Reshape I needed to get comfortable with numpy fast if I was going to be able to read and write code. How do I calculate percentiles with python/numpy? La valeur par défaut est False. asked Sep 17, 2019 in Python by Sammy (47.8k points) Can someone explain to me what is the purpose of meshgrid function in Numpy? What is the easiest way to extend this to three dimensions? Applying monopole to the meshgrid vectors X and Y is easy, because the function only uses numpy's ufuncs that work element-wise on the input arrays.. Non-universal functions: compose input from meshgrids, reshape result¶. I want to create a 2D numpy array where I want to store the coordinates of the pixels such that numpy array looks like this From the coordinate vectors, the meshgrid() function returns the coordinate matrices. Fastest method to create 2D numpy array whose elements are in range (2) . Pour les vecteurs The grid represented by the coordinates X and Y has length(y) rows and length(x) columns. Cette fonction prend en charge les deux conventions d'indexation via l'argument du mot clé d'indexation. In this article we will discuss how to use numpy.reshape() to change the shape of a numpy array. Last Updated: 08-04-2019 The numpy.meshgrid function is used to create a rectangular grid out of two given one-dimensional arrays representing the Cartesian indexing or Matrix indexing. 一句话解释numpy.meshgrid()——生成网格点坐标矩阵。 关键词:网格点,坐标矩阵 网格点是什么?坐标矩阵又是什么鬼? 我先问个问题:这张图你会生成吗? Quelle est la façon la plus simple de l'étendre à trois dimensions? The function of meshgrid is really simple. Here are the examples of the python api numpy.reshape taken from open source projects. numpy.reshape() in Python. You can use the reshape function for this. : ndarray. Voir Entrée 11 ici . numpy.meshgrid(*xi, **kwargs) [source] Return coordinate matrices from coordinate vectors. numpy.mgrid() function . See the following post for views and copies in NumPy. Tableaux . Say you have a list of x and y co-ordinates. The meshgrid function is useful for creating coordinate arrays to vectorize function evaluations over a grid. Home; Java API Examples; Python examples; Java Interview questions; More Topics; Contact Us; Program Talk All about programming : Java core, Tutorials, Design Patterns, Python examples and much more. Voir les notes pour plus de détails. The numpy.reshape() function is available in NumPy package. si les colonnes ont une largeur fixe plutôt qu'un délimiteur, faire delimiter = (4, 6, 5) en donnant la largeur de chaque colonne. pi / 2, 3) >>> x array([-1.57079633, 0. , 1.57079633]) >>> y = np. , I have a long 121 element array where the data is stored in ascending order and I want to reshape to an 11x11 matrix and so I use the NumPy reshape command . NumPy arange () est l’une des routines de création de tableaux basée sur des plages numériques. Tweeter Suivre @CoursPython. Charger un dataset On charge un dataset basic (fleurs Iris très connu). The dimensions and number of the output arrays are equal to the number of indexing dimensions. The meshgrid () function of Python numpy class returns the coordinate matrices from coordinate vectors. On s’en sert ensuite dans l’affichage d’un nuage de points avec Matplotlib. The numpy module of Python provides meshgrid() function for creating a rectangular grid with the help of the given 1-D arrays that represent the Matrix indexing or Cartesian indexing.MATLAB somewhat inspires the meshgrid() function. Learn how to use python api numpy.meshgrid Tableaux 1-D représentant les coordonnées d'une grille. Je sais que cela peut être fait avec le code suivant: g = np. The following are 30 code examples for showing how to use numpy.mgrid().These examples are extracted from open source projects. Reshape Data. Make N-D coordinate arrays for vectorized evaluations of N-D scalar/vector fields over N-D grids, given one-dimensional coordinate arrays x1, x2,…, xn. : {'xy', 'ij'}, facultatif. Aplikasi. Visit the post for more. Giving the string ‘ij’ returns a meshgrid with matrix indexing, while ‘xy’ returns a meshgrid with Cartesian indexing. In some occasions, you need to reshape the data from wide to long. et ainsi de suite. x2 Numpy meshgrid en 3D. numpy.reshape. des tableaux en forme si l'indexation = 'ij' ou 37. python numpy. Retour haut de page. ,…, 'xn' avec des longueurs Can use np.indices or np.meshgrid for more advanced indexing: This may look odd because its really made to do something like this: All are equivalent ways of doing this; however, meshgrid can be used to create non-uniform grids. If you do not mind switching row/column indices you can drop the final swapaxes(0,1). NumPy Array Reshaping Previous Next Reshaping arrays. The new shape should be compatible with the original shape. Numpy meshgrid in 3D (4) Numpy's meshgrid is very useful for converting two vectors to a coordinate grid. Diberi xs, ys, dan zs, Anda akan mendapatkan kembali xcoords, ycoords, zcoords sebagai array 3d. numpy.meshgrid(*xi, **kwargs) Construit N ndarray X1, X2, …, Xn. The axes of numpy arrays are identified by integer indices; for a 2-D array, thus, there is axis=0 and axis=1. print list (np. The numpy module of Python provides meshgrid() function for creating a rectangular grid with the help of the given 1-D arrays that represent the Matrix indexing or Cartesian indexing.MATLAB somewhat inspires the meshgrid() function. Reshape Data. Donc, si je veux créer une grille de la région de (0,0) à (1,1), il contient les points (0,0), (0,1), (1,0), (1,0). 6.1.1. Sometimes, we need to reshape the data from wide to long. Matplotlib & Numpy 1. The numpy.reshape() function enables the user to change the dimensions of the array within which the elements reside. While I’d used np.array() to convert a list to an array many times, I wasn’t prepared for line after line of linspace, meshgrid and vsplit. These examples are extracted from open source projects. 1-D et 0-D sont autorisés. The mgrid() function is used to get a dense multi-dimensional 'meshgrid'. Are your gridpoints always integral? newShape: The new desires shape . The meshgrid function is useful for creating coordinate arrays to vectorize function evaluations over a grid. Si True, une grille fragmentée est renvoyée afin de conserver la mémoire. Can you show us how you are using np.meshgrid? This one - example ranges 3, 4 for clarity - provides the solution for the first variant and produces a 2D array in effect (as the question title suggests) - "listing" all coordinates: The other variant was already shown in another answer by using 2x .swapaxes() - but it could also be done with one np.rollaxis() (or the new np.moveaxis()) : This method also works the same for N-dimensional indices, e.g. Linear algebra¶. You can use the reshape function for this. In the 2-D case with inputs of length M and N, the outputs are of shape (N, M) for ‘xy’ indexing and (M, N) for ‘ij’ … Order: Default is C which is an essential row style. The following are 30 code examples for showing how to use numpy.meshgrid(). La différence est illustrée par l'extrait de code suivant: Dans le cas 1-D et 0-D, les mots-clés d'indexation et clairsemés sont sans effet. numpy.meshgrid() function . While I’d used np.array() to convert a list to an array many times, I wasn’t prepared for line after line of linspace, meshgrid and vsplit. numpy.meshgrid. Parameter. Parameters a array_like. The numpy.reshape() function helps us to get a new shape to an array without changing its data. numpy.reshape¶ numpy.reshape (a, newshape, order='C') [source] ¶ Gives a new shape to an array without changing its data. newShape: The new desires shape . répétés pour remplir la matrice le long de la première dimension pour Numpy can be imported as import numpy as np. The syntax is numpy.reshape(a, newShape, order='C') Here, a: Array that you want to reshape . Catatan, np.meshgridjuga bisa menghasilkan grid untuk dimensi yang lebih tinggi. dépouillé , la seconde pour Experienced NumPy users will have noticed some discrepancy between meshgrid and the mgrid, a function that is used just as often, for exactly the same purpose. Consider the above figure with X-axis ranging from -4 to 4 and Y-axis ranging from -5 to 5. What is the discrepancy, and why does a discrepancy even exist when "there should be one - and preferably only one - obvious way to do it." x1 numpy.i: un fichier d'interface SWIG pour NumPy, numpy.distutils.misc_util.generate_config_py, numpy.distutils.misc_util.get_dependencies, numpy.distutils.misc_util.get_ext_source_files, numpy.distutils.misc_util.get_numpy_include_dirs, numpy.distutils.misc_util.get_script_files, numpy.distutils.misc_util.has_cxx_sources, numpy.distutils.misc_util.is_local_src_dir, numpy.distutils.misc_util.terminal_has_colors, numpy.distutils.system_info.get_standard_file, Module Chebyshev (numpy.polynomial.chebyshev), numpy.polynomial.chebyshev.Chebyshev.__call__, numpy.polynomial.chebyshev.Chebyshev.basis, numpy.polynomial.chebyshev.Chebyshev.cast, numpy.polynomial.chebyshev.Chebyshev.convert, numpy.polynomial.chebyshev.Chebyshev.copy, numpy.polynomial.chebyshev.Chebyshev.cutdeg, numpy.polynomial.chebyshev.Chebyshev.degree, numpy.polynomial.chebyshev.Chebyshev.deriv, numpy.polynomial.chebyshev.Chebyshev.fromroots, numpy.polynomial.chebyshev.Chebyshev.has_samecoef, numpy.polynomial.chebyshev.Chebyshev.has_samedomain, numpy.polynomial.chebyshev.Chebyshev.has_sametype, numpy.polynomial.chebyshev.Chebyshev.has_samewindow, numpy.polynomial.chebyshev.Chebyshev.identity, numpy.polynomial.chebyshev.Chebyshev.integ, numpy.polynomial.chebyshev.Chebyshev.interpolate, numpy.polynomial.chebyshev.Chebyshev.linspace, numpy.polynomial.chebyshev.Chebyshev.mapparms, numpy.polynomial.chebyshev.Chebyshev.roots, numpy.polynomial.chebyshev.Chebyshev.trim, numpy.polynomial.chebyshev.Chebyshev.truncate, Module Hermite, “Physiciens” (numpy.polynomial.hermite), numpy.polynomial.hermite.Hermite.__call__, numpy.polynomial.hermite.Hermite.fromroots, numpy.polynomial.hermite.Hermite.has_samecoef, numpy.polynomial.hermite.Hermite.has_samedomain, numpy.polynomial.hermite.Hermite.has_sametype, numpy.polynomial.hermite.Hermite.has_samewindow, numpy.polynomial.hermite.Hermite.identity, numpy.polynomial.hermite.Hermite.linspace, numpy.polynomial.hermite.Hermite.mapparms, numpy.polynomial.hermite.Hermite.truncate, Module HermiteE, “Probabilists '” (numpy.polynomial.hermite_e), numpy.polynomial.hermite_e.HermiteE.__call__, numpy.polynomial.hermite_e.HermiteE.basis, numpy.polynomial.hermite_e.HermiteE.convert, numpy.polynomial.hermite_e.HermiteE.cutdeg, numpy.polynomial.hermite_e.HermiteE.degree, numpy.polynomial.hermite_e.HermiteE.deriv, numpy.polynomial.hermite_e.HermiteE.fromroots, numpy.polynomial.hermite_e.HermiteE.has_samecoef, numpy.polynomial.hermite_e.HermiteE.has_samedomain, numpy.polynomial.hermite_e.HermiteE.has_sametype, numpy.polynomial.hermite_e.HermiteE.has_samewindow, numpy.polynomial.hermite_e.HermiteE.identity, numpy.polynomial.hermite_e.HermiteE.integ, numpy.polynomial.hermite_e.HermiteE.linspace, numpy.polynomial.hermite_e.HermiteE.mapparms, numpy.polynomial.hermite_e.HermiteE.roots, numpy.polynomial.hermite_e.HermiteE.truncate, Module Laguerre (numpy.polynomial.laguerre), numpy.polynomial.laguerre.Laguerre.__call__, numpy.polynomial.laguerre.Laguerre.convert, numpy.polynomial.laguerre.Laguerre.cutdeg, numpy.polynomial.laguerre.Laguerre.degree, numpy.polynomial.laguerre.Laguerre.fromroots, numpy.polynomial.laguerre.Laguerre.has_samecoef, numpy.polynomial.laguerre.Laguerre.has_samedomain, numpy.polynomial.laguerre.Laguerre.has_sametype, numpy.polynomial.laguerre.Laguerre.has_samewindow, numpy.polynomial.laguerre.Laguerre.identity, numpy.polynomial.laguerre.Laguerre.linspace, numpy.polynomial.laguerre.Laguerre.mapparms, numpy.polynomial.laguerre.Laguerre.truncate, Module Legendre (numpy.polynomial.legendre), numpy.polynomial.legendre.Legendre.__call__, numpy.polynomial.legendre.Legendre.convert, numpy.polynomial.legendre.Legendre.cutdeg, numpy.polynomial.legendre.Legendre.degree, numpy.polynomial.legendre.Legendre.fromroots, numpy.polynomial.legendre.Legendre.has_samecoef, numpy.polynomial.legendre.Legendre.has_samedomain, numpy.polynomial.legendre.Legendre.has_sametype, numpy.polynomial.legendre.Legendre.has_samewindow, numpy.polynomial.legendre.Legendre.identity, numpy.polynomial.legendre.Legendre.linspace, numpy.polynomial.legendre.Legendre.mapparms, numpy.polynomial.legendre.Legendre.truncate, Module polynomial (numpy.polynomial.polynomial), numpy.polynomial.polynomial.Polynomial.__call__, numpy.polynomial.polynomial.Polynomial.basis, numpy.polynomial.polynomial.Polynomial.cast, numpy.polynomial.polynomial.Polynomial.convert, numpy.polynomial.polynomial.Polynomial.copy, numpy.polynomial.polynomial.Polynomial.cutdeg, numpy.polynomial.polynomial.Polynomial.degree, numpy.polynomial.polynomial.Polynomial.deriv, numpy.polynomial.polynomial.Polynomial.fit, numpy.polynomial.polynomial.Polynomial.fromroots, numpy.polynomial.polynomial.Polynomial.has_samecoef, numpy.polynomial.polynomial.Polynomial.has_samedomain, numpy.polynomial.polynomial.Polynomial.has_sametype, numpy.polynomial.polynomial.Polynomial.has_samewindow, numpy.polynomial.polynomial.Polynomial.identity, numpy.polynomial.polynomial.Polynomial.integ, numpy.polynomial.polynomial.Polynomial.linspace, numpy.polynomial.polynomial.Polynomial.mapparms, numpy.polynomial.polynomial.Polynomial.roots, numpy.polynomial.polynomial.Polynomial.trim, numpy.polynomial.polynomial.Polynomial.truncate, numpy.polynomial.hermite_e.hermecompanion, numpy.polynomial.hermite_e.hermefromroots, numpy.polynomial.polynomial.polycompanion, numpy.polynomial.polynomial.polyfromroots, numpy.polynomial.polynomial.polyvalfromroots, numpy.polynomial.polyutils.PolyDomainError, numpy.random.RandomState.multivariate_normal, numpy.random.RandomState.negative_binomial, numpy.random.RandomState.noncentral_chisquare, numpy.random.RandomState.standard_exponential, Transformée de Fourier discrète (numpy.fft), Traitement des erreurs en virgule flottante, Fonctions mathématiques avec domaine automatique (numpy.emath), Routines éventuellement accélérées par Scipy (numpy.dual), Interface de fonction étrangère de type C (numpy.ctypeslib), numpy.core.defchararray.chararray.argsort, numpy.core.defchararray.chararray.endswith, numpy.core.defchararray.chararray.expandtabs, numpy.core.defchararray.chararray.flatten, numpy.core.defchararray.chararray.getfield, numpy.core.defchararray.chararray.isalnum, numpy.core.defchararray.chararray.isalpha, numpy.core.defchararray.chararray.isdecimal, numpy.core.defchararray.chararray.isdigit, numpy.core.defchararray.chararray.islower, numpy.core.defchararray.chararray.isnumeric, numpy.core.defchararray.chararray.isspace, numpy.core.defchararray.chararray.istitle, numpy.core.defchararray.chararray.isupper, numpy.core.defchararray.chararray.nonzero, numpy.core.defchararray.chararray.replace, numpy.core.defchararray.chararray.reshape, numpy.core.defchararray.chararray.searchsorted, numpy.core.defchararray.chararray.setfield, numpy.core.defchararray.chararray.setflags, numpy.core.defchararray.chararray.splitlines, numpy.core.defchararray.chararray.squeeze, numpy.core.defchararray.chararray.startswith, numpy.core.defchararray.chararray.swapaxes, numpy.core.defchararray.chararray.swapcase, numpy.core.defchararray.chararray.tostring, numpy.core.defchararray.chararray.translate, numpy.core.defchararray.chararray.transpose, numpy.testing.assert_array_almost_equal_nulp.

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