Python SciPy - ndimage.map_coordinates() function Last Updated : 09 Aug, 2023 Comments Improve Suggest changes Like Article Like Report This function is used to map the given array to new coordinates by interpolation. The array of coordinates is used to find, for each point in the output, the corresponding coordinates in the input. Syntax: scipy.ndimage.map_coordinates(input, coordinates, output=None, order=3,cval=0.0, prefilter=True) Parameters input: which is of array_like - The input array.coordinates: which is of array_like- The coordinates at which input is evaluated.output: which is an array - The array in which to place the output.order: which is of int, - it is optional,The order of the spline interpolation,cval: it is a scalar,- it is optional,The Value to fill past edges of input if mode is ‘constant’. Default is 0.0.prefilter: it is of boolean type, it is optional. it is used to determine if the input array is prefiltered with spline_filter before interpolation.Returns: map_coordinates: ndarray Example 1: Python3 # importing numpy package for # creating arrays import numpy as np # importing scipy from scipy import ndimage # creating an array from 0 to 15 values a = np.arange(16.).reshape((4, 4)) # finding coordinates ndimage.map_coordinates(a, [[0.3, 1], [0.5, 1]], order=1) Output: array([1.7, 5. ]) Example 2: Python3 # importing numpy package for # creating arrays import numpy as np # importing scipy from scipy import ndimage a = np.arange(25.).reshape((5, 5)) vals = [[0.3, 1], [0.5, 1]] # calculating mode print(ndimage.map_coordinates(a, vals, order=1, mode='nearest')) print(ndimage.map_coordinates(a, vals, order=1, cval=0, output=bool)) print(ndimage.map_coordinates(a, vals, order=1)) Output: [2. 6.][ True True][2. 6.] Comment More infoAdvertise with us Next Article Python SciPy - ndimage.map_coordinates() function sravankumar_171fa07058 Follow Improve Article Tags : Python Python-scipy Practice Tags : python Similar Reads Python Scipy - ndimage.interpolation.geometric_transform() function The given mapping function is used to find, for each point in the output, the corresponding coordinates in the input Syntax: scipy.ndimage.interpolation.geometric_transform(input, mapping,  order=3) Parameters  input : takes an array.mapping : accepts a tuple data structure similar to length of giv 1 min read numpy.matrix.A() function - Python numpy.matrix.A() function return self as an ndarray object. Syntax : numpy.matrix.A() Parameters : None Return : [ndarray] Return self as an ndarray. Code #1 : Python3 # Python program explaining # numpy.matrix.A() function # importing numpy as geek import numpy as geek mat = geek.matrix(geek.arange 1 min read Numpy recarray.dot() function | Python In numpy, arrays may have a data-types containing fields, analogous to columns in a spreadsheet. An example is [(a, int), (b, float)], where each entry in the array is a pair of (int, float). Normally, these attributes are accessed using dictionary lookups such as arr['a'] and arr['b']. Record array 3 min read Numpy recarray.max() function | Python In numpy, arrays may have a data-types containing fields, analogous to columns in a spreadsheet. An example is [(a, int), (b, float)], where each entry in the array is a pair of (int, float). Normally, these attributes are accessed using dictionary lookups such as arr['a'] and arr['b']. Record array 4 min read Numpy MaskedArray.atleast_2d() function | Python numpy.MaskedArray.atleast_2d() function is used to convert inputs to masked arrays with at least two dimension.Scalar and 1-dimensional arrays are converted to 2-dimensional arrays, whilst higher-dimensional inputs are preserved. Syntax : numpy.ma.atleast_2d(*arys) Parameters: arys:[ array_like] One 2 min read numpy.fromiter() function â Python NumPy's fromiter() function is a handy tool for creating a NumPy array from an iterable object. This iterable can be any Python object that provides elements one at a time. The function is especially useful when you need to convert data from a custom data source, like a file or generator, into a Num 2 min read numpy.broadcast_to() function â Python numpy.broadcast_to() function broadcast an array to a new shape. Syntax : numpy.broadcast_to(array, shape, subok = False) Parameters : array : [array_liket] The array to broadcast. shape : [tuple] The shape of the desired array. subok : [bool, optional] If True, then sub-classes will be passed-throu 1 min read Numpy MaskedArray.atleast_1d() function | Python numpy.MaskedArray.atleast_1d() function is used to convert inputs to masked arrays with at least one dimension.Scalar inputs are converted to 1-dimensional arrays, whilst higher-dimensional inputs are preserved. Syntax : numpy.ma.atleast_1d(*arys) Parameters: arys:[ array_like] One or more input arr 2 min read numpy.fromstring() function â Python numpy.fromstring() function create a new one-dimensional array initialized from text data in a string. Syntax : numpy.fromstring(string, dtype = float, count = -1, sep = ' ') Parameters : string : [str] A string that contained the data. dtype : [data-type, optional] Data-type of the array. Default d 1 min read Numpy recarray.flatten() function | Python In numpy, arrays may have a data-types containing fields, analogous to columns in a spreadsheet. An example is [(a, int), (b, float)], where each entry in the array is a pair of (int, float). Normally, these attributes are accessed using dictionary lookups such as arr['a'] and arr['b']. Record array 3 min read Like