other:python:misc_by_jyp
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other:python:misc_by_jyp [2022/02/21 14:47] jypeter [numpy related stuff] Added np.unique example |
other:python:misc_by_jyp [2022/02/21 16:46] jypeter [numpy related stuff] Added reduceat |
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| Line 230: | Line 230: | ||
| >>> vals | >>> vals | ||
| array([1. , 2. , 1. , 2. , 2. , 1.5, 1. , 1.5, 2. , 1.5]) | array([1. , 2. , 1. , 2. , 2. , 1.5, 1. , 1.5, 2. , 1.5]) | ||
| + | |||
| >>> np.unique(vals) | >>> np.unique(vals) | ||
| array([1. , 1.5, 2. ]) | array([1. , 1.5, 2. ]) | ||
| - | >>> np.unique(vals, return_counts=True) | + | >>> unique_vals, nb_unique = np.unique(vals, return_counts=True) |
| - | (array([1. , 1.5, 2. ]), array([3, 3, 4])) | + | >>> unique_vals |
| - | >>> np.sort(vals) # Sorted copy, in order to check the result | + | array([1. , 1.5, 2. ]) |
| + | >>> nb_unique | ||
| + | array([3, 3, 4]) | ||
| + | |||
| + | >>> sorted_vals = np.sort(vals) # Sorted copy, in order to check the result | ||
| + | >>> sorted_vals | ||
| array([1. , 1. , 1. , 1.5, 1.5, 1.5, 2. , 2. , 2. , 2. ])</code> | array([1. , 1. , 1. , 1.5, 1.5, 1.5, 2. , 2. , 2. , 2. ])</code> | ||
| + | === Applying a ufunc over all the elements of an array === | ||
| + | |||
| + | There are all sorts of //ufuncs// (Universal Functions), and we will just use below ''add'' from the [[https://numpy.org/doc/stable/reference/ufuncs.html#math-operations|math operations]], applied on the arrays defined in [[#finding_and_counting_unique_values|Finding and counting unique values]] | ||
| + | |||
| + | <code># Get the sum of all the elements of 'vals' | ||
| + | >>> np.add.reduce(vals) | ||
| + | 15.5 | ||
| + | >>> np.add.reduce(sorted_vals) | ||
| + | 15.5 | ||
| + | >>> vals.sum() # The usual and easy way to do it | ||
| + | 15.5 | ||
| + | |||
| + | # Compute the sum of the elements of 'nb_unique' | ||
| + | # AND keep (accumulate) the intermediate results | ||
| + | >>> nb_unique | ||
| + | array([3, 3, 4]) | ||
| + | >>> np.add.accumulate(nb_unique) | ||
| + | array([ 3, 6, 10]) | ||
| + | |||
| + | # The accumulated values can be used as indices to separate the different groups of sorted values! | ||
| + | >>> sorted_vals | ||
| + | array([1. , 1. , 1. , 1.5, 1.5, 1.5, 2. , 2. , 2. , 2. ]) | ||
| + | >>> sorted_vals[0:3] | ||
| + | array([1., 1., 1.]) | ||
| + | >>> sorted_vals[3:6] | ||
| + | array([1.5, 1.5, 1.5]) | ||
| + | >>> sorted_vals[6:10] | ||
| + | array([2., 2., 2., 2.]) | ||
| + | |||
| + | # Compute the sum of each equal-value group | ||
| + | >>> sorted_vals[0:3].sum(), sorted_vals[3:6].sum(), sorted_vals[6:10].sum() | ||
| + | (3.0, 4.5, 8.0)</code> | ||
| + | |||
| + | === Applying a ufunc over specified sections of an array === | ||
| + | |||
| + | The [[https://numpy.org/doc/stable/reference/generated/numpy.ufunc.reduceat.html#numpy.ufunc.reduceat|reduceat]] function can be used to avoid explicit python loops, and improve the speed (but not the readability...) of a script. The example below //improves// what has been shown above | ||
| + | |||
| + | <code># Define a list with the boundaries of the intervals we want to apply the 'add' function to | ||
| + | # We need to add the beginning index (0), AND remove the last index | ||
| + | # (reduceat will automatically go to the end of the input array | ||
| + | >>> nb_unique | ||
| + | array([3, 3, 4]) | ||
| + | >>> slices_indices = [0] + list(np.add.accumulate(nb_unique)) | ||
| + | >>> slices_indices.pop() # Remove last element | ||
| + | 10 | ||
| + | >>> slices_indices | ||
| + | [0, 3, 6] | ||
| + | |||
| + | # Compute the sums over the selected intervals with just one call | ||
| + | >>> np.add.reduceat(np.sort(vals), slices_indices) | ||
| + | array([3. , 4.5, 8. ])</code> | ||
| /* | /* | ||
other/python/misc_by_jyp.txt · Last modified: 2024/04/19 12:02 by jypeter
