I would agree with your approach to override hashCode() and equals() and use something that implements Set. Doing so also makes it absolutely clear to any other developers that the non-duplicate characteristic is required. Another reason – you get to choose an implementation that meets your needs best now: HashSet TreeSet LinkedHashSet and you don’t … Read more
function get_duplicates ($array) { return array_unique( array_diff_assoc( $array, array_unique( $array ) ) ); }
You can use array_intersect(). $peopleContainsCriminal = !empty(array_intersect($people, $criminals));
This solution uses fnmatch.translate to convert glob patterns to regular expressions (it assumes the includes only is used for files): import fnmatch import os import os.path import re includes = [‘*.doc’, ‘*.odt’] # for files only excludes = [‘/home/paulo-freitas/Documents’] # for dirs and files # transform glob patterns to regular expressions includes = r’|’.join([fnmatch.translate(x) for … Read more
Well, you could do it using preg_grep(): $output = preg_grep(‘!^car_!’, $array); You could use array_filter() too but you have to pass a test function into that.
Convert arrays to a format, where array index is the sight_id: $b1 =array(); foreach($a1 as $x) $b1[$x[‘sight_id’]] = $x[‘location’]; $b2 =array(); foreach($a2 as $x) $b2[$x[‘sight_id’]] = $x[‘location’]; Calculate the differences and intersection: $c_intersect = array_intersect_key($b1,$b2); $c_1 = array_diff_key($b1,$b2); $c_2 = array_diff_key($b2,$b1); Convert arrays back to your format: $intersect_array = array(); foreach($c_intersect as $i=>$v) $intersect_array[] = … Read more
Use groupby filter: In [11]: df = pd.DataFrame([[1, 2], [1, 4], [5, 6]], columns=[‘A’, ‘B’]) In [12]: df Out[12]: A B 0 1 2 1 1 4 2 5 6 In [13]: df.groupby(“A”).filter(lambda x: len(x) > 1) Out[13]: A B 0 1 2 1 1 4 I recommend reading the split-combine-section of the docs.
A few comments: The Nyquist frequency is half the sampling rate. You are working with regularly sampled data, so you want a digital filter, not an analog filter. This means you should not use analog=True in the call to butter, and you should use scipy.signal.freqz (not freqs) to generate the frequency response. One goal of … Read more
Try preg_split. $exploded = preg_split(‘@/@’, ‘1/2//3/’, -1, PREG_SPLIT_NO_EMPTY);
With array_intersect_key and array_flip: var_dump(array_intersect_key($my_array, array_flip($allowed))); array(1) { [“foo”]=> int(1) }