| In [4]: %timeit int(1001) in range(1000) | |
| 558 ns ± 6.22 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each) | |
| In [5]: %timeit int(100001) in range(100000) | |
| 567 ns ± 14.1 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each) | |
| In [6]: %timeit int(10000001) in range(10000000) | |
| 561 ns ± 8.6 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each) | |
| In [7]: %timeit int(1000000001) in range(1000000000) |
| """Example of adding attributes to a function using a decorator.""" | |
| import time | |
| def delayable(func): | |
| def _delayer(delay, *args, **kwargs): | |
| time.sleep(delay) | |
| return func(*args, **kwargs) | |
| setattr(func, 'delay', _delayer) |
| from itertools import zip_longest | |
| def remove_consecutive(lst, item=0): | |
| """Remove consecutive occurrences of `item` from `lst`. | |
| For example: | |
| >>> print(list(remove_consecutive([1, 1, 0, 0, 0, 1, 0, 1, 1], item=0))) | |
| [1, 1, 0, 1, 0, 1, 1] |
| """Converting fasta files to data frames and back again.""" | |
| import pandas as pd | |
| import skbio as sk | |
| def fasta_to_dataframe(path, id_col='id', description_col='description', sequence_col='sequence'): | |
| """Read a FASTA file into a pandas data frame. | |
| This function is the inverse of :func:`dataframe_to_fasta`. |
| """Bash-like string expansion in Python. | |
| Author: Dan Søndergaard <[email protected]> | |
| License: MIT | |
| """ | |
| import re | |
| from itertools import product | |
| GROUP_RE = re.compile(r'\{.+?\}') |
| #!/bin/bash | |
| # | |
| # Short description of what this script does... | |
| # | |
| # This is a bash script template based on the recommendations | |
| # from Julia Evans' excellent blog post about bash quirks and | |
| # how to avoid them: | |
| # | |
| # https://jvns.ca/blog/2017/03/26/bash-quirks/ | |
| # |
| accessibility: [] | |
| additionDate: '2015-12-17T14:23:00Z' | |
| availability: null | |
| canonicalID: null | |
| collectionID: | |
| - CBS | |
| contact: | |
| - email: [email protected] | |
| name: Henrik Nielsen | |
| tel: null |
| def prepend_line(line, rest): | |
| return line + '\n' + rest | |
| class Transformer: | |
| def __call__(self, f): | |
| def wrapper(*args, **kwargs): | |
| options, spec = f(*args, **kwargs) | |
| return self.transform(options, spec) | |
| return wrapper |
| KNIME - Cellular phenotyping | |
| Galaxy Image Analysis | |
| COMPSs | |
| YEASTRACT | |
| WormBase | |
| Reactome knowledgebase of biological pathways | |
| PRIDE | |
| PANTHER | |
| AnalysisServiceService | |
| Kyoto encyclopedia of genes and genomes KEGG |
[Astrid Kousholt] TBA.
[2017/03/08] [Elham Bavafaye Haghighi] Bartsch GJ, Mitra AP, Mitra SA, et al. Use of Artificial Intelligence and Machine Learning Algorithms with Gene Expression Profiling to Predict Recurrent Nonmuscle Invasive Urothelial Carcinoma of the Bladder. J Urol. 2016;195(2):493-498. doi:10.1016/j.juro.2015.09.090.