Mercurial > repos > shellac > guppy_basecaller
annotate env/lib/python3.7/site-packages/networkx/readwrite/leda.py @ 3:758bc20232e8 draft
"planemo upload commit 2a0fe2cc28b09e101d37293e53e82f61762262ec"
author | shellac |
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date | Thu, 14 May 2020 16:20:52 -0400 |
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1 """ |
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2 Read graphs in LEDA format. |
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3 |
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4 LEDA is a C++ class library for efficient data types and algorithms. |
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5 |
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6 Format |
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7 ------ |
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8 See http://www.algorithmic-solutions.info/leda_guide/graphs/leda_native_graph_fileformat.html |
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9 |
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10 """ |
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11 # Original author: D. Eppstein, UC Irvine, August 12, 2003. |
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12 # The original code at http://www.ics.uci.edu/~eppstein/PADS/ is public domain. |
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13 __author__ = """Aric Hagberg (hagberg@lanl.gov)""" |
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14 # Copyright (C) 2004-2019 by |
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15 # Aric Hagberg <hagberg@lanl.gov> |
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16 # Dan Schult <dschult@colgate.edu> |
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17 # Pieter Swart <swart@lanl.gov> |
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18 # All rights reserved. |
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19 # BSD license. |
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20 |
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21 __all__ = ['read_leda', 'parse_leda'] |
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22 |
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23 import networkx as nx |
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24 from networkx.exception import NetworkXError |
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25 from networkx.utils import open_file, is_string_like |
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26 |
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27 |
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28 @open_file(0, mode='rb') |
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29 def read_leda(path, encoding='UTF-8'): |
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30 """Read graph in LEDA format from path. |
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31 |
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32 Parameters |
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33 ---------- |
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34 path : file or string |
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35 File or filename to read. Filenames ending in .gz or .bz2 will be |
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36 uncompressed. |
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37 |
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38 Returns |
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39 ------- |
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40 G : NetworkX graph |
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41 |
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42 Examples |
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43 -------- |
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44 G=nx.read_leda('file.leda') |
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45 |
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46 References |
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47 ---------- |
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48 .. [1] http://www.algorithmic-solutions.info/leda_guide/graphs/leda_native_graph_fileformat.html |
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49 """ |
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50 lines = (line.decode(encoding) for line in path) |
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51 G = parse_leda(lines) |
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52 return G |
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53 |
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54 |
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55 def parse_leda(lines): |
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56 """Read graph in LEDA format from string or iterable. |
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57 |
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58 Parameters |
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59 ---------- |
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60 lines : string or iterable |
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61 Data in LEDA format. |
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62 |
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63 Returns |
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64 ------- |
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65 G : NetworkX graph |
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66 |
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67 Examples |
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68 -------- |
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69 G=nx.parse_leda(string) |
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70 |
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71 References |
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72 ---------- |
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73 .. [1] http://www.algorithmic-solutions.info/leda_guide/graphs/leda_native_graph_fileformat.html |
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74 """ |
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75 if is_string_like(lines): |
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76 lines = iter(lines.split('\n')) |
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77 lines = iter([line.rstrip('\n') for line in lines |
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78 if not (line.startswith('#') or line.startswith('\n') or line == '')]) |
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79 for i in range(3): |
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80 next(lines) |
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81 # Graph |
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82 du = int(next(lines)) # -1=directed, -2=undirected |
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83 if du == -1: |
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84 G = nx.DiGraph() |
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85 else: |
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86 G = nx.Graph() |
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87 |
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88 # Nodes |
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89 n = int(next(lines)) # number of nodes |
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90 node = {} |
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91 for i in range(1, n + 1): # LEDA counts from 1 to n |
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92 symbol = next(lines).rstrip().strip('|{}| ') |
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93 if symbol == "": |
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94 symbol = str(i) # use int if no label - could be trouble |
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95 node[i] = symbol |
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96 |
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97 G.add_nodes_from([s for i, s in node.items()]) |
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98 |
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99 # Edges |
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100 m = int(next(lines)) # number of edges |
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101 for i in range(m): |
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102 try: |
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103 s, t, reversal, label = next(lines).split() |
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104 except: |
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105 raise NetworkXError('Too few fields in LEDA.GRAPH edge %d' % (i + 1)) |
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106 # BEWARE: no handling of reversal edges |
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107 G.add_edge(node[int(s)], node[int(t)], label=label[2:-2]) |
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108 return G |