| Conditions | 11 | 
| Total Lines | 78 | 
| Lines | 0 | 
| Ratio | 0 % | 
Small methods make your code easier to understand, in particular if combined with a good name. Besides, if your method is small, finding a good name is usually much easier.
For example, if you find yourself adding comments to a method's body, this is usually a good sign to extract the commented part to a new method, and use the comment as a starting point when coming up with a good name for this new method.
Commonly applied refactorings include:
If many parameters/temporary variables are present:
Complex classes like skchem.io.read_smiles() often do a lot of different things. To break such a class down, we need to identify a cohesive component within that class. A common approach to find such a component is to look for fields/methods that share the same prefixes, or suffixes.
Once you have determined the fields that belong together, you can apply the Extract Class refactoring. If the component makes sense as a sub-class, Extract Subclass is also a candidate, and is often faster.
| 1 | #! /usr/bin/env python  | 
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| 16 | def read_smiles(smiles_file, smiles_column=0, name_column=None, delimiter='\t',  | 
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| 17 | title_line=False, force=False, *args, **kwargs):  | 
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| 18 | |||
| 19 | """  | 
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| 20 | Read a smiles file into a pandas dataframe. The class wraps the pandas read_csv function.  | 
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| 21 | |||
| 22 | @param smiles_file A file path provided as a :str:, or a :file-like: object.  | 
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| 23 | @param smiles_column The column index as an :int: in which the smiles strings are provided.  | 
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| 24 | Defaults to _zero_.  | 
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| 25 | @param name_column The column index as an :int: in which compound names are provided,  | 
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| 26 | for use as the index in the dataframe. Defaults to _None_.  | 
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| 27 | @param delimiter The delimiter used, specified as a :str:.  | 
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| 28 | Defaults to _<tab>_.  | 
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| 29 | @param title_line A :bool: specifying whether a title line is provided,  | 
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| 30 | to use as column titles.  | 
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| 31 | @param force A :bool: specifying whether poorly parsed molecules should be skipped,  | 
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| 32 | or an error thrown.  | 
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| 33 | Additionally, pandas read_csv arguments may be provided.  | 
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| 34 | |||
| 35 | @returns df A dataframe of type :pandas.core.frame.DataFrame:.  | 
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| 36 | """  | 
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| 37 | |||
| 38 | with Suppressor():  | 
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| 39 | |||
| 40 | # set the header line to pass to the pandas parser  | 
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| 41 | # we accept True as being line zero, as is usual for smiles  | 
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| 42 | # if user specifies a header already, then do nothing  | 
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| 43 | |||
| 44 |         header = kwargs.get('header', None) | 
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| 45 | if title_line is True:  | 
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| 46 | header = 0  | 
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| 47 | elif header is not None:  | 
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| 48 |             kwargs.pop('header') #remove from the kwargs to not pass it twice | 
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| 49 | else:  | 
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| 50 | header = None  | 
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| 51 | |||
| 52 | # open file if not already open  | 
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| 53 | if isinstance(smiles_file, str):  | 
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| 54 | smiles_file = open(smiles_file, 'r')  | 
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| 55 | |||
| 56 | # read the smiles file  | 
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| 57 | df = pd.read_csv(smiles_file, delimiter=delimiter, header=header, *args, **kwargs)  | 
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| 58 | |||
| 59 | # replace the smiles column with the structure column  | 
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| 60 | lst = list(df.columns)  | 
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| 61 | lst[smiles_column] = 'structure'  | 
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| 62 | df.columns = lst  | 
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| 63 | |||
| 64 | # apply the from smiles constructor  | 
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| 65 | if force:  | 
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| 66 | def parse(smiles):  | 
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| 67 | |||
| 68 | """  | 
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| 69 | Parse a molecule from smiles string and return None if it doesn't load  | 
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| 70 | (restoring rdkit functionality)  | 
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| 71 | """  | 
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| 72 | |||
| 73 | try:  | 
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| 74 | return skchem.Mol.from_smiles(smiles)  | 
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| 75 | except ValueError:  | 
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| 76 | return None  | 
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| 77 | else:  | 
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| 78 | def parse(smiles):  | 
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| 79 | """  | 
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| 80 | Parse a molecule from smiles string  | 
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| 81 | """  | 
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| 82 | return skchem.Mol.from_smiles(smiles)  | 
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| 83 | |||
| 84 | df['structure'] = df['structure'].apply(str).apply(parse) #make sure is a string  | 
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| 85 | |||
| 86 | if force:  | 
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| 87 | df = df[df['structure'].notnull()]  | 
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| 88 | |||
| 89 | # set index if passed  | 
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| 90 | if name_column is not None:  | 
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| 91 | df = df.set_index(df.columns[name_column])  | 
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| 92 | |||
| 93 | return df  | 
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| 94 | |||
| 102 | 
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