ilogue /
niprov
| Conditions | 11 |
| Total Lines | 65 |
| Lines | 0 |
| Ratio | 0 % |
| Changes | 1 | ||
| Bugs | 0 | Features | 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 FifFile.inspect() 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 | from __future__ import division |
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| 15 | def inspect(self): |
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| 16 | provenance = super(FifFile, self).inspect() |
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| 17 | """ try: |
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| 18 | img = self.libs.mne.io.Raw(self.path, allow_maxshield=True) |
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| 19 | except ValueError: |
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| 20 | pass |
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| 21 | else: |
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| 22 | inspect file |
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| 23 | Return |
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| 24 | """ |
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| 25 | ftypes = [ |
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| 26 | ('cov', self.libs.mne.read_cov), |
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| 27 | ('epo', self.libs.mne.read_epochs), |
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| 28 | ('ave', self.libs.mne.read_evokeds), |
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| 29 | ('fwd', self.libs.mne.read_forward_solution), |
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| 30 | ('trans', self.libs.mne.read_trans), |
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| 31 | ('raw', partial(self.libs.mne.io.read_raw_fif, |
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| 32 | allow_maxshield=True)), |
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| 33 | ('proj', self.libs.mne.read_proj), |
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| 34 | ] |
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| 35 | oldLevel = logging.getLogger('mne').getEffectiveLevel() |
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| 36 | logging.getLogger('mne').setLevel(logging.ERROR) |
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| 37 | for ftype, readfif in ftypes: |
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| 38 | try: |
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| 39 | img = readfif(self.path) |
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| 40 | if img == []: |
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| 41 | continue |
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| 42 | break |
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| 43 | except (ValueError, IOError): |
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| 44 | continue |
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| 45 | else: |
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| 46 | ftype = 'other' |
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| 47 | logging.getLogger('mne').setLevel(oldLevel) |
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| 48 | |||
| 49 | if ftype == 'raw': |
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| 50 | sub = img.info['subject_info'] |
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| 51 | if sub is not None: |
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| 52 | provenance['subject'] = sub['first_name']+' '+sub['last_name'] |
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| 53 | provenance['project'] = img.info['proj_name'] |
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| 54 | acqTS = img.info['meas_date'][0] |
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| 55 | provenance['acquired'] = datetime.fromtimestamp(acqTS) |
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| 56 | T = img.last_samp - img.first_samp + 1 |
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| 57 | provenance['dimensions'] = [img.info['nchan'], T] |
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| 58 | provenance['sampling-frequency'] = img.info['sfreq'] |
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| 59 | provenance['duration'] = T/img.info['sfreq'] |
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| 60 | |||
| 61 | if ftype == 'epo': |
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| 62 | provenance['lowpass'] = img.info['lowpass'] |
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| 63 | provenance['highpass'] = img.info['highpass'] |
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| 64 | provenance['bad-channels'] = img.info['bads'] |
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| 65 | provenance['dimensions'] = [img.events.shape[0], img.times.shape[0]] |
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| 66 | |||
| 67 | if ftype == 'ave': |
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| 68 | nEvokeds = len(img) |
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| 69 | provenance['dimensions'] = [nEvokeds] + list(img[0].data.shape) |
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| 70 | |||
| 71 | if ftype == 'cov': |
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| 72 | provenance['dimensions'] = list(img.data.shape) |
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| 73 | |||
| 74 | if ftype == 'proj': |
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| 75 | provenance['projection-description'] = img[0]['desc'] |
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| 76 | |||
| 77 | provenance['fif-type'] = ftype |
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| 78 | provenance['modality'] = 'MEG' |
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| 79 | return provenance |
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| 80 | |||
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