st-bender /
sciapy
| Conditions | 10 |
| Total Lines | 81 |
| Code Lines | 62 |
| Lines | 0 |
| Ratio | 0 % |
| Tests | 55 |
| CRAP Score | 10.0138 |
| Changes | 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 sciapy.level1c.scia_limb_txt.read_from_textfile() 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 | # -*- coding: utf-8 -*- |
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| 38 | 1 | def read_from_textfile(self, filename): |
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| 39 | """SCIAMACHY level 1c limb scan text import |
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| 40 | |||
| 41 | Parameters |
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| 42 | ---------- |
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| 43 | filename : str |
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| 44 | The (plain ascii) table text filename to read the data from. |
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| 45 | |||
| 46 | Returns |
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| 47 | ------- |
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| 48 | nothing |
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| 49 | """ |
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| 50 | 1 | import numpy.lib.recfunctions as rfn |
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| 51 | 1 | if hasattr(filename, 'seek'): |
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| 52 | f = filename |
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| 53 | else: |
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| 54 | 1 | f = open(filename, 'rb') |
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| 55 | 1 | h_list = [] |
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| 56 | 1 | nh = int(f.readline()) |
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| 57 | 1 | for _ in range(nh): |
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| 58 | 1 | h_list.append(bytes(f.readline()).decode().rstrip()) |
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| 59 | 1 | self.textheader_length = nh |
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| 60 | 1 | self.textheader = '\n'.join(h_list) |
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| 61 | 1 | self.parse_textheader() |
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| 62 | 1 | self.nalt, self.npix = np.fromstring(f.readline(), dtype=_int_type, sep=' ') |
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| 63 | |||
| 64 | 1 | self.orbit_state = np.fromstring(f.readline(), dtype=_int_type, sep=' ') |
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| 65 | 1 | (self.orbit, self.state_in_orbit, self.state_id, |
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| 66 | self.profiles_per_state, self.profile_in_state) = self.orbit_state |
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| 67 | 1 | self.date = np.fromstring(f.readline(), dtype=_int_type, sep=' ') |
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| 68 | # pre-set the limb_data |
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| 69 | 1 | if self._limb_data_dtype is None: |
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| 70 | 1 | self._limb_data_dtype = _limb_data_dtype[:] |
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| 71 | 1 | self.limb_data = np.zeros((self.nalt), dtype=self._limb_data_dtype) |
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| 72 | 1 | if nh > 27: |
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| 73 | 1 | self.limb_data["sub_sat_lat"] = np.fromstring(f.readline(), dtype=_float_type, sep=' ') |
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| 74 | 1 | self.limb_data["sub_sat_lon"] = np.fromstring(f.readline(), dtype=_float_type, sep=' ') |
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| 75 | 1 | if nh > 29: |
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| 76 | 1 | self.orbit_phase = np.fromstring(f.readline(), dtype=_float_type, sep=' ')[0] |
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| 77 | 1 | self.cent_lat_lon = np.fromstring(f.readline(), dtype=_float_type, sep=' ') |
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| 78 | |||
| 79 | 1 | self.limb_data["tp_lat"] = np.fromstring(f.readline(), dtype=_float_type, sep=' ') |
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| 80 | 1 | self.limb_data["tp_lon"] = np.fromstring(f.readline(), dtype=_float_type, sep=' ') |
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| 81 | 1 | self.limb_data["tp_alt"] = np.fromstring(f.readline(), dtype=_float_type, sep=' ') |
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| 82 | 1 | self.limb_data["tp_sza"] = np.fromstring(f.readline(), dtype=_float_type, sep=' ') |
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| 83 | 1 | self.limb_data["tp_saa"] = np.fromstring(f.readline(), dtype=_float_type, sep=' ') |
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| 84 | 1 | self.limb_data["tp_los"] = np.fromstring(f.readline(), dtype=_float_type, sep=' ') |
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| 85 | 1 | self.limb_data["toa_sza"] = np.fromstring(f.readline(), dtype=_float_type, sep=' ') |
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| 86 | 1 | self.limb_data["toa_saa"] = np.fromstring(f.readline(), dtype=_float_type, sep=' ') |
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| 87 | 1 | self.limb_data["toa_los"] = np.fromstring(f.readline(), dtype=_float_type, sep=' ') |
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| 88 | 1 | self.limb_data["sat_sza"] = np.fromstring(f.readline(), dtype=_float_type, sep=' ') |
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| 89 | 1 | self.limb_data["sat_saa"] = np.fromstring(f.readline(), dtype=_float_type, sep=' ') |
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| 90 | 1 | self.limb_data["sat_los"] = np.fromstring(f.readline(), dtype=_float_type, sep=' ') |
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| 91 | 1 | self.limb_data["sat_alt"] = np.fromstring(f.readline(), dtype=_float_type, sep=' ') |
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| 92 | 1 | self.limb_data["earth_rad"] = np.fromstring(f.readline(), dtype=_float_type, sep=' ') |
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| 93 | |||
| 94 | 1 | tmp_list = [] |
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| 95 | 1 | for _ in range(self.npix): |
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| 96 | 1 | input = np.fromstring(f.readline(), dtype=_float_type, sep=' ') |
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| 97 | 1 | self.wls.append(input[0]) |
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| 98 | 1 | tmp_list.append(input[1:]) |
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| 99 | 1 | tmp_rad_arr = np.asarray(tmp_list).reshape(self.npix, self.nalt).transpose() |
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| 100 | 1 | tmp_list[:] = [] |
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| 101 | 1 | line = f.readline().strip() |
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| 102 | 1 | if bytes(line) == b"ERRORS": |
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| 103 | 1 | for _ in range(self.npix): |
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| 104 | 1 | input = np.fromstring(f.readline(), dtype=_float_type, sep=' ') |
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| 105 | 1 | tmp_list.append(input[1:]) |
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| 106 | else: |
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| 107 | for _ in range(self.npix): |
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| 108 | tmp_list.append(np.zeros(self.nalt)) |
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| 109 | 1 | tmp_err_arr = np.asarray(tmp_list).reshape(self.npix, self.nalt).transpose() |
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| 110 | |||
| 111 | # save to limb_data recarray |
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| 112 | 1 | rads = np.rec.fromarrays([tmp_rad_arr], |
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| 113 | dtype=np.dtype([("rad", 'f4', (self.npix,))])) |
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| 114 | 1 | errs = np.rec.fromarrays([tmp_err_arr], |
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| 115 | dtype=np.dtype([("err", 'f4', (self.npix,))])) |
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| 116 | 1 | self.limb_data = rfn.merge_arrays([self.limb_data, rads, errs], |
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| 117 | usemask=False, asrecarray=True, flatten=True) |
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| 118 | 1 | self._limb_data_dtype = self.limb_data.dtype |
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| 119 | |||
| 182 |
