| Conditions | 3 | 
| Total Lines | 70 | 
| Code Lines | 30 | 
| Lines | 0 | 
| Ratio | 0 % | 
| 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:
| 1 | # Copyright (C) 2019 NRL  | 
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| 139 | def igrf_dipole_axis(date):  | 
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| 140 | """Get Cartesian unit vector pointing at dipole pole in the north,  | 
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| 141 | according to IGRF  | 
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| 142 | |||
| 143 | Parameters  | 
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| 144 | -------------  | 
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| 145 | date : (dt.datetime)  | 
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| 146 | Date and time  | 
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| 147 | |||
| 148 | Returns  | 
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| 149 | ----------  | 
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| 150 | m_0: (np.ndarray)  | 
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| 151 | Cartesian 3 element unit vector pointing at dipole pole in the north  | 
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| 152 | (geocentric coords)  | 
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| 153 | |||
| 154 | Notes  | 
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| 155 | ----------  | 
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| 156 | IGRF coefficients are read from the igrf12coeffs.txt file. It should also  | 
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| 157 | work after IGRF updates. The dipole coefficients are interpolated to the  | 
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| 158 | date, or extrapolated if date > latest IGRF model  | 
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| 159 | """  | 
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| 160 | warnings.warn(dep_str, category=FutureWarning)  | 
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| 161 | |||
| 162 | # get time in years, as float:  | 
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| 163 | year = date.year  | 
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| 164 | doy = date.timetuple().tm_yday  | 
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| 165 |     year_days = int(dt.date(date.year, 12, 31).strftime("%j")) | 
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| 166 | year = year + doy / year_days  | 
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| 167 | |||
| 168 | # read the IGRF coefficients  | 
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| 169 | with open(aacgmv2.IGRF_COEFFS, 'r') as f_igrf:  | 
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| 170 | lines = f_igrf.readlines()  | 
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| 171 | |||
| 172 | years = lines[3].split()[3:][:-1]  | 
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| 173 | years = np.array(years, dtype=float) # time array  | 
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| 174 | |||
| 175 | g10 = lines[4].split()[3:]  | 
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| 176 | g11 = lines[5].split()[3:]  | 
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| 177 | h11 = lines[6].split()[3:]  | 
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| 178 | |||
| 179 | # secular variation coefficients (for extrapolation)  | 
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| 180 | g10sv = np.float32(g10[-1])  | 
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| 181 | g11sv = np.float32(g11[-1])  | 
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| 182 | h11sv = np.float32(h11[-1])  | 
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| 183 | |||
| 184 | # model coefficients:  | 
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| 185 | g10 = np.array(g10[:-1], dtype=float)  | 
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| 186 | g11 = np.array(g11[:-1], dtype=float)  | 
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| 187 | h11 = np.array(h11[:-1], dtype=float)  | 
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| 188 | |||
| 189 | # get the gauss coefficient at given time:  | 
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| 190 | if year <= years[-1]:  | 
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| 191 | # regular interpolation  | 
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| 192 | g10 = np.interp(year, years, g10)  | 
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| 193 | g11 = np.interp(year, years, g11)  | 
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| 194 | h11 = np.interp(year, years, h11)  | 
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| 195 | else:  | 
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| 196 | # extrapolation  | 
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| 197 | dyear = year - years[-1]  | 
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| 198 | g10 = g10[-1] + g10sv * dyear  | 
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| 199 | g11 = g11[-1] + g11sv * dyear  | 
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| 200 | h11 = h11[-1] + h11sv * dyear  | 
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| 201 | |||
| 202 | # calculate pole position  | 
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| 203 | B_0 = np.sqrt(g10**2 + g11**2 + h11**2)  | 
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| 204 | |||
| 205 | # Calculate output  | 
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| 206 | m_0 = -np.array([g11, h11, g10]) / B_0  | 
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| 207 | |||
| 208 | return m_0  | 
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| 209 |