Conditions | 4 |
Total Lines | 68 |
Code Lines | 29 |
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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141 | def igrf_dipole_axis(date): |
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142 | """Get Cartesian unit vector pointing at dipole pole in the north (IGRF). |
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143 | |||
144 | Parameters |
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145 | ---------- |
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146 | date : dt.datetime |
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147 | Date and time |
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148 | |||
149 | Returns |
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150 | ------- |
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151 | m_0 : np.ndarray |
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152 | Cartesian 3 element unit vector pointing at dipole pole in the north |
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153 | (geocentric coords) |
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154 | |||
155 | Notes |
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156 | ----- |
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157 | IGRF coefficients are read from the igrf12coeffs.txt file. It should also |
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158 | work after IGRF updates. The dipole coefficients are interpolated to the |
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159 | date, or extrapolated if date > latest IGRF model |
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160 | |||
161 | """ |
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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 = dt.date(date.year, 12, 31).timetuple().tm_yday |
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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) 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] and year >= years[0]: |
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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 |