| Conditions | 23 |
| Total Lines | 92 |
| Code Lines | 47 |
| 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:
Complex classes like mutis.flares.bayblocks.BayesianBlocks.get_flares() 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 | """ Flare analysis """ |
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| 115 | def get_flares(self, thresh=1): |
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| 116 | """ Get a list of flares following the algorithm proposed in |
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| 117 | [Meyer, Scargle, Blandford (2019)] |
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| 118 | (https://iopscience.iop.org/article/10.3847/1538-4357/ab1651/pdf): |
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| 119 | |||
| 120 | ```There is no generally accepted consensus on the best way to |
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| 121 | determine which data points belong to a flaring state and which |
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| 122 | characterize the quiescent level. Nalewajko (2013)suggested |
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| 123 | the simple definition that a flare is a continuous time interval |
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| 124 | associated with a flux peak in which the flux is larger than half |
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| 125 | the peak flux value. This definition is intuitive, however, and it |
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| 126 | is unclear how to treat overlapping flares and identify flux |
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| 127 | peaks in an objective way. Here we use a simple two-step |
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| 128 | procedure tailored to the block representation: (1)identify a |
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| 129 | block that is higher than both the previous and subsequent |
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| 130 | blocks as a peak, and (2)proceed downward from the peak in |
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| 131 | both directions as long as the blocks are successively lower.``` |
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| 132 | """ |
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| 133 | di_max = 5 |
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| 134 | |||
| 135 | # get list of local maxima |
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| 136 | # bad beheaviour at 0 and -1: |
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| 137 | #imaxL = sp.signal.argrelextrema(self.values, np.greater)[0] |
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| 138 | imaxL = list() |
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| 139 | for i in range(0,len(self.values)): |
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| 140 | if i == 0 and thresh*self.values[0] > self.values[1]: |
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| 141 | imaxL.append(i) |
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| 142 | elif i == len(self.values)-1 and self.values[i-1] < thresh*self.values[i]: |
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| 143 | imaxL.append(i) |
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| 144 | elif self.values[i-1] < thresh*self.values[i] > self.values[i+1]: |
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| 145 | imaxL.append(i) |
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| 146 | imaxL = np.array(imaxL) |
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| 147 | |||
| 148 | |||
| 149 | inflareL = np.full(self.values.shape, False) |
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| 150 | |||
| 151 | # get list of local maxima over a threshold (flare peaks) |
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| 152 | for imax in imaxL: |
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| 153 | inflare = True |
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| 154 | |||
| 155 | if imax == 0: |
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| 156 | pass |
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| 157 | elif not (self.values[imax-1] < thresh*self.values[imax]): |
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| 158 | inflare = False |
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| 159 | |||
| 160 | |||
| 161 | if imax == len(self.values)-1: |
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| 162 | pass |
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| 163 | elif not (thresh*self.values[imax] > self.values[imax+1]): |
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| 164 | inflare = False |
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| 165 | |||
| 166 | inflareL[imax] = inflare |
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| 167 | |||
| 168 | # extend the flare to adyacent blocks |
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| 169 | for imax in np.argwhere(inflareL): |
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| 170 | |||
| 171 | if imax == 0: |
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| 172 | pass |
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| 173 | else: |
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| 174 | di = 1 |
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| 175 | stop = False |
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| 176 | while not stop and di < di_max: |
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| 177 | if imax-di-1 < 0: |
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| 178 | break |
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| 179 | |||
| 180 | if self.values[imax-di-1] < thresh*self.values[imax-di]: |
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| 181 | inflareL[imax-di] = True |
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| 182 | else: |
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| 183 | stop = True |
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| 184 | |||
| 185 | |||
| 186 | di = di + 1 |
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| 187 | |||
| 188 | if imax == len(self.values)-1: |
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| 189 | pass |
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| 190 | else: |
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| 191 | di = 1 |
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| 192 | stop = False |
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| 193 | while not stop and di < di_max: |
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| 194 | if imax+di+1 > len(self.values)-1: |
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| 195 | break |
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| 196 | |||
| 197 | if thresh*self.values[imax+di] > self.values[imax+di+1]: |
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| 198 | inflareL[imax+di] = True |
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| 199 | else: |
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| 200 | stop = True |
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| 201 | |||
| 202 | |||
| 203 | di = di + 1 |
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| 204 | |||
| 205 | |||
| 206 | self.inflare = np.array(inflareL) |
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| 207 | |||
| 232 | return flareL |
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