Sourcery refactored main branch #1
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| hi = {} | ||
| for icode in indice_codes: | ||
| hi[icode] = getattr(indice_class, icode)() | ||
| return hi | ||
| return {icode: getattr(indice_class, icode)() for icode in indice_codes} |
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Function indices refactored with the following changes:
- Inline variable that is immediately returned (
inline-immediately-returned-variable) - Convert for loop into dictionary comprehension (
dict-comprehension)
| # Dry points, namely strict baseflow. | ||
| dry = ~(wet1 + wet2 + wet3 + wet4) | ||
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| return dry | ||
| return ~(wet1 + wet2 + wet3 + wet4) |
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Function strict_baseflow refactored with the following changes:
- Inline variable that is immediately returned (
inline-immediately-returned-variable)
This removes the following comments ( why? ):
# Dry points, namely strict baseflow.
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| # calculate the Kling-Gupta Efficiency KGE | ||
| kge_ = 1 - np.sqrt((r - 1) ** 2 + (alpha - 1) ** 2 + (beta - 1) ** 2) | ||
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| return kge_ | ||
| return 1 - np.sqrt((r - 1) ** 2 + (alpha - 1) ** 2 + (beta - 1) ** 2) |
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Function KGE refactored with the following changes:
- Inline variable that is immediately returned (
inline-immediately-returned-variable)
This removes the following comments ( why? ):
# calculate the Kling-Gupta Efficiency KGE
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| annual_BFI = annual_b / annual_Q | ||
| else: | ||
| idx_year = date.Y - date.Y.min() | ||
| counts = np.bincount(idx_year) | ||
| idx_valid = counts > 0 | ||
| annual_b = np.bincount(idx_year, weights=b)[idx_valid] / counts[idx_valid] | ||
| annual_Q = np.bincount(idx_year, weights=Q)[idx_valid] / counts[idx_valid] | ||
| annual_BFI = annual_b / annual_Q | ||
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| annual_BFI = annual_b / annual_Q |
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Function maxmium_BFI refactored with the following changes:
- Hoist repeated code outside conditional statement (
hoist-statement-from-if)
| year_delete = [] | ||
| for y in year_unique: | ||
| if (Q[year == y] >= 0).sum() < 120: | ||
| year_delete.append(y) | ||
| return year_delete | ||
| return [y for y in year_unique if (Q[year == y] >= 0).sum() < 120] |
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Function clean_streamflow_jit refactored with the following changes:
- Inline variable that is immediately returned (
inline-immediately-returned-variable) - Convert for loop into list comprehension (
list-comprehension)
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| allnp[group_name] = allnp[group_name] + 1 | ||
| allnp[group_name] += 1 | ||
| if flag == 1: | ||
| nnp[group_name] = nnp[group_name] + 1 | ||
| nnp[group_name] += 1 |
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Function Indices._lf refactored with the following changes:
- Replace assignment with augmented assignment [×2] (
aug-assign)
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| if self.use_median is True: | ||
| return stat.median() | ||
| return stat.mean() | ||
| return stat.median() if self.use_median is True else stat.mean() |
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Function Indices.DL18 refactored with the following changes:
- Lift code into else after jump in control flow (
reintroduce-else) - Replace if statement with if expression (
assign-if-exp)
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| stat = self.data_monthly_mean[self.data_monthly_mean == 0].count() | ||
| return stat | ||
| return self.data_monthly_mean[self.data_monthly_mean == 0].count() |
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Function Indices.DL20 refactored with the following changes:
- Inline variable that is immediately returned (
inline-immediately-returned-variable)
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| if self.use_median: | ||
| stat = stat.median() | ||
| else: | ||
| stat = stat.mean() | ||
| stat = stat.median() if self.use_median else stat.mean() |
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Function Indices.DH1 refactored with the following changes:
- Replace if statement with if expression (
assign-if-exp)
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| table = [] | ||
| table.append(lq[lq < 0.1 * lma1]) | ||
| table.append(lq[(lq >= 0.1 * lma1) & (lq < 0.25 * lma1)]) | ||
| table.append(lq[(lq >= 0.25 * lma1) & (lq < 0.5 * lma1)]) | ||
| table.append(lq[(lq >= 0.5 * lma1) & (lq < 0.75 * lma1)]) | ||
| table.append(lq[(lq >= 0.75 * lma1) & (lq < lma1)]) | ||
| table.append(lq[(lq >= lma1) & (lq < 1.25 * lma1)]) | ||
| table.append(lq[(lq >= 1.25 * lma1) & (lq < 1.5 * lma1)]) | ||
| table.append(lq[(lq >= 1.5 * lma1) & (lq < 1.75 * lma1)]) | ||
| table.append(lq[(lq >= 1.75 * lma1) & (lq < 2.0 * lma1)]) | ||
| table.append(lq[(lq >= 2.0 * lma1) & (lq < 2.25 * lma1)]) | ||
| table.append(lq[(lq >= 2.25 * lma1)]) | ||
| table = [ | ||
| lq[lq < 0.1 * lma1], | ||
| lq[(lq >= 0.1 * lma1) & (lq < 0.25 * lma1)], | ||
| lq[(lq >= 0.25 * lma1) & (lq < 0.5 * lma1)], | ||
| lq[(lq >= 0.5 * lma1) & (lq < 0.75 * lma1)], | ||
| lq[(lq >= 0.75 * lma1) & (lq < lma1)], | ||
| lq[(lq >= lma1) & (lq < 1.25 * lma1)], | ||
| lq[(lq >= 1.25 * lma1) & (lq < 1.5 * lma1)], | ||
| lq[(lq >= 1.5 * lma1) & (lq < 1.75 * lma1)], | ||
| lq[(lq >= 1.75 * lma1) & (lq < 2.0 * lma1)], | ||
| lq[(lq >= 2.0 * lma1) & (lq < 2.25 * lma1)], | ||
| lq[lq >= 2.25 * lma1], | ||
| ] |
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Function Indices._pre_ta1_ta2 refactored with the following changes:
- Merge append into list declaration [×11] (
merge-list-append) - Convert for loop into list comprehension (
list-comprehension)
Sourcery Code Quality Report✅ Merging this PR will increase code quality in the affected files by 0.88%.
Here are some functions in these files that still need a tune-up:
Legend and ExplanationThe emojis denote the absolute quality of the code:
The 👍 and 👎 indicate whether the quality has improved or gotten worse with this pull request. Please see our documentation here for details on how these metrics are calculated. We are actively working on this report - lots more documentation and extra metrics to come! Help us improve this quality report! |
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