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36 def _replace_nan(a, val): 37 """ 38 If `a` is of inexact type, make a copy of `a`, replace NaNs with 39 the `val` value, and return the copy together with a boolean mask 40 marking the locations where NaNs were present. If `a` is not of 41 inexact type, do nothing and return `a` together with a mask of None. 42 43 Note that scalars will end up as array scalars, which is important 44 for using the result as the value of the out argument in some 45 operations. 46 47 Parameters 48 ---------- 49 a : array-like 50 Input array. 51 val : float 52 NaN values are set to val before doing the operation. 53 54 Returns 55 ------- 56 y : ndarray 57 If `a` is of inexact type, return a copy of `a` with the NaNs 58 replaced by the fill value, otherwise return `a`. 59 mask: {bool, None} 60 If `a` is of inexact type, return a boolean mask marking locations of 61 NaNs, otherwise return None. 62 63 """ 64 a = np.array(a, subok=True, copy=True) 65 66 if a.dtype == np.object_: 67 # object arrays do not support `isnan` (gh-9009), so make a guess 68 mask = a != a 69 elif issubclass(a.dtype.type, np.inexact): 70 mask = np.isnan(a) 71 else: 72 mask = None 73 74 if mask is not None: 75 np.copyto(a, val, where=mask) 76 77 return a, mask

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24 def fix_nans(mat): 25 """ 26 returns the matrix with average over models if a model, sample, chromosome had nan in it. 27 :param mat: ndarray (model, sample, chromosome) 28 :return: mat ndarray (model, sample, chromosome) 29 """ 30 mat = np.nan_to_num(mat) 31 idx, idy, idz = np.where(mat == 0) 32 for x, y, z in zip(idx, idy, idz): 33 mat[x, y, z] = mat[:, y, z].mean() 34 return mat

694 def remove_nans(self): 695 """Removes cadences where the flux is NaN. 696 697 Returns 698 ------- 699 clean_lightcurve : `LightCurve` 700 A new light curve object from which NaNs fluxes have been removed. 701 """ 702 return self[~np.isnan(self.flux)] # This will return a sliced copy

74 def filter_nan2none(value): 75 """Convert the NaN value to None, leaving everything else unchanged. 76 77 This function is meant to be used as a Django template filter. It 78 is useful in combination with filters that handle None (or any 79 false value) specially, such as the 'default' filter, when one 80 wants special treatment for the NaN value. It is also useful 81 before the 'format' filter to avoid the NaN value being formatted. 82 83 """ 84 if is_nan(value): 85 return None 86 return value

135 def replace_nan(value, default=0): 136 if math.isnan(value): 137 return default 138 return value