Lines Matching refs:np

28 import numpy as np  namespace
68 def check_noise_model_shape(noise_model: np.ndarray) -> None:
87     noise_model: np.ndarray,
133     gains: np.ndarray,
134     sens_max_analog: np.ndarray,
135 ) -> np.ndarray:
149   digital_gains = np.maximum(gains / sens_max_analog, 1)
150   if not np.all(digital_gains == 1):
189     mean_img: np.ndarray,
190     var_img: np.ndarray,
193 ) -> Tuple[np.ndarray, np.ndarray]:
234   means, vars_ = np.asarray(means), np.asarray(vars_)
239     means: np.ndarray,
240     vars_: np.ndarray,
246 ) -> Tuple[np.ndarray, np.ndarray]:
290       med = np.median(vars_i)
296     keep_indices = np.where(np.logical_and.reduce(constraints))
297     if not np.any(keep_indices):
316     means_i = np.asarray(means_i)
317     vars_i = np.asarray(vars_i)
323   means_filtered = np.asarray(means_filtered, dtype=object)
324   vars_filtered = np.asarray(vars_filtered, dtype=object)
371 ) -> Dict[int, List[Tuple[float, np.ndarray, np.ndarray]]]:
534             np.min(means), np.median(means), np.max(means),
538             np.min(vars_), np.median(vars_), np.max(vars_),
568     iso_to_stats_dict: Dict[int, List[Tuple[float, np.ndarray, np.ndarray]]],
627     samples: List[List[Tuple[float, np.ndarray, np.ndarray]]],
629     offset_a: np.ndarray,
630     offset_b: np.ndarray,
632 ) -> np.ndarray:
667     gains = np.asarray(gains).flatten()
668     means = np.asarray(means).flatten()
669     vars_ = np.asarray(vars_).flatten()
707       coeffs = np.append(coeffs, offset_a[pidx])
708       coeffs = np.append(coeffs, offset_b[pidx])
713   noise_model = np.asarray(noise_model)