def __init__(self, cols=["x_resid", "y_resid", "phi_resid"], min_num_obs=20, separate_experiments=True, separate_panels=True, alpha=0.5, max_n_groups=5, min_group_size=300, n_trials=500, k1=2, k2=2, k3=100, threshold_probability=0.975): CentroidOutlier.__init__(self, cols=cols, min_num_obs=min_num_obs, separate_experiments=separate_experiments, separate_panels=separate_panels) # Keep the FastMCD options here self._alpha = alpha self._max_n_groups = max_n_groups self._min_group_size = min_group_size self._n_trials = n_trials self._k1 = k1 self._k2 = k2 self._k3 = k3 # Calculate Mahalanobis distance threshold df = len(cols) self._mahasq_cutoff = qchisq(threshold_probability, df) return
def __init__( self, cols=None, min_num_obs=20, separate_experiments=True, separate_panels=True, block_width=None, px_sz=(1, 1), verbose=False, pdf=None, ): # here the column names are fixed by the algorithm, so what's passed in is # ignored. CentroidOutlier.__init__( self, cols=["miller_index", "xyzobs.px.value", "xyzcal.px"], min_num_obs=min_num_obs, separate_experiments=separate_experiments, separate_panels=separate_panels, block_width=block_width, ) self._px_sz = px_sz self._verbose = verbose self._pdf = pdf return
def __init__(self, cols=["x_resid", "y_resid", "phi_resid"], min_num_obs=20, separate_experiments=True, separate_panels=True, iqr_multiplier=1.5): CentroidOutlier.__init__(self, cols=cols, min_num_obs=min_num_obs, separate_experiments=separate_experiments, separate_panels=separate_panels) self._iqr_multiplier = iqr_multiplier return
def __init__(self, cols=None, min_num_obs=20, separate_experiments=True, separate_panels=True, block_width=None, iqr_multiplier=1.5): if cols is None: cols = ["x_resid", "y_resid", "phi_resid"] CentroidOutlier.__init__(self, cols=cols, min_num_obs=min_num_obs, separate_experiments=separate_experiments, block_width=block_width, separate_panels=separate_panels) self._iqr_multiplier = iqr_multiplier return
def __init__(self, cols=None, min_num_obs=20, separate_experiments=True, separate_panels=True, block_width=None, iqr_multiplier=1.5): if cols is None: cols = ["x_resid", "y_resid", "phi_resid"] CentroidOutlier.__init__(self, cols=cols, min_num_obs=min_num_obs, separate_experiments=separate_experiments, block_width=block_width, separate_panels=separate_panels) self._iqr_multiplier = iqr_multiplier return
def __init__( self, cols=None, min_num_obs=20, separate_experiments=True, separate_panels=True, block_width=None, alpha=0.5, max_n_groups=5, min_group_size=300, n_trials=500, k1=2, k2=2, k3=100, threshold_probability=0.975, ): if cols is None: cols = ["x_resid", "y_resid", "phi_resid"] CentroidOutlier.__init__( self, cols=cols, min_num_obs=min_num_obs, separate_experiments=separate_experiments, separate_panels=separate_panels, block_width=block_width, ) # Keep the FastMCD options here self._alpha = alpha self._max_n_groups = max_n_groups self._min_group_size = min_group_size self._n_trials = n_trials self._k1 = k1 self._k2 = k2 self._k3 = k3 # Calculate Mahalanobis distance threshold df = len(cols) self._mahasq_cutoff = qchisq(threshold_probability, df) return
def __init__(self, cols=None, min_num_obs=20, separate_experiments=True, separate_panels=True, px_sz=(1,1), verbose=False, pdf=None): # here the column names are fixed by the algorithm, so what's passed in is # ignored. CentroidOutlier.__init__(self, cols=["miller_index", "xyzobs.px.value", "xyzcal.px"], min_num_obs=min_num_obs, separate_experiments=separate_experiments, separate_panels=separate_panels) self._px_sz = px_sz self._verbose = verbose self._pdf = pdf return