Coverage for python/lsst/meas/algorithms/accumulator_mean_stack.py: 9%
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1# This file is part of meas_algorithms.
2#
3# LSST Data Management System
4# This product includes software developed by the
5# LSST Project (http://www.lsst.org/).
6# See COPYRIGHT file at the top of the source tree.
7#
8# This program is free software: you can redistribute it and/or modify
9# it under the terms of the GNU General Public License as published by
10# the Free Software Foundation, either version 3 of the License, or
11# (at your option) any later version.
12#
13# This program is distributed in the hope that it will be useful,
14# but WITHOUT ANY WARRANTY; without even the implied warranty of
15# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
16# GNU General Public License for more details.
17#
18# You should have received a copy of the LSST License Statement and
19# the GNU General Public License along with this program. If not,
20# see <https://www.lsstcorp.org/LegalNotices/>.
21#
22import warnings
24import numpy as np
27__all__ = ['AccumulatorMeanStack']
30class AccumulatorMeanStack:
31 """Stack masked images.
33 Parameters
34 ----------
35 shape : `tuple`
36 Shape of the input and output images.
37 bit_mask_value : `int`
38 Bit mask to flag for "bad" inputs that should not be stacked.
39 mask_threshold_dict : `dict` [`int`: `float`], optional
40 Dictionary of mapping from bit number to threshold for flagging.
41 Only bad bits (in bit_mask_value) which mask fractional weight
42 greater than this threshold will be flagged in the output image.
43 mask_map : `list` [`tuple`], optional
44 Mapping from input image bits to aggregated coadd bits.
45 no_good_pixels_mask : `int`, optional
46 Bit mask to set when there are no good pixels in the stack.
47 If not set then will set coadd masked image 'NO_DATA' bit.
48 calc_error_from_input_variance : `bool`, optional
49 Calculate the error from the input variance?
50 compute_n_image : `bool`, optional
51 Calculate the n_image map as well as stack?
52 """
53 def __init__(self, shape,
54 bit_mask_value, mask_threshold_dict={},
55 mask_map=[], no_good_pixels_mask=None,
56 calc_error_from_input_variance=True,
57 compute_n_image=False):
58 self.shape = shape
59 self.bit_mask_value = bit_mask_value
60 self.mask_map = mask_map
61 self.no_good_pixels_mask = no_good_pixels_mask
62 self.calc_error_from_input_variance = calc_error_from_input_variance
63 self.compute_n_image = compute_n_image
65 # Only track threshold bits that are in the bad bit_mask_value.
66 self.mask_threshold_dict = {}
67 for bit in mask_threshold_dict:
68 if (self.bit_mask_value & 2**bit) > 0:
69 self.mask_threshold_dict[bit] = mask_threshold_dict[bit]
71 # sum_weight holds the sum of weights for each pixel.
72 self.sum_weight = np.zeros(shape, dtype=np.float64)
73 # sum_wdata holds the sum of weight*data for each pixel.
74 self.sum_wdata = np.zeros(shape, dtype=np.float64)
76 if calc_error_from_input_variance:
77 # sum_w2var holds the sum of weight**2 * variance for each pixel.
78 self.sum_w2var = np.zeros(shape, dtype=np.float64)
79 else:
80 # sum_weight2 holds the sum of weight**2 for each pixel.
81 self.sum_weight2 = np.zeros(shape, dtype=np.float64)
82 # sum_wdata2 holds the sum of weight * data**2 for each pixel.
83 self.sum_wdata2 = np.zeros(shape, dtype=np.float64)
85 self.or_mask = np.zeros(shape, dtype=np.int64)
86 self.rejected_weights_by_bit = {}
87 for bit in self.mask_threshold_dict:
88 self.rejected_weights_by_bit[bit] = np.zeros(shape, dtype=np.float64)
90 self.masked_pixels_mask = np.zeros(shape, dtype=np.int64)
92 if self.compute_n_image:
93 self.n_image = np.zeros(shape, dtype=np.int32)
95 def reset(self):
96 """Reset all accumulator arrays."""
97 self.sum_weight[...] = 0
98 self.sum_wdata[...] = 0
99 if self.calc_error_from_input_variance:
100 self.sum_w2var[...] = 0
101 else:
102 self.sum_weight2[...] = 0
103 self.sum_wdata2[...] = 0
104 self.or_mask[...] = 0
105 for bit in self.mask_threshold_dict:
106 self.rejected_weights_by_bit[bit][...] = 0
107 self.masked_pixels_mask[...] = 0
108 if self.compute_n_image:
109 self.n_image[...] = 0
111 def add_masked_image(self, masked_image, weight=1.0):
112 """Add a masked image to the stack.
114 Parameters
115 ----------
116 masked_image : `lsst.afw.image.MaskedImage`
117 Masked image to add to the stack.
118 weight : `float`, optional
119 Weight to apply for weighted mean.
120 """
121 good_pixels = np.where(((masked_image.mask.array & self.bit_mask_value) == 0)
122 & np.isfinite(masked_image.mask.array))
124 self.sum_weight[good_pixels] += weight
125 self.sum_wdata[good_pixels] += weight*masked_image.image.array[good_pixels]
127 if self.compute_n_image:
128 self.n_image[good_pixels] += 1
130 if self.calc_error_from_input_variance:
131 self.sum_w2var[good_pixels] += (weight**2.)*masked_image.variance.array[good_pixels]
132 else:
133 self.sum_weight2[good_pixels] += weight**2.
134 self.sum_wdata2[good_pixels] += weight*(masked_image.image.array[good_pixels]**2.)
136 # Mask bits are propagated for good pixels
137 self.or_mask[good_pixels] |= masked_image.mask.array[good_pixels]
139 # Bad pixels are only tracked if they cross a threshold
140 for bit in self.mask_threshold_dict:
141 bad_pixels = ((masked_image.mask.array & 2**bit) > 0)
142 self.rejected_weights_by_bit[bit][bad_pixels] += weight
143 self.masked_pixels_mask[bad_pixels] |= 2**bit
145 def fill_stacked_masked_image(self, stacked_masked_image):
146 """Fill the stacked mask image after accumulation.
148 Parameters
149 ----------
150 stacked_masked_image : `lsst.afw.image.MaskedImage`
151 Total masked image.
152 """
153 with warnings.catch_warnings():
154 # Let the NaNs through and flag bad pixels below
155 warnings.simplefilter("ignore")
157 # The image plane is sum(weight*data)/sum(weight)
158 stacked_masked_image.image.array[:, :] = self.sum_wdata/self.sum_weight
160 if self.calc_error_from_input_variance:
161 mean_var = self.sum_w2var/(self.sum_weight**2.)
162 else:
163 # Compute the biased estimator
164 variance = self.sum_wdata2/self.sum_weight - stacked_masked_image.image.array[:, :]**2.
165 # De-bias
166 variance *= (self.sum_weight**2.)/(self.sum_weight**2. - self.sum_weight2)
168 # Compute the mean variance
169 mean_var = variance*self.sum_weight2/(self.sum_weight**2.)
171 stacked_masked_image.variance.array[:, :] = mean_var
173 # Propagate bits when they cross the threshold
174 for bit in self.mask_threshold_dict:
175 hypothetical_total_weight = self.sum_weight + self.rejected_weights_by_bit[bit]
176 self.rejected_weights_by_bit[bit] /= hypothetical_total_weight
177 propagate = np.where(self.rejected_weights_by_bit[bit] > self.mask_threshold_dict[bit])
178 self.or_mask[propagate] |= 2**bit
180 # Map mask planes to new bits for pixels that had at least one
181 # bad input rejected and are in the mask_map.
182 for mask_tuple in self.mask_map:
183 self.or_mask[(self.masked_pixels_mask & mask_tuple[0]) > 0] |= mask_tuple[1]
185 stacked_masked_image.mask.array[:, :] = self.or_mask
187 if self.no_good_pixels_mask is None:
188 mask_dict = stacked_masked_image.mask.getMaskPlaneDict()
189 no_good_pixels_mask = 2**(mask_dict['NO_DATA'])
190 else:
191 no_good_pixels_mask = self.no_good_pixels_mask
193 bad_pixels = (self.sum_weight <= 0.0)
194 stacked_masked_image.mask.array[bad_pixels] |= no_good_pixels_mask
196 def add_image(self, image, weight=1.0):
197 """Add an image to the stack.
199 No bit-filtering is performed when adding an image.
201 Parameters
202 ----------
203 image : `lsst.afw.image.Image`
204 Image to add to the stack.
205 weight : `float`, optional
206 Weight to apply for weighted mean.
207 """
208 self.sum_weight[:, :] += weight
209 self.sum_wdata[:, :] += weight*image.array[:]
211 if self.compute_n_image:
212 self.n_image[:, :] += 1
214 def fill_stacked_image(self, stacked_image):
215 """Fill the image after accumulation.
217 Parameters
218 ----------
219 stacked_image : `lsst.afw.image.Image`
220 Total image.
221 """
222 with warnings.catch_warnings():
223 # Let the NaNs through, this should only happen
224 # if we're stacking with no inputs.
225 warnings.simplefilter("ignore")
227 # The image plane is sum(weight*data)/sum(weight)
228 stacked_image.array[:, :] = self.sum_wdata/self.sum_weight
230 @staticmethod
231 def stats_ctrl_to_threshold_dict(stats_ctrl):
232 """Convert stats control to threshold dict.
234 Parameters
235 ----------
236 stats_ctrl : `lsst.afw.math.StatisticsControl`
238 Returns
239 -------
240 threshold_dict : `dict`
241 Dict mapping from bit to propagation threshold.
242 """
243 threshold_dict = {}
244 for bit in range(64):
245 threshold_dict[bit] = stats_ctrl.getMaskPropagationThreshold(bit)
247 return threshold_dict