def identify_outliers(self, params, experiments, indexed):
if not params.indexing.stills.candidate_outlier_rejection:
return flex.bool(len(indexed), True)
logger.info("$$$ stills_indexer::identify_outliers")
refiner = e_refine(params, experiments, indexed, graph_verbose=False)
RR = refiner.predict_for_reflection_table(indexed)
px_sz = experiments[0].detector[0].get_pixel_size()
class Match(object):
pass
matches = []
for item in RR.rows():
m = Match()
m.x_obs = item["xyzobs.px.value"][0] * px_sz[0]
m.y_obs = item["xyzobs.px.value"][1] * px_sz[1]
m.x_calc = item["xyzcal.px"][0] * px_sz[0]
m.y_calc = item["xyzcal.px"][1] * px_sz[1]
m.miller_index = item["miller_index"]
matches.append(m)
from rstbx.phil.phil_preferences import indexing_api_defs
import iotbx.phil
hardcoded_phil = iotbx.phil.parse(input_string=indexing_api_defs).extract()
from rstbx.indexing_api.outlier_procedure import OutlierPlotPDF
# comment this in if PDF graph is desired:
# hardcoded_phil.indexing.outlier_detection.pdf = "outlier.pdf"
# new code for outlier rejection inline here
if hardcoded_phil.indexing.outlier_detection.pdf is not None:
hardcoded_phil.__inject__(
"writer", OutlierPlotPDF(hardcoded_phil.indexing.outlier_detection.pdf)
)
# execute Sauter and Poon (2010) algorithm
from rstbx.indexing_api import outlier_detection
od = outlier_detection.find_outliers_from_matches(
matches, verbose=True, horizon_phil=hardcoded_phil
)
if hardcoded_phil.indexing.outlier_detection.pdf is not None:
od.make_graphs(canvas=hardcoded_phil.writer.R.c, left_margin=0.5)
hardcoded_phil.writer.R.c.showPage()
hardcoded_phil.writer.R.c.save()
return od.get_cache_status()
def _detect_outliers(self, cols):
# cols is guaranteed to be a list of three flex arrays, containing miller
# indices, observed pixel coordinates and calculated pixel coordinates.
# Copy the data into matches
class match:
pass
matches = []
for hkl in cols[0]:
m = match()
m.miller_index = hkl
matches.append(m)
for obs, m in zip(cols[1], matches):
m.x_obs = obs[0] * self._px_sz[0]
m.y_obs = obs[1] * self._px_sz[1]
for calc, m in zip(cols[2], matches):
m.x_calc = calc[0] * self._px_sz[0]
m.y_calc = calc[1] * self._px_sz[1]
import iotbx.phil
from rstbx.phil.phil_preferences import indexing_api_defs
hardcoded_phil = iotbx.phil.parse(
input_string=indexing_api_defs).extract()
# set params into the hardcoded_phil
hardcoded_phil.indexing.outlier_detection.verbose = self._verbose
hardcoded_phil.indexing.outlier_detection.pdf = self._pdf
from rstbx.indexing_api.outlier_procedure import OutlierPlotPDF
if self._pdf is not None:
## new code for outlier rejection inline here
hardcoded_phil.__inject__(
"writer",
OutlierPlotPDF(hardcoded_phil.indexing.outlier_detection.pdf))
# execute Sauter and Poon (2010) algorithm
from rstbx.indexing_api import outlier_detection
od = outlier_detection.find_outliers_from_matches(
matches, verbose=self._verbose, horizon_phil=hardcoded_phil)
# flex.bool of the inliers
outliers = ~od.get_cache_status()
return outliers
def _detect_outliers(self, cols):
# cols is guaranteed to be a list of three flex arrays, containing miller
# indices, observed pixel coordinates and calculated pixel coordinates.
# Copy the data into matches
class match: pass
matches = []
for hkl in cols[0]:
m = match()
m.miller_index = hkl
matches.append(m)
for obs, m in zip(cols[1], matches):
m.x_obs = obs[0]*self._px_sz[0]
m.y_obs = obs[1]*self._px_sz[1]
for calc, m in zip(cols[2], matches):
m.x_calc = calc[0]*self._px_sz[0]
m.y_calc = calc[1]*self._px_sz[1]
from rstbx.phil.phil_preferences import indexing_api_defs
import iotbx.phil
hardcoded_phil = iotbx.phil.parse(
input_string=indexing_api_defs).extract()
# set params into the hardcoded_phil
hardcoded_phil.indexing.outlier_detection.verbose = self._verbose
hardcoded_phil.indexing.outlier_detection.pdf = self._pdf
from rstbx.indexing_api.outlier_procedure import OutlierPlotPDF
if self._pdf is not None:
## new code for outlier rejection inline here
hardcoded_phil.__inject__("writer",OutlierPlotPDF(hardcoded_phil.indexing.outlier_detection.pdf))
# execute Sauter and Poon (2010) algorithm
from rstbx.indexing_api import outlier_detection
hardcoded_phil = hardcoded_phil
od = outlier_detection.find_outliers_from_matches(
matches,
verbose=self._verbose,
horizon_phil=hardcoded_phil)
# flex.bool of the inliers
outliers = ~od.get_cache_status()
return outliers
def identify_outliers(self, params, experiments, indexed):
print "$$$ stills_indexer::identify_outliers"
refiner = e_refine(params, experiments, indexed, graph_verbose=False)
RR = refiner.predict_for_reflection_table(indexed)
px_sz = experiments[0].detector[0].get_pixel_size()
class match: pass
matches = []
for item in RR:
m = match()
m.x_obs = item["xyzobs.px.value"][0]*px_sz[0]
m.y_obs = item["xyzobs.px.value"][1]*px_sz[1]
m.x_calc= item["xyzcal.px"][0]*px_sz[0]
m.y_calc= item["xyzcal.px"][1]*px_sz[1]
m.miller_index = item["miller_index"]
matches.append(m)
from rstbx.phil.phil_preferences import indexing_api_defs
import iotbx.phil
hardcoded_phil = iotbx.phil.parse(
input_string=indexing_api_defs).extract()
from rstbx.indexing_api.outlier_procedure import OutlierPlotPDF
#comment this in if PDF graph is desired:
#hardcoded_phil.indexing.outlier_detection.pdf = "outlier.pdf"
# new code for outlier rejection inline here
if hardcoded_phil.indexing.outlier_detection.pdf is not None:
hardcoded_phil.__inject__("writer",OutlierPlotPDF(hardcoded_phil.indexing.outlier_detection.pdf))
# execute Sauter and Poon (2010) algorithm
from rstbx.indexing_api import outlier_detection
od = outlier_detection.find_outliers_from_matches(
matches,
verbose=True,
horizon_phil=hardcoded_phil)
if hardcoded_phil.indexing.outlier_detection.pdf is not None:
od.make_graphs(canvas=hardcoded_phil.writer.R.c,left_margin=0.5)
hardcoded_phil.writer.R.c.showPage()
hardcoded_phil.writer.R.c.save()
return od.get_cache_status()
