From a157f453c390e0c1b99ac87135c01f1259fb7070 Mon Sep 17 00:00:00 2001
From: "Doucet, Mathieu" <doucetm@ornl.gov>
Date: Tue, 27 Oct 2015 09:24:09 -0400
Subject: [PATCH] Re #14104 Clean up python
---
.../algorithms/LRReflectivityOutput.py | 93 +++++++++----------
.../LiquidsReflectometryReduction.py | 4 +-
2 files changed, 47 insertions(+), 50 deletions(-)
diff --git a/Framework/PythonInterface/plugins/algorithms/LRReflectivityOutput.py b/Framework/PythonInterface/plugins/algorithms/LRReflectivityOutput.py
index b5ded9f545a..1e6d8b9e2ca 100644
--- a/Framework/PythonInterface/plugins/algorithms/LRReflectivityOutput.py
+++ b/Framework/PythonInterface/plugins/algorithms/LRReflectivityOutput.py
@@ -1,4 +1,4 @@
-#pylint: disable=no-init,invalid-name
+# pylint: disable=no-init,invalid-name
import math
import time
import mantid
@@ -30,27 +30,27 @@ class LRReflectivityOutput(PythonAlgorithm):
self.declareProperty(FloatArrayProperty("OutputBinning", [0.005, -0.01, 1.0], direction=Direction.Input))
self.declareProperty("DQConstant", 0.0004, "Constant factor for the resolution dQ = dQ0 + Q dQ/Q")
self.declareProperty("DQSlope", 0.025, "Slope for the resolution dQ = dQ0 + Q dQ/Q")
- self.declareProperty(FileProperty('OutputFilename', '', action=FileAction.Save, extensions = ["txt"]),
+ self.declareProperty(FileProperty('OutputFilename', '', action=FileAction.Save, extensions=["txt"]),
doc='Name of the reflectivity file output')
self.declareProperty("MetaData", "", "Additional meta-data to add to the top of the output file")
-
+
def PyExec(self):
# Check that all the input workspaces are scaled
workspace_list = self.getProperty("ReducedWorkspaces").value
if self.check_scaling(workspace_list) == False:
logger.error("Absolute normalization not available!")
-
+
# Put the workspaces together
self.average_points_for_single_q(workspace_list)
-
+
def check_scaling(self, workspace_list):
"""
Check that all the workspaces are on an absolute scale.
@param workspace_list: list of workspaces to put together
"""
- #TODO: Store scaling factors and clocking correction in header
+ # TODO: Store scaling factors and clocking correction in header
scaling_cutoff = self.getProperty("ScalingWavelengthCutoff")
-
+
normalization_available = True
for ws in workspace_list:
if mtd[ws].getRun().hasProperty("isSFfound"):
@@ -58,7 +58,7 @@ class LRReflectivityOutput(PythonAlgorithm):
try:
wl = mtd[ws].getRun().getProperty("LambdaRequest").value[0]
# Scaling factor above the wavelength cutoff are assumed to be 1
- normalization_available = wl>scaling_cutoff
+ normalization_available = wl > scaling_cutoff
logger.notice("%s: no normalization for wl=%s" % (ws, str(wl)))
except:
logger.notice("%s: could not find LambdaRequest" % ws)
@@ -69,8 +69,7 @@ class LRReflectivityOutput(PythonAlgorithm):
logger.notice("%s: no normalization info" % ws)
normalization_available = False
return normalization_available
-
-
+
def average_points_for_single_q(self, scaled_ws_list):
"""
Take the point with the smalled error when multiple points are
@@ -88,7 +87,7 @@ class LRReflectivityOutput(PythonAlgorithm):
# Convert each histo to histograms and rebin to final binning
for ws in scaled_ws_list:
new_name = "%s_histo" % ws
- #ConvertToHistogram(InputWorkspace=ws, OutputWorkspace=new_name)
+ # ConvertToHistogram(InputWorkspace=ws, OutputWorkspace=new_name)
mtd[ws].setDistribution(True)
Rebin(InputWorkspace=ws, Params=binning_parameters,
OutputWorkspace=new_name)
@@ -113,9 +112,9 @@ class LRReflectivityOutput(PythonAlgorithm):
header_info += "# %-9s %-9s %-14.6g %-14.6g %-12.6g %-12.6s %-12.6s %-12.6s %-12.6s %-12.6s\n" % value_list
# Take the first rebinned histo as our output
- data_x = mtd[scaled_ws_list[0]+'_histo'].dataX(0)
- data_y = mtd[scaled_ws_list[0]+'_histo'].dataY(0)
- data_e = mtd[scaled_ws_list[0]+'_histo'].dataE(0)
+ data_x = mtd[scaled_ws_list[0] + '_histo'].dataX(0)
+ data_y = mtd[scaled_ws_list[0] + '_histo'].dataY(0)
+ data_e = mtd[scaled_ws_list[0] + '_histo'].dataE(0)
# Skip first point and last one
points_to_skip = 1
@@ -123,9 +122,9 @@ class LRReflectivityOutput(PythonAlgorithm):
skipped_points = 0
distribution_started = False
- data_y_i = mtd[scaled_ws_list[i]+'_histo'].dataY(0)
- data_e_i = mtd[scaled_ws_list[i]+'_histo'].dataE(0)
- for j in range(len(data_y_i)-1):
+ data_y_i = mtd[scaled_ws_list[i] + '_histo'].dataY(0)
+ data_e_i = mtd[scaled_ws_list[i] + '_histo'].dataE(0)
+ for j in range(len(data_y_i) - 1):
# Check whether we need to skip this point
if data_y_i[j] > 0:
distribution_started = True
@@ -134,23 +133,23 @@ class LRReflectivityOutput(PythonAlgorithm):
continue
# If this is the last point of the distribution, skip it
- if distribution_started and data_y_i[j+1]==0 and data_e_i[j+1]==0:
+ if distribution_started and data_y_i[j + 1] == 0 and data_e_i[j + 1] == 0:
break
-
- if data_y_i[j]>0:
- if data_y[j]>0:
- data_y[j] = 0.5*(data_y[j]+data_y_i[j])
- data_e[j] = 0.5*math.sqrt(data_e[j]*data_e[j]+data_e_i[j]*data_e_i[j])
+
+ if data_y_i[j] > 0:
+ if data_y[j] > 0:
+ data_y[j] = 0.5 * (data_y[j] + data_y_i[j])
+ data_e[j] = 0.5 * math.sqrt(data_e[j] * data_e[j] + data_e_i[j] * data_e_i[j])
else:
data_y[j] = data_y_i[j]
data_e[j] = data_e_i[j]
# Skip the first point
for i in range(len(data_y)):
- if data_y[i]>0:
+ if data_y[i] > 0:
data_y[i] = 0.0
break
-
+
# Scale to unity
scale_to_unity = self.getProperty("ScaleToUnity").value
specular_cutoff = self.getProperty("SpecularCutoff").value
@@ -159,7 +158,7 @@ class LRReflectivityOutput(PythonAlgorithm):
y_values = []
e_values = []
for i in range(len(data_y)):
- if data_y[i]>0 and data_x[i]<specular_cutoff:
+ if data_y[i] > 0 and data_x[i] < specular_cutoff:
y_values.append(data_y[i])
e_values.append(data_e[i])
@@ -167,26 +166,26 @@ class LRReflectivityOutput(PythonAlgorithm):
total = 0.0
weights = 0.0
for i in range(len(y_values)):
- total += e_values[i]*y_values[i]
+ total += e_values[i] * y_values[i]
weights += e_values[i]
- scaling_factor = total/weights
-
- Scale(InputWorkspace=scaled_ws_list[0]+'_histo', OutputWorkspace=scaled_ws_list[0]+'_scaled',
- Factor=1.0/scaling_factor, Operation='Multiply')
-
+ scaling_factor = total / weights
+
+ Scale(InputWorkspace=scaled_ws_list[0] + '_histo', OutputWorkspace=scaled_ws_list[0] + '_scaled',
+ Factor=1.0 / scaling_factor, Operation='Multiply')
+
# Save the data
file_path = self.getProperty("OutputFilename").value
dq0 = self.getProperty("DQConstant").value
dq_over_q = self.getProperty("DQSlope").value
meta_data = self.getProperty("MetaData").value
- data_x = mtd[scaled_ws_list[0]+'_scaled'].dataX(0)
- data_y = mtd[scaled_ws_list[0]+'_scaled'].dataY(0)
- data_e = mtd[scaled_ws_list[0]+'_scaled'].dataE(0)
+ data_x = mtd[scaled_ws_list[0] + '_scaled'].dataX(0)
+ data_y = mtd[scaled_ws_list[0] + '_scaled'].dataY(0)
+ data_e = mtd[scaled_ws_list[0] + '_scaled'].dataE(0)
- start_time = mtd[scaled_ws_list[0]+'_scaled'].getRun().getProperty("start_time").value
- experiment = mtd[scaled_ws_list[0]+'_scaled'].getRun().getProperty("experiment_identifier").value
- run_number = mtd[scaled_ws_list[0]+'_scaled'].getRun().getProperty("run_number").value
+ start_time = mtd[scaled_ws_list[0] + '_scaled'].getRun().getProperty("start_time").value
+ experiment = mtd[scaled_ws_list[0] + '_scaled'].getRun().getProperty("experiment_identifier").value
+ run_number = mtd[scaled_ws_list[0] + '_scaled'].getRun().getProperty("run_number").value
content = '# Experiment %s Run %s\n' % (experiment, run_number)
content += '# Run start time: %s\n' % start_time
@@ -195,7 +194,7 @@ class LRReflectivityOutput(PythonAlgorithm):
content += header_info
try:
- if len(meta_data.strip())>0:
+ if len(meta_data.strip()) > 0:
content += '#\n'
lines = meta_data.strip().split('\n')
for l in lines:
@@ -207,10 +206,10 @@ class LRReflectivityOutput(PythonAlgorithm):
content += '# dQ0[1/Angstrom] = %g\n' % dq0
content += '# dQ/Q = %g\n' % dq_over_q
content += '# Q[1/Angstrom] R delta_R Precision\n'
-
+
for i in range(len(data_x)):
# Skip point where the error is much larger than the reflectivity value
- if (data_y[i]>data_e[i]/100.0):
+ if (data_y[i] > data_e[i] / 100.0):
content += str(data_x[i])
content += ' ' + str(data_y[i])
content += ' ' + str(data_e[i])
@@ -218,14 +217,14 @@ class LRReflectivityOutput(PythonAlgorithm):
content += ' ' + _precision
content += '\n'
- f=open(file_path,'w')
+ f = open(file_path, 'w')
f.write(content)
f.close()
for ws in scaled_ws_list:
- if AnalysisDataService.doesExist(ws+'_histo'):
- AnalysisDataService.remove(ws+'_histo')
- if AnalysisDataService.doesExist(ws+'_scaled'):
- AnalysisDataService.remove(ws+'_scaled')
-
+ if AnalysisDataService.doesExist(ws + '_histo'):
+ AnalysisDataService.remove(ws + '_histo')
+ if AnalysisDataService.doesExist(ws + '_scaled'):
+ AnalysisDataService.remove(ws + '_scaled')
+
AlgorithmFactory.subscribe(LRReflectivityOutput)
diff --git a/Framework/PythonInterface/plugins/algorithms/LiquidsReflectometryReduction.py b/Framework/PythonInterface/plugins/algorithms/LiquidsReflectometryReduction.py
index e9b941b4588..1b43e5112d9 100644
--- a/Framework/PythonInterface/plugins/algorithms/LiquidsReflectometryReduction.py
+++ b/Framework/PythonInterface/plugins/algorithms/LiquidsReflectometryReduction.py
@@ -232,7 +232,7 @@ class LiquidsReflectometryReduction(PythonAlgorithm):
m = 1.675e-27 # kg
constant = 4e-4 * math.pi * m * source_detector_distance / h * math.sin(theta)
q_range = [qMin, qStep, constant / TOFrange[0] * 1.2]
-
+
q_min_from_data = constant / TOFrange[1]
q_max_from_data = constant / TOFrange[0]
AddSampleLog(Workspace=q_workspace, LogName='q_min', LogText=str(q_min_from_data), LogType='Number')
@@ -322,7 +322,6 @@ class LiquidsReflectometryReduction(PythonAlgorithm):
self.setProperty('OutputWorkspace', mtd[name_output_ws])
-
def calculate_scattering_angle(self, ws_event_data):
"""
Compute the scattering angle
@@ -567,5 +566,4 @@ class LiquidsReflectometryReduction(PythonAlgorithm):
logger.error("Could not find scaling factor for %s" % str(workspace))
return workspace
-
AlgorithmFactory.subscribe(LiquidsReflectometryReduction)
--
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