diff --git a/Code/Mantid/Framework/PythonInterface/mantid/simpleapi.py b/Code/Mantid/Framework/PythonInterface/mantid/simpleapi.py
index a8504f916262da4b9f247d1dd228e5a850b044ca..1ded6b0bf0352e05f97a0fe0054a9010ac70383b 100644
--- a/Code/Mantid/Framework/PythonInterface/mantid/simpleapi.py
+++ b/Code/Mantid/Framework/PythonInterface/mantid/simpleapi.py
@@ -20,6 +20,7 @@
"""
from __future__ import absolute_import
+import os, string
import mantid.api as _api
import mantid.kernel as _kernel
@@ -31,7 +32,7 @@ from mantid.api._aliases import *
#------------------------ Specialized function calls --------------------------
# List of specialized algorithms
-__SPECIALIZED_FUNCTIONS__ = ["Load", "Fit"]
+__SPECIALIZED_FUNCTIONS__ = ["Load", "Fit", "CutMD"]
# List of specialized algorithms
__MDCOORD_FUNCTIONS__ = ["PeakIntensityVsRadius", "CentroidPeaksMD","IntegratePeaksMD"]
# The "magic" keyword to enable/disable logging
@@ -257,22 +258,124 @@ def FitDialog(*args, **kwargs):
#--------------------------------------------------- --------------------------
-#This dictionary maps algorithm names to functions that preprocess their inputs
-#in the simpleapi. The functions take args and kwargs as regular arguments and
-#modify them as required.
+def CutMD(*args, **kwargs):
+ """
+ Slices multidimensional workspaces using input projection information and binning limits.
+ """
+ (in_wss,) = _get_mandatory_args('CutMD', ["InputWorkspace"], *args, **kwargs)
+
+ # If the input isn't a list, wrap it in one so we can iterate easily
+ if isinstance(in_wss, list):
+ in_list = in_wss
+ handling_multiple_workspaces = True
+ else:
+ in_list = [in_wss]
+ handling_multiple_workspaces = False
+
+ # Remove from keywords so it is not set twice
+ if "InputWorkspace" in kwargs:
+ del kwargs['InputWorkspace']
+
+ #Make sure we were given some output workspace names
+ lhs = _kernel.funcreturns.lhs_info()
+ if lhs[0] == 0 and 'OutputWorkspace' not in kwargs:
+ raise RuntimeError("Unable to set output workspace name. Please either assign the output of "
+ "CutMD to a variable or use the OutputWorkspace keyword.")
+
+ #Take what we were given
+ if "OutputWorkspace" in kwargs:
+ out_names = kwargs["OutputWorkspace"]
+ else:
+ out_names = list(lhs[1])
+
+ #Ensure the output names we were given are valid
+ if handling_multiple_workspaces:
+ if not isinstance(out_names, list):
+ raise RuntimeError("Multiple OutputWorkspaces must be given as a list when processing multiple InputWorkspaces.")
+ else:
+ #We wrap in a list for our convenience. The user musn't pass us one though.
+ if not isinstance(out_names, list):
+ out_names = [out_names]
+ elif len(out_names) != 1:
+ raise RuntimeError("Only one OutputWorkspace required")
+
+ if len(out_names) != len(in_list):
+ raise RuntimeError("Different number of input and output workspaces given.")
+
+ # Split PBins up into P1Bin, P2Bin, etc.
+ if "PBins" in kwargs:
+ bins = kwargs["PBins"]
+ del kwargs["PBins"]
+ if isinstance(bins, tuple) or isinstance(bins, list):
+ for bin in range(len(bins)):
+ kwargs["P{0}Bin".format(bin+1)] = bins[bin]
+
+ # Create and execute
+ algm = _create_algorithm_object('CutMD')
+ _set_logging_option(algm, kwargs)
-_algorithm_preprocessors = dict()
+ # Now check that all the kwargs we've got are correct
+ for key in kwargs.keys():
+ if key not in algm:
+ raise RuntimeError("Unknown property: {0}".format(key))
+
+ # We're now going to build to_process, which is the list of workspaces we want to process.
+ to_process = list()
+ for i in range(len(in_list)):
+ ws = in_list[i]
+
+ if isinstance(ws, _api.Workspace):
+ #It's a workspace, do nothing to it
+ to_process.append(ws)
+ elif isinstance(ws, str):
+ if ws in mtd:
+ #It's a name of something in the ads, just take it from the ads
+ to_process.append(_api.AnalysisDataService[ws])
+ else:
+ #Let's try treating it as a filename
+ load_alg = AlgorithmManager.create("Load")
+ load_alg.setLogging(True)
+ load_alg.setAlwaysStoreInADS(False)
+ load_alg.setProperty("Filename", ws)
+ load_alg.setProperty("OutputWorkspace", "__loaded_by_cutmd_{0}".format(i+1))
+ load_alg.execute()
+ if not load_alg.isExecuted():
+ raise TypeError("Failed to load " + ws)
+ wsn = load_alg.getProperty("OutputWorkspace").valueAsStr
+ to_process.append(_api.AnalysisDataService[wsn])
+ else:
+ raise TypeError("Unexpected type: " + type(ws))
+
+ #Run the algorithm across the inputs and outputs
+ for i in range(len(to_process)):
+ _set_properties(algm, **kwargs)
+ algm.setProperty('InputWorkspace', to_process[i])
+ algm.setProperty('OutputWorkspace', out_names[i])
+ algm.execute()
-def _pp_cutmd(args, kwargs):
- if "PBins" in kwargs:
- bins = kwargs["PBins"]
- del kwargs["PBins"]
- if isinstance(bins, tuple) or isinstance(bins, list):
- #PBin has been provided, we need to split it out into P1Bin, P2Bin, etc.
- for bin in range(len(bins)):
- kwargs["P{0}Bin".format(bin+1)] = bins[bin]
+ #Get the workspace objects so we can return them
+ for i in range(len(out_names)):
+ out_names[i] = _api.AnalysisDataService[out_names[i]]
-_algorithm_preprocessors["CutMD"] = _pp_cutmd
+ #We should only return a list if we're handling multiple workspaces
+ if handling_multiple_workspaces:
+ return out_names
+ else:
+ return out_names[0]
+
+# Have a better load signature for autocomplete
+_signature = "\bInputWorkspace"
+# Getting the code object for Load
+_f = CutMD.func_code
+# Creating a new code object nearly identical, but with the two variable names replaced
+# by the property list.
+_c = _f.__new__(_f.__class__, _f.co_argcount, _f.co_nlocals, _f.co_stacksize, _f.co_flags, _f.co_code, _f.co_consts, _f.co_names,\
+ (_signature, "kwargs"), _f.co_filename, _f.co_name, _f.co_firstlineno, _f.co_lnotab, _f.co_freevars)
+
+# Replace the code object of the wrapper function
+CutMD.func_code = _c
+
+#--------------------------------------------------- --------------------------
def _get_function_spec(func):
"""Get the python function signature for the given function object
@@ -568,10 +671,6 @@ def _create_algorithm_function(algorithm, version, _algm_object):
the proper version of the algorithm without failing.
"""
- # If needed, preprocess this algorithm's input
- if algorithm in _algorithm_preprocessors:
- _algorithm_preprocessors[algorithm](args, kwargs)
-
_version = version
if "Version" in kwargs:
_version = kwargs["Version"]
diff --git a/Code/Mantid/docs/source/algorithms/CutMD-v1.rst b/Code/Mantid/docs/source/algorithms/CutMD-v1.rst
index 98b00565f70354061e153679fb13593d870b82d5..da2f0f251e15456314cd10bdb50cc2a8e139241e 100644
--- a/Code/Mantid/docs/source/algorithms/CutMD-v1.rst
+++ b/Code/Mantid/docs/source/algorithms/CutMD-v1.rst
@@ -9,11 +9,20 @@
Description
-----------
-This algorithm performs slicing of multiDimensional data according to a chosen projection, and binning choice. The algorithm uses :ref:`algm-BinMD` or
-:ref:`algm-SliceMD` to achieve the binning of the data. The choice of child algorithm used for the slicing is controlled by the NoPix option.
+This algorithm performs slicing of multiDimensional data according to a chosen
+projection, and binning choice. The algorithm uses :ref:`algm-BinMD` or
+:ref:`algm-SliceMD` to achieve the binning of the data. The choice of child
+algorithm used for the slicing is controlled by the NoPix option.
The synax is similar to that used by `Horace <http://horace.isis.rl.ac.uk/Manipulating_and_extracting_data_from_SQW_files_and_objects#cut_sqw>`__.
+Unlike most Mantid algorithms, CutMD can accept a list of workspaces as the
+input workspace, given as the name of a workspace in the analysis data service
+or the path to a workspace, or simply a workspace object in python. These will
+all then be processed sequentially with the same options. The only requirement
+is that the same number of output workspaces are also given so that CutMD knows
+what to call each output workspace created.
+
Usage
-----
@@ -39,6 +48,10 @@ Usage
# Apply the cut (PBins property sets the P1Bin, P2Bin, etc. properties for you)
out_md = CutMD(to_cut, Projection=proj_ws, PBins=([0.1], [0.1], [0.1], [-5,5]), NoPix=True)
+
+ #Another way we can call CutMD:
+ #[out1, out2, out3] = CutMD([to_cut, "some_other_file.nxs", "some_workspace_name"], ...)
+
print 'number of dimensions', out_md.getNumDims()
print 'number of dimensions not integrated', len(out_md.getNonIntegratedDimensions())
dim_dE = out_md.getDimension(3)