createMadrigalHdf5FromMatlab.py
161 lines
| 4.7 KiB
| text/x-python
|
PythonLexer
| r0 | #!PYTHONEXE | |||
| """createMadrigalHdf5FromMatlab.py reads in Matlab file with all information for a CEDAR file | ||||
| and creates an output file. | ||||
| $Id: createMadrigalHdf5FromMatlab.py 6540 2018-07-05 19:28:51Z brideout $ | ||||
| """ | ||||
| usage = """"python createMadrigalHdf5FromMatlab.py <input_mat_file> [output_Madrigal_file] | ||||
| If output_Madrigal_file not given, uses filename set in .mat file | ||||
| """ | ||||
| # standard python imports | ||||
| import os, os.path, sys | ||||
| import datetime | ||||
| # third party imports | ||||
| import scipy.io | ||||
| import numpy | ||||
| # Madrigal imports | ||||
| import madrigal.cedar | ||||
| if len(sys.argv) not in (2, 3): | ||||
| print(usage) | ||||
| sys.exit() | ||||
| matlabFile = sys.argv[1] | ||||
| # read in all local variables from *.mat file | ||||
| mat_dict = scipy.io.loadmat(matlabFile) | ||||
| if len(sys.argv) == 2: | ||||
| filename = mat_dict['filename'][0] | ||||
| else: | ||||
| filename = sys.argv[2] | ||||
| # convert all parm names with __plus__ to + | ||||
| oneDParms = [str(parm[0][0]).replace('__plus__', '+') for parm in mat_dict['oneDParms']] | ||||
| independent2DParms = [str(parm[0][0]).replace('__plus__', '+') for parm in mat_dict['independent2DParms']] | ||||
| twoDParms = [str(parm[0][0]).replace('__plus__', '+') for parm in mat_dict['twoDParms']] | ||||
| arraySplittingParms = [str(parm[0][0]).replace('__plus__', '+') for parm in mat_dict['arraySplittingParms']] | ||||
| data = mat_dict['data'] | ||||
| principleInvestigator = mat_dict['principleInvestigator'] | ||||
| if len(principleInvestigator): | ||||
| principleInvestigator = str(principleInvestigator[0]) | ||||
| else: | ||||
| principleInvestigator = None | ||||
| expPurpose = mat_dict['expPurpose'] | ||||
| if len(expPurpose): | ||||
| expPurpose = str(expPurpose[0]) | ||||
| else: | ||||
| expPurpose = None | ||||
| expMode = mat_dict['expMode'] | ||||
| if len(expMode): | ||||
| expMode = str(expMode[0]) | ||||
| else: | ||||
| expMode = None | ||||
| cycleTime = mat_dict['cycleTime'] | ||||
| if len(cycleTime): | ||||
| cycleTime = float(cycleTime[0]) | ||||
| else: | ||||
| cycleTime = None | ||||
| correlativeExp = mat_dict['correlativeExp'] | ||||
| if len(correlativeExp): | ||||
| correlativeExp = str(correlativeExp[0]) | ||||
| else: | ||||
| correlativeExp = None | ||||
| sciRemarks = mat_dict['sciRemarks'] | ||||
| if len(sciRemarks): | ||||
| sciRemarks = str(sciRemarks[0]) | ||||
| else: | ||||
| sciRemarks = None | ||||
| instRemarks = mat_dict['instRemarks'] | ||||
| if len(instRemarks): | ||||
| instRemarks = str(instRemarks[0]) | ||||
| else: | ||||
| instRemarks = None | ||||
| kindatDesc = mat_dict['kindatDesc'] | ||||
| if len(kindatDesc): | ||||
| kindatDesc = str(kindatDesc[0]) | ||||
| else: | ||||
| kindatDesc = None | ||||
| analyst = mat_dict['analyst'] | ||||
| if len(analyst): | ||||
| analyst = str(analyst[0]) | ||||
| else: | ||||
| analyst = None | ||||
| comments = mat_dict['comments'] | ||||
| if len(comments): | ||||
| comments = str(comments[0]) | ||||
| else: | ||||
| comments = None | ||||
| history = mat_dict['history'] | ||||
| if len(history): | ||||
| history = str(history[0]) | ||||
| else: | ||||
| history = None | ||||
| skipArray = bool(mat_dict['skipArray']) | ||||
| all2DParms = independent2DParms + twoDParms # in python these are combined | ||||
| recnoArr = numpy.unique(data[:,0]) | ||||
| newFile = madrigal.cedar.MadrigalCedarFile(filename, True, skipArray=skipArray) | ||||
| # add each record individually | ||||
| for recno in recnoArr: | ||||
| indices = numpy.where(data[:,0] == recno)[0] | ||||
| numRows = len(indices) | ||||
| kinst = int(data[indices[0],1]) | ||||
| kindat = int(data[indices[0],2]) | ||||
| ut1_unix = data[indices[0],3] | ||||
| ut2_unix = data[indices[0],4] | ||||
| sDT = datetime.datetime.utcfromtimestamp(ut1_unix) | ||||
| eDT = datetime.datetime.utcfromtimestamp(ut2_unix) | ||||
| newRec = madrigal.cedar.MadrigalDataRecord(kinst, | ||||
| kindat, | ||||
| sDT.year,sDT.month,sDT.day,sDT.hour,sDT.minute,sDT.second, 0, | ||||
| eDT.year,eDT.month,eDT.day,eDT.hour,eDT.minute,eDT.second, 0, | ||||
| oneDParms, | ||||
| all2DParms, | ||||
| numRows, | ||||
| ind2DList=independent2DParms) | ||||
| # set 1D parms | ||||
| numSkip = 5 # 5 std parms in data to be skipped | ||||
| for i, parm in enumerate(oneDParms): | ||||
| newRec.set1D(parm, data[indices[0], numSkip+i]) | ||||
| # set independent parms | ||||
| numSkip = 5 + len(oneDParms) | ||||
| for i, parm in enumerate(independent2DParms): | ||||
| newRec.set2DParmValues(parm, data[indices, numSkip+i]) | ||||
| # set dependent 2D parms | ||||
| numSkip = 5 + len(oneDParms) + len(independent2DParms) | ||||
| for i, parm in enumerate(twoDParms): | ||||
| newRec.set2DParmValues(parm, data[indices, numSkip+i]) | ||||
| # done - append record | ||||
| newFile.append(newRec) | ||||
| # write result | ||||
| newFile.write(arraySplittingParms=arraySplittingParms) | ||||
| # add catalog and header | ||||
| creatorObj = madrigal.cedar.CatalogHeaderCreator(filename) | ||||
| creatorObj.createCatalog(principleInvestigator, | ||||
| expPurpose, | ||||
| expMode, | ||||
| cycleTime, | ||||
| correlativeExp, | ||||
| sciRemarks, | ||||
| instRemarks) | ||||
| creatorObj.createHeader(kindatDesc, | ||||
| analyst, | ||||
| comments, | ||||
| history) | ||||
| creatorObj.write() | ||||
