def mod_database(input_scale, prefix, start, stop):
vars_in_file = spssaux.VariableDict().Variables
df_dict = {}
for i in range(start, stop):
df = input_scale.reset_index()
df['time'] = prefix + str(i)
for col in gb.columns_to_use:
df[col] = prefix + str(i) + df[col]
df_dict[i] = df
db = pd.concat(df_dict, ignore_index=True)
diff_vars = db['items'][~db['items'].isin(vars_in_file)].values.tolist()
db2 = db[db['items'].isin((vars_in_file))]
spss.Submit(recode(db2))
rename_db = db2.loc[db['rename'].notnull(), ['items', 'rename']]
for i, row in rename_db.iterrows():
orgname = row['items']
new_name = row['rename']
spss.Submit('RENAME VARIABLES {orgname}={new_name}.\nEXECUTE.'.format(
orgname=orgname, new_name=new_name))
vars_in_file = spssaux.VariableDict().Variables
diff_vars = db['items'][~db['items'].isin(vars_in_file)].values.tolist()
vars_not_in_file = 'Variables not in file\n' + '\n'.join(diff_vars) + '\n'
print(
str(len(diff_vars)) +
' variables not in file. Check log.txt for specification\n')
db = db[db['items'].isin((vars_in_file))]
return db, vars_not_in_file
def get_median_cut():
cmd = ''
cmd2 = ''
input_dep = db_glob[db_glob['dependent'] == 1 & db_glob[version].notnull()]
for dep in input_dep[version].unique():
db = input_dep['items'][input_dep[version] == dep]
for time in range(start,stop):
list_of_dep_items = [prefix + str(time) + var for var in db]
cmd += 'COMPUTE {var}=mean.1({var_list}).\n'.format(var=prefix+str(time)+dep,var_list=','.join(list_of_dep_items))
cmd += 'EXECUTE.\n'
#print(cmd)
spss.Submit(cmd)
list_of_dep = [prefix + str(start) + dep for dep in input_dep[version].unique()]
tag, err = spssaux.CreateXMLOutput("""FREQUENCIES VARIABLES={vars}
/STATISTICS=MEDIAN.\n""".format(vars=' '.join(list_of_dep)))
spss.GetXmlUtf16(tag,folder+'/fest.xml')
context = "/outputTree"
xpath = "//category[@text='Median']/dimension[@text='Variables']/category[@variable='true']//@*[name()='number' or name()='varName']"
median_list = spss.EvaluateXPath(tag, context, xpath)
median_list = [[prefix + str(i) + median_list[n][4:],median_list[n+1]] for i in range(start,stop) for n in range(0,len(median_list),2)]
for sublist in median_list:
cmd2 += 'RECODE {var} (Lowest thru {mean}=0) (sysmis,77,88,99,0=sysmis) (else=1) into {var}_cut.\n'.format(var=sublist[0],mean=sublist[1])
cmd2 += 'EXECUTE.\n'
#print(cmd2)
spss.Submit(cmd2)
return list_of_dep
def synthesize(name=None, dir=None, mode=None):
proc = "synthesize"
args = srclib.arguments(name, dir, mode, proc) # get the arguments
if not args:
return 1
path = args[0]
datain = args[1]
dataout = args[2]
rc = srclib.metadata(datain, path) # get the metadata
if rc != 0:
return rc
msg = None
rc = srclib.execute("iveset", path, mode, None) # execute iveset
if rc != 0:
msg = "Abnormal termination of iveset"
else:
if not os.path.exists(path + ".inp"): # get the data
msg = "Missing " + path + ".inp file"
else:
f = open(path + ".inp", "r")
cmd = f.read()
f.close()
spss.Submit(cmd)
if not mode:
os.remove(path + ".inp")
rc = srclib.execute("impute", path, mode, None) # execute impute
if rc != 0:
msg = "Abnormal termination of synthesize"
else:
if dataout: # output the synthesized data
rc = srclib.execute("putdata", path, mode, None) # execute putdata
if rc != 0:
msg = "Abnormal termination of putdata"
else:
if not os.path.exists(dataout + ".out"): # write the synthesized data
msg = "Missing " + dataout + ".out file"
else:
f = open(dataout + ".out", "r")
cmd = f.read()
f.close()
spss.Submit(cmd) # execute the command
if not mode:
os.remove(dataout + ".imp")
os.remove(dataout + ".out")
if msg:
if os.path.exists(path + ".log"): # copy the log
print
f = open(path + ".log", "r")
print f.read()
f.close()
print msg # print the error message
else:
if not os.path.exists(path + ".lst"): # copy the listing
msg = "Missing " + path + ".lst file"
else:
print
f = open(path + ".lst", "r")
print f.read()
f.close()
def OpenExcelFile(self, existingfile, ext, sheet, readnames,
assumedstrwidth, datasetname):
spss.Submit(
r"""get data /type=%s /file="%s" /sheet=name "%s" /readnames=%s
/assumedstrwidth=%s.""" %
(ext[1:], existingfile, sheet, readnames, assumedstrwidth))
if datasetname:
spss.Submit("dataset name %s." % datasetname)
def dolabels(variables=None, varpattern=None,
lblvars=None, lblpattern=None, execute=True,
varsperpass=20, syntax=None):
"""Execute STATS VALLBLS FROMDATA"""
# debugging
# makes debug apply only to the current thread
#try:
#import wingdbstub
#if wingdbstub.debugger != None:
#import time
#wingdbstub.debugger.StopDebug()
#time.sleep(1)
#wingdbstub.debugger.StartDebug()
#import thread
#wingdbstub.debugger.SetDebugThreads({thread.get_ident(): 1}, default_policy=0)
## for V19 use
### ###SpssClient._heartBeat(False)
#except:
#pass
try:
vardict = spssaux.VariableDict(caseless=True)
except:
raise ValueError(_("""This command requires a newer version the spssaux module. \n
It can be obtained from the SPSS Community website (www.ibm.com/developerworks/spssdevcentral)"""))
varstolabel = resolve(vardict, _("variables to label"), variables, varpattern, stringonly=False)
labelvars = resolve(vardict, _("label variables"), lblvars, lblpattern, stringonly=True)
if len(varstolabel) == 0 or len(labelvars) == 0:
raise ValueError(_("""No variables to label or no labelling variables were specified.
If a pattern was used, it may not have matched any variables."""))
if len(labelvars) > 1 and len(labelvars) != len(varstolabel):
raise ValueError(_("The number of label variables is different from the number of variables to label"))
if min([vardict[item].VariableType for item in labelvars]) == 0:
raise ValueError(_("""The label variables must all have type string"""))
dsname = spss.ActiveDataset()
if dsname == "*":
raise ValueError(_("""The active dataset must have a dataset name in order to use this procedure"""))
if syntax:
syntax = syntax.replace("\\", "/")
syntax = FileHandles().resolve(syntax)
mkvl = Mkvls(varstolabel, labelvars, varsperpass, execute, syntax, vardict)
for i in range(0, len(varstolabel), varsperpass):
spss.Submit("""DATASET ACTIVATE %s""" % dsname)
mkvl.doaggr(i)
spss.Submit("""DATASET ACTIVATE %s""" % dsname)
labelsyntax = mkvl.dolabels()
if labelsyntax and execute:
spss.Submit(labelsyntax)
mkvl.report(labelsyntax)
if labelsyntax and syntax:
writesyntax(labelsyntax, syntax, mkvl)
def main():
spssaux.OpenDataFile(data)
spss.SetOutput("off")
spss.Submit('SET TNumbers=Values ONumbers=Labels OVars=Labels.')
db_prefixed = mod_database(db_glob, prefix, start, stop)
#print(recode_cut(db_prefixed))
spss.Submit(recode_cut(db_prefixed))
#print(make_long_exp_vars())
spss.Submit(make_long_exp_vars())
spss.Submit("SAVE OUTFILE='%s'\n/COMPRESSED." % save_data)
def bbdesign(name=None, dir=None, mode=None):
proc = "bbdesign"
args = srclib.arguments(name, dir, mode, proc) # get the arguments
if not args:
return 1
path = args[0]
datain = args[1]
dataout = args[2]
rc = srclib.metadata(datain, path) # get the metadata
if rc != 0:
return rc
msg = None
args = [] # execute putdata
args.append("/setup")
rc = srclib.execute("bbdesign", path, mode, args) # execute bbdesign setup
if rc != 0:
msg = "Abnormal termination of bbdesign"
else:
if not os.path.exists(path + ".inp"): # get the data
msg = "Missing " + path + ".inp file"
else:
f = open(path + ".inp", "r")
cmd = f.read()
f.close()
spss.Submit(cmd)
if not mode:
os.remove(path + ".inp")
rc = srclib.execute("bbdesign", path, mode, None) # execute bbdesign go
if rc != 0:
msg = "Abnormal termination of bbdesign"
else:
if os.path.exists(path + ".out"): # write the samples
f = open(path + ".out", "r")
cmd = f.read()
f.close()
spss.Submit(cmd) # execute the command
if not mode:
os.remove(path + ".out")
if msg:
if os.path.exists(path + ".log"): # copy the log
print
f = open(path + ".log", "r")
print f.read()
f.close()
print msg # print the error message
else:
if not os.path.exists(path + ".lst"): # copy the listing
msg = "Missing " + path + ".lst file"
else:
print
f = open(path + ".lst", "r")
print f.read()
f.close()
def doaggr(self, doindex):
"""create an aggregate dataset and tally values
doindex is the index into varstolabel at which to start"""
vtl = self.varstolabel[doindex:doindex+self.varsperpass]
vtllen = len(vtl)
if len(self.labelvars) == 1:
lbls = self.labelvars
lastlbl = vtllen + 1
else:
lbls = self.labelvars[doindex:doindex+self.varsperpass]
lastlbl = 3 * vtllen - 1
brkvarlist = "\n".join(textwrap.wrap(" ".join(vtl), width=100))
outvars = ["/min_%s=MIN(%s)/max_%s=MAX(%s)" % (mkrandomname(), v, mkrandomname(), v) for v in lbls]
aggrcmd = Mkvls.aggrtemplate % (self.aggrdsname, self.aggrdsname, brkvarlist) + "\n".join(outvars)
spss.Submit(aggrcmd)
spss.Submit("DATASET ACTIVATE %s" % self.aggrdsname)
# for each variable, build label information based on data
# AGGREGATE dataset structure:
# var1value, var2value,..., min(text lbl1), max(text lbl1), min(text lbl2), max(text lbl2)...
# but if only one label set, only one pair of label aggregates is produced
# user missing values are exposed and subject to labelling
curs = spssdata.Spssdata(names=False, convertUserMissing=False)
for case in curs:
for v, vname in enumerate(vtl):
value = case[v]
minlbl = self.truncate(case[min(vtllen + v*2, lastlbl-1)], 120).rstrip()
maxlbl = self.truncate(case[min(vtllen + v*2 + 1, lastlbl)], 120).rstrip()
# more than one label for the same value?
if minlbl != maxlbl and (minlbl != "" and minlbl is not None):
self.conflicts[vname].add(value)
# ignore empty or missing labels
if maxlbl != "" and maxlbl is not None:
# if the value has already been seen but with a different label, it's a conflict
if value in self.values[vname] and not (value, maxlbl) in self.vlabels[vname]:
self.conflicts[vname].add(value)
else:
self.vlabels[vname].add((value, maxlbl)) # first one wins
self.values[vname].add(value)
# tally instances where the same label used for different value
# need to see whether labels has been assigned to a different value
previousvalue = self.labelusage[vname].get(maxlbl, None)
if previousvalue is not None and value != previousvalue:
###self.duplabels[vname] = self.duplabels[vname] + 1
self.duplabels[vname].add(maxlbl)
self.labelusage[vname][maxlbl] = value
curs.CClose()
spss.Submit("DATASET CLOSE %s" % self.aggrdsname)
def runscript(scriptname, params={}):
"""Construct a parameter dictionary and run a Python script.
scriptname is the path to run.
params is a Python dictionary of parameter names and values.
The total size of the parameter dictionary is limited to 4K (after pickling).
This function returns a dictionary of values set by the script via setreturnvalue.
If the script sets no return value, the result is an empty dictionary."""
fnparams = tempfile.gettempdir() + os.sep + "__SCRIPT__"
fnreturn = tempfile.gettempdir() + os.sep + "__SCRIPTRETURN__"
f = open(fnparams, "w+")
# ensure file size is 4096 for *nix os's.
f.write(1024 * "0000")
f.flush()
shmem = mmap.mmap(f.fileno(), 4096, access=mmap.ACCESS_WRITE)
shmem.write(pickle.dumps(params))
f.close()
try:
os.remove(fnreturn) # ensure that no stale returns file exists
except:
pass
###import wingdbstub
spss.Submit("SCRIPT " + spssaux._smartquote(scriptname))
shmem.close()
# The _SYNC command is required in order to ensure that the script has completed
spss.Submit("_SYNC")
# The parameter file will be removed by the script if it calls getscriptparam, but
# the following code will clean up in case the script doesn't make that call.
try:
os.remove(fnparams)
except:
pass
# get the return value, if any
###import wingdbstub
try:
f = open(fnreturn, "r")
shmem = mmap.mmap(f.fileno(), 4096, access=mmap.ACCESS_READ)
ret = pickle.loads(shmem.read(4096))
shmem.close()
f.close()
os.remove(fnreturn)
except:
ret = {}
return ret
def getsav(self, filespec, delete=True):
"""Open sav file and return all contents
filespec is the file path
filespec is deleted after the contents are read unless delete==False"""
item = self.wdsname
spss.Submit(r"""get file="%(filespec)s".
DATASET NAME %(item)s.
DATASET ACTIVATE %(item)s.""" % locals())
contents = spssdata.Spssdata(names=False).fetchall()
spss.Submit("""DATASET CLOSE %(item)s.
NEW FILE.""" % locals())
if delete:
os.remove(filespec)
return contents
def genData(self):
"""Generate variables holding all the dichotomies for values"""
valcount = len(self.vvalues)
computes = []
# The VALUE function only works for numeric variables :-(
# In ANY, all string values are considered valid.
if self.string:
setvars = ",".join(self.setvars)
else:
setvars = ",".join(["VALUE(%s)" % v for v in self.setvars])
values = sorted(self.vvalues)
varprefix = self.varprefix
# if any generated variables already exist, they will be overwritten.
# if they exist and are strings, the procedure will fail.
for v in range(valcount):
v1 = v + 1
vname = "%(varprefix)s_%(v1)02d" % locals()
self.generatednames.append(vname)
val = values[v]
vallabel = self.valuelabels.get(
val, val) # try to pick up a value label
self.generatedvalues.append(val)
self.generatedlabels.append(vallabel)
if self.string:
val = spssaux._smartquote("%s" % val)
cmd = """COMPUTE %(vname)s = any(%(val)s, %(setvars)s).
VARIABLE LABEL %(vname)s %(vallabel)s.
VARIABLE LEVEL %(vname)s (NOMINAL).""" % locals()
computes.append(cmd)
spss.Submit(computes)
def list_of_cut_old(indep):
temp = sys.stdout
sys.stdout = open('descript.txt', 'w')
spss.Submit("""FREQUENCIES VARIABLES=%s
/STATISTICS=MINIMUM MAXIMUM""" % indep)
sys.stdout.close()
sys.stdout = temp
with open('descript.txt','r') as indata:
valid_flag = 0
listaallarader = []
cut_vals = []
for line in indata:
listaallarader.append(line)
for row in listaallarader:
if 'Valid' in row:
valid_flag = valid_flag +1
if '__' not in row and valid_flag == 3:
if 'Total' in row:
valid_flag = valid_flag+1
break
e,e,value,e2,e,e,e,e = row.split('|')
value = value.replace(',','.')
value = value.replace(' ','')
cut_vals.append(value)
return cut_vals
def rename_vars(vars_in_file):
for i, row in rename.iterrows():
orgname = row['old']
new_name = row['new']
if orgname in vars_in_file:
spss.Submit(
'RENAME VARIABLES {orgname}={new_name}.\nEXECUTE.'.format(
orgname=orgname, new_name=new_name))
def dohistogram(finalweightvar):
"""Display unweighted histogram of weights
finalweightvar is the generated weight variable"""
title = _("Raked Weights Histogram before Applying New Weights")
cmd = histtem % locals()
spss.Submit(cmd)
def doactions(filespec=None, conflict="noname", currentactivedsn=None):
"""Execute command"""
# debugging
# makes debug apply only to the current thread
try:
import wingdbstub
if wingdbstub.debugger != None:
import time
wingdbstub.debugger.StopDebug()
time.sleep(1)
wingdbstub.debugger.StartDebug()
import _thread
wingdbstub.debugger.SetDebugThreads({_thread.get_ident(): 1}, default_policy=0)
# for V19 use
## ###SpssClient._heartBeat(False)
except:
pass
if filespec is None and currentactivedsn is None:
raise ValueError(_("No actions were specified for this command"))
activeds = spss.ActiveDataset().lower()
alldatasets = getAllDatasetNames()
if currentactivedsn is not None:
if currentactivedsn.lower() != activeds and \
currentactivedsn.lower() in alldatasets:
raise ValueError(_("""The dataset name to be assigned is already in use for another dataset: %s""")\
% currentactivedsn)
spss.Submit("""DATASET NAME %(currentactivedsn)s.
DATAFILE ATTRIBUTE ATTRIBUTE=%(customdsattr)s(%(currentactivedsn)s).""")
if filespec is not None:
# The unnamed active dataset might be empty, but we preserve it in case it isn't
if activeds == "*":
spss.Submit("""DATASET NAME %s.""" % ("D" + random.ranunif(.1, 1.)))
spss.Submit("""GET FILE="%s". """ % filespec)
thedsn = spss.GetDataFileAttributes(customdsattr)
if len(thedsn) == 0:
print(_("The data file does not contain a permanent dataset name. No session dataset name has been assigned."))
else:
if thedsn[0].lower() in alldatasets:
if conflict != "override":
print(_("The permanent dataset name is already in use in this session. No session dataset name has been assigned."))
else:
print(_("The dataset name has been removed from an already open dataset: %s") % thedsn[0])
spss.Submit("""DATASET NAME %s.""" % thedsn[0])
def list_of_cut(indep):
context = '/outputTree'
xpath = '//pivotTable[@subType="Frequencies"]//group[@text="Valid"]//category/@number'
spss.Submit('SET TNumbers=Values ONumbers=Labels OVars=Labels.')
tag, err = spssaux.CreateXMLOutput('FREQUENCIES VARIABLES={}.'.format(indep))
cut_list = spss.EvaluateXPath(tag, context, xpath)
spss.DeleteXPathHandle(tag)
return sorted(list((set(cut_list))))
def __enter__(self):
"""initialization for with statement"""
try:
spss.StartDataStep()
except:
spss.Submit("EXECUTE")
spss.StartDataStep()
return self
def doheatmap(variables, yvar, xvar, paneldownvar, panelacrossvar, finalweightvar, autoheatmap):
"""produce a heatmap of the weights with 2 to 4 variables if requested
variables is the set of variables with control totals
yvar and xvar are the main variables for the heatmap (as lists)
paneldownvar and panelacrossvar are variables for paneling down and across
finalweightvar is the output weight variable
autoheatmap specifies that the first two to four control variables
define the heatmap dimensions"""
# autoheatmap is used to get around dialog box limitations and overrides other
# related variable specifications without a warning
if len(variables) == 1:
return # No heatmap available
if autoheatmap:
yvar = [variables[0]]
xvar = [variables[1]]
hmsize = min(autoheatmap, len(variables), 4)
if hmsize >=3:
paneldownvar = [variables[2]]
else:
paneldownvar = None
if hmsize >=4:
panelacrossvar = [variables[3]]
else:
panelacrossvar = None
plotvars = (yvar, xvar, paneldownvar, panelacrossvar)
if not any(plotvars):
return # no plot requested
if not all([yvar, xvar]):
print(_("No heatmap produced: both y and x variables must be specified"))
return
if set(v[0] for v in plotvars if v is not None) - set(variables):
print(_("No heatmap produced: only raking variables can be specified"))
return
if not paneldownvar:
panelingdownvars = ""
panelingdowntem = ""
else:
panelingdownvar = paneldownvar[0]
panelingdownvars = """%(panelingdownvar)s[LEVEL=nomimal]""" % locals()
panelingdowntem = """ "Panel down"="%(panelingdownvar)s"[DATASET="graphdataset"]""" % locals()
if not panelacrossvar:
panelingacrossvars = ""
panelingacrosstem = ""
else:
panelingacrossvar = panelacrossvar[0]
panelingacrossvars = """%(panelingacrossvar)s[LEVEL=nomimal]""" % locals()
panelingacrosstem = """ "Panel across"="%(panelingacrossvar)s"[DATASET="graphdataset"]""" % locals()
title = _("Unweighted Heatmap of Weights by Raking Variables")
label = _("Weight Heatmap")
yvar = yvar[0]
xvar = xvar[0]
cmd = hmtemplate % locals()
spss.Submit(cmd)
def doblock(blocknum, atstart, atend, errorprint, errorcont, errorcall,
syntax):
"""Execute block of syntax
blocknum is the condition number
atstart and atend are text to be displayed before and after
errorprint is what to display on a syntax error
errrcont is whether to continue running lines or stop
syntax is a list of syntax lines to execute."""
if not atstart is None:
print(atstart.replace(")BLOCK", str(blocknum + 1)))
lastline = len(syntax) - 1
if lastline < 0:
raise ValueError(_("""A syntax command block contains no syntax"""))
# Submit each command one by one and handle error conditions
cmd = []
inmatrix = False
for linenum, line in enumerate(syntax):
cmd.append(line)
# block or pseudo-block commands have to be submitted in a single call
testline = line.rstrip().lower()
if testline in ["matrix", "matrix."]:
inmatrix = True
dosubmit = not inmatrix and (linenum == lastline or (testline.endswith(".")\
and (syntax[linenum+1].lower().strip() not in ["begin gpl", "begin gpl."])))
if testline == "end matrix.":
inmatrix = False
dosubmit = True
if dosubmit:
try:
spss.Submit(cmd)
cmd = []
except:
if not errorprint is None:
print(errorprint.replace(")BLOCK", str(blocknum + 1)))
if not errorcall is None:
# an error function can take control on error.
# It can return "stop" or "continue" to override the action specified in STATS IF
action = errorcall(blocknum + 1, cmd,
spss.GetLastErrorLevel(),
spss.GetLastErrorMessage())
if action == "stop":
break
elif action == "continue":
cmd = []
continue
if not errorcont:
break
cmd = []
if not atend is None:
print(atend.replace(")BLOCK", str(blocknum + 1)))
def main():
os.chdir(imputations)
spssaux.OpenDataFile(gb.select_rev)
cmd = 'DATASET NAME orginal.\n'
cmd += 'cd "{cwd}".\n'.format(cwd=imputations)
spss.SetOutput("off")
cmd += fix_value_lables()
with open(imputation_syntax, 'w') as out:
out.write(cmd)
spss.Submit(cmd)
run_cmd = impute_item_for_item()
with open(imputation_syntax, 'a') as out:
out.write('\n' + run_cmd)
spss.Submit(run_cmd)
match_files()
cmd = recode()
with open(imputation_syntax, 'a') as out:
out.write('\n' + cmd)
spss.Submit("""SAVE OUTFILE = '{imputed_data}'
/COMPRESSED.\n""".format(imputed_data=gb.imputed_data))
def get_hostname(lines):
m = re.search('\[Contacting .* host "(.*)".*\]', "\n".join(lines))
if m:
hostname = m.group(1)
if hostname.lower() == "no-net":
msg = "This program is intended for concurrent licenses only"
issue_warning(msg)
spss.Submit("show license.")
return hostname
else:
raise ValueError("Hostname not found")
def setgen(self):
"""construct a new MR set of the appropriate type"""
cmd = """MRSETS /MDGROUP NAME=%(outputname)s LABEL="%(label)s"
VARIABLES = %(variables)s VALUE=1
/DISPLAY NAME=[%(outputname)s]"""
outputname = self.setname
label = self.mrsetinfo.getSetLabel(self.mcset)
variables = " ".join(self.generatednames)
spss.Submit(cmd % locals())
def __init__(self):
try:
spss.StartDataStep()
except:
spss.Submit("EXECUTE.")
spss.StartDataStep()
self.ds = spss.Dataset()
self.varlist = self.ds.varlist
self.mrsets = {}
# the api always returns the set name in upper case
for name, theset in self.ds.multiResponseSet.data.items():
self.mrsets[name.upper()] = theset
def addinfo(filespec):
"""open the file if appropriate type, extract variable information, and add it to dataset dsname.
filespec is the file to open
dsname is the dataset name to append to
filetypes is the list of file types to include."""
fnsplit = os.path.split(filespec)[1]
fn, ext = os.path.splitext(fnsplit)
for ft in filetypes:
if ext in ftdict[ft]:
if pat is None or pat.match(fn):
try:
spss.Submit(spsscmd[ft] % filespec)
spss.Submit("DATASET NAME @__GATHERMD__.")
except:
if not isinstance(filespec, str):
filespec = str(filespec, encoding)
raise EnvironmentError(_("File could not be opened, skipping: %s") % filespec)
break
else:
return addinfo
with DataStep():
ds = spss.Dataset(name=dsname) # not the active dataset
dssource = spss.Dataset(name="*") # The dataset to examine
numvars = spss.GetVariableCount() # active dataset
variables = dssource.varlist
for v in range(numvars):
lis = [filespec.replace("\\","/"), spss.GetVariableName(v), spss.GetVariableLabel(v)]
lis.extend(blanks)
lis = [item+ 256*" " for item in lis]
ds.cases.append(lis)
#ds.cases.append([filespec.replace("\\","/"), spss.GetVariableName(v), spss.GetVariableLabel(v), *blanks])
if includeAttrs:
attrs = variables[v].attributes.data
for a in attrs:
if a.lower() in attrindexes:
ds.cases[-1, attrindexes[a.lower()]+ 3] = attrs[a][0] + attrlength * " "# allow for standard variables
spss.Submit("DATASET CLOSE @__GATHERMD__.")
def execute(self, commands):
transformedCommands = ['* Encoding: UTF-8.']
for command in commands:
# command = command.replace("\n", " ");
if (len(command) >= 1 and not (command[-1] == '.')):
command += '.'
# SPSS probably only understands ASCII
transformedCommands.append(command)
#try:
"""
Execute all commands as batch; this allows to execute BEGIN DUMMY. [...] END DUMMY. as well
i.e. BEGIN MATRIX. [...] END MATRIX.
"""
spss.Submit(transformedCommands)
def add_suffix(folder):
for item in ["/no_strings", "/strings"]:
datafiles = get_filelist(folder + item, 'sav')
for file in datafiles:
print(file)
exclude = ['kod_id'] #Ange namnet på id_variablen
spssaux.OpenDataFile(file)
basename = os.path.basename(file).strip('.sav')
suffix = basename #önskat suffix
print(basename)
vars = spssaux.VariableDict().variables
for i in exclude:
if i in vars:
vars.remove(i)
oldnames = spssaux.VariableDict().expand(vars)
newnames = [varnam + "_" + suffix for varnam in oldnames]
spss.Submit('rename variables (%s=%s).' %
('\n'.join(oldnames), '\n'.join(newnames)))
spss.Submit("""
SAVE OUTFILE = "%s%s".
DATASET CLOSE ALL.
NEW FILE.
""" % (folder + item + '/suffix/', basename + '.sav'))
def match_files():
cmd1 = ''
cmd2 = ''
A = funcs.get_filelist(imputations, 'sav')
f = lambda A, n=30: [A[i:i + n] for i in range(0, len(A), n)]
cmd1 += '\n'.join([
'MATCH FILES\n' + '\n'.join([' /FILE="%s"' % fil for fil in dl]) +
'\n /BY Imputation_ %s.\n' % gb.id +
'SAVE OUTFILE="part_%s.sav"\n/COMPRESSED.\n' % f(A).index(dl)
for dl in f(A)
])
with open(imputation_syntax, 'a') as out:
out.write('\n' + cmd1)
spss.Submit(cmd1)
B = [x for x in funcs.get_filelist(imputations, 'sav') if 'part_' in x]
cmd2 += '\n'.join([
'MATCH FILES\n' + '\n'.join([' /FILE="%s"' % fil for fil in B]) +
'\n /BY Imputation_ %s.\n' % gb.id +
'SAVE OUTFILE="COMPLETE.sav"\n/COMPRESSED.\n'
])
cmd2 += 'DATASET CLOSE ALL.\n'
with open(imputation_syntax, 'a') as out:
out.write('\n' + cmd2)
spss.Submit(cmd2)
def createHandle(self, handle, spec, encoding=None):
"""Create a file handle and update the handle list accordingly
handle is the name of the handle
spec is the location specification, i.e., the /NAME value
encoding optionally specifies the encoding according to the valid values in the FILE HANDLE syntax."""
spec = re.sub(r"[\\/]", re.escape(os.path.sep), spec) # clean up path separator
cmd = """FILE HANDLE %(handle)s /NAME="%(spec)s" """ % locals()
# Note the use of double quotes around the encoding name as there are some encodings that
# contain a single quote in the name
if encoding:
cmd += ' /ENCODING="' + encoding + '"'
spss.Submit(cmd)
self.fhdict[handle.lower()] = (spec, encoding)
def search(name=None, dir=None, mode=None):
proc = "search"
args = srclib.arguments(name, dir, mode, proc) # get the arguments
if not args:
return 1
path = args[0]
datain = args[1]
dataout = args[2]
rc = srclib.metadata(datain, path) # get the metadata
if rc != 0:
return rc
msg = None
rc = srclib.execute("srchset", path, mode, None) # execute srchset
if rc != 0:
msg = "Abnormal termination of srchset"
else:
if not os.path.exists(path + ".inp"): # get the data
msg = "Missing " + path + ".inp file"
else:
f = open(path + ".inp", "r")
cmd = f.read()
f.close()
spss.Submit(cmd)
if not mode:
os.remove(path + ".inp")
rc = srclib.execute("search", path, mode, None) # execute search
if rc != 0:
msg = "Abnormal termination of search"
else:
if dataout:
srclib.residuals(datain, dataout, path,
mode) # output the residuals
if msg:
if os.path.exists(path + ".log"): # copy the log
print
f = open(path + ".log", "r")
print f.read()
f.close()
print msg # print the error message
else:
if not os.path.exists(path + ".lst"): # copy the listing
msg = "Missing " + path + ".lst file"
else:
print
f = open(path + ".lst", "r")
print f.read()
f.close()
def calculate_odds(db_prefixed,start,stop,prefix):
cmd_recode = ''
cmd_log_reg = ''
db_list = []
db_err_list = []
#input_indep = db_glob.query('dependent != 1')
input_dep = db_glob.query('dependent == 1')
dep_list = get_median_cut()
n = 0
for time in range(start,stop):
dep_list_time = [prefix+str(time)+item[4:] for item in dep_list]
myset = set(dep_list_time)
dep_list_time = list(myset)
input_indep = db_prefixed.query('dependent != 1 and time == "%s%s"' % (prefix,str(time)))
for typ in columns_to_use:
if typ == version:
suffix = '_middle'
if typ == single_items:
suffix = '_single'
for indep in input_indep[typ].unique():
if str(indep) != 'nan':
indep = indep + suffix
cutoffs = list_of_cut(indep)
for dep in dep_list_time:
for x in range(len(cutoffs) - 1):
if n >= error_search:
#cmd_log_reg += recode_cut(indep,cutoffs,x)
#cmd_log_reg += log_reg_cmd(dep,indep)
spss.Submit(recode_cut(indep,cutoffs, x))
db, db_err = log_reg(dep,indep,cutoffs[x+1],n)
db_list.append(db)
db_err_list.append(db_err)
# spss.Submit(odds(dep, indep))
#db_list.append(log_reg(dep,indep,cutoffs[x+1],n))
n += 2
#db_list.append(find_value(indep,dep,prefix+str(time)))
db = pd.concat(db_list)
db_err = pd.concat(db_err_list)
#print(cmd_log_reg)
return db, db_err