def ppStitch(self):
self.csvList_LW.clear()
for i in range(len(self.simplelist)):
if self.simplelist[i].name=="Stitch":
del self.simplelist[i]
for i in range(len(self.simplelist)):
self.csvList_LW.addItem(self.simplelist[i].name)
self.ppPopulateStitchList()
csvcombined=self.stitchlist[0]
for i in range(len(self.stitchlist)):
try:
csvcombined=csvcombined.combine_first(self.stitchlist[i+1])
except IndexError:
break
csvcombined.to_csv("%s\stitch.csv" % self.path,index_label=False,sep=',')
stitchPPinstance= postProcessing.postProcessing("Stitch","%s\stitch.csv" % self.path,
self.pp_TV,self.ppFileLoaded_L,
self.plot_L,
'%s\Calibration_files\stitch.cal' % self.path,
1,
self.framerate,self.path)
self.simplelist.append(stitchPPinstance)
self.csvList_LW.addItem( self.simplelist[-1].name)
self.simplelist[-1].show(0)
def pp_openCSV(self):
self.ppnamelist=[]
self.ppfileobj=[]
self.calfile=[]
self.ppfileobj=QFileDialog.getOpenFileNames(self,"CSV files", self.path, filter="Text Files (*.csv)")
for i in range(len(self.ppfileobj)):
self.pppath, self.ppfilename=os.path.split(os.path.abspath(self.ppfileobj[i]))
slicestr=self.ppfilename[0:6]
self.cameraid=self.ppfilename[0:2]
self.ppnamelist.append(slicestr)
self.csvList_LW.addItem(self.ppnamelist[i])
for name in glob("%s\Calibration_files\*" % self.path):
a="%s\Calibration_files\\%s.cal" % (self.path,self.cameraid)
if name == a:
self.calfile.append(name)
self.newlist=[postProcessing.postProcessing(self.ppnamelist[i],self.ppfileobj[i],self.pp_TV,self.ppFileLoaded_L,self.plot_L,self.calfile[i],0,self.framerate,self.path) for i in range(len(self.ppnamelist))]
if len(self.simplelist) == 0:
self.simplelist=self.newlist
else:
self.simplelist=self.simplelist+self.newlist
def predictDataWithOnlyPostScript(self, pmmlstoragepointer, filpath,
extenFile):
import importlib.util
print('Step 11.1')
global PMMLMODELSTORAGE
pointerObj = PMMLMODELSTORAGE[pmmlstoragepointer]
#####################>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>###############
codePart2 = pointerObj['postProcessScript']
fnaMe2 = './trainModel/testPostprocessing.py'
with open(fnaMe2, 'w') as k:
k.write(codePart2)
print('Code File written POST Process')
spec2 = importlib.util.spec_from_file_location('postProcessing',
fnaMe2)
postProcessing = spec2.loader.load_module()
print('Step 11.2')
#####################>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>###############
from postProcessing import postProcessing
from string import ascii_uppercase
from random import choice
print('Step 11.5')
if extenFile in ['.jpg', '.JPG', '.png', '.PNG']:
target_path2 = './logs/' + ''.join(
choice(ascii_uppercase) for i in range(12)) + '/'
target_path = target_path2 + '/test/'
self.checkCreatePath(target_path2)
self.checkCreatePath(target_path)
print('Step 11.6')
targetResult = self.predictImagedata(pmmlstoragepointer, filpath)
print('Step 11.9')
target_path3 = './logs/' + ''.join(
choice(ascii_uppercase) for i in range(12)) + '/'
self.checkCreatePath(target_path3)
print('Step 11.11')
outPutAfterPostProcess = postProcessing(targetResult)
print('Step 11.12')
elif scriptOutput == 'DATA':
print('Use case need to be discussed')
try:
os.remove(fnaMe2)
except:
pass
return outPutAfterPostProcess
def main(self):
if self.target <= 2:
target = 10
else:
target = self.target
"""
主函数
"""
'''
parser = argparse.ArgumentParser(description="***** this is auto-generate-expression *****")
parser.add_argument("-n", metavar = "--number", dest = "expnum_arg", help = "Generate a given number of expressions" )
parser.add_argument("-r", metavar = "--range", dest = "range_arg", help = "Specify the range of operands")
parser.add_argument("-e", metavar = "--exercise file", dest = "exercise_arg", help = "Given four arithmetic problem files")
parser.add_argument("-a", metavar = "--answer file", dest = "answer_arg", help = "Given four arithmetic problem answer files")
args = parser.parse_args()
'''
try:
with open(self.expFile, "w+", encoding="utf-8") as f:
f.truncate()
f.close()
with open(self.answerFile, "w+", encoding="utf-8") as f:
f.truncate()
f.close()
print(("%s" + "Clear existing expressions....done." + "%s") %
(white, end))
except:
print(("%s" + "No need to clear existing expressions." + "%s") %
(white, end))
#判断生成的表达式的数目
if self.expNum:
#表达式的范围
if self.target:
config = Config(exp_num=int(self.expNum),
num_range=int(target))
else:
config = Config(exp_num=int(self.expNum))
print(("%s" + "Arithmetic expression replacing literal (" +
str(self.target) + ") is being generated." + "%s") %
(green, end))
generator = Generator()
res_list = generator.generate(config)
generator.normalize_exp(res_list)
expression_result(res_list)
print(("%s" + 'Generation is complete.' + "%s") % (green, end))
#后期处理表达式的值
#print(args.exercise_arg, args.answer_arg)
pp = postProcessing(self.expFile, self.answerFile, self.target)
return pp.run()
print '\n', \
'Welcome to: softGNSS\n\n', \
'An open source GNSS SDR software project initiated by:\n\n', \
' Danish GPS Center/Aalborg University\n\n', \
'The code was improved by GNSS Laboratory/University of Colorado.\n\n', \
'The software receiver softGNSS comes with ABSOLUTELY NO WARRANTY;\n', \
'for details please read license details in the file license.txt. This\n', \
'is free software, and you are welcome to redistribute it under\n', \
'the terms described in the license.\n\n', \
' -------------------------------\n\n'
## Initialize constants, settings =========================================
settings = initSettings.Settings()
## Generate plot of raw data and ask if ready to start processing =========
try:
print 'Probing data "%s"...' % settings.fileName
# probeData.probeData(settings)
# probeData.probeData('/Users/yangsu/Downloads/GNSS_signal_records/GPS_and_GIOVE_A-NN-fs16_3676-if4_1304.bin', settings)
finally:
pass
print ' Raw IF data plotted '
print ' (run setSettings or change settings in "initSettings.m" to reconfigure)'
print ' '
gnssStart = True
# gnssStart = int(raw_input('Enter "1" to initiate GNSS processing or "0" to exit : ').strip())
if gnssStart:
print ' '
postProcessing.postProcessing(settings)
def calculateHouse(self):
print('calculating..')
#getNumerical Data
try:
self.getNumerical()
except KeyError:
print(
"Something went wrong with getting the numerical data, modify your data and try again"
)
#get the Categorical Data
try:
self.getCategorical()
except KeyError:
print(
"Something went wrong with getting the categorical data, modify your data and try again"
)
self.df.rename({"stFlrSF": "1stFlrSF"}, axis='columns', inplace=True)
#write to file
self.df.to_csv('user_input.csv', sep=',', index=False, header=True)
#
#map the user input to a format that our training engine can process in order to predict
try:
postProcessing.postProcessing()
except KeyError:
print(
"Something went wrong while converting your user input, modify your data and try again"
)
#predicion of output from user input
sample = pd.read_csv('user_input.csv')
try:
(price, imputedSample) = predictionFramework.run(sample)
except ValueError:
print(
"Some field from the alternative is missing, modify your data and try again"
)
price = "Not available"
#compare the input price with the expected price ValueError
try:
self.comparePrices(price)
except ValueError:
print(
"It appears you have found an edge of our model. The expected price is too high"
)
#search alternatives
try:
similar.run('processed_user_input.csv')
except KeyError:
print(
"Sorry, we did not find any good alternatives, modify your data and try again"
)
#extract alternatives
try:
postProcessing.convertAlternatives()
except KeyError:
print(
"We could not convert the alternatives file we found back to user input.Maybe have a look at the 'test_similarhouses.csv' or modify your data and try again"
)
#converting back to human readable code and putting it into the GUI
try:
postProcessing.postProcessing2()
except KeyError:
print(
"We could not convert the alternatives file we found back to user input.Maybe have a look at the 'finished_alternative.csv' or modify your data and try again"
)
self.setAlternatives()
def predictDataWithPostScript(self, pmmlstoragepointer, filpath,
scriptOutput):
print('Step 1.1')
global PMMLMODELSTORAGE
pointerObj = PMMLMODELSTORAGE[pmmlstoragepointer]
#####################>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>###############
codePart = pointerObj['preProcessScript']
print('Step 1.2')
fnaMe = './trainModel/testPreprocessing.py'
with open(fnaMe, 'w') as k:
k.write(codePart)
print('Code File written Pre Process')
import importlib.util
spec = importlib.util.spec_from_file_location('preProcessing', fnaMe)
preProcessing = spec.loader.load_module()
print('Step 1.3')
#####################>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>###############
codePart2 = pointerObj['postProcessScript']
fnaMe2 = './trainModel/testPostprocessing.py'
with open(fnaMe2, 'w') as k:
k.write(codePart2)
print('Code File written POST Process')
spec2 = importlib.util.spec_from_file_location('postProcessing',
fnaMe2)
postProcessing = spec2.loader.load_module()
print('Step 1.4')
#####################>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>###############
from preProcessing import preProcessing
from postProcessing import postProcessing
from string import ascii_uppercase
from random import choice
print('Step 1.5')
if scriptOutput == 'IMAGE':
target_path2 = './logs/' + ''.join(
choice(ascii_uppercase) for i in range(12)) + '/'
target_path = target_path2 + '/test/'
self.checkCreatePath(target_path2)
self.checkCreatePath(target_path)
print('Step 1.6')
preProcessing(filpath, target_path)
print('Step 1.7')
print(' Image chunk saving done')
print('Step 1.8')
listOfFiles = os.listdir(target_path)
if len(listOfFiles) > 1000000:
tempRunMemory = self.predictFolderDataInBatch(
pmmlstoragepointer, target_path2, len(listOfFiles))
tempRunMemory['inTask'] = True
return tempRunMemory
else:
targetResult = self.predictFolderdata(pmmlstoragepointer,
target_path2)
print('Step 1.9')
# outputOfPreScript=open(targetResult,'r').read()
# outputOfPreScript=json.loads(outputOfPreScript)
print('Step 1.10')
target_path3 = './logs/' + ''.join(
choice(ascii_uppercase) for i in range(12)) + '/'
self.checkCreatePath(target_path3)
print('Step 1.11')
outPutAfterPostProcess = postProcessing(filpath, targetResult,
target_path3)
print('Step 1.12')
elif scriptOutput == 'DATA':
print('Use case need to be discussed')
try:
os.remove(fnaMe2)
except:
pass
try:
os.remove(fnaMe)
except:
pass
return outPutAfterPostProcess
