def roots_interval(exp,a,b):
roots=[]
h=(b-a)/1000
try:
while(a<=b):
f1=extract.main(exp,[a])
f2=extract.main(exp,[a+h])
if(f1*f2<=0 and (f1*f2)>-10):
roots.append(a+h/2)
else:
if(math.fabs(f1)<0.01):
roots.append(a)
a=a+h
actual=[]
if(len(roots)!=0):
actual.append(roots[0])
i=1
count=0
while(i<len(roots)):
if(math.fabs(roots[i]-roots[i-1])>0.1):
actual.append(roots[i])
count=count+1
i=i+1
else:
j=i
sum=0
while(i<len(roots) and math.fabs(roots[i]-roots[i-1])<0.1):
sum+=roots[i]
i=i+1
actual[count]=(actual[count]+sum)/(i-j+1)
return actual
except:
return("unexpected value experienced.Try again with a different interval")
def main(args):
img_paths = sorted(list_images(args['folder_images']))
i = 1
print('[INFO] Starting ...')
ls = os.listdir('output/')
ls = [item.split('.')[0] for item in ls]
s_t = time.time()
for path in img_paths:
name = path.split(os.path.sep)[-1].split('.')[0]
if name in ls:
print('[INFO] Processed {}'.format(name))
i += 1
continue
try:
start = time.time()
if name < 'K6171-0012':
intro.main({'image': path, 'intro': 'True'})
elif name == 'K6171-0012':
intro.main({'image': path, 'intro': 'False'})
elif name < 'K6171-0754':
extract.main({'image': path})
end = time.time()
print('[INFO] processing {} - {} - {:.4f}s'.format(
i,
path.split(os.path.sep)[-1], end - start))
except:
print('[INFO] ERROR! {}'.format(path))
i += 1
e_t = time.time()
print('[INFO] Finished. Total: {} images - Total time: {:.4f}s'.format(
i - 1, e_t - s_t))
def main():
if parser.extract:
extract.main()
if parser.train:
train.main()
def integrate(exp,downer,upper):
if(upper=='inf'):
upper=100000
if(exp.isnumeric()):
return(int(exp)*(upper-downer))
dx=(upper-downer)/1000
val1=cython.declare(cython.float)
val2=cython.declare(cython.float)
arr=[0]
if(upper==downer):
sum=0.0
elif(upper>downer):
j=downer
sum=0.0
while(j<upper):
arr[0]=j
val1=extract.main(exp,arr)
arr[0]=j+dx
val2=extract.main(exp,arr)
sum+=(val1+val2)/2*dx
j=j+dx
else:
j=upper
sum=0.0
while(j<downer):
arr[0]=j
val1=extract.main(exp,arr)
arr[0]=j-dx
val2=extract.main(exp,arr)
sum+=(val1+val2)/2*dx
j=j-dx
return sum
def send_data_to_graphite(self):
extract.main()
d = os.listdir()
delay = 1
for files in d:
if (("RL" in files or "TU" in files) and ".txt" in files):
data = json.load(open(files))
f = files[0:7]
if f in data:
rc = int(data[f][3].get('rc'))
vst = int(data[f][3].get('vst'))
nmt = int(data[f][3].get('nmt'))
frc = int(data[f][3].get('frc'))
reboots = rc/((vst+1)/(3600*24))
upTime = (1-(nmt-vst)/nmt)*100
#return(f,getReboots(rc,vst),getUpTime(nmt,vst),getFilesPerDay(frc,vst))
print ('RLname : %s Reboot : %d Uptime : %d' % (f,reboots,upTime))
timestamp = int(time.time())
lines = [
'RLname.%s.UpTime %d %d' % (f, upTime, timestamp),
'RLname.%s.Restarts %s %d' % (f, reboots, timestamp)
]
message = '\n'.join(lines) + '\n'
print(message)
self.sock.send(message.encode("utf-8"))
time.sleep(delay)
def main():
#Extract data from images
target_dir = '../../Dataset/'
if extract_data_again:
ext.main()
xtrain, ytrain, ytrain_label = getdata_file(target_dir+'DS2full-train.csv')
xtest, ytest, ytest_label = getdata_file(target_dir+'DS2full-test.csv')
#Scale Data
scaler = pp.MinMaxScaler()
scaler.fit(np.concatenate((xtrain,xtest)))
xtrain = scaler.transform(xtrain)
xtest = scaler.transform(xtest)
original = sys.stdout
eta = 0.1
filelog=open('result_'+str(eta)+'.txt','w')
original = sys.stdout
sys.stdout = Tee(sys.stdout, filelog)
print('##########################################')
print('Eta:',eta)
n_epoch = 80
no_hidden = 30
NeuralNet = nn.neural_network(len(xtrain[0]),[30],len(ytrain[0]))
NeuralNet.train(xtrain, ytrain,ytrain_label,eta, n_epoch,test_while_train = True, xtest = xtest,ytest_label= ytest_label, draw_plot=True)
eta = eta*10
sys.stdout = original
def main2():
#Extract data from images
target_dir = '../../Dataset/'
if extract_data_again:
ext.main()
xtrain, ytrain, ytrain_label = getdata_file(target_dir+'DS2full-train.csv')
xtest, ytest, ytest_label = getdata_file(target_dir+'DS2full-test.csv')
#Scale Data
scaler = pp.MinMaxScaler()
scaler.fit(np.concatenate((xtrain,xtest)))
xtrain = scaler.transform(xtrain)
xtest = scaler.transform(xtest)
original = sys.stdout
eta = 0.01
gamma = 0.01
for i in range(5):
filelog=open('result2_'+str(gamma)+'.txt','w')
original = sys.stdout
sys.stdout = Tee(sys.stdout, filelog)
print('##########################################')
print('Eta:',eta)
print('Gamma:',gamma)
#Set number of epochs
n_epoch = 250
no_hidden = 20
NeuralNet = nn2.neural_network(len(xtrain[0]),[20],len(ytrain[0]))
NeuralNet.train(xtrain, ytrain,ytrain_label,eta,gamma, n_epoch,test_while_train = True, xtest = xtest,ytest_label= ytest_label, draw_plot=True)
sys.stdout = original
gamma = gamma*10.0
def main():
# Get the name of the config file
config_file = leverage_efficiency.base.get_config_filename(sys.argv)
# Extract the data from source data folder into common format
import extract
extract.main(config_file)
# Update data with most recent values (optional)
#import update # This doesn't connect to the rest of the pipeline yet
#update.main(config_file)
# Calculate derived quantities like returns for input into calculations
import transform
transform.main(config_file)
# Perform leverage efficiency calculations
import analysis
analysis.main(config_file)
# Create figures
import plots
plots.main(config_file)
# Create exact figures used in the paper
import paper_plots
paper_plots.main(config_file)
# Create figures used in the EE lecture notes
import lecture_plots
lecture_plots.main(config_file)
def test_it_clears_the_database_when_reuploading_a_file():
extract.main(['./extract.py', 'fixture/simple.xlsx'])
extract.main(['./extract.py', 'fixture/mps.xlsx'])
sheet1 = scraperwiki.sql.select('count(*) as n from Sheet1')
assert_equals(sheet1[0]['n'], 308)
sheet2 = scraperwiki.sql.select('count(*) as n from Sheet2')
assert_equals(sheet2[0]['n'], 1073)
def main():
# run scrape
extract.main()
# run link
link.main()
# run make
make.main()
def isMonotone(exp,a,b):
begin=min(a,b)
end=max(a,b)
arr=[]
interval=(end-begin)/1000
while(begin<=end):
arr.append(extract.main(exp,[begin]))
begin+=interval
arr.sort()
if(math.fabs(arr[0]-extract.main(exp,[begin]))<0.01 and math.fabs(arr[len(arr)-1]-extract.main(exp,[end])<0.01)):
return True
return False
def integrate(exp,downer,upper):
try:
if(downer=='-inf'):
downer=-300
if(upper=='inf'):
upper=300
if(not('x' in exp)):
return(float(exp)*(upper-downer))
if(math.fabs(downer)==math.fabs(upper)):
flag=funcType(exp,upper)
if(flag==10):
exp="2*("+exp+")"
downer=0
if(flag==-10):
return 0
dx=(upper-downer)/1000
if(math.fabs(dx)<1):
arr=[0]
if(upper==downer):
sum=0.0
elif(upper>downer):
j=downer
sum=0.0
while(j<upper):
arr[0]=j
val1=extract.main(exp,arr)
arr[0]=j+dx
val2=extract.main(exp,arr)
sum+=(val1+val2)/2*dx
j=j+dx
else:
j=upper
sum=0.0
while(j<downer):
arr[0]=j
val1=extract.main(exp,arr)
arr[0]=j-dx
val2=extract.main(exp,arr)
sum+=(val1+val2)/2*dx
j=j-dx
return sum
else:
down_lim=[downer]
up_lim=[downer+(upper-downer)/10]
sum=0
for i in range(10):
sum+=integrate(exp,down_lim[i],up_lim[i])
down_lim.append(up_lim[i])
up_lim.append(up_lim[i]+(upper-downer)/10)
return sum
except:
return("unexpected input")
def funcType(exp,upper):
flag=0
for i in range(10):
t1=random.random()*(upper)-upper
val1=extract.main(exp,[t1])
val2=extract.main(exp,[-1*t1])
if(val1==val2):
flag+=1
elif(val1==-1*val2):
flag-=1
else:
flag=0
return flag
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--crawl')
args = parser.parse_args()
if args.crawl == 'yes':
c = raw_input("This will crawl the websites and will take very long time. Do you want to continue? ")
if c == 'yes':
crawl.crawl_trains()
extract.main()
transform.main()
os.system('bash export.sh')
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--crawl')
args = parser.parse_args()
if args.crawl == 'yes':
c = raw_input(
"This will crawl the websites and will take very long time. Do you want to continue? "
)
if c == 'yes':
crawl.crawl_trains()
extract.main()
transform.main()
os.system('bash export.sh')
def setUp(self):
# Skip this test because it takes too long (>1 minute)
# TODO: figure out how to declare a "long-running" test suite
# and add this test to it.
raise SkipTest()
global SETUP_HAS_RUN
# Subtract 1 second to help comparisons with file-modify time succeed,
# since os.path.getmtime() is not millisecond-accurate
self.start_time = datetime.now(UTC) - timedelta(seconds=1)
super(TestExtract, self).setUp()
if not SETUP_HAS_RUN:
# Run extraction script. Warning, this takes 1 minute or more
extract.main()
SETUP_HAS_RUN = True
def test_main_return_values(arguments, return_code, message, monkeypatch,
capsys):
monkeypatch.setattr('extract.call_docker', lambda **_: None)
monkeypatch.setattr('extract.sys.argv', arguments)
monkeypatch.setattr('extract.subprocess.run', exec_stub)
assert main() == return_code
assert message in capsys.readouterr().err
def main():
parser = argparse.ArgumentParser()
parser.add_argument("input", help="File of URLs to be analyzed")
parser.add_argument("output", help="Output File")
args = parser.parse_args()
if args.input and args.output:
# Update phishtank database
print('Download and update phishtank database...')
update_db()
# Starts extraction
print('Starts extraction...')
extract.main(args.input, args.output)
print('''
#######################################
# Dataset generated successfully! #
#######################################
''')
def test_main_return_values(arguments, return_code, message, monkeypatch,
capsys):
monkeypatch.setattr(
'extract.call_docker',
lambda input_file, container, target, report_file, memory_limit: None)
monkeypatch.setattr('extract.sys.argv', arguments)
monkeypatch.setattr('extract.subprocess.run', exec_stub)
assert main() == return_code
assert message in capsys.readouterr().err
def input_num(self):
input_window = tk.Tk()
input_window.geometry('170x100+' + str(int(0.5 * input_window.winfo_screenwidth())) +
'+' + str(int(0.5 * input_window.winfo_screenheight())))
input_window.title('输入')
input_entry = tk.Entry(input_window, width = 15, font = ('Arial', 10))
input_entry.place(x = 30, y = 25, anchor = 'w')
start = tk.Button(input_window, text = '开始',
command = lambda : extract.main(input_entry.get(), self.extracted_name, self.show_res))
start.place(x = 70, y = 65, anchor = 'w')
def dirDeriv(exp,point,vector):
dim=len(vector)
dird=[]
for i in range(dim):
dird.append(CallDiff(exp,var_list[i]))
comp=[]
try:
for i in range(dim):
comp.append(extract.main(dird[i],point)*vector[i])
return comp
except:
return("function not derivable")
def differentiate(exp,x,order):
arr=[x]
i=1
for i in range(1,order+1):
if(not('x' in exp)):
return 0
exp=CallDiff(exp,'x')
try:
der=extract.main(exp,arr)
return der
except:
return("function not derivable")
def EqnSolver(exp,x0,x1):
count=0
try:
while((math.fabs(x1-x0))>0.01 and count<100):
func1=extract.main(exp,[x1])
func0=extract.main(exp,[x0])
if(math.fabs(func1)<0.09 and math.fabs(func0)<0.09):
break
x2=x1-func1*(x1-x0)/(func1-func0)
x0,x1=x1,x2
count=count+1
if(count==100):
return("please try again with better root approximation.Perhaps no roots exist")
if(math.fabs(x1-x0)<0.1):
if(math.fabs(func1)<0.09 and math.fabs(func0)<0.09):
return x1
else:
return("high probability that roots don't exist")
else:
return("two roots were found"+str(x0)+","+str(x1))
except:
return("method failed.Please try again with another approximation")
def test(text1,text2,t1,t2,text3,text4,item):
x2=arange(t1,t2,0.01)
y1=zeros(len(x2))
y2=zeros(len(x2))
z=[None]
if item==0:
plt.xlabel(text3)
plt.ylabel(text4)
for i in range(len(x2)):
z=[x2[i]]
y2[i]=extract.main(text1,z)
y1[i]=extract.main(text2,z)
plot(y2,y1)
xlabel(text3)
ylabel(text4)
title('f(x) vs g(x)')
show()
if item==1:
xlabel(text3)
ylabel(text4)
for i in range(len(x2)):
z=[x2[i]]
y2[i]=extract.main(text1,z)
#y1[i]=extract.main(text2,z)
plot(x2,y2)
title('f(x) vs x')
show()
if item==3:
xlabel(text3)
ylabel(text4)
for i in range(len(x2)):
z=[x2[i]]
y2[i]=extract.main(text1,z)
#y1[i]=extract.main(text2,z)
plot(x2,y2)
title('g(x) vs x')
show()
if item==2:
xlabel(text3)
ylabel(text4)
for i in range(len(x2)):
z=[x2[i]]
y2[i]=extract.main(text1,z)
y1[i]=extract.main(text2,z)
plot(x2,y2)
plot(x2,y1)
title('g(x) vs x')
show()
def test(text1,text2,t1,t2,text3,text4,item):
n=50*(t2-t1)/2
x2=linspace(t1,t2,n)
y1=zeros(len(x2))
y2=zeros(len(x2))
z=[None]
if item==0:
plt.xlabel(text3)
plt.ylabel(text4)
for i in range(len(x2)):
z=[x2[i]]
y2[i]=extract.main(text1,z)
y1[i]=extract.main(text2,z)
plt.figure()
plt.plot(y1,y2)
plt.savefig("example.png")
if item==1:
plt.xlabel(text3)
plt.ylabel(text4)
for i in range(len(x2)):
z=[x2[i]]
y2[i]=extract.main(text1,z)
#y1[i]=extract.main(text2,z)
plt.figure()
plt.plot(x2,y2)
plt.savefig("example.png")
if item==3:
plt.xlabel(text3)
plt.ylabel(text4)
for i in range(len(x2)):
z=[x2[i]]
y2[i]=extract.main(text1,z)
#y1[i]=extract.main(text2,z)
plt.figure()
plt.plot(x2,y2)
plt.savefig("example.png")
if item==2:
plt.xlabel("X axis")
plt.ylabel("Y axis")
for i in range(len(x2)):
z=[x2[i]]
y2[i]=extract.main(text1,z)
y1[i]=extract.main(text2,z)
plt.figure()
plt.plot(x2,y2,label=text3)
plt.plot(x2,y1,label=text4)
plt.savefig("example.png")
def optimize_min(exp, dim, arrmin, arrmax):
flag = -1
count = 0
while count < 50:
count = count + 1
if flag == 0 or flag == -1:
points = [] * (dim + 1)
values = []
for i in range(dim + 1):
points.append([0] * (dim))
temp = [] * (dim)
for j in range(dim):
temp.append(arrmin[j] + (arrmax[j] - arrmin[j]) * random.random())
points[i] = temp
values.append(extract.main(exp, points[i]))
points[i] = numpy.array(points[i])
values = numpy.array(values)
index = values.argsort()
temp = points
for i in range(dim + 1):
points[i] = temp[index[i]]
values.sort()
if math.fabs((values[dim] - values[0]) / values[0]) < 0.01:
break
if True in (points[dim] - points[0]) < numpy.array([0.05] * (dim)) and True in (
points[dim] - points[0]
) > numpy.array([-0.05] * (dim)):
break
x0 = numpy.array([0] * dim)
for i in range(dim):
x0 = x0 + points[dim]
x0 = x0 / dim
xr = numpy.array([0] * (dim))
xr = 2 * x0 - points[dim]
if extract.main(exp, xr) < values[dim] and extract.main(exp, xr) >= values[0]:
gr = xr >= numpy.array(arrmin)
sm = xr <= numpy.array(arrmax)
if False in gr or False in sm:
flag = 0
continue
points[dim] = xr
values[dim] = extract.main(exp, xr)
flag = 1
continue
elif extract.main(exp, xr) < values[0]:
xe = 2 * xr - x0
gr = xr >= numpy.array(arrmin)
sm = xr <= numpy.array(arrmax)
if False in gr or False in sm:
flag = 0
continue
if extract.main(exp, xe) < extract.main(exp, xr):
points[dim] = xe
values[dim] = extract.main(exp, xe)
flag = 1
continue
else:
points[dim] = xr
values[dim] = extract.main(exp, xr)
flag = 1
continue
else:
xc = 0.5 * xr + 0.5 * x0
gr = xr >= numpy.array(arrmin)
sm = xr <= numpy.array(arrmax)
if False in gr or False in sm:
flag = 0
continue
if extract.main(exp, xc) < extract.main(exp, xr):
points[dim] = xc
values[dim] = extract.main(exp, xc)
flag = 1
continue
elif extract.main(exp, xr) > values[dim]:
xc = 0.5 * values[dim] + 0.5 * x0
if extract.main(exp, xc) < values[dim]:
points[dim] = xc
values[dim] = extract.main(exp, xc)
flag = 1
continue
else:
for i in range(1, dim + 1):
points[i] = 0.5 * points[0] + 0.5 * points[i]
values[i] = extract.main(exp, points[i])
flag = 1
if math.fabs((values[dim] - values[0]) / values[0]) < 0.01:
return values[0]
if True in (points[dim] - points[0]) < ([0.05] * (dim)) and True in (points[dim] - points[0]) > ([-0.05] * (dim)):
return min(values)
return min(values)
def main():
embed.main()
extract.main()
evaluate.main()
import eval
import extract
if __name__ == '__main__':
extract.main('data/Corpus.DEV.txt', 'data/Corpus.TRAIN.txt',
'data/TRAIN.annotations', 'output.dev.txt')
eval.main('data/DEV.annotations', 'output.dev.txt')
while choice != 5:
print('\nMenu:')
print(
'1 -> Create Dataset.\n2 -> Classify using kNN.\n3 -> Classify using MNB.\n4 -> Exit.'
)
choice = int(input('Enter your choice: '))
if choice == 1:
# Create Dataset
reply = input(
'Do you want get emails from your email account? (y/n): '
)[0].lower()
if reply == 'n':
dataset_name = input(
'Enter the name of existing dataset folder: ')
ex.main(dataset_name)
elif reply == 'y':
usr = input('Email: ')
pwd = getpass('Password: '******'Enter a name for your dataset: ')
if platform.system() == 'Windows':
dataset_path = current_path + dataset_name + "\\"
elif platform.system() == 'Linux':
dataset_path = current_path + dataset_name + "/"
if not os.path.exists(dataset_path):
os.mkdir(dataset_path)
print('Your Folders:')
if platform.system() == 'Windows':
current_path = os.path.dirname(os.path.abspath(__file__)) + "\\"
elif platform.system() == 'Linux':
current_path = os.path.dirname(os.path.abspath(__file__)) + "/"
choice = 0
while choice != 5:
print('\nMenu:')
print('1 -> Create Dataset.\n2 -> Classify using kNN.\n3 -> Classify using MNB.\n4 -> Exit.')
choice = int(input('Enter your choice: '))
if choice == 1:
# Create Dataset
reply = input('Do you want get emails from your email account? (y/n): ')[0].lower()
if reply == 'n':
dataset_name = input('Enter the name of existing dataset folder: ')
ex.main(dataset_name)
elif reply == 'y':
usr = input('Email: ')
pwd = getpass('Password: '******'Enter a name for your dataset: ')
if platform.system() == 'Windows':
dataset_path = current_path + dataset_name + "\\"
elif platform.system() == 'Linux':
dataset_path = current_path + dataset_name + "/"
if not os.path.exists(dataset_path):
os.mkdir(dataset_path)
print('Your Folders:')
def main():
usage = "Usage: vcfPytools.py [tool] [options]\n\n" + \
"Available tools:\n" + \
" annotate:\n\tAnnotate the vcf file with membership in other vcf files.\n" + \
" extract:\n\tExtract vcf records from a region.\n" + \
" filter:\n\tFilter the vcf file.\n" + \
" indel:\n\tIndel manipulation tools.\n" + \
" intersect:\n\tGenerate the intersection of two vcf files.\n" + \
" merge:\n\tMerge a list of vcf files.\n" + \
" multi:\n\tFind the intersections and unique fractions of multiple vcf files.\n" + \
" sort:\n\tSort a vcf file.\n" + \
" stats:\n\tGenerate statistics from a vcf file.\n" + \
" union:\n\tGenerate the union of two vcf files.\n" + \
" unique:\n\tGenerate the unique fraction from two vcf files.\n" + \
" validate:\n\tValidate the input vcf file.\n\n" + \
"vcfPytools.py [tool] --help for information on a specific tool."
# Determine the requested tool.
if len(sys.argv) > 1:
tool = sys.argv[1]
else:
print >> sys.stderr, usage
exit(1)
if tool == "annotate":
import annotate
success = annotate.main()
elif tool == "extract":
import extract
success = extract.main()
elif tool == "filter":
import filter
success = filter.main()
elif tool == "intersect":
import intersect
success = intersect.main()
elif tool == "indel":
import indel
success = indel.main()
elif tool == "multi":
import multi
success = multi.main()
elif tool == "merge":
import merge
success = merge.main()
elif tool == "sort":
import sort
success = sort.main()
elif tool == "stats":
import stats
success = stats.main()
elif tool == "union":
import union
success = union.main()
elif tool == "unique":
import unique
success = unique.main()
elif tool == "test":
import test
success = test.main()
elif tool == "validate":
import validate
success = validate.main()
elif tool == "--help" or tool == "-h" or tool == "?":
print >> sys.stderr, usage
else:
print >> sys.stderr, "Unknown tool: ",tool
print >> sys.stderr, "\n", usage
exit(1)
# If program completed properly, terminate.
if success == 0: exit(0)
def multiDimensionInteg(exp,dim,down,up):
minarr=[None]*dim
maxarr=[None]*dim
res=0.0
pro=1.0
j=0
flag_down=[]
flag_up=[]
while(j<dim):
flag_down.append(0)
flag_up.append(0)
k=j-1
while(k>=0):
if(var_list[k] in str(up[j])):
flag_up[j]=1
if(var_list[k] in str(down[j])):
flag_down[j]=1
k=k-1
if(flag_up[j]==0 and flag_down[j]==0):
minarr[j],maxarr[j]=float(down[j]),float(up[j])
elif(flag_up[j]==1 and flag_down[j]==0):
temp1=(Max(up[j],j,minarr[0:j],maxarr[0:j]))
temp2=(Min(up[j],j,minarr[0:j],maxarr[0:j]))
temp3=float(down[j])
if(temp1>=temp3):
maxarr[j]=temp1
else:
maxarr[j]=temp3
if(temp2<=temp3):
minarr[j]=temp2
else:
minarr[j]=temp3
elif(flag_up[j]==0 and flag_down[j]==1):
temp1=(Max(down[j],j,minarr[0:j],maxarr[0:j]))
temp2=(Min(down[j],j,minarr[0:j],maxarr[0:j]))
temp3=float(up[j])
if(temp1>=temp3):
maxarr[j]=temp1
else:
maxarr[j]=temp3
if(temp2<=temp3):
minarr[j]=temp2
else:
minarr[j]=temp3
else:
temp1=(Max(up[j],j,minarr[0:j],maxarr[0:j]))
temp2=(Min(up[j],j,minarr[0:j],maxarr[0:j]))
temp3=(Max(down[j],j,minarr[0:j],maxarr[0:j]))
temp4=(Min(down[j],j,minarr[0:j],maxarr[0:j]))
if(temp1>=temp3):
maxarr[j]=temp1
else:
maxarr[j]=temp3
if(temp2<=temp4):
minarr[j]=temp2
else:
minarr[j]=temp4
j=j+1
if(minarr[0]>maxarr[0]):
minarr[0],maxarr[0]=maxarr[0],minarr[0]
try:
for i in range(1000):
arr=[]
j=0
while(j<dim):
arr.append((maxarr[j]-minarr[j])*random.random()+minarr[j])
j=j+1
f1=extract.main(exp,arr)
flag=1
for k in range(dim):
temp1=extract.main(down[k],arr[0:k])
temp2=extract.main(up[k],arr[0:k])
if(not((arr[k]<temp1 and arr[k]>temp2) or (arr[k]>temp1 and arr[k]<temp2))):
flag=0
if(temp1>temp2):
flag=-1*flag
res+=f1*flag
res/=1000
for i in range(dim):
res*=(maxarr[i]-minarr[i])
return res
except:
return("unexpected input")
console.setLevel(logging.CRITICAL)
# Set a format which is simpler for console use
formatter = logging.Formatter('%(message)s')
# Tell the handler to use this format
console.setFormatter(formatter)
# Add the handler to the root logger
logging.getLogger('').addHandler(console)
# Create a logger
log = logging.getLogger(__name__)
#
# RUN SELECTED COMMAND
#
if cliArgs.command == "simulate":
# Run simulate.py
simulate.main(cliArgs)
elif cliArgs.command == "list":
# Run list.py
list.main()
elif cliArgs.command == "convert":
# Run convert.py
convert.main(cliArgs)
elif cliArgs.command == "extract":
# Run extract.py
extract.main(cliArgs)
def test_it_returns_error_json():
rawOutput = extract.main(['./extract.py','fixture/empty-header-cells.csv'])
output = json.loads(rawOutput)
assert("errorType" in output)
assert_equals(output["errorType"],"NullHeaderError")
def answer(self,event):
f=open("data.txt",'w')
t1=self.tc.GetValue()
t2=self.tc2.GetValue()
self.imp.Append(t1)
f.write(t1+'\n')
self.imp.Append(t2)
f.write(t2+'\n')
print self.k
if self.k<=5 and self.k>=0: # matrices (part 1)
text=self.tc.GetValue()
print(text)
a=self.conv(text)
text2=self.tc2.GetValue()
print(text2)
b=self.conv(text2)
if self.k==0:
try:
ans=matrix(a)+matrix(b)
ans=str(ans)
self.text12.SetValue(ans)
except:
ans='Either of the matrix is not given in the correct format'
self.text12.SetValue(ans)
if self.k==1:
try:
ans=matrix(a)-matrix(b)
ans=str(ans)
self.text12.SetValue(ans)
except:
ans='Either of the matrix is not given in the correct format'
self.text12.SetValue(ans)
if self.k==2:
try:
ans=dot(matrix(a),matrix(b))
ans=str(ans)
self.text12.SetValue(ans)
except:
ans='Either of the matrix is not given in the correct format'
self.text12.SetValue(ans)
if self.k==3:
try:
if determinant.determinant(a)!=0:
v=matrix(a).I
ans=dot(v,matrix(b))
ans=str(ans)
self.text12.SetValue(ans)
elif determinant.determinant(a)==0:
ans='Given an invertible matrix'
self.text12.SetValue(ans)
except:
ans='Either of the matrix is not given in the correct format'
self.text12.SetValue(ans)
if self.k==4:
try:
if determinant.determinant(a)!=0:
ans=matrix(a).I
ans=str(ans)
self.text12.SetValue(ans)
elif determinant.determinant(a)==0:
ans='The matrix is not invertible'
self.text12.SetValue(ans)
except:
ans='The matrix is not given inthe correct format'
self.text12.SetValue(ans)
if self.k==5:
try:
c=determinant.determinant(a)
ans=str(c)
self.text12.SetValue(ans)
except:
ans='The matrix given is not in the correct format'
self.text12.SetValue(ans)
if self.k>5 and self.k<=14 :
self.text2.SetLabel('')
text=self.tc.GetValue() # matrices (part 2)
print(text)
a=self.conv(text)
if self.k==6:
try:
a=matrix(a)
m=rank(a)
m=str(m)
self.text12.SetValue(m)
except:
m='The given input is not in the correct format'
self.text12.SetValue(m)
if self.k==7:
try:
c=matrix(a)
ans=gauss.test(c,len(a))
ans=str(ans)
self.text12.SetValue(ans)
except:
ans='The given input is not in the correct format'
self.text12.SetValue(ans)
if self.k==8:
try:
l,u,d=ludecomposition.test(a)
ans='l is ',l,'u is',u,'and d is',d
ans=str(ans)
self.text12.SetValue(ans)
except:
ans='The given input is not in the correct format'
self.text12.SetValue(ans)
if self.k==9:
try:
ans=matrix(a).T
ans=str(ans)
self.text12.SetValue(ans)
except:
ans='The given input is not in the correct format'
self.text12.SetValue(ans)
if self.k==10:
try:
a=matrix(a)
ans1,ans2=qr_decomposition.test(text)
ans='Q is ',ans1,'R is ',ans2,'in QR decomposition'
ans=str(ans)
self.text12.SetValue(ans)
except:
ans='The given input is not correct'
self.text12.SetValue(ans)
if self.k==11:
try:
ans=qr_decomposition2.test(text)
ans=str(ans)
self.text12.SetValue(ans)
except:
ans='The given input is not correct'
self.text12.SetValue(ans)
if self.k==12:
try:
ans=eigen.test(text)
ans=str(ans)
self.text12.SetValue(ans)
except:
ans='The given input is incorrect'
self.text12.SetValue(ans)
if self.k>=16 and self.k<20: # differential calculus
text2=self.tc2.GetValue()
text=self.tc.GetValue()
if self.k==16:
try:
b=self.conv(text2)
print b[0],b[1]
ans=symbolic.differentiate(text,float(b[0]),int(b[1]))
ans=str(ans)
self.text12.SetValue(ans)
except:
ans='please check the input that you have given'
self.text12.SetValue(ans)
if self.k==17:
try:
ans=symbolic.CallDiff(text,text2)
ans=str(ans)
self.text12.SetValue(ans)
except:
ans='The given input is not correct'
self.text12.SetValue(ans)
if self.k==18:
try:
b=self.conv(text2)
ans=symbolic.dirDeriv(text,b[0],b[1])
ans=str(ans)
self.text12.SetValue(ans)
except:
ans='The given input is not correct'
self.text12.SetValue(ans)
if self.k==19:
try:
ans=symbolic.Laplacian(text)
ans=str(ans)
self.text12.SetValue(ans)
except:
ans='The given input is not correct'
self.text12.SetValue(ans)
if self.k>=21 and self.k<=25: # polynomials
text=self.tc.GetValue()
text2=self.tc2.GetValue()
Poly=Polynomial()
if self.k==21:
try:
ans=Poly.addPolynomial(text,text2)
ans=str(ans)
self.text12.SetValue(ans)
except:
ans='The given input is not correct'
self.text12.SetValue(ans)
if self.k==22:
try:
ans=Poly.subtractPolynomial(text,text2)
ans=str(ans)
self.text12.SetValue(ans)
except:
ans='The given input is not correct'
self.text12.SetValue(ans)
if self.k==23:
try:
ans=Poly.MultiplyPolynomial(text,text2)
ans=str(ans)
self.text12.SetValue(ans)
except:
ans='The given input is not correct'
self.text12.SetValue(ans)
if self.k==24:
try:
ans=Poly.dividePolynomial(text,text2)
ans=str(ans)
self.text12.SetValue(ans)
except:
ans='The given input is not correct'
self.text12.SetValue(ans)
if self.k==25:
try:
self.tc2.SetValue('')
ans=rootpoly(text)
ans=str(ans) # karna hain
self.text12.SetValue(ans)
except:
ans='he choice not selected'
self.text12.SetValue(ans)
if self.k>=27 and self.k<=30: # integral calculus
text=self.tc.GetValue()
text2=self.tc2.GetValue()
print(text2)
if self.k==27:
try:
b=self.conv(text2)
lower=[None]*len(b)
upper=[None]*len(b)
ans=symbolic.integrate(text,b[0],b[1])
ans=str(ans)
self.text12.SetValue(ans)
except:
ans='The given input is not correct'
self.text12.SetValue(ans)
if self.k==28:
try:
b=self.conv3(text2)
print 'mridul',b
lower=[None]*len(b)
upper=[None]*len(b)
for i in range(len(b)):
lower[i]=b[i][0]
upper[i]=b[i][1]
print 'mridul',lower,upper
ans=symbolic.multiDimensionInteg(text,len(b),lower,upper)
ans=str(ans)
self.text12.SetValue(ans)
except:
ans='The given input is not correct'
self.text12.SetValue(ans)
if self.k==29:
try:
b=self.conv(text2)
lower=[None]*len(b)
upper=[None]*len(b)
if len(b)==6:
ans=diffeqn.solveDiffEqn4(text,b[:len(b)-2],b[len(b)-2],b[len(b)-1]) # the first element b[len(b)-1] is the point where the f(x) has to be calculated and the rest are the initial conditions
if len(b)==5:
ans=diffeqn.solveDiffEqn3(text,b[:len(b)-2],b[len(b)-2],b[len(b)-1])
if len(b)==4:
ans=diffeqn.solveDiffEqn2(text,b[:len(b)-2],b[len(b)-2],b[len(b)-1]) # the first element b[0] is the point where the f(x) has to be calculated and the rest are the initial conditions
if len(b)==3:
ans=diffeqn.solveDiffEqn1(text,b[:len(b)-2],b[len(b)-2],b[len(b)-1])
ans=str(ans)
self.text12.SetValue(ans)
except:
ans='The given input is not in not in the standard format demanded by CAS'
self.text12.SetValue(ans)
if self.k==30:
try:
b=self.conv3(text2)
lower=[None]*len(b)
upper=[None]*len(b)
print b
ans=diffeqn.LaplaceEqn(text,b[0],b[2],b[1],b[3])
ans=str(ans)
self.text12.SetValue(ans)
except:
ans='The given input is not correct'
self.text12.SetValue(ans)
if self.k>=37 and self.k<=44: # statistics the data points
text=self.tc.GetValue()
print(text)
b=self.conv(text)
lower=[None]*len(b)
middle=[None]*len(b)
upper=[None]*len(b)
if self.k==37:
try:
text2=self.tc2.GetValue()
for i in range(len(b)):
lower[i]=b[i][0]
upper[i]=b[i][1]
ans=regression.LeastSquare(int(text2),lower,upper)
ans=str(ans)
self.text12.SetValue(ans)
except:
ans='The given input is not correct format'
self.text12.SetValue(ans)
if self.k==38:
try:
for i in range(len(b)):
lower[i]=b[i][0]
middle[i]=b[i][1]
upper[i]=b[i][2]
ans=regression.PlaneFit(lower,middle,upper)
ans=str(ans)
self.text12.SetValue(ans)
except:
ans='The given input is not correct'
self.text12.SetValue(ans)
if self.k==39:
try:
for i in range(len(b)):
lower[i]=b[i][0]
upper[i]=b[i][1]
ans=regression.ExponentialCurve(lower,upper)
ans=str(ans)
self.text12.SetValue(ans)
except:
ans='The given input is not correct'
self.text12.SetValue(ans)
if self.k==40:
try:
for i in range(len(b)):
lower[i]=b[i][0]
upper[i]=b[i][1]
ans=regression.LogarithmicCurve(lower,upper)
ans=str(ans)
self.text12.SetValue(ans)
except:
ans='The given input is not correct'
self.text12.SetValue(ans)
if self.k==41:
try:
ans=regression.SplineInterpolation(b)
ans=str(ans)
self.text12.SetValue(ans)
except:
ans='The given input is not correct'
self.text12.SetValue(ans)
if self.k==42:
try:
for i in range(len(b)):
lower[i]=b[i][0]
upper[i]=b[i][1]
ans=regression.SpearsmanCoff(lower,upper)
ans=str(ans)
self.text12.SetValue(ans)
except:
ans='The given input is not correct'
self.text12.SetValue(ans)
if self.k==43:
try:
for i in range(len(b)):
lower[i]=b[i][0]
upper[i]=b[i][1]
ans=regression.Pearson(lower,upper)
ans=str(ans)
self.text12.SetValue(ans)
except:
ans='The given input is not correct'
self.text12.SetValue(ans)
if self.k==44:
try:
ans=regression.tau(b)
ans=str(ans)
self.text12.SetValue(ans)
except:
ans='The given input is not correct'
self.text12.SetValue(ans)
if self.k>=32 and self.k<=36: # playing with Fourier
text=self.tc.GetValue()
print (text)
if self.k==32:
try:
text2=self.tc2.GetValue()
b=self.conv(text2)
ans=SinCoeff(text,b[0],b[1],b[2])
ans=str(ans)
self.text12.SetValue(ans)
except:
ans='The given input is not correct'
self.text12.SetValue(ans)
if self.k==33:
try:
text2=self.tc2.GetValue()
b=self.conv(text2)
ans=CosCoeff(text,b[0],b[1],b[2])
ans=str(ans)
self.text12.SetValue(ans)
except:
ans='The given input is not correct'
self.text12.SetValue(ans)
if self.k==34:
try:
text2=self.tc2.GetValue()
b=self.conv(text2)
ans=FourierCoeff(text,b[0],b[1],b[2])
ans=str(ans)
self.text12.SetValue(ans)
except:
ans='The given input is not correct'
self.text12.SetValue(ans)
if self.k==35:
try:
b=self.conv2(text)
ans=discreteFourier(b,len(b))
ans=str(ans)
self.text12.SetValue(ans)
except:
ans='The given input is not correct'
self.text12.SetValue(ans)
if self.k==36:
try:
b=self.conv2(text)
ans=inverseVal(b,len(b))
ans=str(ans)
self.text12.SetValue(ans)
except:
ans='The given input is not correct'
self.text12.SetValue(ans)
if self.k>=46 and self.k<=47: # optimization
text=self.tc.GetValue()
print (text)
text2=self.tc2.GetValue()
b=self.conv(text2)
lower=[None]*len(b)
upper=[None]*len(b)
for i in range(len(b)):
lower[i]=b[i][0]
upper[i]=b[i][1]
if self.k==46:
try:
m=symbolic.Max(text,len(b),lower,upper)
ans=str(m)
self.text12.SetValue(ans)
except:
ans='The given input is not correct'
self.text12.SetValue(ans)
if self.k==47:
try:
m=symbolic.Min(text,len(b),lower,upper)
ans=str(m)
self.text12.SetValue(ans)
except:
ans='The given input is not correct'
self.text12.SetValue(ans)
if self.k>=49: #evaluating expression
text=self.tc.GetValue()
print (text)
text2=self.tc2.GetValue()
b=self.conv(text2)
if self.k==49:
try:
m=symbolic.roots_interval(text,b[0],b[1])
ans=str(m)
self.text12.SetValue(ans)
except:
ans='The given input is not correct'
self.text12.SetValue(ans)
if self.k==50:
try:
m=symbolic.EqnSolver(text,b[0],b[1])
ans=str(m)
self.text12.SetValue(ans)
except:
ans='The given input is not correct'
self.text12.SetValue(ans)
if self.k==51:
try:
m=extract.main(text,b)
ans=str(m)
self.text12.SetValue(ans)
except:
ans='The given input is not correct'
self.text12.SetValue(ans)
t3=self.text12.GetValue()
self.imp.Append(t3)
f.write(t3+'\n')
f.close()
def main():
usage = "Usage: vcfPytools.py [tool] [options]\n\n" + \
"Available tools:\n" + \
" annotate:\n\tAnnotate the vcf file with membership in other vcf files.\n" + \
" extract:\n\tExtract vcf records from a region.\n" + \
" filter:\n\tFilter the vcf file.\n" + \
" indel:\n\tIndel manipulation tools.\n" + \
" intersect:\n\tGenerate the intersection of two vcf files.\n" + \
" merge:\n\tMerge a list of vcf files.\n" + \
" multi:\n\tFind the intersections and unique fractions of multiple vcf files.\n" + \
" sort:\n\tSort a vcf file.\n" + \
" stats:\n\tGenerate statistics from a vcf file.\n" + \
" union:\n\tGenerate the union of two vcf files.\n" + \
" unique:\n\tGenerate the unique fraction from two vcf files.\n" + \
" validate:\n\tValidate the input vcf file.\n\n" + \
"vcfPytools.py [tool] --help for information on a specific tool."
# Determine the requested tool.
if len(sys.argv) > 1:
tool = sys.argv[1]
else:
print >> sys.stderr, usage
exit(1)
if tool == "annotate":
import annotate
success = annotate.main()
elif tool == "extract":
import extract
success = extract.main()
elif tool == "filter":
import filter
success = filter.main()
elif tool == "intersect":
import intersect
success = intersect.main()
elif tool == "indel":
import indel
success = indel.main()
elif tool == "multi":
import multi
success = multi.main()
elif tool == "merge":
import merge
success = merge.main()
elif tool == "sort":
import sort
success = sort.main()
elif tool == "stats":
import stats
success = stats.main()
elif tool == "union":
import union
success = union.main()
elif tool == "unique":
import unique
success = unique.main()
elif tool == "test":
import test
success = test.main()
elif tool == "validate":
import validate
success = validate.main()
elif tool == "--help" or tool == "-h" or tool == "?":
print >> sys.stderr, usage
else:
print >> sys.stderr, "Unknown tool: ", tool
print >> sys.stderr, "\n", usage
exit(1)
# If program completed properly, terminate.
if success == 0: exit(0)
def test_it_logs_progress_to_a_text_file():
extract.main(['./extract.py','fixture/simple.xls'])
with open('log.txt', 'r') as f:
assert("STARTED" in f.read())
import traceback
import time
import extract
import read_csv
import write_csv
import read_lines
import processsing
if __name__ == "__main__":
print("_" * 50)
print("Start script 'time_reporting.py'")
try:
dir, csv_list = extract.main()
files = read_csv.main(csv_list)
datas = read_lines.main(files, csv_list)
datas_transformed = processsing.main(datas)
write_csv.main(dir, datas_transformed)
print("Finish script 'time_reporting.py'")
except:
traceback.print_exc()
time.sleep(24 * 60 * 60)
def test_it_returns_success_json():
rawOutput = extract.main(['./extract.py', 'fixture/simple.xls'])
output = json.loads(rawOutput)
assert ("errorType" in output)
assert_equals(output["errorType"], None)
def test_it_returns_error_json():
rawOutput = extract.main(
['./extract.py', 'fixture/empty-header-cells.csv'])
output = json.loads(rawOutput)
assert ("errorType" in output)
assert_equals(output["errorType"], "NullHeaderError")
import sys import extract as ex import eval as ev train_file, annotation_file, output_file = sys.argv[1:] ex.main(train_file, output_file) ev.main(annotation_file, output_file)
def LaplaceEqn(g,boundary,boundary1,pts,pt):
for i in range(len(pts)):
pts[i]=int(pts[i])
#print(pts)
pt[0]=float(pt[0])
pt[1]=float(pt[1])
# print(pt)
m=int(math.fabs(int(pts[0]/1)))
n=int(math.fabs(int(pts[1]/1)))
t1=1
t2=1
A=[0]*((m-1)*(n-1))
for i in range((m-1)*(n-1)):
A[i]=[0]*((m-1)*(n-1))
D=[0]*(m-1)
I=[0]*(m-1)
for i in range(m-1):
D[i]=[0]*(m-1)
I[i]=[0]*(m-1)
for j in range(m-1):
if(i==j):
D[i][j]=4
I[i][j]=-1
elif(math.fabs(i-j)==1):
D[i][j]=-1
i=0
while(i<((m-1)*(n-1))):
j=0
while(j<((m-1)*(n-1))):
MatrixFill(A,i,j,D,I,m-1)
j+=(m-1)
i+=(m-1)
val=[]
if(pts[0]<0):
t1=-1
if(pts[1]<0):
t2=-1
y_cor=1*t2
for i in range(n-1):
x_cor=1*t1
for j in range(m-1):
if(math.fabs(x_cor)==1 and math.fabs(y_cor)==1):
val.append(extract.main(boundary[0],[x_cor,y_cor])+extract.main(boundary[1],[x_cor,y_cor])-extract.main(g,[x_cor,y_cor])*(math.pow(1,2)))
elif((x_cor)==(pts[0]-1*t1) and (y_cor)==(pts[1]-1*t2)):
val.append(extract.main(boundary1[0],[x_cor,y_cor])+extract.main(boundary1[1],[x_cor,y_cor])-extract.main(g,[x_cor,y_cor])*(math.pow(1,2)))
elif((x_cor)==(pts[0]-1*t1) and math.fabs(y_cor)==1):
val.append(extract.main(boundary1[0],[x_cor,y_cor])+extract.main(boundary[1],[x_cor,y_cor])-extract.main(g,[x_cor,y_cor])*(math.pow(1,2)))
elif((y_cor)==(pts[1]-1*t2) and math.fabs(x_cor)==1):
val.append(extract.main(boundary[0],[x_cor,y_cor])+extract.main(boundary1[1],[x_cor,y_cor])-extract.main(g,[x_cor,y_cor])*(math.pow(1,2)))
elif(math.fabs(x_cor)==1):
val.append(extract.main(boundary[1],[x_cor,y_cor])-extract.main(g,[x_cor,y_cor])*(math.pow(1,2)))
elif(math.fabs(y_cor)==1):
val.append(extract.main(boundary[0],[x_cor,y_cor])-extract.main(g,[x_cor,y_cor])*(math.pow(1,2)))
elif((x_cor)==(pts[0]-1*t1)):
val.append(extract.main(boundary1[1],[x_cor,y_cor])-extract.main(g,[x_cor,y_cor])*(math.pow(1,2)))
elif((y_cor)==(pts[1]-1*t2)):
val.append(extract.main(boundary1[0],[x_cor,y_cor])-extract.main(g,[x_cor,y_cor])*(math.pow(1,2)))
else:
val.append(extract.main(g,[x_cor,y_cor])*(math.pow(1,2))*-1)
x_cor+=1*t1
y_cor+=1*t2
A=matrix(A)
val=matrix(val)
u=(A.I)*(val.T)
u.tolist()
x1=int(pt[0])
y1=int(pt[1])
w1=max(1-math.sqrt((x1-pt[0])**2+(y1-pt[1])**2),0)
w2=max(1-math.sqrt((x1+t1-pt[0])**2+(y1-pt[1])**2),0)
w3=max(1-math.sqrt((x1-pt[0])**2+(y1+t2-pt[1])**2),0)
w4=max(1-math.sqrt((x1+t1-pt[0])**2+(y1+t2-pt[1])**2),0)
if(x1!=0 and y1!=0):
u1=u[math.fabs(x1)-1+(m-1)*(math.fabs(y1)-1)]
else:
if(x1==0):
u1=extract.main(boundary[1],[x1,y1])
elif(y1==0):
u1=extract.main(boundary[0],[x1,y1])
if(y1!=0 and x1!=pts[0]-t1):
u2=u[math.fabs(x1)+(m-1)*(math.fabs(y1)-1)]
else:
if(y1==0):
u2=extract.main(boundary[0],[x1,y1])
elif(x1==pts[0]-t1):
u2=extract.main(boundary1[1],[x1,y1])
if(x1!=0 and y1!=pts[1]-t2):
u3=u[math.fabs(x1)-1+(m-1)*(math.fabs(y1))]
else:
if(x1==0):
u3=extract.main(boundary[1],[x1,y1])
elif(y1==pts[1]-t2):
u3=extract.main(boundary1[1],[x1,y1])
if(x1!=pts[0]-t1 and y1!=pts[1]-t2):
u4=u[math.fabs(x1)+(m-1)*(math.fabs(y1))]
else:
if(x1==pts[0]-t1):
u4=extract.main(boundary1[1],[x1,y1])
elif(y1==pts[1]-t2):
u4=extract.main(boundary1[1],[x1,y1])
u_final=(w1*u1+w2*u2+w3*u3+w4*u4)/(w1+w2+w3+w4)
return(u_final)
def test_it_logs_stack_traces():
extract.main(['./extract.py','fixture/empty-header-cells.csv'])
with open('log.txt', 'r') as f:
logOutput = f.read()
assert('Traceback (most recent call last):' in logOutput)
assert('NullHeaderError' in logOutput)
def extract(text=None, filename=None):
from extract import main
if filename:
with open(filename) as f:
text = f.read()
print(main(text))
def test_it_clears_the_log_before_each_run():
extract.main(['./extract.py','fixture/simple.xls'])
with open('log.txt', 'r') as f:
assert_equals(f.read().count('STARTED'), 1)
import cv2
import os
import numpy as np
import extract
e = extract.main()
print(e)
a0 = e[0][0]
a1 = e[0][1]
b0 = e[1][0]
b1 = e[1][1]
c0 = e[2][0]
c1 = e[2][1]
cv2.destroyAllWindows()
rgb = np.zeros((100, 512, 3), np.uint8)
lower_range = np.zeros((100, 512, 3), np.uint8)
upper_range = np.zeros((100, 512, 3), np.uint8)
draw = np.zeros((480, 640, 3), np.uint8)
cap = cv2.VideoCapture(0)
def nothing(x):
pass
cv2.namedWindow('control & lower_range')
cv2.createTrackbar('r', 'control & lower_range', 0, 255, nothing)
def test_it_returns_success_json():
rawOutput = extract.main(['./extract.py','fixture/simple.xls'])
output = json.loads(rawOutput)
assert("errorType" in output)
assert_equals(output["errorType"],None)