def getType():
tests = []
tests.append(["anata ha BAKA desu.", "r"])
tests.append(["あなたはバカです。", "k"])
tests.append([["a", "na", "ta", "ha", "ba", "ka", "de", "su", "."], "i"])
for x in tests:
result = JapaneseTools.Conversion.getType(x[0])
if result != x[1]: return Tester.Result(False, "Failed on " + str(x))
return Tester.Result(True, "All Types were properly detected")
def ktor():
tests = collections.OrderedDict()
tests["ぼく"] = "boku"
tests["あたい"] = "atai"
tests["しんぶん"] = "shin'bun'"
tests["しんおさか"] = "shin'osaka"
tests["ネイセン"] = "NEISEN'"
tests["てゐ"] = "tewi"
tests["ハニュー"] = "HANYU-"
for x in tests:
result = JapaneseTools.Conversion.convert(x, sourcetype="k", targettype="r")
if result != tests[x]: return Tester.Result(False, "Failed on '" + x + "'")
return Tester.Result(True, "All Kana to Romaji worked properly")
def rtok():
tests = collections.OrderedDict()
tests["boku"] = "ぼく"
tests["atai"] = "あたい"
tests["shinbun"] = "しんぶん"
tests["shin'osaka"] = "しんおさか"
tests["NEISEN"] = "ネイセン"
tests["tewi"] = "てゐ"
tests["HANYU-"] = "ハニュー"
for x in tests:
result = JapaneseTools.Conversion.convert(x, sourcetype="r", targettype="k")
if result != tests[x]: return Tester.Result(False, "Failed on '" + x + "'")
return Tester.Result(True, "All Romaji to Kana worked properly")
def learn():
# splits()
print("Done Splitting...")
tester = Tester()
test_set = tester.getTestSet()
print("Finished tester Stuff")
answers = []
reader = FileReader("training.txt")
X = reader.read_file()
print("Starting SVD..")
svd = TruncatedSVD(n_components=10, n_iter=10, random_state=42)
dense = svd.fit_transform(X)
print("Done with SVD, starting K Means...")
km = KMeans(n_clusters=100)
ans = km.fit_predict(dense)
print("Done with K Means...")
inverseAns = {cluster: [] for cluster in range(100)}
# centroids = svd.inverse_transform(km.cluster_centers_)
for trainingProdKey, trainingProdIndex in reader.product.items():
inverseAns[ans[trainingProdIndex]].append(trainingProdKey)
print('Done inverting clusters')
i = 0
for prod in test_set:
# print("Inside Loop")
answers.append(predict(prod, reader.product, ans, inverseAns))
if i % (len(test_set) // 100) == 0:
print("\rDone with {}% of predicting...".format(i / len(test_set)),
end='')
i = i + 1
print()
print(tester.checkAnswers(answers))
def gen_medium(iSeq1_len, sSeq1_len):
return Tester.MediumData(smallData=gen_small(iSeq1_len),
n3=random.randint(1, 1000),
n4=random.randint(1, 1000),
d1=random.uniform(1, 100.0),
d2=random.uniform(1, 100.0),
s2=random_string(10),
b2=random_bool(),
sSeq1=random_s_list(sSeq1_len))
def runTests(self):
tester = Tester.Tester(self.stop, self.color, self.verbose)
tester.offline = self.offline
try:
for test in self.test.getTests():
test.run(tester)
except KeyboardInterrupt:
sys.stderr.write('\n%s\n' % ('=' * 72))
sys.stderr.write('\nTesting interrupted\n')
if self.report:
tester.report()
return
def process_input():
ticker = e1.get()
start_date = e2.get()
end_date = e3.get()
candle_interval = e4.get()
ma_period = int(e5.get())
path = e6.get()
df = tr.MAIN_Tester(ticker, start_date, end_date, candle_interval,
ma_period).run()
writer = pd.ExcelWriter(path)
sheet_name = ticker + " " + start_date + " " + end_date
df.to_excel(writer, sheet_name)
writer.save()
def configure(logging=None, noLogEvents=None):
# """Sets the global event manager's logging options."""
# if g_eventManager:
# g_eventManager.setLogging(logging)
# g_eventManager.setNoLogEvents(noLogEvents)
# else:
# BugUtil.error("BugEventManager - BugEventManager not setup before configure()")
# K-Mod. I've expanded the purpose of this function.
"""Sets the global event manager's logging options. And registers some BUG events handlers."""
if not g_eventManager:
BugUtil.error(
"BugEventManager - BugEventManager not setup before configure()")
return
g_eventManager.setLogging(logging)
g_eventManager.setNoLogEvents(noLogEvents)
# K-Mod. Don't use register BUG events for PitBoss host.
# (Note: actually, if this is a PitBoss host, this function won't even be called
# because the BUG core will not initialize any mod components in PitBoss mode.)
if CyGame().isPitbossHost():
BugUtil.debug(
"BugEventManager - skipping event registration for PitBoss host")
return
# K-Mod end
# --------- Better BTS AI (2/2) (moved by K-Mod) -------------
# K-Mod, only enable these features if the cheat mode is enabled.
#if getChtLvl():
# advc.127: Replacing the above. ChtLvl is always 0 in multiplayer.
if getChtLvl() or (CyGame().isGameMultiPlayer()
and gc.getDefineINT("ENABLE_AUTOPLAY_MULTIPLAYER") > 0):
AIAutoPlay.AIAutoPlay(g_eventManager)
ChangePlayer.ChangePlayer(g_eventManager)
Tester.Tester(g_eventManager)
# advc.106c: Changed OnLoad handler
g_eventManager.addEventHandler("kbdEvent", g_eventManager.onKbdEvent)
g_eventManager.addEventHandler("OnLoad",
g_eventManager.resetActiveTurnAfterLoad)
g_eventManager.addEventHandler("GameStart", g_eventManager.resetActiveTurn)
g_eventManager.addEventHandler("gameUpdate", g_eventManager.onGameUpdate)
def main():
net = YoloNet.YOLONet()
criterion = Loss.MyLoss()
if input('Do you want to load network?').upper() == 'N':
optimizer = optim.Adam(net.parameters(), lr=1e-4)
train_data = Dataset.DetectionDataSet()
trainer = Trainer.Trainer(net=net,
data_set=train_data,
optimizer=optimizer,
criterion=criterion)
trainer.train()
else:
net.load_state_dict(torch.load('yolo_cpu.pt'))
test_data = Dataset.DetectionDataSet(paths='numpy_test/paths.txt',
label_dir='numpy_test/',
root_dir='test/')
tester = Tester.Tester(net=net,
test_criterion=criterion,
data_set=test_data)
tester.test()
def configure(logging=None, noLogEvents=None):
# """Sets the global event manager's logging options."""
# if g_eventManager:
# g_eventManager.setLogging(logging)
# g_eventManager.setNoLogEvents(noLogEvents)
# else:
# BugUtil.error("BugEventManager - BugEventManager not setup before configure()")
# K-Mod. I've expanded the purpose of this function.
"""Sets the global event manager's logging options. And registers some BUG events handlers."""
if not g_eventManager:
BugUtil.error(
"BugEventManager - BugEventManager not setup before configure()")
return
g_eventManager.setLogging(logging)
g_eventManager.setNoLogEvents(noLogEvents)
# K-Mod. Don't use register BUG events for PitBoss host.
# (Note: actually, if this is a PitBoss host, this function won't even be called
# because the BUG core will not initialize any mod components in PitBoss mode.)
if CyGame().isPitbossHost():
BugUtil.debug(
"BugEventManager - skipping event registration for PitBoss host")
return
# K-Mod end
# --------- Better BTS AI (2/2) (moved by K-Mod) -------------
AIAutoPlay.AIAutoPlay(g_eventManager)
ChangePlayer.ChangePlayer(g_eventManager)
Tester.Tester(g_eventManager)
# advc.106c: Changed OnLoad handler
g_eventManager.addEventHandler("kbdEvent", g_eventManager.onKbdEvent)
g_eventManager.addEventHandler("OnLoad",
g_eventManager.resetActiveTurnAfterLoad)
g_eventManager.addEventHandler("GameStart", g_eventManager.resetActiveTurn)
g_eventManager.addEventHandler("gameUpdate", g_eventManager.onGameUpdate)
def gen_big(iSeq1_len, sSeq1_len, iSeq2_len, sSeq2_len, dSeq1_len, dSeq2_len):
return Tester.BigData(mediumData=gen_medium(iSeq1_len, sSeq1_len),
n5=random.randint(1, 1000),
n6=random.randint(1, 1000),
n7=random.randint(1, 1000),
n8=random.randint(1, 1000),
n9=random.randint(1, 1000),
n10=random.randint(1, 1000),
s3=random_string(10),
s4=random_string(10),
s5=random_string(10),
s6=random_string(10),
s7=random_string(10),
s8=random_string(10),
d3=random.uniform(1, 100.0),
d4=random.uniform(1, 100.0),
d5=random.uniform(1, 100.0),
b3=random_bool(),
b4=random_bool(),
b5=random_bool(),
iSeq2=random_i_list(iSeq2_len),
sSeq2=random_s_list(sSeq2_len),
dSeq1=random_d_list(dSeq1_len),
dSeq2=random_d_list(dSeq2_len))
test_loader = torch.utils.data.DataLoader(test_set,
batch_size=1,
shuffle=False,
num_workers=0)
if name == 'densenet':
model = models.densenet161(pretrained=False)
model.classifier = nn.Sequential(
nn.Dropout(0.3), nn.Linear(model.classifier.in_features, num_classes))
model = torch.nn.DataParallel(model).module
model_path = os.path.join(workspace, 'checkpoints',
trained + str(num_classes), 'densenet.pth')
model.load_state_dict(torch.load(model_path, map_location='cuda:0'))
elif name == 'inception':
model = models.inception_v3(pretrained=True, aux_logits=False)
model.fc = nn.Linear(model.fc.in_features, num_classes)
model_path = os.path.join(workspace, 'checkpoints',
trained + str(num_classes), 'inception.pth')
model.load_state_dict(torch.load(model_path, map_location='cuda:0'))
else:
model = models.resnext50_32x4d(pretrained=True)
model.fc = nn.Sequential(nn.Dropout(0.3),
nn.Linear(model.fc.in_features, num_classes))
model_path = os.path.join(workspace, 'checkpoints',
trained + str(num_classes), 'resnext.pth')
model.load_state_dict(torch.load(model_path, map_location='cuda:0'))
test_acc, f1_test, cm, out, labels, preds = tester.test(
model, device, test_loader, test_size, name)
#utils.plot_confusion_matrix(cm, classes, name + '_' + str(num_classes) + '_' + trained, workspace, name + ' - acc: ' + str(test_acc.item()), save=False)
rms = audioop.rms(data, 2) # here's where you calculate the volume
#print("yo: ",audioop.avg(data, 2), rms)
# difference between current volume and last volume
#
diff = rms - lastRms
percentage = rms / 100
# checks if |diff| exceeds the difference_threshold
if (abs(diff) >= DIFFERENCE_THRESHOLD):
# sets the brightness value
bri = int(BRI_MODIFIER * percentage)
if (bri < 0):
bri = 0
elif (bri > 254):
bri = 254
print("Difference: ", diff, "\nBrightness: ", bri)
t.change_bri(bri)
lastRms = rms
print("* done")
stream.stop_stream()
stream.close()
p.terminate()
def target_spotted(direction):
gamefile.pointgun(direction * 1.4)
gamefile.fire()
gamefile.fire()
gamefile.fire()
test_labs[n_label_0 : ,1 ] = 1
test_cls = np.argmax(test_labs,axis=1)
print len(paths_0)
print len(paths_1)
# Image
for i,path in enumerate(paths_0 + paths_1):
print i,os.path.split(path)[-1]
img=np.asarray(Image.open(path).convert('RGB'))
imgs.append(img)
test_imgs=np.asarray(imgs)
assert len(test_labs) == len(test_imgs)
print np.shape(test_imgs)
tester=Tester.Tester(None)
tester._reconstruct_model(restore_model)
tester.n_classes =2
test_imgs = test_imgs/255.
tester.validate(test_imgs , test_labs, 60 ,0 ,False)
for i,path in enumerate(paths_0 + paths_1):
print os.path.split(path)[-1].split('_')[0] , tester.pred_all[i] ,test_cls[i]
inspect_cam(tester.sess , tester.classmap_op , tester.top_conv ,test_imgs , test_labs , 0 , tester.x_ , tester.y_ , tester.is_training , tester.logits_ )
actmap=tester.sess.run(tester.classmap_op , {tester.x_: test_imgs[0:1] , tester.is_training :False})
actmap=np.squeeze(actmap)
print np.shape(actmap)
plt.imsave('actmap_0.png' , actmap)
print ''
parser.add_argument(
'--lrde',
default=20,
type=int,
help='[net] divided the learning rate 10 by every lrde epochs')
parser.add_argument('--mom', default=0.9, type=float, help='[net] momentum')
parser.add_argument('--wd',
default=1e-3,
type=float,
help='[net] weight decay')
parser.add_argument('--lr',
default=0.01,
type=float,
help='[net] learning rate')
parser.add_argument('--ep', default=60 * 1, type=int, help='[net] epoch')
parser.add_argument('--beta',
default=0.3,
type=float,
help='[net] hyperparameter for pre-class loss weight')
parser.add_argument('--pmp',
default=pmp,
type=str,
help='[net] pre-trained model path')
args = parser.parse_args()
print args
print int((args.sr * args.msc) / args.hs)
Trer = Tester(args)
pred = Trer.run()
print("Error: missing files")
exit(-1)
classes = 3
training_data = numpy.genfromtxt(sys.argv[1], delimiter=',', dtype="|U5")
training_labels = numpy.genfromtxt(sys.argv[2], delimiter=',')
test_data = numpy.genfromtxt(sys.argv[3], delimiter=',', dtype="|U5")
training_data, test_data = Preparations.Preparations(
training_data, test_data).prepare(1)
perceptron_weights, svm_weights, pa_weights = Trainer.Trainer(
training_data, training_labels, classes).train_all_simul()
tester = Tester.Tester(test_data, perceptron_weights, svm_weights,
pa_weights)
tester.test()
if len(sys.argv) == 5 and True: # debug mode
perceptron_success_rate, svm_success_rate, pa_success_rate = tester.calculate_statistics(
numpy.genfromtxt(sys.argv[4], delimiter=','))
print("succeeds rate: per: {}, svm:{}, pa: {}".format(
perceptron_success_rate, svm_success_rate, pa_success_rate))
Grapher.Grapher(training_data, training_labels, test_data,
numpy.genfromtxt(sys.argv[4]),
classes).perceptron_graph()
else: # testing mode
tester.test()
tests["しんおさか"] = "shin'osaka"
tests["ネイセン"] = "NEISEN'"
tests["てゐ"] = "tewi"
tests["ハニュー"] = "HANYU-"
for x in tests:
result = JapaneseTools.Conversion.convert(x, sourcetype="k", targettype="r")
if result != tests[x]: return Tester.Result(False, "Failed on '" + x + "'")
return Tester.Result(True, "All Kana to Romaji worked properly")
#TODO : Make n/n' more intelligent in the k to r converter
#print(JapaneseTools.Conversion.convert("しんぶん", targettype="r"))
#print(JapaneseTools.Conversion.convert("ハニュー", targettype="r"))
t = Tester.Tester()
t.addTest("Romaji To Kana", "Tests Romaji to Kana conversion", rtok)
t.addTest("Type Detection", "Tests the capacity to determine the type of a string", getType)
t.addTest("Kana To Romaji", "Tests Kana to Romaji conversion", ktor)
Tester.makeGUI(t)
t.doAllTests()
print("t")
r = t.getAllTests()
for x in r:
print(str(x))
print("")
def commands():
gamefile.move(180)
gamefile.fire()
gamefile.stop(180)
gamefile.fire()
gamefile.turn_left(120)
gamefile.fire()
gamefile.turn_right(120)
gamefile.fire()
gamefile.turn_right(120)
gamefile.fire()
gamefile.turn_left(120)
gamefile.fire()
gamefile.done()
)
powershellRunner.t1(username, password)
print(
"Analyzing Process stage 1 [OK - NO ERROR]"
)
print(
"Analyzing Process stage 2: Extracts IP addresses [START]"
)
csvAnalyzer.analyzer()
print(
"Analyzing Process stage 2: [OK - NO ERROR]"
)
print(
"Analyzing Process stage 3: Ip Analyzing - Finding Ips GeoLocation etc [START]"
)
Tester.ipAnalyzer()
print(
"Analyzing Process stage 3 [OK - NO ERROR]"
)
print(
"Analyzing Process stage 4: Ip Analyzing - Extracting rules [START]"
)
powershellRunner.t2(username, password)
print(
"Analyzing Process stage 4: [OK - NO ERROR]"
)
print(
"Analyzing Process stage 5: Logon Analyzing [START]"
)
GeoLogonalyzer.geoLogonAnalyer()
powershellRunner.t3()
def main():
workspace = os.path.abspath("../")
num_classes = 5
trained = 'last'
# test_fold = 1
data_dir = os.path.join(workspace, 'Datasets',
str(num_classes) + '-classes')
# ResNeXt and DenseNet
transform0 = transforms.Compose([
transforms.Resize(224),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
# Inception
transform1 = transforms.Compose([
transforms.Resize(299),
transforms.CenterCrop(299),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
test_dir = os.path.join(data_dir, 'Val')
test_set_0 = datasets.ImageFolder(test_dir, transform0)
test_set_1 = datasets.ImageFolder(test_dir, transform1)
test_size = len(test_set_0)
classes = test_set_0.classes
print(classes)
test_loader0 = torch.utils.data.DataLoader(test_set_0,
batch_size=1,
shuffle=False,
num_workers=0)
test_loader1 = torch.utils.data.DataLoader(test_set_1,
batch_size=1,
shuffle=False,
num_workers=0)
loaders = [test_loader0, test_loader1, test_loader0]
model_path = os.path.join(workspace, 'checkpoints',
trained + str(num_classes))
nets = [os.path.join(model_path, x) for x in os.listdir(model_path)]
model_list, model_name = load_model(model_path, nets, num_classes)
test_accs, test_f1s, cms, outs = [], [], [], []
softmax = nn.Softmax(dim=0)
for i in range(len(model_list)):
test_acc, f1_test, cm, out, labels, _ = tester.test(
model_list[i], device, loaders[i], test_size, model_name[i])
out = [softmax(x).cpu().numpy() for x in out]
out = np.asmatrix(out)
test_accs.append(test_acc)
test_f1s.append(f1_test)
cms.append(cm)
outs.append(out)
#utils.plot_confusion_matrix(cm, classes, model_name[i] + '_' + str(num_classes), workspace, model_name[i] + ' - Acc: ' + str(round(test_acc.item(), 3)) + '%', save=False)
#utils.create_test_log(workspace, cm, test_acc, f1_test, model_name[i], test_fold)
start = datetime.datetime.now()
preds, labels, total_correct = predict_with_ensemble(
outs, utils.list_toTorch(labels))
end = datetime.datetime.now()
elapsed = end - start
# for i in range(len(preds)):
# if labels[i] == 1 and preds[i] != labels[i]:
# sample_fname, _ = test_loader0.dataset.samples[i]
# print(sample_fname.split('\\')[-1])
# print('pred', preds[i].item(),'label', labels[i].item())
total_acc = total_correct.numpy() / len(preds.numpy())
total_fscore = f1_score(labels, preds, average='micro')
total_cm = confusion_matrix(labels, preds)
print('\n[INFO] ensemble model testing complete')
print('- total accuracy = ', total_acc)
print('- total F1-score = ', total_fscore)
print('- elapsed time (microsec) = ', elapsed.microseconds)
#utils.compute_AUC_scores(labels, preds, classes)
#timestamp = str(datetime.datetime.now()).split('.')[0]
#utils.plot_confusion_matrix(total_cm, classes, 'ensamble_' + str(num_classes) + '_' + trained, workspace, 'Ensamble - acc: ' + str(round(total_acc.item(),3)) + '%', save=False)
return labels, preds, classes, total_cm
def run(self):
for i in self.data["Insurance"]:
ins = Insurance(i["insuranceId"])
ins.setType(i["type"])
ins.setInsuranceCeiling(i["ceil"])
ins.setInsuranceRate(i["rate"])
self.insurances.append(ins)
for i in self.data["Patient"]:
p = Patient(i["id"])
p.setName(i["name"])
p.setUsername(i["username"])
p.setLastName(i["family"])
p.setGender(i["gender"])
p.setPhoneNumber(i["phone"])
p.setPassword(i["pass"])
p.setAge(i["age"])
p.setDisease(i["disease"])
for ins in self.insurances:
if ins.getInsuranceId() == i["insurance"]["insuranceId"]:
p.setInsurance(ins)
p.setPrescription(i["prescription"])
self.patients.append(p)
for i in self.data["Test"]:
t = Test(i["id"])
t.setTestDescription(i["description"])
t.setTestPreCondition(i["preCondition"])
t.setBasePrice(i["basePrice"])
self.tests.append(t)
for i in self.data["TimeSlot"]:
t = TimeSlot(i["year"], i["month"], i["day"], i["start"], i["end"],
i["id"], i["status"])
self.timeSlots.append(t)
for i in self.data["Tester"]:
t = Tester(i["id"])
t.setName(i["name"])
t.setLastName(i["family"])
t.setGender(i["gender"])
t.setPhoneNumber(i["phone"])
allTimes = []
for atid in i["available_time"]:
for ts in self.timeSlots:
if atid == ts.getId():
allTimes.append(ts)
t.setAllTimes(allTimes)
self.testers.append(t)
for i in self.data["Labratory"]:
l = Labratory(i["id"])
l.setName(i["name"])
l.setAvailableTests(i["availableTests"])
l.setPriceRate(i["priceRate"])
allTesters = []
for t in i["testers"]:
for tester in self.testers:
if t == tester.getId():
allTesters.append(tester)
l.setTesters(allTesters)
self.labratories.append(l)
def gen_small(iSeq1_len):
return Tester.SmallData(n1=random.randint(1, 1000),
n2=random.randint(1, 1000),
s1=random_string(10),
b1=random_bool(),
iSeq1=random_i_list(iSeq1_len))
if outcome.upper() == 'PASS':
result = 1
else:
result = 0
output = 'Result: '
if result == 1:
output += 'PASS\n'
else:
output += 'FAIL\n'
return result, output
def test_01():
output = ('test_01\n' '\t1. Run the test\n' '\t2. Check if it passes\n')
print output
result, passed = isPassed()
output += passed
return (result, output)
def test_02():
output = ('test_01\n' '\t1. Run the test\n' '\t2. Check if it passes\n')
print output
result, passed = isPassed()
output += passed
return (result, output)
if __name__ == '__main__':
Tester.run('SampleTest')
def menu():
continueGame = Button((255, 255, 255), "Buttons/ContinueButton.png",
(0, 200))
newGame = Button((255, 255, 255), "Buttons/NewGameButton.png", (0, 250))
instructions = Button((255, 255, 255), "Buttons/InstructionButton.png",
(200, 200))
sentences = Button((255, 255, 255), "Buttons/SentencesButton.png",
(200, 250), (100, 40))
customizations = Button((255, 255, 255),
"Buttons/CustomizationsButton.png", (400, 200))
credits = Button((255, 255, 255), "Buttons/creditsButton.png", (400, 250))
quit = Button((255, 255, 255), "Buttons/QuitButton.png", (200, 450))
jetpackMode = False
jetpack = Button((255, 255, 255), "res/testButton.png", (450, 450),
(50, 50))
twoPlayerMode = False
twoPlayer = Button((255, 255, 255), "res/testButton.png", (0, 450),
(50, 50))
Tester.restart()
state = 0
while state == 0:
screen.fill([255, 255, 255])
screen.blit(background, backgroundRect)
screen.blit(continueGame.image, continueGame)
screen.blit(newGame.image, newGame)
screen.blit(instructions.image, instructions)
screen.blit(sentences.image, sentences)
screen.blit(customizations.image, customizations)
screen.blit(credits.image, credits)
screen.blit(jetpack.image, jetpack)
screen.blit(twoPlayer.image, twoPlayer)
screen.blit(quit.image, quit)
pygame.display.update()
for event in pygame.event.get():
if event.type == QUIT or (event.type == KEYDOWN
and event.key == K_ESCAPE):
sys.exit("quit game")
if event.type == MOUSEBUTTONDOWN:
loc = pygame.mouse.get_pos()
if (continueGame.clicked(loc[0], loc[1])):
Tester.setBoatImage(Customizations.getSelectedBoat())
Tester.resume()
elif (newGame.clicked(loc[0], loc[1])):
Tester.setSentenceFile(SentenceSelector.getSelected())
Tester.restart()
Tester.setBoatImage(Customizations.getSelectedBoat())
Tester.resume()
elif (instructions.clicked(loc[0], loc[1])):
print("instruction menu")
Instructions.load()
elif (sentences.clicked(loc[0], loc[1])):
SentenceSelector.load()
pass
elif (customizations.clicked(loc[0], loc[1])):
Customizations.load()
pass
elif (credits.clicked(loc[0], loc[1])):
Credits.load()
pass
elif (jetpack.clicked(loc[0], loc[1])):
jetpackMode = not jetpackMode
print "Jetpack mode: " + str(jetpackMode)
Tester.setJetpackMode(jetpackMode)
elif (twoPlayer.clicked(loc[0], loc[1])):
twoPlayerMode = not twoPlayerMode
print "Two player mode: " + str(twoPlayerMode)
Tester.setTwoPlayerMode(twoPlayerMode)
elif (quit.clicked(loc[0], loc[1])):
return
def test_01():
testName = 'Camera Connection'
output = (testName + '\n'
'\t1. Connect the camera via USB\n'
'\t2. Verify log includes "Camera Connected!" message\n'
'\t3. Disconnect the camera\n'
'\t4. Verify log includes "Camera Disconnected!" message\n')
print output
result, passed = isPassed()
output += passed
return (result, output)
def test_02():
testName = 'Downloaded Images stay on Camera'
output = (testName + '\n'
'\t1. Connect the camera via USB\n'
'\t2. Open up the directory specified in the config file\n'
'\t3. Take a new photo\n'
'\t4. Verify photo appears in specified directory\n'
'\t5. Open up the directory the filesystem gives the camera\n'
'\t6. Verify photo appears in camera memory\n')
print output
result, passed = isPassed()
output += passed
return (result, output)
if __name__ == '__main__':
Tester.run('ReaderTest')
def test_01():
testName = 'Camera Connection'
output = (testName + '\n'
'\t1. Connect the camera via USB\n'
'\t2. Verify log includes "Camera Connected!" message\n'
'\t3. Disconnect the camera\n'
'\t4. Verify log includes "Camera Disconnected!" message\n')
print output
result, passed = isPassed()
output += passed
return (result, output)
def test_02():
testName = 'Downloaded Images stay on Camera'
output = (testName + '\n'
'\t1. Connect the camera via USB\n'
'\t2. Open up the directory specified in the config file\n'
'\t3. Take a new photo\n'
'\t4. Verify photo appears in specified directory\n'
'\t5. Open up the directory the filesystem gives the camera\n'
'\t6. Verify photo appears in camera memory\n')
print output
result, passed = isPassed()
output += passed
return (result, output)
if __name__ == '__main__':
Tester.run('ReaderTest')
else:
result = 0
output = 'Result: '
if result == 1:
output += 'PASS\n'
else:
output += 'FAIL\n'
return result, output
def test_01():
output = ('test_01\n'
'\t1. Run the test\n'
'\t2. Check if it passes\n')
print output
result, passed = isPassed()
output += passed
return (result, output)
def test_02():
output = ('test_01\n'
'\t1. Run the test\n'
'\t2. Check if it passes\n')
print output
result, passed = isPassed()
output += passed
return (result, output)
if __name__ == '__main__':
Tester.run('SampleTest')
import Tester
import csv
import json
typetester = Tester.Tester()
lineswithpoints = {}
longeststrings = {}
linespecialchars = {}
outputdictlist = []
text = typetester.retrieveText()
text = typetester.addMarkers(text)
text = text.replace("\n", " ")
lines = text.split("<>")
longestwords = typetester.longestWords(lines, longeststrings)
lineratings = typetester.rateSpecialChars(lines, linespecialchars)
for line in lines:
line.rstrip("\n")
linedifficulty = typetester.calculateLineComplexity(
line, longeststrings, linespecialchars)
lineswithpoints[line] = linedifficulty
with open("Difficulties.json", 'w') as outfile:
for item in lineswithpoints.items():
outputdict = {
} #Creates a new dictionary for each tuple because in the test file there is a dictionary for each line and its difficulty
outputdict["text"] = item[
0] #Stores the line under the "text" key ready for uploading to the database
outputdict["difficulty"] = item[
1] #Stores the line's difficulty under the "difficulty" key ready for uploading to the database
outputdictlist.append(
outputdict
