def update(self, dt):
super().update(dt)
if self.alive:
self.barrel.baseX, self.barrel.baseY = functions.convert(
(self.body.position[0], self.body.position[1]))
self.barrel.endX = self.barrel.baseX - (92 / 2) * math.cos(
self.barrel.currAngle)
self.barrel.endY = self.barrel.baseY + (92 / 2) * math.sin(
self.barrel.currAngle)
characterPos = functions.convert(self.player.body.position)
self.barrel.delta_x = characterPos[0] - self.barrel.baseX
self.barrel.delta_y = characterPos[1] - self.barrel.baseY
self.barrel.currAngle = math.pi - (
math.atan2(self.barrel.delta_y, self.barrel.delta_x) +
(20 * (math.pi / 180)))
if self.barrel.cooldown <= 0:
self.barrel.createProjectile(0)
self.barrel.cooldown = random.uniform(2, 2.75)
dir_path = os.path.dirname(os.path.realpath(__file__))
gunSound = pygame.mixer.Sound(
os.path.join(dir_path, "sounds/sound_effects/Gun8.wav"))
gunSound.set_volume(0.15)
pygame.mixer.Channel(2).play(gunSound)
self.barrel.cooldown -= dt
else:
self.remove()
def main():
path = sys.argv[1]
with open(path) as f:
config = json.load(f)
# unzip the zip file
zip_ref = zipfile.ZipFile(config['zip'], 'r')
zip_ref.extractall(config['src'])
zip_ref.close()
if os.path.exists(config['src']):
functions.convert(config['src'], config['des'])
def __init__(self,
screen,
space,
entities,
pos,
speed,
parentVelocity,
angle,
radius,
mass,
friendly=False):
self.mass = mass
self.radius = radius
super().__init__(screen, space, entities)
self.body = pymunk.Body(self.mass,
pymunk.moment_for_circle(self.mass, 0, radius))
self.body.entity_ref = self
self.body.position = functions.convert(pos)
self.body.velocity = (speed * math.cos(angle),
parentVelocity[1] - speed * math.sin(angle))
self.shape = pymunk.Circle(self.body, radius)
if friendly:
self.shape.collision_type = 1
self.image = playerBulletImg
else:
self.shape.collision_type = 2
self.image = pygame.transform.scale(enemyBulletImg,
(radius * 2, radius * 2))
self.space = space
self.space.add(self.body, self.shape)
def update(self, dt):
#if pygame.key.get_pressed()[pygame.K_s]:
# if self.currAngle < self.maxAngle:
# self.currAngle += math.pi / 48
#if pygame.key.get_pressed()[pygame.K_w]:
# if self.currAngle > self.minAngle:
# self.currAngle -= math.pi / 48
self.centreX, self.centreY = functions.convert(
(self.parent.body.position[0] - 4,
self.parent.body.position[1] + 24))
mouseX, mouseY = pygame.mouse.get_pos()
try:
angle = math.atan(
(mouseY - self.centreY) / (mouseX - self.centreX))
if angle >= self.minAngle and angle <= self.maxAngle:
self.currAngle = angle
elif angle < self.minAngle:
self.currAngle = self.minAngle
else:
self.currAngle = self.maxAngle
except:
angle = math.pi / 2
if pygame.mouse.get_pressed()[0] and self.cooldown <= 0:
self.createProjectile()
self.cooldown = 0.25
self.cooldown -= dt
self.endX = self.centreX + self.length * math.cos(self.currAngle +
0.40489)
self.endY = self.centreY + self.length * math.sin(self.currAngle +
0.40489)
def read_score(file,gene_convert):
score_pair=dict()
list_pair=[]
f=open(file,'r')
for line in f:
line=line.rstrip().split("\t")
gene1=functions.convert(line[0],gene_convert)
gene2=functions.convert(line[1],gene_convert)
pair=gene1+":"+gene2
score=float(line[2])
if gene1==gene2: continue
score_pair[pair]=score
list_pair.append(pair)
f.close()
return score_pair,list_pair
def read_score(file, gene_convert):
score_pair = dict()
list_pair = []
f = open(file, 'r')
for line in f:
line = line.rstrip().split("\t")
gene1 = functions.convert(line[0], gene_convert)
gene2 = functions.convert(line[1], gene_convert)
pair = gene1 + ":" + gene2
score = float(line[2])
if gene1 == gene2: continue
score_pair[pair] = score
list_pair.append(pair)
f.close()
return score_pair, list_pair
def formHistogram(outputFile, listOfDirs):
global maximum, minimum, numBins, volts, eSqH, binSize, listofBins, shifts
fs = None
listOfBins = functions.createZeroedList(numBins)
print("Number of files opened: " + str(len(listOfDirs)))
print("Min = " + str(minimum) + " Max = " + str(maximum))
binSize = (maximum - minimum) / (numBins - 1)
print("BinSize = " + str(binSize))
#go through file sorting data
for i in range(0, len(listOfDirs), 1):
success = True
try:
fs = open(listOfDirs[i], "r")
#print("Successfully opened "+listOfDirs[i])
except:
print("FAILED to open " + listOfDirs[i])
success = False
if success == True:
#read file
for line in fs:
cell = line.split(",")
cell[4] = functions.convert(float(cell[4]), volts,
eSqH) - shifts[i]
if cell[4] < 14 and cell[4] > 0:
listOfBins[int(float(cell[4] - minimum) / (binSize))] += 1
fs.close()
print("Finished.")
#Estimate peaks
baseAvg = functions.findBaselineAvg(listOfBins, numBins)
peakData = functions.findpeaks(listOfBins, numBins, binSize, baseAvg)
print("Calculating peaks...")
peakXVal = []
for peak in peakData:
peakXVal.append(peak * binSize)
print("Peaks found at ")
print(peakXVal)
#write results to a file
fs = open(outputFile, "w")
j = 0
halfBin = binSize / 2
for i in range(0, numBins, 1):
try:
if peakData[j] == i:
fs.write((str((i * binSize) + minimum + halfBin)) + "," +
str(listOfBins[i]) + ",10000," + str(baseAvg[i]) +
"\n")
j += 1
else:
fs.write((str((i * binSize) + minimum + halfBin)) + "," +
str(listOfBins[i]) + ",0," + str(baseAvg[i]) + "\n")
except:
fs.write((str((i * binSize) + minimum + halfBin)) + "," +
str(listOfBins[i]) + ",0," + str(baseAvg[i]) + "\n")
print("Output File created.")
fs.close()
def read_score(file,gene_convert,sem_sim):
score_pair=dict()
f=open(file,'r')
for line in f:
line=line.rstrip().split("\t")
gene1=functions.convert(line[0],gene_convert)
gene2=functions.convert(line[1],gene_convert)
pair=gene1+":"+gene2
score=float(line[2])
if gene1==gene2: continue
if sem_sim.has_key(gene1) and sem_sim[gene1].has_key(gene2):
score_pair[pair]=score
if sem_sim.has_key(gene2) and sem_sim[gene2].has_key(gene1):
score_pair[pair]=score
f.close()
return score_pair
def read_score(file, gene_convert, sem_sim):
score_pair = dict()
f = open(file, 'r')
for line in f:
line = line.rstrip().split("\t")
gene1 = functions.convert(line[0], gene_convert)
gene2 = functions.convert(line[1], gene_convert)
pair = gene1 + ":" + gene2
score = float(line[2])
if gene1 == gene2: continue
if sem_sim.has_key(gene1) and sem_sim[gene1].has_key(gene2):
score_pair[pair] = score
if sem_sim.has_key(gene2) and sem_sim[gene2].has_key(gene1):
score_pair[pair] = score
f.close()
return score_pair
def main():
create_database(['104', '732'])
request = YandexApiRequest(Request.GET_STOP_INFO, '732')
# request = YandexApiRequest(Request.GET_LINE, '732') # TODO doesnt work
request.run()
print(convert(request.obtained_data))
def formHistogram(outputFile, listOfDirs):
global maximum, minimum, numBins, volts, eSqH, binSize, listofBins, shifts
fs = None
listOfBins = functions.createZeroedList(numBins)
print("Number of files opened: "+str(len(listOfDirs)))
print("Min = "+str(minimum)+" Max = "+str(maximum))
binSize = (maximum-minimum)/(numBins-1)
print("BinSize = "+str(binSize))
#go through file sorting data
for i in range(0, len(listOfDirs), 1):
success = True
try:
fs = open(listOfDirs[i], "r")
#print("Successfully opened "+listOfDirs[i])
except:
print("FAILED to open "+listOfDirs[i])
success = False
if success == True:
#read file
for line in fs:
cell = line.split(",")
cell[4] = functions.convert(float(cell[4]), volts, eSqH) - shifts[i]
if cell[4] < 14 and cell[4] > 0:
listOfBins[int(float(cell[4]-minimum)/(binSize))] += 1
fs.close()
print("Finished.")
#Estimate peaks
baseAvg = functions.findBaselineAvg(listOfBins, numBins)
peakData = functions.findpeaks(listOfBins, numBins, binSize, baseAvg)
print("Calculating peaks...")
peakXVal = []
for peak in peakData:
peakXVal.append(peak*binSize)
print("Peaks found at ")
print(peakXVal)
#write results to a file
fs = open(outputFile, "w")
j = 0
halfBin = binSize/2
for i in range(0, numBins, 1):
try:
if peakData[j] == i:
fs.write((str((i*binSize)+minimum+halfBin))+","+str(listOfBins[i])+",10000,"+str(baseAvg[i])+"\n")
j+=1
else:
fs.write((str((i*binSize)+minimum+halfBin))+","+str(listOfBins[i])+",0,"+str(baseAvg[i])+"\n")
except:
fs.write((str((i*binSize)+minimum+halfBin))+","+str(listOfBins[i])+",0,"+str(baseAvg[i])+"\n")
print("Output File created.")
fs.close()
def printList(self):
initial = 0
for list in self.attrName:
self.attributes[initial] = func.convert(self.attributes[initial],
"float")
vartype = type(self.attributes[initial]).__name__
print("{:15}{}".format(list, self.attributes[initial]
) if vartype == "str" else "{:15}{:.2f}".
format(list, self.attributes[initial]))
initial += 1
def draw(self):
x, y = functions.convert(self.body.position)
if self.thrusting:
self.imageIndex += 0.5
self.screen.blit(self.sheetThrust[int(self.imageIndex % 4)],
(x - 100, y - 120))
functions.rotate(self.screen, self.armImg, (x - 4, y - 24),
(96, 96), -math.degrees(self.target.currAngle))
else:
self.imageIndex += 0.25
self.screen.blit(self.sheetIdle[int(self.imageIndex % 2)],
(x - 100, y - 120))
functions.rotate(self.screen, self.armImg, (x - 4, y - 24),
(96, 96), -math.degrees(self.target.currAngle))
self.screen.blit(self.shldrImg, (x - 100, y - 120))
def findMinAndMax(
): # function to find the minimum and maximum across all of the data, and find how much each file needs to be shifted to reach 0
global minimum, maximum, shifts
fs = None
print("Reading in data...")
# read through files and find minimum/maximum data
for i, directory in enumerate(listOfDirs):
data = []
success = True
try:
fs = open(directory, "r")
#print("Successfully opened "+directory)
except:
print("FAILED to open " + directory)
success = False
if success == True:
#read file
for line in fs:
cell = line.split(",")
cell[4] = functions.convert(float(cell[4]), volts, eSqH)
if cell[4] < 0.3:
data.append(cell[4])
if float(cell[4]) > maximum:
maximum = float(cell[4])
elif float(cell[4]) < minimum:
minimum = float(cell[4])
if len(data) > 20:
avgData = 0
for item in data:
avgData += item
avgData = avgData / len(data)
shifts.append(avgData)
else:
shifts.append(-10000)
avgShift = 0
count = 0
for item in shifts:
if item != -10000:
avgShift += item
count += 1
avgShift = avgShift / count
for i, item in enumerate(shifts):
if item == -10000:
shifts[i] = avgShift
def findMinAndMax(): # function to find the minimum and maximum across all of the data, and find how much each file needs to be shifted to reach 0
global minimum, maximum, shifts
fs = None
print("Reading in data...")
# read through files and find minimum/maximum data
for i, directory in enumerate(listOfDirs):
data = []
success = True
try:
fs = open(directory, "r")
#print("Successfully opened "+directory)
except:
print("FAILED to open "+directory)
success = False
if success == True:
#read file
for line in fs:
cell = line.split(",")
cell[4] = functions.convert(float(cell[4]), volts, eSqH)
if cell[4] < 0.3:
data.append(cell[4])
if float(cell[4]) > maximum:
maximum = float(cell[4])
elif float(cell[4]) < minimum:
minimum = float(cell[4])
if len(data) > 20:
avgData = 0
for item in data:
avgData += item
avgData = avgData/len(data)
shifts.append(avgData)
else:
shifts.append(-10000)
avgShift = 0
count = 0
for item in shifts:
if item != -10000:
avgShift += item
count += 1
avgShift = avgShift/count
for i, item in enumerate(shifts):
if item == -10000:
shifts[i] = avgShift
def throws(self):
# total sum of 3 throws is going to deducted
score = 0
# total sum of 3 throws (temporary)
pre_score = 0
print(f'Ход {self.name}:')
if checkout(self.score_remain - pre_score):
print(
f'Остаток: {self.score_remain} = {checkout(self.score_remain)} \n'
)
else:
print(f'Остаток: {self.score_remain}\n')
for attempt in range(3):
# receives a list of [k, v] where k - multiply and v - sector
throw = convert(input(f'Дротик #{attempt + 1}: '))
# throw score
throw_score = throw[0] * throw[1]
pre_score += throw_score
if not self.is_end_of_turn(pre_score):
if checkout(self.score_remain - pre_score):
print(
f'Остаток: {self.score_remain - pre_score} = {checkout(self.score_remain - pre_score)}\n'
)
else:
print(f'Остаток: {self.score_remain - pre_score}\n')
score = pre_score if attempt == 2 else 0
else:
if self.is_checkout(throw, (self.score_remain - pre_score)):
print('Победа!')
score = pre_score
self.score_remain -= score
self.end = True
break
print(f'Перебор!\nОстаток: {self.score_remain}\n')
break
self.score_remain -= score
if not self.end:
print(f'Игрок {self.name} выбил {score}\n\n')
def handle_source(json_data):
text = fs.convert((json_data['message'].encode()))
socketio.emit('echo', {'echo': str(text)})
count += 1
avgShift = avgShift / count
for i, item in enumerate(shifts):
if item == -10000:
shifts[i] = avgShift
findMinAndMax()
print("Reading in Data...")
data = []
for i, directory in enumerate(listOfDirs):
fs = open(directory, "r")
for line in fs:
column = line.split(",")
data.append(
functions.convert(float(column[4]), volts, eSqH) -
shifts[i]) # <-----TOM make this one point to the correct column
fs.close()
binSize = (maximum - minimum) / (numBins - 1)
print("Sorting data...")
for item in data:
if item < 14 and item > 0:
listOfBins[int(float(item - minimum) / binSize)] += 1
print("Writing data...")
fs = open("somefile.csv", "w")
halfBin = binSize / 2
for i, item in enumerate(listOfBins):
fs.write(str((i * binSize) + halfBin + minimum) + "," + str(item) + "\n")
print("Finished.")
fs.close()
def formHistogram(outputFile, listOfDirs):
global maximum, minimum, numBins, volts, eSqH, binSize, listofBins, listOfMinimums
global prevcell, listdiff, listdata,value, runningavg, base, markers,k, total, count2, l, kref
fs = None
listOfBins = functions.createZeroedList(numBins)
listOfMinimums = functions.createZeroedList(len(listOfDirs))
print("Reading in data...")
# read through files and fine minimum/maximum data
count = 0
for directory in listOfDirs:
success = True
try:
fs = open(directory, "r")
#print("Successfully opened "+directory)
except:
print("FAILED to open "+directory)
success = False
if success == True:
#read file
minTemp = 0
maxTemp = 0
for line in fs:
cell = line.split(",")
cell[4] = functions.convert(float(cell[4]), volts, eSqH)
if float(cell[4]) > maximum:
maximum = float(cell[4])
elif float(cell[4]) < minimum:
minimum = float(cell[4])
#if float(cell[4]) > maxTemp:
# maxTemp = float(cell[4])
#elif float(cell[4]) < minTemp:
# minTemp = float(cell[4])
#listOfMinimums[count] = abs(minTemp)
count = count + 1
print("Number of files opened: "+str(len(listOfDirs)))
#print("Min = "+str(minimum)+" Max = "+str(maximum))
binSize = (maximum-minimum)/(numBins-1)
print("BinSize = "+str(binSize))
#go through file sorting data
for i in range(0, len(listOfDirs), 1):
success = True
try:
fs = open(listOfDirs[i], "r")
#print("Successfully opened "+listOfDirs[i])
except:
print("FAILED to open "+listOfDirs[i])
success = False
if success == True:
#read file
listdiff = []
runningavg = []
markers = []
listData = []
for x,line in enumerate(fs):
listData.append(cell[4])
cell = line.split(",")
cell[4] = functions.convert(float(cell[4]), volts, eSqH)
#cell[4] = float(cell[4]) + float(listOfMinimums[i])
#listOfBins[int(float(cell[4]+abs(minimum))/(binSize))] += 1
if x >= stepsize:
listdiff.append((cell[4] - listData[x-stepsize]) / ( stepsize*4*10e-7 ) )
#fopen=open("c:\\Users\\Thomas\\Desktop\\QCNLab\\qcnlab\\New folder\\outputFile_" + str(i)+ ".csv","w")
count=len(listdiff)
for x in range (3,count-2,1):
#fopen.write(str((listdiff[x-2]+listdiff[x-1]+listdiff[x]+listdiff[x+1]+listdiff[x+2])/5) + "\n")
runningavg.append((listdiff[x-2]+listdiff[x-1]+listdiff[x]+listdiff[x+1]+listdiff[x+2])/5)
#functions.writeList(listdiff,fopen)
avg = sum(runningavg)/count
base = ((avg)+min(runningavg))/2
for x in range (1,count-5,1):
if runningavg[x]<base:
markers.append(x)
count=len(markers)
k=markers[0]+1
for x in range (0,count,1):
if k!=markers[x]-1:
count2 = 0
total = 0
#print("HI")
for l in range (markers[x-1]+1,markers[x],1):
count2 = count2 + 1
total = listData[l]+total
#print("HELLO")
if count2>0:
#print("HOWDY")
value.append(total/count2)
k=markers[x]
#fopen.close()
fs.close()
minimum = 0
maximum = max(value)
print(maximum)
binSize = (maximum)/(numBins-1)
count=len(value)
print(count)
print(binSize)
for i in range (0, count, 1):
if value[i]>=0:
listOfBins[int(value[i]/binSize)] += 1
#print(int(value[i]/binsize))
else:
print("ERROR")
#fopen=open("c:\\Users\\Thomas\\Desktop\\QCNLab\\qcnlab\\New folder\\outputFile.csv","w")
fopen = open(outputFile, "w")
for i in range (0,numBins,1):
fopen.write(str((i*binSize)+(binSize/2))+","+str(listOfBins[i])+"\n")
fopen.close()
print("Finished.")
'''
#Estimate peaks
baseAvg = functions.findBaselineAvg(listOfBins, numBins)
peakData = functions.findpeaks(listOfBins, numBins, binSize, baseAvg)
print("Calculating peaks...")
peakXVal = []
for peak in peakData:
peakXVal.append(peak*binSize)
print("Peaks found at ")
print(peakXVal)
'''
'''
def formHistogram(outputFile, listOfDirs):
global maximum, minimum, numBins, volts, eSqH, binSize, listofBins, condData, timeData, stripSize, numSteps, steps, shifts, numEmpty
listOfBins = functions.createZeroedList(numBins)
print("Number of files opened: " + str(len(listOfDirs)))
print("Min = " + str(minimum) + " Max = " + str(maximum))
binSize = (maximum - minimum) / (numBins - 1)
print("BinSize = " + str(binSize))
print("Finding steps...")
#go through file sorting data
for i in range(0, len(listOfDirs), 1):
success = True
try:
fs = open(listOfDirs[i], "r")
#print("Successfully opened "+directory)
except:
print("FAILED to open " + listOfDirs[i])
success = False
if success == True:
#read file
Empty = True
for line in fs:
cell = line.split(",")
condData.append(
functions.convert(float(cell[4]), volts, eSqH) - shifts[i])
timeData.append(float(cell[3]))
marker = 0
while marker < stripSize:
steps = []
while marker < (2500 - stripSize):
stripDataX = []
stripDataY = []
for i in range(marker, marker + stripSize, 1):
stripDataX.append(timeData[i])
stripDataY.append(condData[i])
b = functions.regressionFindB(stripDataX, stripDataY)
sumY = 0
if abs(b) < 10000: # Fudge Factor
numSteps += 1
for item in stripDataY:
sumY += item
sumY = sumY / stripSize
steps.append(sumY)
if len(steps) > 0:
steps = functions.removeDuplicates(steps)
steps = functions.removeMinMaxValues(
steps, max(condData))
Empty = False
marker += stripSize
for item in steps:
listOfBins[int(float(item + abs(minimum)) /
(binSize))] += 1
marker += 1
if Empty == True:
numEmpty += 1
condData = []
timeData = []
fs.close()
print("Number of files where steps could not be detected: " +
str(numEmpty))
#Estimate peaks
baseAvg = functions.findBaselineAvg(listOfBins, numBins)
peakData = functions.findpeaks(listOfBins, numBins, binSize, baseAvg)
print("Calculating peaks...")
peakXVal = []
for peak in peakData:
peakXVal.append(peak * binSize)
print("Peaks found at ")
print(peakXVal)
#write results to a file
fs = open(outputFile, "w")
j = 0
halfBin = binSize / 2
for i in range(0, numBins, 1):
try:
if peakData[j] == i:
fs.write((str((i * binSize) - abs(minimum) + halfBin)) + "," +
str(listOfBins[i]) + ",20," + str(baseAvg[i]) + "\n")
j += 1
else:
fs.write((str((i * binSize) - abs(minimum) + halfBin)) + "," +
str(listOfBins[i]) + ",0," + str(baseAvg[i]) + "\n")
except:
fs.write((str((i * binSize) - abs(minimum) + halfBin)) + "," +
str(listOfBins[i]) + ",0," + str(baseAvg[i]) + "\n")
print("Output File created.")
fs.close()
def formHistogram(outputFile, listOfDirs):
global maximum, minimum, numBins, volts, eSqH, binSize, listofBins, condData, steps, timeData, shifts, numEmpty
fs = None
listOfBins = functions.createZeroedList(numBins)
print("Number of files opened: " + str(len(listOfDirs)))
print("Min = " + str(minimum) + " Max = " + str(maximum))
binSize = (maximum - minimum) / (numBins - 1)
print("BinSize = " + str(binSize))
#go through file sorting data
for i in range(0, len(listOfDirs), 1):
success = True
try:
fs = open(listOfDirs[i], "r")
#print("Successfully opened "+directory)
except:
print("FAILED to open " + listOfDirs[i])
success = False
if success == True:
#read file
for line in fs:
cell = line.split(",")
condData.append(functions.convert(float(cell[4]), volts, eSqH))
timeData.append(float(cell[3]))
recursiveStepSearch(0, len(condData) - 1, condData, timeData)
steps = functions.removeDuplicates(steps)
steps = functions.removeMinMaxValues(steps, max(condData))
if len(steps) == 0:
numEmpty += 1
for item in steps:
listOfBins[int(float(item + abs(minimum)) / (binSize))] += 1
steps = []
condData = []
timeData = []
fs.close()
print("Number of files where steps could not be detected: " +
str(numEmpty))
#Estimate peaks
baseAvg = functions.findBaselineAvg(listOfBins, numBins)
peakData = functions.findpeaks(listOfBins, numBins, binSize, baseAvg)
print("Calculating peaks...")
peakXVal = []
for peak in peakData:
peakXVal.append(peak * binSize)
print("Peaks found at ")
print(peakXVal)
#write results to a file
fs = open(outputFile, "w")
j = 0
halfBin = binSize / 2
for i in range(0, numBins, 1):
try:
if peakData[j] == i:
fs.write((str((i * binSize) - abs(minimum) + halfBin)) + "," +
str(listOfBins[i]) + ",20," + str(baseAvg[i]) + "\n")
j += 1
else:
fs.write((str((i * binSize) - abs(minimum) + halfBin)) + "," +
str(listOfBins[i]) + ",0," + str(baseAvg[i]) + "\n")
except:
fs.write((str((i * binSize) - abs(minimum) + halfBin)) + "," +
str(listOfBins[i]) + ",0," + str(baseAvg[i]) + "\n")
print("Output File created.")
fs.close()
def formHistogram(outputFile, listOfDirs):
valueList = []
stepListVoltageValues = []
fs = None
z = 3
something = int(z / 3)
j = 0
Top = 0
Bottom = 0
global numBins, volts, eSqH, binSize, listOfBins
listOfBins = functions.createZeroedList(numBins)
for count, directory in enumerate(listOfDirs):
success = True
try:
fs = open(directory, "r")
#print("Successfully opened "+directory)
except:
print("FAILED to open " + directory)
success = False
if success == True:
valueList = []
for line in fs:
cell = line.split(",")
print(cell[4] + " " + str(float(cell[4])))
cell[4] = functions.convert(float(cell[4]), volts,
eSqH) - shifts[count]
if cell[4] > 0.3 and cell[4] < 14:
valueList.append(float(cell[4]))
for i in range(z, len(valueList),
1): #Start loop for at top value search
Top = None
if ((valueList[i] - valueList[i - something]) >
((valueList[i - something] -
valueList[i - 2 * something]))):
Bottom = None
Top = valueList[i - something]
j = i - something #store index for last value assessed
for z in range(
j, len(valueList), 1
): #now carry on only looking for a much smaller increment size to give bottom of step
if (((valueList[z] - valueList[z - something]) <=
((valueList[z - something] -
valueList[z - 2 * something])) * (1 - 0))):
Bottom = valueList[z]
#store last index assessed
break #break out of loop so we can calc the step
try:
if (((Top - Bottom) >= 0.2 *
eSqH)): #filtering out the accidental mini steps
stepListVoltageValues.append(Top)
else:
#print("false step between " + str(j) +"-" + str(i)) #explaining where we miscalc a step to then assess for debug
print("nboithing")
except:
print("Nout")
#minimum = min(stepListVoltageValues) #analyzing for the max and min range of step sizes,
#maximum = max(stepListVoltageValues)
binSize = (maximum - minimum) / (
numBins - 1
) #To be done after data set has been full analyzed, so once while loop is ended
print("min = " + str(minimum) + " max = " + str(maximum) +
" BinSize = " + str(binSize))
print(functions.printList(stepListVoltageValues))
for i in range(0, len(stepListVoltageValues), 1):
listOfBins[int(
(stepListVoltageValues[i] - minimum) / binSize)] += 1
#print(int((stepListVoltageValues[i]-minimum)/binSize))
fs = open(outputFile, "w") #make the histo
halfBin = binSize / 2
for i, item in enumerate(listOfBins):
fs.write((str((i * binSize) + halfBin)) + "," + str(item) + "\n")
print("Output File created.")
fs.close()
def formHistogram(outputFile, listOfDirs):
valueList = []
stepListVoltageValues = []
fs = None
z=3
something = int(z/3)
j=0
Top=0
Bottom=0
global numBins, volts, eSqH, binSize, listOfBins
listOfBins = functions.createZeroedList(numBins)
for count, directory in enumerate(listOfDirs):
success = True
try:
fs = open(directory, "r")
#print("Successfully opened "+directory)
except:
print("FAILED to open "+directory)
success = False
if success == True:
valueList = []
for line in fs:
cell = line.split(",")
print(cell[4]+" "+str(float(cell[4])))
cell[4] = functions.convert(float(cell[4]), volts, eSqH) - shifts[count]
if cell[4] > 0.3 and cell[4] < 14:
valueList.append(float(cell[4]))
for i in range (z, len(valueList), 1):#Start loop for at top value search
Top=None
if((valueList[i]-valueList[i-something])>((valueList[i-something]-valueList[i-2*something]))):
Bottom = None
Top = valueList[i-something]
j=i-something #store index for last value assessed
for z in range (j, len(valueList), 1): #now carry on only looking for a much smaller increment size to give bottom of step
if (((valueList[z]-valueList[z-something])<=((valueList[z-something]-valueList[z-2*something]))*(1-0))):
Bottom = valueList[z]
#store last index assessed
break #break out of loop so we can calc the step
try:
if(((Top-Bottom) >= 0.2*eSqH)): #filtering out the accidental mini steps
stepListVoltageValues.append(Top)
else:
#print("false step between " + str(j) +"-" + str(i)) #explaining where we miscalc a step to then assess for debug
print("nboithing")
except:
print("Nout")
#minimum = min(stepListVoltageValues) #analyzing for the max and min range of step sizes,
#maximum = max(stepListVoltageValues)
binSize = (maximum - minimum)/(numBins-1) #To be done after data set has been full analyzed, so once while loop is ended
print("min = "+str(minimum)+" max = "+str(maximum)+" BinSize = "+str(binSize))
print(functions.printList(stepListVoltageValues))
for i in range (0, len(stepListVoltageValues), 1):
listOfBins[int((stepListVoltageValues[i]-minimum)/binSize)] +=1
#print(int((stepListVoltageValues[i]-minimum)/binSize))
fs = open(outputFile, "w") #make the histo
halfBin = binSize/2
for i, item in enumerate(listOfBins):
fs.write((str((i*binSize)+halfBin))+","+str(item)+"\n")
print("Output File created.")
fs.close()
def draw(self):
functions.rotate(self.screen, self.texture, functions.convert(self.body.position), (9, self.height//2), math.degrees(self.body.angle))
def main():
functions.clear()
if not functions.exists():
try:
functions.intro()
except KeyboardInterrupt:
functions.clear()
import sys
sys.exit(0)
functions.clear()
try:
password = getpass.getpass("Password: "******"Invalid password")
import sys
sys.exit(1)
while True:
functions.clear()
print("Vault")
print(
"Use Ctrl+C to return back to this menu, or use it here to exit.\n"
)
try:
action = input(
"1: Encrypt a file\n2: Download and encrypt a file from a URL\n3: Decrypt a file\n4: Change password\nSelect an option: "
)
except:
break
action = action.strip().lstrip()
try:
action = int(action)
except:
continue
if action not in [1, 2, 3, 4]:
continue
if action == 1:
functions.clear()
try:
path = input("Please specify a file to encrypt: ")
except KeyboardInterrupt:
continue
if functions.validate(path) and not path.endswith(".png"):
functions.clear()
try:
response = input(
"This file may be a valid image file. Convert to .png (y/n)? "
)
except KeyboardInterrupt:
continue
if response.lower().lstrip().strip() == "y":
temp = path
path = functions.convert(path)
delete = input("Delete original file (y/n)? ")
if delete.lower().lstrip().strip() == "y":
if not windows:
temp = temp.replace("\\", "")
try:
directory = os.path.dirname(os.path.realpath(temp))
os.chdir(directory)
os.remove(temp)
except:
pass
functions.clear()
try:
save = functions.encodepath(path)
if save == None:
continue
except Exception as e:
print(str(e))
try:
input("Invalid file path. Press enter to continue. ")
except KeyboardInterrupt:
continue
continue
else:
try:
input(
"File encrypted.\nEncrypted file path: {}\nPress enter to continue. "
.format(save))
except KeyboardInterrupt:
continue
continue
elif action == 2:
functions.clear()
try:
url = input("Please specify a file URL to download: ")
except KeyboardInterrupt:
continue
name = os.path.split(url)[-1]
base = os.path.dirname(os.path.realpath(__file__))
path = os.path.join(base, name)
functions.clear()
try:
r = requests.get(url, stream=True)
with open(path, 'wb') as out_file:
shutil.copyfileobj(r.raw, out_file)
except:
try:
input("Invalid URL: {}\nPress enter to continue. ".format(
url))
except KeyboardInterrupt:
continue
continue
if functions.validate(path) and not path.endswith(".png"):
functions.clear()
try:
response = input(
"This file may be a valid image file. Convert to .png (y/n)? "
)
except KeyboardInterrupt:
continue
if response.lower().lstrip().strip() == "y":
temp = path
path = functions.convert(path)
delete = input("Delete original file (y/n)? ")
if delete.lower().lstrip().strip() == "y":
if not windows:
temp = temp.replace("\\", "")
try:
directory = os.path.dirname(os.path.realpath(temp))
os.chdir(directory)
os.remove(temp)
except:
pass
functions.clear()
try:
save = functions.encodepath(path)
except:
try:
input("Invalid file path. Press enter to continue. ")
except KeyboardInterrupt:
continue
continue
else:
try:
input(
"File encrypted.\nEncrypted file path: {}\nPress enter to continue. "
.format(save))
except KeyboardInterrupt:
continue
continue
elif action == 3:
functions.clear()
try:
path = input("Please specify a .enc file to decrypt: ")
except KeyboardInterrupt:
continue
functions.clear()
try:
save = functions.decodepath(path)
except Exception as e:
print(str(e))
try:
input("Invalid file path. Press enter to continue. ")
except KeyboardInterrupt:
continue
continue
else:
try:
input(
"File decrypted.\nDecrypted file path: {}\nPress enter to continue. "
.format(save))
except KeyboardInterrupt:
continue
continue
elif action == 4:
try:
functions.clear()
current = input("Current password: "******"Passwords do not match. Press enter to continue. ")
continue
new = input("New password: "******"You will need to decrypt all files with your old password. Your new password will not be able to decrypt already encrypted files.\nRetype your password to confirm: "
)
if confirm != new:
input("Passwords do not match. Press enter to continue. ")
continue
functions.writepw(new)
functions.clear()
input("Password changed to: {}.\nPress enter to continue. ".
format(new))
except KeyboardInterrupt:
continue
functions.clear()
def formHistogram(outputFile, listOfDirs):
global maximum, minimum, numBins, volts, eSqH, binSize, listofBins, condData, steps, timeData, shifts, numEmpty
fs = None
listOfBins = functions.createZeroedList(numBins)
print("Number of files opened: "+str(len(listOfDirs)))
print("Min = "+str(minimum)+" Max = "+str(maximum))
binSize = (maximum-minimum)/(numBins-1)
print("BinSize = "+str(binSize))
#go through file sorting data
for i in range(0, len(listOfDirs), 1):
success = True
try:
fs = open(listOfDirs[i], "r")
#print("Successfully opened "+directory)
except:
print("FAILED to open "+listOfDirs[i])
success = False
if success == True:
#read file
for line in fs:
cell = line.split(",")
condData.append(functions.convert(float(cell[4]), volts, eSqH))
timeData.append(float(cell[3]))
recursiveStepSearch(0, len(condData)-1, condData, timeData)
steps = functions.removeDuplicates(steps)
steps = functions.removeMinMaxValues(steps, max(condData))
if len(steps) == 0:
numEmpty+=1
for item in steps:
listOfBins[int(float(item+abs(minimum))/(binSize))] += 1
steps = []
condData = []
timeData =[]
fs.close()
print("Number of files where steps could not be detected: "+str(numEmpty))
#Estimate peaks
baseAvg = functions.findBaselineAvg(listOfBins, numBins)
peakData = functions.findpeaks(listOfBins, numBins, binSize, baseAvg)
print("Calculating peaks...")
peakXVal = []
for peak in peakData:
peakXVal.append(peak*binSize)
print("Peaks found at ")
print(peakXVal)
#write results to a file
fs = open(outputFile, "w")
j = 0
halfBin = binSize/2
for i in range(0, numBins, 1):
try:
if peakData[j] == i:
fs.write((str((i*binSize)-abs(minimum)+halfBin))+","+str(listOfBins[i])+",20,"+str(baseAvg[i])+"\n")
j+=1
else:
fs.write((str((i*binSize)-abs(minimum)+halfBin))+","+str(listOfBins[i])+",0,"+str(baseAvg[i])+"\n")
except:
fs.write((str((i*binSize)-abs(minimum)+halfBin))+","+str(listOfBins[i])+",0,"+str(baseAvg[i])+"\n")
print("Output File created.")
fs.close()
def test_convert():
assert convert(1.00) == 'um real'
assert convert(1000.54) == 'mil reais e cinquenta e quatro centavos'
assert convert(1000.10) == 'mil reais e dez centavos'
assert convert(100000.10) == 'cem mil reais e dez centavos'
assert convert(0.10) == 'dez centavos'
assert convert(0.01) == 'um centavo'
assert convert(0.99) == 'noventa e nove centavos'
assert convert(1.00) == 'um real'
assert convert(10.00) == 'dez reais'
assert convert(
999000.01) == 'novecentos e noventa e nove mil reais e um centavo'
assert convert(100000.01) == 'cem mil reais e um centavo'
assert convert(60000.11) == 'sessenta mil reais e onze centavos'
assert convert(1000.00) == 'mil reais'
assert convert(1000000.00) == 'um milhão de reais'
assert convert(
1130200.24
) == 'um milhão cento e trinta mil duzentos reais e vinte e quatro centavos'
assert convert(5000000.00) == 'cinco milhões de reais'
assert convert(50000000.00) == 'cinquenta milhões de reais'
assert convert(53128040.32) == 'cinquenta e três milhões cento e vinte e oito mil e quarenta reais ' \
'e trinta e dois centavos'
assert convert(99999999.99) == 'noventa e nove milhões novecentos e noventa e nove mil novecentos e ' \
'noventa e nove reais e noventa e nove centavos'
assert convert(999999999.99) == 'novecentos e noventa e nove milhões novecentos e noventa e nove mil novecentos e ' \
'noventa e nove reais e noventa e nove centavos'
def formHistogram(outputFile, listOfDirs):
global maximum, minimum, numBins, volts, eSqH, binSize, listofBins, condData, timeData, stripSize, numSteps, steps, shifts, numEmpty
listOfBins = functions.createZeroedList(numBins)
print("Number of files opened: "+str(len(listOfDirs)))
print("Min = "+str(minimum)+" Max = "+str(maximum))
binSize = (maximum-minimum)/(numBins-1)
print("BinSize = "+str(binSize))
print("Finding steps...")
#go through file sorting data
for i in range(0, len(listOfDirs), 1):
success = True
try:
fs = open(listOfDirs[i], "r")
#print("Successfully opened "+directory)
except:
print("FAILED to open "+listOfDirs[i])
success = False
if success == True:
#read file
Empty = True
for line in fs:
cell = line.split(",")
condData.append(functions.convert(float(cell[4]), volts, eSqH) - shifts[i])
timeData.append(float(cell[3]))
marker = 0
while marker < stripSize:
steps = []
while marker < (2500-stripSize):
stripDataX = []
stripDataY = []
for i in range (marker, marker+stripSize, 1):
stripDataX.append(timeData[i])
stripDataY.append(condData[i])
b = functions.regressionFindB(stripDataX, stripDataY)
sumY = 0
if abs(b) < 10000: # Fudge Factor
numSteps += 1
for item in stripDataY:
sumY += item
sumY = sumY/stripSize
steps.append(sumY)
if len(steps) > 0:
steps = functions.removeDuplicates(steps)
steps = functions.removeMinMaxValues(steps, max(condData))
Empty = False
marker += stripSize
for item in steps:
listOfBins[int(float(item+abs(minimum))/(binSize))] += 1
marker += 1
if Empty == True:
numEmpty+=1
condData = []
timeData =[]
fs.close()
print("Number of files where steps could not be detected: "+str(numEmpty))
#Estimate peaks
baseAvg = functions.findBaselineAvg(listOfBins, numBins)
peakData = functions.findpeaks(listOfBins, numBins, binSize, baseAvg)
print("Calculating peaks...")
peakXVal = []
for peak in peakData:
peakXVal.append(peak*binSize)
print("Peaks found at ")
print(peakXVal)
#write results to a file
fs = open(outputFile, "w")
j = 0
halfBin = binSize/2
for i in range(0, numBins, 1):
try:
if peakData[j] == i:
fs.write((str((i*binSize)-abs(minimum)+halfBin))+","+str(listOfBins[i])+",20,"+str(baseAvg[i])+"\n")
j+=1
else:
fs.write((str((i*binSize)-abs(minimum)+halfBin))+","+str(listOfBins[i])+",0,"+str(baseAvg[i])+"\n")
except:
fs.write((str((i*binSize)-abs(minimum)+halfBin))+","+str(listOfBins[i])+",0,"+str(baseAvg[i])+"\n")
print("Output File created.")
fs.close()
# -*- coding: utf-8 -*- ########################################################################## # Project: COMP6004 - Machine learning pipeline for data analysis # File: 03-featureExtraction.py # Author: Diego Bueno - [email protected] # Date: 20/04/2021 # Description: Applying feature extraction to step03 of ML pipeline. # ########################################################################## # Maintenance # Author: # Date: # Description: A # ##########################################################################> import numpy as np import pandas as pd from functions import openfile from functions import savefile from functions import convert from sklearn.feature_selection import VarianceThreshold from sklearn.model_selection import train_test_split from sklearn.linear_model import LinearRegression from sklearn.neighbors import LocalOutlierFactor from sklearn.metrics import mean_absolute_error #calling the function to Load data pre-reading on task 1 print("\nReading the step02 file\n") db = openfile('data/step02.csv') print("\nChecking the current shape of the data:") rows, columns = db.shape
#correlations in heatmap are aligned with feature selection results: with unknown name and intervention variables being the most strongly correlated with the outcome #we need to select non-repeated features according to the scores #how many features should we select? Just try the methods with different number of features and select best? #think we can check feature importance and impact of using different features when testing classifiers, but maybe not as a pre-step given that the choice will be biased by the specific method being used and is only possible for tree based classifiers #Top features from correlation are also high in PCA importance rank #final features: keep common_name_85 and common_colour_98; keep age and bins but never use together (same for weekend and weekday_sin) #classifiers: linear SVC, random forest, logistic regression with multinomial distribution #df = func.balance_dataset(df, 'OutcomeType') #print(df['OutcomeType'].value_counts()) #OAO baseline classification #func.OAO_classif(df.drop(['AnimalID'],axis=1), 'OutcomeType') #OAA baseline classification cols = func.convert(features_select_K, 'list') X = df[cols] y = df['OutcomeType'] func.OAA_classif(X, y) #baseline classification: compare onevsone, onevsall and all&one approaches with each other and Andreia's baseline (starting with all variables and imbalanced sample); use not only accuracy but also confusion matrix and multiclass AUC #no proved best approach for imbalanced multiclass problems, depends on the dataset #gradient boosting seems promising; SVC slightly better with OAO approach; LR also not too bad; rest really bad #balancing the classes reduces the overall accuracy for all OAA models, but improves classification of smaller classes (Died and Euthanasia), since precision is simillar in both circumstancies both recall improves generally with a balanced dataset #Gradient boosting seems to be the only OAA classifier worth keeping #OAO SVC benefits highly from balancing the dataset #predict_proba to return probabilities (cross validation already included in this function); probability = True in model definition #with log-loss, it seems better to not balance the dataset #dataset unbalanced towards neutered/spayed, which is more than double 'intact' animals (might need to omit this variable); gender balance is fine
from functions import convert
print('Digite "s" para sair.')
while True:
number = input('Digite um número:\n').strip()
if number == 's':
break
print(convert(number), '\n')
def __init__(self, screen, space, entities, pos):
super().__init__(screen, space, entities)
self.pos = functions.convert((pos[0] - 96, pos[1] + 96))
self.index = 0
self.screen = screen
def draw(self):
functions.rotate(self.screen, self.bodyImg,
functions.convert(self.body.position),
(132 / 2, 192 / 2), math.degrees(self.body.angle))
self.barrel.draw()
def __str__(self):
return convert(self.dice_dict)
def draw(self):
self.screen.blit(
self.image,
functions.convert((self.body.position[0] - self.radius,
self.body.position[1] + self.radius)))
def data():
mazeArray = pfun.convert().tolist()
return jsonify({'results': mazeArray})
os.chdir(current_path)
# Check if output directory exists
if os.path.exists(OUTPUT):
print("Output directory already exists")
exit()
# Create directories
os.makedirs("{}/img_bmp".format(OUTPUT))
os.makedirs("{}/img_potrace".format(OUTPUT))
os.makedirs("{}/quote_pdf".format(OUTPUT))
os.makedirs("{}/grid_pdf".format(OUTPUT))
# Create bmp files of the images
for i in range(1, IMAGES + 1):
functions.convert('data/img/{i}.png'.format(i=i), '{out}/img_bmp/{i}.bmp'.format(out=OUTPUT, i=i))
functions.negate('{out}/img_bmp/{i}.bmp'.format(out=OUTPUT, i=i))
# Create pdf of the images by running potrace
for i in range(1, IMAGES + 1):
functions.potrace('{out}/img_bmp/{i}.bmp'.format(out=OUTPUT, i=i),
'{out}/img_potrace/{i}.pdf'.format(out=OUTPUT, i=i))
# Create the quotes pdf
line_no = 1
with open("data/quotes", 'r') as quotes_file:
for quote in quotes_file:
print("Creating quote {}".format(line_no))
if settings.args.preview:
functions.quote_pdf(quote, settings.args.font, "{out}/quote_pdf/{i}.pdf".format(out=OUTPUT, i=line_no))
else:
