def score_distribution(file, peptide, charge, modi_list=['cc'], mass_list=[0]):
peptide_scores=[]
# print 'peptide: ',peptide,', charge:',charge, ' , mass:', mass_list, 'modi:',modi_list
for mass in mass_list:
y_scores=[]
b_scores=[]
# fetch matching hits
y_seq,y_mz,y_amp, b_seq,b_mz,b_amp = id.identify(file,peptide,charge,modi_list,mass)
# loop over given peptide match according to localise()
for i in np.arange(0,len(peptide)):
#if not a gap, rise temp score and scale with amplitude of hit
if y_seq[i]!= '-':
temp_score=y_amp[i]
else:
temp_score= 0
#assemble list of temp scores, building a seprate row in matrix
y_scores.append(temp_score)
if b_seq[i]!= '-':
temp_score=b_amp[i]
else:
temp_score = 0
b_scores.append(temp_score)
#append temp scores to peptide score list
peptide_scores.append(b_scores)
peptide_scores.append(y_scores)
# cast peptide score list of arrays together as matrix
A = np.matrix(peptide_scores)
return(A)
def main():
print('Type in the C declaration (or \'f\' to input a .txt file):',
end=' ')
response = input()
if response == 'f':
print('.txt file name:', end=' ')
filename = input()
try:
f = open(filename, 'r')
except IOError:
print('The file \'' + filename + '\' could not be found.')
return 0
response = f.read()
identify(response.splitlines())
def predict(self):
if os.path.exists(self.image_dir):
pic = misc.imread(self.image_dir)
result = identify(pic)
self.result.setText(result)
else:
reply = QMessageBox.information(self,
"Error",
"FileNotFoundError",
QMessageBox.Yes | QMessageBox.No)
def run(self):
global result_name
# 进行任务操作
if os.path.exists(self.image_dir):
pic = misc.imread(self.image_dir)
result = identify(pic)
self.signal.emit(result)
else:
reply = QMessageBox.information(self, "Error", "FileNotFoundError",
QMessageBox.Yes | QMessageBox.No)
def peptide_score(file, peptide_list,max_charge,mass_list, command=''):
#item holder for output:
score_list=[]
# loop over every peptide, charge status, modification type and mass patter
for peptide in peptide_list:
peptide_score=0
for mass in mass_list:
# fetch matching hits
y_seq,y_mz,y_amp, b_seq,b_mz,b_amp = id.identify(file,peptide,max_charge,['cc'],mass)
# counters
score=0
consecutive_hits=0
number_of_hits=0
# raise the score through summing the matches for y series, scaled over their amplitude
for i in np.arange(0,len(peptide)):
if y_seq[i]!= '-':
number_of_hits+=1
score +=y_amp[i]
# if adjacent series, raise counter
if i >0 and y_seq[i-1]!= '-':
consecutive_hits +=1
if b_seq[i]!= '-':
number_of_hits+=1
score +=b_amp[i]
if i >0 and b_seq[i-1]!= '-':
consecutive_hits +=1
# compute final score: sum up intensities of hits (peptide_score), multiply with number of hits,
# add bonus for consecutive hits and divide through number of possible hits (len(peptide)= #ions)
peptide_score += (score*number_of_hits*(1+consecutive_hits))/len(peptide)
#print peptide+' at '+str(int(mass))+': '+str(int(peptide_score))+ ' and consecutive hits:'+str(consecutive_hits)
score_list.append((peptide_score))
#output single computation results
if command=='-o':
print 'peptide:',peptide, ' -> score:', int(peptide_score)
#find max value in scores
if len(peptide_list)>1:
max_score = max(score_list)
max_score_peptide= peptide_list[score_list.index(max_score)]
best_score=[max_score_peptide,max_score]
else:
best_score=[peptide_list,score_list]
#report best score
if command=='-o' or command=='-b':
print 'best score: ', best_score
return_list=zip(peptide_list,score_list)
return(best_score,return_list)
def picture_identify(name):
original = cv2.imread(name)
rawImage = cv2.imread(name)
#高斯模糊
image = cv2.GaussianBlur(rawImage, (3, 3), 0)
#转为灰度图像
image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
#Canny边缘检测
image = cv2.Canny(image, 100, 300)
#Sobel算子
Sobel_x = cv2.Sobel(image, cv2.CV_16S, 1, 0)
absX = cv2.convertScaleAbs(Sobel_x)
image = absX
#图像二值化
ret, image = cv2.threshold(image, 0, 255, cv2.THRESH_OTSU)
#闭操作
kernelX = cv2.getStructuringElement(cv2.MORPH_RECT, (30, 10))
image = cv2.morphologyEx(image, cv2.MORPH_CLOSE, kernelX, iterations=1)
# #膨胀腐蚀
kernelX = cv2.getStructuringElement(cv2.MORPH_RECT, (19, 1))
kernelY = cv2.getStructuringElement(cv2.MORPH_RECT, (1, 19))
image = cv2.dilate(image, kernelX)
image = cv2.erode(image, kernelX)
image = cv2.erode(image, kernelY)
image = cv2.dilate(image, kernelY)
#中值滤波
image = cv2.medianBlur(image, 15)
#查找轮廓
contours, hierarchy = cv2.findContours(image, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
for item in contours:
x, y, weight, height = cv2.boundingRect(item)
if weight > (height * 2) and weight > 50 and height > 25:
# 裁剪区域图片
cv2.rectangle(image, (x, y), (x + weight, y + height), (0, 255, 0),
2)
chepai = original[y:y + height, x:x + weight]
list = identify(chepai)
print(list)
if (len(list) > 3):
cv2.resize(chepai, (300, 100))
cv2.imwrite("pai.jpg", chepai)
return list
return [0]
def movienamer(movie):
directory = '/'.join(movie.split('/')[:-1])
filename, extension = os.path.splitext(os.path.basename(movie))
results = identify(filename, directory)
if len(results) == 0:
print 'No results found. Skipping movie file\n'
return False
action = confirm(results, filename, extension)
if action == 'SKIP':
print 'Skipping movie file\n'
return False
elif action == 'QUIT':
print 'Exiting movienamer'
sys.exit()
else:
i = int(action)
result = results[i-1]
if directory == '':
directory = '.'
dest = (directory + '/' +
result['title'] +
' [' + result['year'] + ']' +
extension)
if os.path.isfile(dest):
print 'File already exists: ' + dest
print 'Overwrite?'
final_confirmation = raw_input('([y]/n/q)'.encode('utf-8')).lower()
if final_confirmation == '':
final_confirmation = 'y'
if final_confirmation not in ['y', 'n', 'q']:
final_confirmation = raw_input(
'([y]/n/q)'.encode('utf-8')).lower()
if final_confirmation == '':
final_confirmation = 'y'
if final_confirmation == 'n':
print 'Skipping movie file\n'
return False
elif final_confirmation == 'q':
print 'Exiting movienamer'
sys.exit()
return movie, dest
def movienamer(movie):
directory = '/'.join(movie.split('/')[:-1])
filename, extension = os.path.splitext(os.path.basename(movie))
results = identify(filename, directory)
if len(results) == 0:
print movie + ': No results found. Skipping movie file\n'
return False
action = confirm(results, filename, extension)
if action == 'SKIP':
print 'Skipping movie file\n'
return False
elif action == 'QUIT':
print 'Exiting movienamer'
sys.exit()
else:
i = int(action)
result = results[i - 1]
if directory == '':
directory = '.'
title = makeValidFilename(result['title'], False, True, "", "_")
dest = (directory + '/' + title + ' (' + result['year'] + ')' +
extension)
if os.path.isfile(dest):
print 'File already exists: ' + dest
print 'Overwrite?'
final_confirmation = raw_input('([y]/n/q)'.encode('utf-8')).lower()
if final_confirmation == '':
final_confirmation = 'y'
if final_confirmation not in ['y', 'n', 'q']:
final_confirmation = raw_input(
'([y]/n/q)'.encode('utf-8')).lower()
if final_confirmation == '':
final_confirmation = 'y'
if final_confirmation == 'n':
print 'Skipping movie file\n'
return False
elif final_confirmation == 'q':
print 'Exiting movienamer'
sys.exit()
return movie, dest
def binstatus1(items):
object = itemsensor()
open = door.door()
while object == False and open == True:
object = itemsensor()
open = door.door()
if object == True:
time.sleep(2)
rotate.rotate(
) #rotate objects in the top compartment 90 degrees and update items
photo.photo() #capture image of the object and assign it to variable
a = identify.identify() #identify object in the image captured
time.sleep(7) #wait 7 seconds for object identification
items[1] = a #assign object type to compartments array
print(items)
return (items)
if object == False and open == False:
return (items)
def xcorr(file,peptide,max_charge, modi_list=['cc'], mass=0,command='-o',error=0.5):
#read file
mz,params,amplitudes= id.read_mgf(file)
#create theoretical spectra of peptide
theo_y= list(id.fragments_y(peptide,maxcharge=max_charge))
theo_b= list(id.fragments_b(peptide,maxcharge=max_charge))
#fetch data
y_seq,y_mz,y_amp, b_seq,b_mz,b_amp = id.identify(file,peptide,max_charge,modi_list,mass,'',error)
# correlation coefficient; return matrix with XX,XY,YX,XX. we need xy, so enter only this part of matrix
xcorr_y = np.round(np.corrcoef(theo_y,y_mz)[0,1],3)
xcorr_b= np.round(np.corrcoef(theo_b,b_mz)[0,1],3)
if command=='-o':
print 'seq: ',peptide, '==> xcorr y:', xcorr_y,', xcorr b:', xcorr_b
return(xcorr_y,xcorr_b)
def identify(self, upload):
fileobjs = self.getuploadfiles(upload)
files = []
for fileobj in fileobjs:
f = {}
f["obj"] = fileobj
f["name"] = fileobj.name
f["mtime"] = fileobj.mtime.timestamp()
f["size"] = fileobj.size
files.append(f)
clusters = identify.identify(files)
for c in clusters:
cobj = Cluster()
self.session.add(cobj)
for f in c["files"]:
fobj = f["obj"]
fobj.cluster = cobj
for e in c["events"]:
self.session.add(Eventconnector(cluster=cobj, event=e["lecture_id"]))
def binstatus1(items):
print("Checking item")
object = itemsensor.itemsensor()
print("Checking door")
open = door.door()
while object == False and open == True:
print("Checking item2")
object = itemsensor.itemsensor()
print("Checking door2")
open = door.door()
if object == True:
print("rotating the plate")
centralmotor.anticlock(
) #rotate objects in the top compartment 90 degrees and update items
photo.photo() #capture image of the object and assign it to variable
a = identify.identify() #identify object in the image captured
time.sleep(7) #wait 7 seconds for object identification
items[1] = a #assign object type to compartments array
print(items)
return (items)
if object == False and open == False:
return (items)
def scan_person():
video = cv2.VideoCapture(0)
for x in range(5):
_, frame = video.read()
cv2.imwrite('capture.jpg', frame)
rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2BGRA) #TODO is this necessary? I forgot what it does.
person, encoding = identify.identify(frame)
#if it is a list it is "unknown" or "no faces"
if person != 'no faces': #maybe save unknown images where there was a face so if last one doesn't find a face we can still do it.
break #maybe don't check 5 times as it's like p-hacking. Will have to see how accuracy is affected when done.
else:
print('No face found; error code: ' + person)
video.release()
cv2.destroyAllWindows()
if person == 'no faces':
say('No face found.')
elif person == 'unknown':
say('Unknown')
clicks = input('Clicks: ') #this would happen right after conversation
if clicks == 'x':
#create directory entry
say('Who is it?')
name = listen(3)
with open('assets/directory.txt') as file:
filename = str(len(file.readlines()))
with open('assets/directory.txt', 'a') as file:
file.write(filename + ',' + name + ',"random data yay"\n') #TODO this needs to be cleaned up to look something like the style in the readme
#save encoding to knowns file
np.save((os.path.join('assets', 'knowns', filename) + '.npy'), encoding) #TODO learn more about os.path.join. Do I want my standard to be "/" or that?
else: #if you double click
unix_time = str(int(datetime.datetime.utcnow().timestamp())) #utc timezone to avoid overwriting pictures when switching timezones
np.save((os.path.join('assets', 'unknowns', unix_time) + '.npy'), encoding) #TODO change this to store face encodings if they are saved for later to speed it up when this is classified
return
else:
say(get_info(int(person)))
def color_identify(name):
#颜色范围
lower = np.array([70, 110, 110])
upper = np.array([130, 255, 255])
raw = cv2.imread(name)
original = cv2.imread(name)
#中值滤波
hsv_one = cv2.medianBlur(raw, 5)
hsv = cv2.cvtColor(hsv_one, cv2.COLOR_BGR2HSV)
#颜色提取
mask_blue = cv2.inRange(hsv, lower, upper)
res = cv2.bitwise_and(raw, raw, mask=mask_blue)
res = cv2.cvtColor(res, cv2.COLOR_BGR2GRAY)
ret, iIMAGE = cv2.threshold(res, 0, 255, cv2.THRESH_OTSU)
#查找轮廓
con, hierarchy = cv2.findContours(iIMAGE, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
img = cv2.imread(name)
for item in con:
x, y, w, h = cv2.boundingRect(item)
if w > (h * 2) and w > 100 and h > 100:
# 裁剪区域图片
cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
chepai = original[y:y + h, x:x + w]
list = identify(chepai)
print(list)
if (len(list) > 3):
cv2.resize(chepai, (300, 100))
cv2.imwrite("pai.jpg", chepai)
return list
return [0]
def respond(voice_data):
if 'your name' in voice_data:
speak.jarvis_speak(
'My name is Jarvis, just a rather very intelligent system, except a lobotomized version'
)
if 'chemistry question' in voice_data:
chem_element = inputs.record_audio(
'I know the periodic table, what element do you want to know about?'
)
chem_qs.el_lookup(chem_element)
if 'time is it' in voice_data:
speak.jarvis_speak(ctime())
if 'search' in voice_data or 'can you find' in voice_data:
search = inputs.record_audio('What do you want to search for?')
url = 'https://google.com/search?q=' + search
webbrowser.get().open(url)
speak.jarvis_speak('Here is what i found for ' + search)
if 'find location' in voice_data:
location = inputs.record_audio('What is the location?')
url = 'https://google.nl/maps/place/' + location + '/&'
webbrowser.get().open(url)
speak.jarvis_speak('Here is the location of ' + location)
if 'exit' in voice_data or "good bye" in voice_data or "ok bye" in voice_data:
speak.jarvis_speak('Always happy to help')
exit()
if 'weather' in voice_data:
city_name = inputs.record_audio('whats the city name')
report = weather.weather_report(city_name)
speak.jarvis_speak(report)
if 'quick question' in voice_data:
question = inputs.record_audio('whats the question')
answer = quick_question.ask(question)
speak.jarvis_speak(answer)
if 'say hello' in voice_data or 'introduce yourself' in voice_data:
names = identify.identify(identify.peek())
hour = datetime.datetime.now().hour
for name in names:
if hour >= 0 and hour < 12:
speak.jarvis_speak('Hello, Good Morning ' + name)
elif hour >= 12 and hour < 18:
speak.jarvis_speak('Hello, Good Afternoon ' + name)
else:
speak.jarvis_speak('Hello, Good Evening ' + name)
if 'open my email' in voice_data:
url = 'https://mail.google.com/mail/u/0/#inbox'
webbrowser.get().open(url)
speak.jarvis_speak('Opening email')
if 'play some music' in voice_data:
url = 'https://www.youtube.com/playlist?list=PLBuxhzrxSigRFEI1WIMgsLvSUAMXr3-xX'
webbrowser.get().open(url)
speak.jarvis_speak('Dropping needle')
def identify_plot(file, peptide, charge, modi_mass=[], zoom=1):
#read file
values, params, amplitude = id.read_mgf(file)
#procentual amplitdues for relative abundance
ampl = (amplitude / max(amplitude)) * 100
#offset and scales for zoom into the spectra
if zoom > 0 and zoom <= 1:
offset = (max(ampl) * zoom) / 4
y_lim_scale = max(ampl) * zoom
one_per = y_lim_scale / 100
# create theoretical fragmentation of given peptide
theo_y = list(id.fragments_y(peptide, maxcharge=charge))
theo_b = list(id.fragments_b(peptide, maxcharge=charge))
# create plot title
headline = 'File: ' + file + ', Peptide: ' + peptide + ' (charge ' + str(
charge) + ')'
#headline = 'File: '+ file
#create figure
py.figure()
py.xlabel('m/z')
py.ylabel('Intensity, rel. units %')
py.title(headline)
# plot experimental spectra in grey
py.bar(values,
ampl,
width=2.5,
linewidth=0.1,
color='grey',
alpha=0.3,
label='data')
# some label and axis configs
py.xlim(xmin=0)
py.ylim(ymax=y_lim_scale)
py.xticks(np.arange(0, max(values) + 1, 50))
py.tick_params(axis='both', which='major', labelsize=8)
py.tick_params(axis='both', which='minor', labelsize=5)
#
# --- 0. benchmarks
#0.1 plot theoretical spectras if no modi given
if modi_mass == []:
py.bar(theo_y,
np.ones(len(theo_y)) * offset,
linewidth=0.1,
width=2.5,
alpha=0.3,
color='red',
label='theo y')
py.bar(theo_b,
np.ones(len(theo_y)) * offset,
linewidth=0.1,
width=3.5,
alpha=0.3,
color='lightgreen',
label='theo b')
# 0.2 annotate peaks
#0.2.1 theo y ions
for i in np.arange(0, len(peptide)):
py.annotate(peptide[i],
xy=(theo_y[i], offset),
xytext=(theo_y[i], offset + 2 * one_per),
bbox=dict(boxstyle='round,pad=0.2',
fc='red',
alpha=0.5))
# 0.2.1 theo b ion
py.annotate(peptide[i],
xy=(theo_b[i], offset),
xytext=(theo_b[i], offset + 6 * one_per),
bbox=dict(boxstyle='round,pad=0.2',
fc='lightgreen',
alpha=0.5))
alpha = 0.75
for mass in modi_mass:
# --- 1. mono matches
if mass == 0:
# 1.0 use def identify to find matching aa sequences
y_seq, y_mz, y_amp, b_seq, b_mz, b_amp = id.identify(
file, peptide, charge, ['cc'], 0)
# 1.1 plot peaks
py.bar(y_mz,
y_amp,
width=5,
linewidth=0.1,
color='red',
label='y [M+1H]+' + str(charge))
py.bar(b_mz,
b_amp,
width=5,
linewidth=0.1,
color='lightgreen',
label='b [M+1H]+' + str(charge))
# 1.2 annotate labels to peak hits
# 1.2.1 y ion label
for i in np.arange(0, len(peptide)):
if y_seq[i] == '-':
py.annotate(y_seq[i],
xy=(theo_y[i], 0),
xytext=(theo_y[i], offset + 4 * one_per))
else:
py.annotate(y_seq[i],
xy=(y_mz[i], offset),
xytext=(y_mz[i], offset + 4 * one_per),
bbox=dict(boxstyle='round,pad=0.2', fc='red'))
# 1.2.2 b ion label
if b_seq[i] == '-':
py.annotate(b_seq[i],
xy=(theo_b[i], 0),
xytext=(theo_b[i], offset + 8 * one_per))
else:
py.annotate(b_seq[i],
xy=(b_mz[i], offset),
xytext=(b_mz[i], offset + 8 * one_per),
bbox=dict(boxstyle='round,pad=0.2',
fc='lightgreen'))
# --- 2. cc matches
else:
#compute sequence
y_seq, y_mz, y_amp, b_seq, b_mz, b_amp = id.identify(
file, peptide, charge, ['cc'], mass)
# 2.1. plot peaks
py.bar(y_mz,
y_amp,
width=5,
linewidth=0.1,
color='orange',
label='y + ' + str(int(mass)) + '[M+1H]+' + str(charge),
alpha=alpha)
py.bar(b_mz,
b_amp,
width=5,
linewidth=0.1,
color='blue',
label='b + ' + str(int(mass)) + '[M+1H]+' + str(charge),
alpha=alpha)
# 2.2.annotate labels to peak
# 2.2.1 y ions
offset = offset + 12 * one_per
for i in np.arange(0, len(peptide)):
if y_seq[i] == '-':
py.annotate(y_seq[i],
xy=(theo_y[i] + (mass / charge), 0),
xytext=(theo_y[i] + (mass / charge), offset))
else:
py.annotate(y_seq[i],
xy=(y_mz[i], offset),
xytext=(y_mz[i], offset),
bbox=dict(boxstyle='round,pad=0.2',
fc='orange',
alpha=alpha))
# 2.2.2 b ions
if b_seq[i] == '-':
py.annotate(b_seq[i],
xy=(theo_b[i] + (mass / charge), 0),
xytext=(theo_b[i] + (mass / charge),
offset + 4 * one_per))
else:
py.annotate(b_seq[i],
xy=(b_mz[i], offset),
xytext=(b_mz[i], offset + 4 * one_per),
bbox=dict(boxstyle='round,pad=0.2',
fc='blue',
alpha=alpha))
alpha = alpha - 0.25
#--- 3. reporter matches
reporter = [284, 447]
r_val = []
r_amp = []
# 3.1 check for occurence
for i in reporter:
closest = min(values, key=lambda x: abs(x - i))
if round(closest) == i:
r_val.append(closest)
r_amp.append(ampl[values.tolist().index(closest)])
# 3.2 plot occurence
if r_val != []:
py.bar(r_val, r_amp, width=5, color='yellow', label='reporter')
# 3.2.1 annotate occurence
for i in np.arange(0, len(r_val)):
py.annotate(int(round(r_val[i])),
xy=(r_val[i], r_amp[i]),
ha='center',
xytext=(r_val[i], r_amp[i] + 2 * one_per),
bbox=dict(boxstyle='round,pad=0.2', fc='yellow'))
py.legend(loc=2, prop={'size': 11})
py.show()
def identify_recording(self):
language = identify()
self.status.configure(text=language)
self.root.update_idletasks()
def getSong(username, token, sp): #record, identify, return id
record()
return identify()