def check(arg):
#plik wejsciowy
infile = open(arg[1], "r")
char = infile.read().split(' ')
infile.close()
#obliczanie minimum
if int(char[1]) == 0:
result = fn.find(int(char[0]), fn.happycat)
else:
result = fn.find(int(char[0]), fn.griewank)
#plik wyjsciowy
outfile = open(arg[2], "w")
outfile.write(' '.join(str(r) for r in result))
outfile.close()
def init():
tab = [
"-h", "--help", "-dir", "-er", "--encrypt", "-e", "decrypt", "-d",
"--replace", "-r"
]
if sys.argv[1] == "-h" or sys.argv[1] == "--help":
functions.display_help()
exit(0)
if len(sys.argv) < 2:
print("\033[1;31;40mBlad! Brak argumentow!")
exit(1)
if sys.argv[1] == "-dir":
functions.find()
exit(0)
if (sys.argv[1] == "-c"):
if len(sys.argv[1]) < 2:
print("\033[1;31;40mZbyt mala ilosc argumentow!")
exit(1)
else:
if (len(sys.argv) > 3):
print("\033[1;31;40mBledne argumenty!")
exit(1)
else:
functions.find_in_current_directory(sys.argv[2])
exit(0)
if sys.argv[1] == "--encrypt" or sys.argv[1] == "-e" or sys.argv[
1] == "-er":
functions.encrypt(sys.argv[1], sys.argv[2])
exit(0)
if sys.argv[1] == "--decrypt" or sys.argv[1] == "-d":
functions.decrypt(sys.argv[2])
exit(0)
if sys.argv[1] == "--replace" or sys.argv[1] == "-r":
functions.replace(sys.argv[2], sys.argv[3], sys.argv[4])
exit(0)
if sys.argv[1] == "-s":
functions.file_statistics(sys.argv[2])
exit(0)
if sys.argv[1] not in tab:
print("\033[1;31;40mBledne argumenty!")
exit(1)
def test_find(): test_list = ( "info hello, world", "info hello, world", " info hello, world", "info\thello, world", "info", "info ", "info ", "info \tfoo", ) for test in test_list: fn = functions.find(test)
def test_find():
test_list = (
"info hello, world",
"info hello, world",
" info hello, world",
"info\thello, world",
"info",
"info ",
"info ",
"info \tfoo",
)
for test in test_list:
fn = functions.find(test)
async def echo_message(message: types.Message):
if message.text:
db.add_message(user_id=message.from_user.id,
text=message.text,
time=message.date,
name=message.chat.first_name)
try:
movies, length, titles = functions.find(message.text)
if length > 0:
if length > 5:
k = 5
else:
k = length
for i in range(k):
movie = movies.iloc[i]
msg = text(bold(movie.title),
f'Описание: {movie.description}...',
f'Год: {movie.year}',
f'Страна: {movie.country}',
f'Жанр: {movie.genre}',
f'Время: {movie.runtime} минуты',
f'Режисер: {movie.film_director}',
f'Актеры: {movie.cast}',
sep='\n')
await bot.send_photo(chat_id=message.from_user.id,
photo=movie.img_url,
parse_mode=ParseMode.MARKDOWN,
caption=msg)
if length > 5:
if length > 50:
length = 50
await bot.send_message(message.from_user.id,
bold('Я также нашел по вашему запросу:'),
parse_mode=ParseMode.MARKDOWN)
for i in range(5, length):
await bot.send_message(message.from_user.id, titles.iloc[i])
if length == 0:
await bot.send_message(
message.from_user.id,
'К сожалению, я ничего не нашел по вашему запросу. Проверьте правильность написания 😌😌😌'
)
except:
await bot.send_message(
message.from_user.id,
'К сожалению, я ничего не нашел по вашему запросу. Проверьте правильность написания 😌😌😌'
)
if coords[0][i][4]-coords[0][i][2]>0 and coords[0][i][3]-coords[0][i][1]>0: imin=max(coords[0][i][2]-10,0) imax=min(coords[0][i][4]+10,Nx) jmin=max(coords[0][i][1]-10,0) jmax=min(coords[0][i][3]+10,Ny) cos=SbFun.isCosmicRay(data[jmin:jmax,imin:imax],100,50) if cos[0]==False: imin=max(coords[0][i][2]-5,0) imax=min(coords[0][i][4]+5,Nx) jmin=max(coords[0][i][1]-5,0) jmax=min(coords[0][i][3]+5,Ny) I=np.sum(data[jmin:jmax,imin:imax]-background_mean) r=SbFun.find(data,imin,imax,jmin,jmax,background_mean,background_std,I) if r[0]!=-1.0 and r[1]<5.0 and r[3]<5.0: model=np.zeros((jmax-jmin,imax-imin)) g=SbFun.Goodness2(data,jmin,jmax,imin,imax,r[0],r[2],r[4],r[6],r[8],r[10],background_mean,background_std) np.append(r,g) if g<0.1: np.append(r,g) #Cat.append(r) stars+=1 ##asignar coordenadas
norm_data_js_exl.loc[norm_data_js_exl['Ulica'] == 'nan'] = None
norm_data_js_exl.loc[norm_data_js_exl['Nr domu'] == 'nan'] = None
norm_data_js_exl.loc[norm_data_js_exl['Kod poczt.'] == 'nan'] = None
norm_data_js_exl['ID'] = norm_data_js_exl['ID'].fillna(0.0).astype(float)
norm_data_js_exl['ID'] = norm_data_js_exl['ID'].astype(int)
norm_data_js_exl['PATRON'] = js_exl["PATRON"]
# utworznie slownika z miastami, wywolanie funkcji dopasowujacej
# of_exl = of_exl.drop(columns=['Patron'])
print('Rozpoczęcie dopasowywania ... ')
of_exl['Miejscowość'] = of_exl['Miejscowość'].apply(fun.dash_out)
of_exl = of_exl.reset_index(drop=True)
of_exl = of_exl.sort_values(by='Miejscowość')
dict_of_names = fun.dictionary_of_cities(of_exl)
of_exl = of_exl.sort_values(by='Miejscowość')
norm_data_loc_of_school, norm_data_prop, status_tab, norm_data_of_school_org = fun.find(min, max, norm_data_js_exl, of_exl, dict_of_names)
print('Dopasoswyanie zakończone')
print('Tworzenie pliku wynikowego ...')
# utworzenie excela zawierajacego dane wejsiowe z jsosa i dopasowania
final_data = {'ID kandydata': js_exl.iloc[min:max, 0], 'Miejscowość szkoły (wprowadzona)': js_exl.iloc[min:max, 2],
'Miejscowość szkoły (znormalizowana)': norm_data_loc_of_school, 'Zgodność danych (%)': norm_data_prop,
'Status': status_tab, 'Nazwa szkoły (niezmieniona)': js_kopia.iloc[min:max, 1],
'Nazwa szkoły z bazy danych (niezmieniona)': norm_data_of_school_org}
norm_data_final = pd.DataFrame(data=final_data)
while True:
try:
norm_data_final.to_excel('plik_wynikowy.xlsx')
break
def main(unused_argv):
data_path = FLAGS.rawDir
p_num = 1
incomplete = []
if FLAGS.pointArray:
patient_sets = find('*.mat', data_path)
patient_sets.sort()
for patient in patient_sets:
data = sio.loadmat(patient)
data_lens = sio.loadmat(
patient.replace('\\data\\', '\\Lens_label\\').replace(
'.mat', '_Lens_label.mat'))
vect_lens = data_lens['vect']
vect = data['vect']
scan = vect['img'][0, 0]
mask = vect['label'][0, 0]
mask_lens = vect_lens['lens_label'][0, 0]
## Needed if passing through CERR
scan_rot = scan.transpose(2, 1, 0)
mask_rot = mask.transpose(2, 1, 0)
mask_lens_rot = mask_lens.transpose(2, 1, 0)
unique, counts = np.unique(mask_lens_rot, return_counts=True)
vals = dict(zip(unique, counts))
print(patient)
print(vals)
unique, counts = np.unique(mask_rot, return_counts=True)
vals = dict(zip(unique, counts))
print(vals)
# parotids
np.place(mask_rot, mask_rot == 2, 2)
# submands
np.place(mask_rot, mask_rot == 3, 3)
np.place(mask_rot, mask_rot == 4, 4)
# bps
np.place(mask_rot, mask_rot == 5, 5)
np.place(mask_rot, mask_rot == 6, 6)
# mandible
np.place(mask_rot, mask_rot == 7, 7)
# cord
np.place(mask_rot, mask_rot == 8, 8)
# brainstem
np.place(mask_rot, mask_rot == 9, 9)
# OC
np.place(mask_rot, mask_rot == 10, 10)
# larynx
np.place(mask_rot, mask_rot == 11, 11)
# chiasm
np.place(mask_rot, mask_rot == 12, 12)
# optics
np.place(mask_rot, mask_rot == 13, 13)
np.place(mask_rot, mask_rot == 14, 14)
# eyes
np.place(mask_rot, mask_rot == 15, 15)
np.place(mask_rot, mask_rot == 16, 16)
# lenses
np.place(mask_lens_rot, mask_lens_rot == 17, 100)
np.place(mask_lens_rot, mask_lens_rot == 18, 100)
np.place(mask_rot, mask_rot == 19, 19)
mask_rot = mask_lens_rot + mask_rot
#
# np.place(mask_rot, mask_rot > 13, 12)
np.place(mask_rot, mask_rot == 115, 17)
np.place(mask_rot, mask_rot == 116, 18)
np.place(mask_rot, mask_rot == 117, 17)
np.place(mask_rot, mask_rot == 118, 18)
unique, counts = np.unique(mask_rot, return_counts=True)
print(unique)
if len(unique) >= 19:
data_export_MR_3D(scan_rot, mask_rot, FLAGS.saveDir, p_num,
FLAGS.datasetName)
print(p_num)
p_num = p_num + 1
else:
incomplete.append(p_num)
create_tfrecord(os.path.join(FLAGS.saveDir, FLAGS.datasetName))
with open('incomplete.pickle', 'wb') as f:
pickle.dump(incomplete, f)
elif FLAGS.MHD:
patient_sets = find('*segmentation.mhd', data_path)
patient_sets.sort()
for patient in patient_sets:
s = sitk.ReadImage(patient.replace('_segmentation', ''))
m = sitk.ReadImage(patient)
# scan, mask should be up shape: (scan length, height, width)
scan = sitk.GetArrayFromImage(s)
mask = sitk.GetArrayFromImage(m)
unique, counts = np.unique(mask, return_counts=True)
print('Saving patient dataset: ' + patient)
data_export_MR_3D(scan, mask, FLAGS.saveDir, p_num,
FLAGS.datasetName)
p_num = p_num + 1
create_tfrecord(os.path.join(FLAGS.saveDir, FLAGS.datasetName))
else:
patient_sets = find('mask_total*', data_path)
patient_sets.sort()
patient_sets = []
for patient in patient_sets:
print(patient)
s = h5py.File(patient.replace('mask_total', 'scan'), 'r')
m = h5py.File(patient, 'r')
scan = s['scan'][:]
mask = m['mask_total'][:]
unique, counts = np.unique(mask, return_counts=True)
print(unique)
if (len(unique) >= 1) and (p_num >= 0):
data_export_MR_3D(scan, mask, FLAGS.saveDir, p_num,
FLAGS.datasetName, 7)
print(p_num)
p_num = p_num + 1
else:
incomplete.append(p_num)
p_num = p_num + 1
create_tfrecord(os.path.join(FLAGS.saveDir, FLAGS.datasetName))
def create_tfrecord(structure_path):
planeList = ['ax', 'cor', 'sag']
planeDir = ['Axial', 'Coronal', 'Sag']
filename_train = 'train_'
filename_val = 'val_'
i = 0
for plane in planeList:
file_base = os.path.join(structure_path, 'processed', 'ImageSets',
planeDir[i])
if not os.path.exists(file_base):
os.makedirs(file_base)
f = open(os.path.join(file_base, filename_train + plane + '.txt'), 'a')
f.truncate()
k = 0
path = os.path.join(structure_path, 'processed', 'PNGImages')
pattern = plane + '*.png'
files = find(pattern, path)
for file in files:
if file.find(plane) > 0 \
and (file.find(plane + '1011_') < 1 and
file.find(plane + '1511_') < 1 and
file.find(plane + '2011_') < 1 and
file.find(plane + '2511_') < 1 and
file.find(plane + '3011_') < 1 and
file.find(plane + '3511_') < 1 and
file.find(plane + '4011_') < 1 and
file.find(plane + '4511_') < 1 and
file.find(plane + '511_') < 1):
h = file.split(os.sep)
f.write(h[-1].replace('.png', '') + '\n')
k = k + 1
f.close()
print(filename_train + plane, k)
if not os.path.exists(file_base):
os.makedirs(file_base)
f = open(os.path.join(file_base, filename_val + plane + '.txt'), 'a')
f.truncate()
k = 0
for file in files:
if file.find(plane) > 0 \
and (file.find(plane + '511_') > 0 or
file.find(plane + '1011_') > 0 or
file.find(plane + '1511_') > 0 or
file.find(plane + '2011_') > 0 or
file.find(plane + '2511_') > 0 or
file.find(plane + '3011_') > 0 or
file.find(plane + '3511_') > 0 or
file.find(plane + '4011_') > 0 or
file.find(plane + '4511_') > 0):
h = file.split(os.sep)
f.write(h[-1].replace('.png', '') + '\n')
k = k + 1
f.close()
print(filename_val + plane, k)
i = i + 1
dataset_splits = glob.glob(os.path.join(file_base, '*.txt'))
for dataset_split in dataset_splits:
_convert_dataset(dataset_split, FLAGS.numShards, structure_path,
plane)
return
db = pd.read_excel(contourDatabase, index=False)
for column in columns:
if column not in db:
db[column] = ""
else:
db[column] = db[column].astype('str')
for directory in dirList[1:]:
# if directory is CT, store scanData matrix
structureSets = []
dataset_ct = None
if 'CT' in directory.split(os.sep)[-1][0:2] or 'MR' in directory.split(
os.sep)[-1][0:2] or 'SQ' in directory.split(os.sep)[-1][0:2]:
dcmFiles = find('*.dcm', directory)
if dcmFiles:
arrayTuple = []
for dcmFile in dcmFiles:
dataset = pydicom.dcmread(dcmFile)
filename = dataset.StudyInstanceUID
file = os.path.join(HDF5_DIR, filename + '.h5')
if not os.path.isfile(file) or os.path.isfile(file):
if dataset.Modality == 'CT' or dataset.Modality == 'MR':
pixelSpacing = dataset.PixelSpacing
pixelSpacing.append(dataset.SliceThickness)
ImagePosition = dataset.ImagePositionPatient
ImagePosition.append(1)
X = dataset.ImageOrientationPatient[0:3]
Y = dataset.ImageOrientationPatient[3:]
coordinateSystemTransform = np.zeros((4, 4))