def mian(self):
os.chdir("./DATA")
files = os.lisdir()
for filename in files:
htmlfile = open(filename, 'r',encoding="utf-8")
htmlpage = htmlfile.read()
soup = BeautifulSoup(htmlpage, 'html.parser', from_encoding='utf-8')
table_obj = soup.find_all('table', class_="grace-grid-body")
table_head = table_obj[0].find("thead").find_all("td")
table_head = [x.extract().get_text() for x in table_head]
table_body = table_obj[0].find("tbody").find_all("tr")
coll_tb = []
for trdata in table_body:
if has_class(trdata):
each_row = [tdf.extract().get_text() for tdf in trdata.find_all("td")]
print(each_row)
trfirst = each_row[0]
print("----first-----",trfirst)
else:
print("doing else......")
each_row = [trfirst] + [tdf.extract().get_text() for tdf in trdata.find_all("td")]
coll_tb.append(tuple(each_row))
print(coll_tb)
import csv
csvname = filename + ".csv"
with open(csvname, 'w', newline='') as f:
writer = csv.writer(f)
writer.writerows(coll_tb)
def repo_create(path):
"""Create a new repository at path."""
repo = GitRepository(path, True)
# First, we make sure the path either does't exit or
# is an empty dir
if os.path.exists(repo.worktree):
if not os.path.isdir(repo.worktree):
raise Exception("%s is not a directory" % path)
if os.lisdir(repo.worktree):
raise Exception("%s is not empty!" % path)
else:
os.makedirs(repo.worktree)
assert (repo_dir(repo, "branches", mkdir=True))
assert (repo_dir(repo, "objects", mkdir=True))
assert (repo_dir(repo, "refs", "tags", mkdir=True))
assert (repo_dir(repo, "refs", "heads", mkdir=True))
# .git/description
with open(repo_file(repo, "description"), "w") as f:
f.write(
"Unnamed repository; edit this file 'description' to name the repository.\n"
)
# .git/HEAD
with open(repo_file(repo, "HEAD"), "w") as f:
f.write("ref: refs/head/master\n")
with open(repo_file(repo, "config"), "w") as f:
config = repo_default_config()
config.write(f)
return repo
def getSentences():
data_dir = "Coref_Data"
for folder in os.lisdir(data_dir):
path = os.path.join(data_dir, folder)
fp = open(path + "smk_list.pickle")
A = pickle.load(fp)
Sentences = Sentences + A
def img2np(imgPath):
resData = []
for file in os.lisdir(imgPath):
img = Image.open(imgPat+"/{}".format(file))
img_data = np.array(img)
resData.append(img_data)
resData = np.array(resData)
return resData
def get_all_files(path, dirs):
all_files = []
for d in dirs:
cur_path = path + '/' + d
files = os.lisdir(cur_path)
for f in files:
all_files.append(cur_path + '/' + f)
return all_files
def find_motivation_letter(cv_name):
"""
:param cv_name: (string) Name of the CV/Resume file
:return: (String) The name of the file that is the most likely to be the candidate's motivation letter
"""
# If a file contains the word "motivation", returns directly that file name
for name in os.listdir('PDF_Converted_Files'):
if name == cv_name: # The motivation letter can't be the same file as the resume.
continue
if "motivation" in name.lower():
return name
# Else, we return the second file submitted by the candidate, which probably is his motivation letter.
if len(os.lisdir('PDF_Converted_Files')) < 2:
return
return os.listdir('PDF_Converted_Files')[1] if os.listdir(
'PDF_Converted_Files')[1] != cv_name else os.listdir(
'PDF_Converted_Files')[0]
def __init__(self, data_dir, transform, data_type="train"):
#path to images
path2data = os.path.join(data_dir, data_type)
filenames = os.lisdir(path2data)
self.full_filenames = [os.path.join(path2data, f) for f in filenames]
csv_filename = data_type + "_labaels.csv"
path2csvLabels = os.path.join(data_dir, csv_filename)
labels_df = pd.read_csv(path2csvLabels)
labels_df.set_index("id", inplace=True)
self.labels = [
labels_df.iloc[csv_filename[:-4]].values[0]
for filename in filenames
]
self.transform = transform
def df(filename):
path = '/mnt/volume_nyc3_01/gutenberg/archive/'
mp = '/mnt/volume_nyc3_01/gutenberg/metadata/'
all = os.lisdir(path)
part = all[:10]
i = part[1] #used later for stem metadata lookup
file = open(path + i, 'rt',
errors='ignore') #not ideal to ignore encoding errors
df = file.read()
file.close()
#lookup stem metadata
stm = Path(i).stem
#should be conditional string split
i = i.split('-')[0]
#xml decode to read .rdf files
obj = untangle.parse(mp + i + '/' + i + '.rdf')
def batch_process_epochs(path, **parameters):
"""This function batch processes a serie of eeg files, and saves it as a
PSD of format out. This take an argument a path leading to a folder
containing all the files of epochs of format epo-fif"""
import os
from backend.epochs_psd import EpochsPSD
from mne import read_epochs
# Init a value files with all the paths of the files to process
if path.endswith('-epo.fif'):
files_path = [path]
else:
files = [path + file for file in os.lisdir(path)]
for file in files:
epochs = read_epochs(file)
psd = EpochsPSD(epochs, **parameters)
psd.save_avg_matrix_sef()
def get_model_filenames(self, model_dir):
''' Returns the path of the meta file and the path of the checkpoint file.
Parameters:
model_dir: (string), the path to model dir.
Returns:
meta_file: (string), the path of the meta file
ckpt_file: (string), the path of the checkpoint file
'''
#bookmark 09/02
files = os.lisdir(model_dir)
meta_files = [s for s in files if s.endswith('.meta')]
if len(meta_files) == 0:
raise ValueError('No meta file found in the model directory (%s)' %
model_dir)
elif len(meta_files) > 1:
raise ValueError(
'There should not be more than one meta file in the model directory (%s)'
% model_dir)
meta_file = meta_files[0]
ckpt = tf.train.get_checkpoint_state(model_dir)
if ckpt in ckpt.model_checkpoint_path:
ckpt_file = os.path.basename(ckpt.model_checkpoint_path)
return meta_file, ckpt_file
def run(**args):
print "[*] in drlister module."
files = os.lisdir(".")
return str(files)
def get_images(path):
return [
os.join(path, each) for each in os.lisdir(path)
if each.endswith('.png') or each.endswith('.jpg')
]
from chatterbot import ChatBot
from chatterbot.trainers import ListTrainer
import os
bot = ChatBot('Bot')
bot.set_trainer(ListTrainer)
for files in os.lisdir(
'C:/Users/jaydesai571/Desktop/jay/Devepolment/Chat bot/chatterbot-corpus-master/chatterbot_corpus/data/english/'
):
data = open(
'C:/Users/jaydesai571/Desktop/jay/Devepolment/Chat bot/chatterbot-corpus-master/chatterbot_corpus/data/english/'
+ files, 'r').readlines()
bot.train(data)
while True:
message = input('You:')
if message.strip() != 'Bye':
reply = bot.get_response(message)
print('ChatBot :', reply)
if message.strip() == 'Bye':
print('ChatBot : Bye')
break
#sacar ejecutables
import os
x=os.lisdir("C:\Windows\System32")
for z in x:
if z[-3:]=="exe":
print z
import os
home = os.getcwd()
for i in os.lisdir():
end = i[len(i)-5:]
if end == ".calc":
file_handle = open("i")
operation = file_handle.read().split("\n")
for row in operation:
def filenames():
return [d for d in os.lisdir(fordir) if not d.endswith(".dat") and not d.endswith(".u8")]
def main():
progname = os.path.basename(sys.argv[0])
usage = """
This program takes a subtomgoram tiltseries (subtiltseries) as extracted with
e2spt_subtilt.py, and computes the resolution of two volumes reconstructed with
the even and the odd images in the tilt series. Must be in HDF format.
Note that the apix in the header must be accurate to get sensible results.
(You can fix the header of an image with e2fixheaderparam.py).
"""
parser = EMArgumentParser(usage=usage,version=EMANVERSION)
parser.add_argument("--inputstem", type=str, default='', help="""Default=None. Aligned tilt series. String common to all files to be processed, in the current folder. For example, if you have many subtiltseries named subt00.hdf, subt01.hdf, ...subt99.hdf, you would supply --stem=subt to have all these processed.""")
parser.add_argument('--path',type=str,default='sptintrafsc',help="""Default=sptintrafsc. Directory to save the results.""")
parser.add_argument('--nonewpath',action='store_true',default=False,help="""Default=False. If True, a new --path directory will not be made. Therefore, whatever is sepcified in --path will be used as the output directory. Note that this poses the risk of overwriting data.""")
parser.add_argument('--input',type=str,default='',help="""Default=None. Subtiltseries file to process. If processing a single file, --inputstem will work too, but you can also just provide the entire filename here --input=subt00.hdf""")
parser.add_argument('--savehalftiltseries',action='store_true',default=False,help="""Default=False. If this parameter is on, the odd and even subtiltseries will be saved.""")
parser.add_argument('--savehalfvolumes',action='store_true',default=False,help="""Default=False. If this parameter is on, the odd and even volumes will be saved.""")
parser.add_argument("--reconstructor", type=str,default="fourier:mode=gauss_2",help="""Default=fourier:mode=gauss_2. The reconstructor to use to reconstruct the tilt series into a tomogram. Type 'e2help.py reconstructors' at the command line to see all options and parameters available. To specify the interpolation scheme for the fourier reconstructor, specify 'mode'. Options are 'nearest_neighbor', 'gauss_2', 'gauss_3', 'gauss_5'. For example --reconstructor=fourier:mode=gauss_5 """)
parser.add_argument("--pad2d", type=float,default=0.0,help="""Default=0.0. Padding factor (e.g., 2.0, to make the box twice as big) to zero-pad the 2d images in the tilt series for reconstruction purposes (the final reconstructed subvolumes will be cropped back to the original size though).""")
parser.add_argument("--pad3d", type=float,default=0.0,help="""Default=0.0. Padding factor (e.g., 2.0, to make the box twice as big) to zero-pad the volumes for reconstruction purposes (the final reconstructed subvolumes will be cropped back to the original size though).""")
parser.add_argument("--averager",type=str,default="mean.tomo",help="""Default=mean.tomo. The type of averager used to produce the class average.""")
parser.add_argument("--averagehalves",action="store_true", default=False,help="""Default=False. This will averager the even and odd volumes.""")
parser.add_argument("--ppid", type=int, default=-1, help="""default=-1. Set the PID of the parent process, used for cross platform PPID.""")
parser.add_argument("--verbose", "-v", dest="verbose", action="store", metavar="n",type=int, default=0, help="verbose level [0-9], higner number means higher level of verboseness")
parser.add_argument("--nolog",action="store_true",default=False,help="Default=False. Turn off recording of the command ran for this program onto the .eman2log.txt file")
(options, args) = parser.parse_args()
#if options.reconstructor == 'None' or options.reconstructor == 'none':
# options.reconstructor = None
#if options.reconstructor and options.reconstructor != 'None' and options.reconstructor != 'none':
# options.reconstructor=parsemodopt(options.reconstructor)
#if options.averager:
# options.averager=parsemodopt(options.averager)
from e2spt_classaverage import sptOptionsParser
options = sptOptionsParser( options )
logger = E2init( sys.argv, options.ppid )
'''
Make the directory where to create the database where the results will be stored
'''
if not options.nonewpath:
from e2spt_classaverage import sptmakepath
options = sptmakepath (options, 'sptintrafsc')
else:
try:
findir = os.lisdir( options.path )
except:
print "ERROR: The path specified %s does not exist" %( options.path )
sys.exit()
inputfiles = []
if options.inputstem:
c = os.getcwd()
findir = os.listdir( c )
for f in findir:
if '.hdf' in f and options.inputstem in f:
if options.verbose > 8:
print "\nFound tiltseries!", f
inputfiles.append( f ) #C:The input files are put into a dictionary in the format {originalseriesfile:[originalseriesfile,volumefile]}
elif options.input:
inputfiles.append( options.input )
for fi in inputfiles:
#genOddAndEvenVols( options, fi )
ret = genOddAndEvenVols( options, fi,[] )
volOdd = ret[1]
volEven = ret[0]
if options.savehalfvolumes and volOdd and volEven:
volOdd.write_image( options.path + '/' + fi.replace('.hdf','_ODDVOL.hdf'), 0 )
volEven.write_image( options.path + '/' + fi.replace('.hdf','_EVENVOL.hdf'), 0 )
retfsc = fscOddVsEven( options, fi, volOdd, volEven )
fscfilename = retfsc[0]
fscarea = retfsc[1]
if options.averagehalves:
avgr = Averagers.get( options.averager[0], options.averager[1] )
avgr.add_image( recOdd )
avgr.add_image( recEven )
avg = avgr.finish()
avg['origin_x'] = 0
avg['origin_y'] = 0
avg['origin_z'] = 0
avg['apix_x'] = apix
avg['apix_y'] = apix
avg['apix_z'] = apix
avgfile = options.path + '/AVG.hdf'
avg.write_image( avgfile, 0 )
E2end(logger)
return
def serveFile(filepath):
#shlex etc sanitise
if [f for f in os.lisdir(os.getcwd()) if os.path.isfile(f) and if f==filepath]:
return f
import tensorflow as tf from tensorflow import keras import string import cv2 immport random import os PATH='characters' all_symbols=string.ascii_uppercase + string.ascii_lowercase +'0123456789' + '^%/*+-' traindata=[] for char in all_symbols: path=os.path.join(PATH,char) for img in os.lisdir(path): symb_index=all_symbol.index(char) img_array=cv2.immread(os.path.join(path,img),cv2.IMREAD_GRAYSCALE) image_array=cv2.resize(img_array,(100,100)) traindata.append([image_array,symb_index]) random.shuffle(traindata) Xfull=[] yfull=[] for freatures,lables in traindata: Xfull.append(freatues) yfull.append(labels) X=np.array(Xfull,dtype='float64') y=np.array(yfull,dtype='float64') def model(): inputs = keras.Input(shape=(100,100,1))
# C:\Users\victo\OneDrive\Área de Trabalho\Pessoal\Python
# .\ pasta atual
# ..\ pasta acima
os.path.abspath()
os.path.isabs()
os.path.dirname()
os.path.basename()
os.path.exists()
os.path.isfile()
os.path.isdir()
os.path.getsize()
os.walk()
os.lisdir() #lista pastas e arquivos
os.makedirs() #cria pastas
arquivo = open('c:\\Users\\victo\\oi.txt',
'a') # abrir, 'w' para sobrescrever, 'a' para append
arquivo.read() # ler
arquivo.write('lalala') # escrever
arquivo.close() # fechar
# import shelve para arquivos com dados complexos (dicionários, listas)
# deletes
shutil.copy('origem', 'destino\\(renomear)?')
shutil.copytree('or', 'dest')
File "<pyshell#4>", line 1, in <module>
os.listir()
AttributeError: module 'os' has no attribute 'listir'
>>> os.chdir('C:/Users/이도영/Desktop/수업 2-2/2D겜플/2DGP_03')
>>> os.listir()
Traceback (most recent call last):
File "<pyshell#6>", line 1, in <module>
os.listir()
AttributeError: module 'os' has no attribute 'listir'
>>> os.chdir('C:/Users/이도영/Desktop/수업 2-2/2D겜플/2DGP_03/res')
>>> os.listir()
Traceback (most recent call last):
File "<pyshell#8>", line 1, in <module>
os.listir()
AttributeError: module 'os' has no attribute 'listir'
>>> os.lisdir()
Traceback (most recent call last):
File "<pyshell#9>", line 1, in <module>
os.lisdir()
AttributeError: module 'os' has no attribute 'lisdir'
>>> os.listdir()
['animation_sheet.png', 'character.png', 'grass.png', 'run_animation.png']
>>>
>>> image = load_image('character.png')
>>> for y in range(100, 501, 80):
for x in range(100,701, 35):
image.draw_now(x,y)
>>> open canvase()
SyntaxError: invalid syntax
def occurence(self, dossier, dossier_image, nom, image):
user = "******"
self.dossier_image = dossier_image
self.dossier = dossier
self.nom = nom
self.image = image
liste_travailler = []
os.chdir(self.dossier)
liste = os.lisdir(".")
for i in liste:
liste_travailler.append(i[:-4])
liste_travailler.sort()
voiture = liste_travailler[:13]
tram = liste_travailler[13:25]
panneau = liste_travailler[26:49]
immeuble = liste_travailler[50:76]
se_placer(self, self.dossier_image)
for i in liste_travailler:
if i in range(1, 13):
self.cursor.execute("""
INSERT INTO image_ciel
(categorie, nom, image)
VALUES (%s, %s),""" (voiture, self.nom, self.image))
self.connexion.commit()
elif i in range(13, 25):
self.cursor.execute("""
INSERT INTO image_ciel
(categorie, nom, image)
VALUES (%s, %s),""" (tram, self.nom, self.image))
self.connexion.commit()
elif i in range(26, 49):
self.cursor.execute("""
INSERT INTO image_ciel
(categorie, nom, image)
VALUES (%s, %s),""" (panneau, self.nom, self.image))
self.connexion.commit()
elif i in range(50, 76):
self.cursor.execute("""
INSERT INTO image_ciel
(categorie, nom, image)
VALUES (%s, %s),""" (immeuble, self.nom, self.image))
self.connexion.commit()
import os path = "/home/yashaswini/Downloads" l = os.lisdir(path) print(l)
# 100 or so links. We want this function to have integer inputs as to make multiprocessing
# a little easier.
def process_file(i):
# This function downloads the html text from each page downloaded in the previous
# steps.
df = pd.read_pickle('ProPublica/'+str(i))
df['html'] = None
for j, row in enumerate(df.iterrows()):
df.iloc[j]['html'] = process_page(row[1]['url'])
df.to_pickle('ProPublicaProcessed/'+str(i))
# In[ ]:
# We'll open up five threads and go to work.
files = os.lisdir('ProPublica')
# Remember p = Pool() was defined previously.
p.map(process_file, files)
p.terminate()
p.join()
# #### Phew. We did it.
#
# We now have all the HTML content downloaded locally to our folder ProPublicaProcessed. We'll need to clean it and extract text, but that's for the next segment.
import re
import os
files=os.lisdir()
print(files)
flag=0
for item in files:
match=re.match(r"[A-Z_a-z0-9]+.py$",item)
if match:
if(flag==0):
c_size=os.path.getsize(item)
large=c_size
flag=flag+1
c_size=os.path.getsize(item)
large=max(large,c_size)
if large==c_size:
large_file=item
else:
print("not python file")
#busy_days.py","date_month.py","last_name_sort.py"]
print("Largest file is: ",large_file)
'''for file in list_files:
with open(file,mode="r") as files:
files.read()
s=files.tell()
file_size[int(s)]=file'''
'''largest= max(file_size.keys())
print(file_size)
print("Largest file is: ",file_size[largest])'''
def scan_zip():
files = os.lisdir()
for f in files:
if f.endswith('.zip'):
return f
oneday = timedelta(days=number)
now = datetime.now()
today = datetime(now.year, now.month, now.day) + oneday
today = today.strftime('%Y%m%d')
return today
ssh = connect()
dirs = exec_commands(ssh, 'ls /data/all')
dirs = str(dirs, 'utf-8')
dirs = dirs.split('\n')
print(type(dirs))
command = 'ls path'
r = os.popen(command)
info = os.lisdir('path')
download_list = []
with open('path/total_data.txt', 'r', encoding='utf-8') as f:
for i in f:
i = i.replace('\n', '')
download_list.append(i)
number = 0
for dir in dirs:
if dir not in download_list:
if number >= 80:
break
if dir != '':
print(dir)
number += 1
os.system('scp -r [email protected]:path/{} path'.format(dir))
import os
import shutil
folder1 = os.lisdir('path to your imagefolder')
folder2 = os.listdir('path to your xml labeled folder')
cnt = 0
for item1 in folder1:
for item2 in folder2:
if cnt < 2001: #counting trough 2000 files/images.
if (item1.strip('.jpg') == item2.strip('.xml')):
shutil.copy('path to your imagefolder' + item1,
'destination folder for image folder')
shutil.copy('path to your xml folder' + item2,
'destination folder for xml files')
op_path = op_dir + str(case) + '.json'
if os.path.isfile(op_path):
if degrees[case] == 0:
opinion = json_to_dict(op_path)
for key in opinion.keys():
value = opinion[key]
if type(value) is unicode:
if 'denied' in value or 'certiorari' in value:
case_metadata.loc[case, 'cert_case'] = True
else:
missing_opinion.append(case)
if remove:
# remove opinion files
for file_name in os.lisdir(op_dir):
os.remove(file_name)
os.rmdir(op_dir)
print 'there were %d cases missing opinions' % len(missing_opinion)
print missing_opinion
return case_metadata[case_metadata['cert_case']].index.tolist()
def find_time_travelers(data_dir):
"""
Some edges cite forwards in time...
"""
case_metadata = pd.read_csv(data_dir + 'raw/case_metadata_master_r.csv',
def main():
progname = os.path.basename(sys.argv[0])
usage = """
This program takes a subtomgoram tiltseries (subtiltseries) as extracted with
e2spt_subtilt.py, and computes the resolution of two volumes reconstructed with
the even and the odd images in the tilt series. Must be in HDF format.
Note that the apix in the header must be accurate to get sensible results.
(You can fix the header of an image with e2fixheaderparam.py).
"""
parser = EMArgumentParser(usage=usage, version=EMANVERSION)
parser.add_argument(
"--inputstem",
type=str,
default='',
help=
"""Default=None. Aligned tilt series. String common to all files to be processed, in the current folder. For example, if you have many subtiltseries named subt00.hdf, subt01.hdf, ...subt99.hdf, you would supply --stem=subt to have all these processed."""
)
parser.add_argument(
'--path',
type=str,
default='sptintrafsc',
help="""Default=sptintrafsc. Directory to save the results.""")
parser.add_argument(
'--nonewpath',
action='store_true',
default=False,
help=
"""Default=False. If True, a new --path directory will not be made. Therefore, whatever is sepcified in --path will be used as the output directory. Note that this poses the risk of overwriting data."""
)
parser.add_argument(
'--input',
type=str,
default='',
help=
"""Default=None. Subtiltseries file to process. If processing a single file, --inputstem will work too, but you can also just provide the entire filename here --input=subt00.hdf"""
)
parser.add_argument(
'--savehalftiltseries',
action='store_true',
default=False,
help=
"""Default=False. If this parameter is on, the odd and even subtiltseries will be saved."""
)
parser.add_argument(
'--savehalfvolumes',
action='store_true',
default=False,
help=
"""Default=False. If this parameter is on, the odd and even volumes will be saved."""
)
parser.add_argument(
"--reconstructor",
type=str,
default="fourier:mode=gauss_2",
help=
"""Default=fourier:mode=gauss_2. The reconstructor to use to reconstruct the tilt series into a tomogram. Type 'e2help.py reconstructors' at the command line to see all options and parameters available. To specify the interpolation scheme for the fourier reconstructor, specify 'mode'. Options are 'nearest_neighbor', 'gauss_2', 'gauss_3', 'gauss_5'. For example --reconstructor=fourier:mode=gauss_5 """
)
parser.add_argument(
"--pad2d",
type=float,
default=0.0,
help=
"""Default=0.0. Padding factor (e.g., 2.0, to make the box twice as big) to zero-pad the 2d images in the tilt series for reconstruction purposes (the final reconstructed subvolumes will be cropped back to the original size though)."""
)
parser.add_argument(
"--pad3d",
type=float,
default=0.0,
help=
"""Default=0.0. Padding factor (e.g., 2.0, to make the box twice as big) to zero-pad the volumes for reconstruction purposes (the final reconstructed subvolumes will be cropped back to the original size though)."""
)
parser.add_argument(
"--averager",
type=str,
default="mean.tomo",
help=
"""Default=mean.tomo. The type of averager used to produce the class average."""
)
parser.add_argument(
"--averagehalves",
action="store_true",
default=False,
help="""Default=False. This will averager the even and odd volumes.""")
parser.add_argument(
"--ppid",
type=int,
default=-1,
help=
"""default=-1. Set the PID of the parent process, used for cross platform PPID."""
)
parser.add_argument(
"--verbose",
"-v",
dest="verbose",
action="store",
metavar="n",
type=int,
default=0,
help=
"verbose level [0-9], higner number means higher level of verboseness")
parser.add_argument(
"--nolog",
action="store_true",
default=False,
help=
"Default=False. Turn off recording of the command ran for this program onto the .eman2log.txt file"
)
(options, args) = parser.parse_args()
#if options.reconstructor == 'None' or options.reconstructor == 'none':
# options.reconstructor = None
#if options.reconstructor and options.reconstructor != 'None' and options.reconstructor != 'none':
# options.reconstructor=parsemodopt(options.reconstructor)
#if options.averager:
# options.averager=parsemodopt(options.averager)
from e2spt_classaverage import sptOptionsParser
options = sptOptionsParser(options)
logger = E2init(sys.argv, options.ppid)
'''
Make the directory where to create the database where the results will be stored
'''
if not options.nonewpath:
from e2spt_classaverage import sptmakepath
options = sptmakepath(options, 'sptintrafsc')
else:
try:
findir = os.lisdir(options.path)
except:
print "ERROR: The path specified %s does not exist" % (
options.path)
sys.exit()
inputfiles = []
if options.inputstem:
c = os.getcwd()
findir = os.listdir(c)
for f in findir:
if '.hdf' in f and options.inputstem in f:
if options.verbose > 8:
print "\nFound tiltseries!", f
inputfiles.append(
f
) #C:The input files are put into a dictionary in the format {originalseriesfile:[originalseriesfile,volumefile]}
elif options.input:
inputfiles.append(options.input)
for fi in inputfiles:
#genOddAndEvenVols( options, fi )
ret = genOddAndEvenVols(options, fi, [])
volOdd = ret[1]
volEven = ret[0]
if options.savehalfvolumes and volOdd and volEven:
volOdd.write_image(
options.path + '/' + fi.replace('.hdf', '_ODDVOL.hdf'), 0)
volEven.write_image(
options.path + '/' + fi.replace('.hdf', '_EVENVOL.hdf'), 0)
retfsc = fscOddVsEven(options, fi, volOdd, volEven)
fscfilename = retfsc[0]
fscarea = retfsc[1]
if options.averagehalves:
avgr = Averagers.get(options.averager[0], options.averager[1])
avgr.add_image(recOdd)
avgr.add_image(recEven)
avg = avgr.finish()
avg['origin_x'] = 0
avg['origin_y'] = 0
avg['origin_z'] = 0
avg['apix_x'] = apix
avg['apix_y'] = apix
avg['apix_z'] = apix
avgfile = options.path + '/AVG.hdf'
avg.write_image(avgfile, 0)
E2end(logger)
return
def test_load_data(datafiles):
path = str(datafiles)
assert len(os.lisdir(path)) == 1
assert os.path.isfile(os.path.join(path, 'CPAC2019.xlsx'))
assert len(datafiles.listdir()) == 1
assert (datafiles / 'CPAC2019.xlsx').check(file=1)
