def run(self): print "STARTING PROCESS " + str(self.year) for month in range(1,13): print "***Starting to clean %d %d" % (self.year, month) #clean_text(self.year, month) combine_data.combine_data(self.year, month) print "***Done with %d %d" % (self.year, month) #content = 'Clean-Up done with %s' % str(self.year) #send_email(content) print "EXITING PROCESS " + str(self.year)
def collapse_data(data_dir, incl_excl_list, n_b0s_list, sep_av_list, transform_list, roi_list):
"""
collapse_data reads in files from a series of results_files and collapses
across all of them so they can be plotted together
Inputs: data_dir
incl_excl_list
n_b0s_list
sep_av_list
transform_list
roi_list
Output: data array
"""
#==========================================================================
import os
import numpy as np
import numpy.lib.recfunctions as rfn
from glob import glob
import itertools as it
#------------------------------------------------------------------------------
from combine_data import combine_data
from get_b0_orders import get_b0_orders
#==========================================================================
print ' Collapsing data: B0 orders by ec volume'
# Find all the results files in all the b0_order folders
for incl_excl, n_b0s, sep_av, transform, roi_name in it.product(incl_excl_list, n_b0s_list, sep_av_list, transform_list, roi_list):
# Start off with an empty data array
data_allorders = None
b0_orders = get_b0_orders(np.int(n_b0s))
for b0_order in b0_orders:
glob_string = os.path.join(data_dir, 'RESULTS', incl_excl, 'B0S_{}'.format(n_b0s),
'B0_ORDER_{}'.format(b0_order), sep_av, transform, '{}_FA_MD_vol.txt'.format(roi_name))
files = glob(glob_string)
dict = {'b0_order': b0_order}
# Read in every file and combine them
for file in files:
data = np.genfromtxt(file, dtype=None, delimiter=' ', names=True)
data_allorders = combine_data(data_allorders, data, dict)
# Name the results dir that this is going into:
results_allorders_dir = os.path.join(data_dir, 'RESULTS', incl_excl, 'B0S_{}'.format(n_b0s),
'ALL_ORDERS', sep_av, transform)
return data_allorders, results_allorders_dir
def one_round_of_simulation(cwd,simultaneous_worker,wait_minute):
#the first step we do is to generate the sh files
write_sh_file_and_submit(simultaneous_worker,cwd)
#the second step is to check whether the final data is generated
start_time = int(time.time())
finished_generating_data=False
succeed=np.zeros((simultaneous_worker,1))
while not finished_generating_data:
for index in range(simultaneous_worker):
x_file_name = os.path.join(cwd, "X_training" + str(index) + ".p")
y_file_name = os.path.join(cwd, "Y_training" + str(index) + ".p")
try:
#try to open those files
#as sometimes the files can still not be generated
pickle.load(open(x_file_name, "rb"))
pickle.load(open(y_file_name, "rb"))
succeed[index,0]=1
except:
pass
time.sleep(15)
if sum(sum(succeed))==simultaneous_worker:
finished_generating_data=True
print('finished this round of simulation in normal end')
print(' ')
print('for this round of simulation, the time has elapsed this much',int(time.time())-start_time,'seconds')
if int(time.time())-start_time>60*wait_minute:
print('stop this round of simulation because time constraint')
subprocess.call('qdel -u zh296', shell=True)
finished_generating_data = True
#the third step is to increase the data
# append new data to the total pickle file
total_good_data_so_far = combine_data(simultaneous_worker)
return total_good_data_so_far
def Q_ec_vol_n6(data_dir, incl_excl_list, sep_av_list, transform_list, roi_list, colors, shapes):
"""
Q_ec_vol_n6 asks the question:
"How does the volume that you register to affect the measurement
when you use all the data"
It reads in all the necessary files from a series of results_files and collapses
across all of them so they can be plotted together
Inputs: data_dir
incl_excl_list
sep_av_list
transform_list
roi_list
Output: data array
"""
#==========================================================================
import os
import numpy as np
import numpy.lib.recfunctions as rfn
from glob import glob
import itertools as it
#---------------------------------------------------------------------------
from combine_data import combine_data
from get_b0_orders import get_b0_orders
from plot_data import plot_data
from read_in_data import read_in_data
#==========================================================================
print ' Question: How does the choice of eddy correct volume affect the measurements?'
# Find all the results files in all the b0_order folders
for incl_excl, sep_av, transform, roi_name in it.product(incl_excl_list, sep_av_list, transform_list, roi_list):
# Start off with an empty data array
data_allorders = None
b0_orders = get_b0_orders(np.int(6))
for b0_order in b0_orders:
glob_string = os.path.join(data_dir, 'RESULTS', incl_excl, 'B0S_6',
'B0_ORDER_{}'.format(b0_order), sep_av, transform, '{}_FA_MD_vol.txt'.format(roi_name))
files = glob(glob_string)
dict = {'b0_order': b0_order}
# Read in every file and combine them
for file in files:
data = read_in_data(file)
data_allorders = combine_data(data_allorders, data, dict)
# Name the results dir that this is going into:
results_allorders_dir = os.path.join(data_dir, 'RESULTS', incl_excl, 'B0S_6',
'ALL_ORDERS', sep_av, transform)
# Now plot the data
plot_data(data_allorders, results_allorders_dir, roi_name, colors, shapes)
if __name__ == '__main__':
time_start = time.time()
for i in range(LAX_ROUND):
time_start_round = time.time()
if i == 0:
print('round:', i, 'is processing......')
splite_data.splite_data(__ORIGIN_FOLDER__, __SPLITE_FOLDER__)
print('Compelte splite data in round:', i)
driver_origin_data_flix.driver_flix(__SPLITE_FOLDER__,
__FLIXED_FOLDER__)
print('Compelte flix data in round:', i)
print('Deleting splite data in round:', i)
os.system(
"cd /home/youyizhe/TrafficDataProcesser/test_script/splite/;rm *.txt"
)
combine_data.combine_data(__FLIXED_FOLDER__, __COMBINE_FOLDER__)
print('Compelte combine data in round:', i)
print('Deleting flixed data in round:', i)
os.system(
"cd /home/youyizhe/TrafficDataProcesser/test_script/flixed/;rm *.txt"
)
else:
print('round:', i, 'is processing......')
splite_data.splite_data(__COMBINE_FOLDER__, __SPLITE_FOLDER__)
print('Compelte splite data in round:', i)
driver_origin_data_flix.driver_flix(__SPLITE_FOLDER__,
__FLIXED_FOLDER__)
print('Compelte flix data in round:', i)
print('Deleting splite data in round:', i)
os.system(
"cd /home/youyizhe/TrafficDataProcesser/test_script/splite/;rm *.txt"
#!/home/vagrant/miniconda3/bin/python
import docx
from combine_data import combine_data
combined_data, temperature_fail, unknown_fail = combine_data()
total_tests = len(combined_data)
tests_failed = len(temperature_fail) + len(unknown_fail)
test_passed = total_tests - tests_failed
test_fail_temp = len(temperature_fail)
test_fail_unknown = len(unknown_fail)
document = docx.Document()
document.add_heading("Acme Test report", 0)
document.add_paragraph('')
test_ran_dates_str = "Tests Summary: Tests ran from: " + combined_data[0][0] + " to " + combined_data[-1][0]
document.add_heading(test_ran_dates_str, level = 1)
document.add_paragraph('')
p = document.add_paragraph("Total number of tests: ")
#!/home/vagrant/miniconda3/bin/python
import docx
from combine_data import combine_data
combined_data, temperature_fail, unknown_fail = combine_data()
total_tests = len(combined_data)
tests_failed = len(temperature_fail) + len(unknown_fail)
test_passed = total_tests - tests_failed
test_fail_temp = len(temperature_fail)
test_fail_unknown = len(unknown_fail)
document = docx.Document()
document.add_heading("Acme Test report", 0)
document.add_paragraph('')
test_ran_dates_str = "Tests Summary: Tests ran from: " + combined_data[0][
0] + " to " + combined_data[-1][0]
document.add_heading(test_ran_dates_str, level=1)
document.add_paragraph('')
p = document.add_paragraph("Total number of tests: ")
p.add_run(str(total_tests)).bold = True
for file_extension in file_extensions:
files.extend([
obj.key for obj in s3.Bucket(manager_data['s3_bucket']).objects.all()
if obj.key.startswith(f"results/{instance_id}")
and obj.key.endswith(f".{file_extension}")
])
logging.info(f"Combining {len(files)} Partial Data Files")
os.makedirs(f"results/{instance_id}")
for file in files:
response = s3.meta.client.download_file(manager_data['s3_bucket'], file,
file)
fileout = f"results/{instance_id}_{output_file}"
combine_data(files, fileout)
logging.info(f"Uploading combined data file '{fileout}' to S3 bucket")
response = s3.meta.client.upload_file(fileout, manager_data['s3_bucket'],
f"{fileout}")
for file in files:
try:
os.remove(file)
response = s3.meta.client.delete_object(
Bucket=manager_data['s3_bucket'], Key=file)
except FileNotFoundError:
pass
log_files = [
obj.key for obj in s3.Bucket(manager_data['s3_bucket']).objects.all() if
def Q_n_b0s(data_dir, incl_excl_list, sep_av_list, transform_list, roi_list,
ec_b0_list, colors, shapes):
"""
Q_ec_vol_n6 asks the question:
"How does the number of B0s you include change your measurement?"
It reads in all the necessary files from a series of results_files and collapses
across all of them so they can be plotted together
Inputs: data_dir
incl_excl_list
sep_av_list
transform_list
roi_list
Output: data array
"""
#==========================================================================
import os
import numpy as np
import numpy.lib.recfunctions as rfn
from glob import glob
import itertools as it
#---------------------------------------------------------------------------
from combine_data import combine_data
from get_b0_orders import get_b0_orders
from plot_data import plot_data
from read_in_data import read_in_data
#==========================================================================
print ' Question: How does the number of B0s change your measurement'
# Find all the results files in all the b0_order folders
for incl_excl, sep_av, transform, roi_name, ec_b0 in it.product(
incl_excl_list, sep_av_list, transform_list, roi_list, ec_b0_list):
# Start off with an empty data array
data_allorders_allb0s = None
for n_b0s in range(1, 7):
b0_orders = get_b0_orders(np.int(n_b0s))
b0_orders = [order for order in b0_orders if order[:2] == ec_b0]
for b0_order in b0_orders:
glob_string = os.path.join(data_dir, 'RESULTS', incl_excl,
'B0S_{}'.format(n_b0s),
'B0_ORDER_{}'.format(b0_order),
sep_av, transform,
'{}_FA_MD_vol.txt'.format(roi_name))
files = glob(glob_string)
dict = {'b0_order': b0_order, 'n_b0s': n_b0s}
# Read in every file and combine them
for file in files:
data = read_in_data(file)
data_allorders_allb0s = combine_data(
data_allorders_allb0s, data, dict)
# Name the results dir that this is going into:
results_allorders_allb0s_dir = os.path.join(data_dir, 'RESULTS',
incl_excl, 'ALL_B0S',
'B0_{}'.format(ec_b0),
sep_av, transform)
# Now plot the data
plot_data(data_allorders_allb0s, results_allorders_allb0s_dir,
roi_name, colors, shapes)
# Now do the same thing, but with REALLY all the B0s
# Find all the results files in all the b0_order folders
for incl_excl, sep_av, transform, roi_name in it.product(
incl_excl_list, sep_av_list, transform_list, roi_list):
# Start off with an empty data array
data_allorders_allb0s = None
for n_b0s in range(1, 7):
b0_orders = get_b0_orders(np.int(n_b0s))
for b0_order in b0_orders:
glob_string = os.path.join(data_dir, 'RESULTS', incl_excl,
'B0S_{}'.format(n_b0s),
'B0_ORDER_{}'.format(b0_order),
sep_av, transform,
'{}_FA_MD_vol.txt'.format(roi_name))
files = glob(glob_string)
dict = {'b0_order': b0_order, 'n_b0s': n_b0s}
# Read in every file and combine them
for file in files:
data = read_in_data(file)
data_allorders_allb0s = combine_data(
data_allorders_allb0s, data, dict)
# Name the results dir that this is going into:
results_allorders_allb0s_dir = os.path.join(data_dir, 'RESULTS',
incl_excl, 'ALL_B0S',
'ALL_B0S', sep_av,
transform)
# Now plot the data
plot_data(data_allorders_allb0s, results_allorders_allb0s_dir,
roi_name, colors, shapes)
def collapse_data(data_dir, incl_excl_list, n_b0s_list, sep_av_list,
transform_list, roi_list):
"""
collapse_data reads in files from a series of results_files and collapses
across all of them so they can be plotted together
Inputs: data_dir
incl_excl_list
n_b0s_list
sep_av_list
transform_list
roi_list
Output: data array
"""
#==========================================================================
import os
import numpy as np
import numpy.lib.recfunctions as rfn
from glob import glob
import itertools as it
#------------------------------------------------------------------------------
from combine_data import combine_data
from get_b0_orders import get_b0_orders
#==========================================================================
print ' Collapsing data: B0 orders by ec volume'
# Find all the results files in all the b0_order folders
for incl_excl, n_b0s, sep_av, transform, roi_name in it.product(
incl_excl_list, n_b0s_list, sep_av_list, transform_list, roi_list):
# Start off with an empty data array
data_allorders = None
b0_orders = get_b0_orders(np.int(n_b0s))
for b0_order in b0_orders:
glob_string = os.path.join(data_dir, 'RESULTS', incl_excl,
'B0S_{}'.format(n_b0s),
'B0_ORDER_{}'.format(b0_order), sep_av,
transform,
'{}_FA_MD_vol.txt'.format(roi_name))
files = glob(glob_string)
dict = {'b0_order': b0_order}
# Read in every file and combine them
for file in files:
data = np.genfromtxt(file,
dtype=None,
delimiter=' ',
names=True)
data_allorders = combine_data(data_allorders, data, dict)
# Name the results dir that this is going into:
results_allorders_dir = os.path.join(data_dir, 'RESULTS',
incl_excl,
'B0S_{}'.format(n_b0s),
'ALL_ORDERS', sep_av,
transform)
return data_allorders, results_allorders_dir
def Q_ec_vol_n6(data_dir, incl_excl_list, sep_av_list, transform_list,
roi_list, colors, shapes):
"""
Q_ec_vol_n6 asks the question:
"How does the volume that you register to affect the measurement
when you use all the data"
It reads in all the necessary files from a series of results_files and collapses
across all of them so they can be plotted together
Inputs: data_dir
incl_excl_list
sep_av_list
transform_list
roi_list
Output: data array
"""
#==========================================================================
import os
import numpy as np
import numpy.lib.recfunctions as rfn
from glob import glob
import itertools as it
#---------------------------------------------------------------------------
from combine_data import combine_data
from get_b0_orders import get_b0_orders
from plot_data import plot_data
from read_in_data import read_in_data
#==========================================================================
print ' Question: How does the choice of eddy correct volume affect the measurements?'
# Find all the results files in all the b0_order folders
for incl_excl, sep_av, transform, roi_name in it.product(
incl_excl_list, sep_av_list, transform_list, roi_list):
# Start off with an empty data array
data_allorders = None
b0_orders = get_b0_orders(np.int(6))
for b0_order in b0_orders:
glob_string = os.path.join(data_dir, 'RESULTS', incl_excl, 'B0S_6',
'B0_ORDER_{}'.format(b0_order), sep_av,
transform,
'{}_FA_MD_vol.txt'.format(roi_name))
files = glob(glob_string)
dict = {'b0_order': b0_order}
# Read in every file and combine them
for file in files:
data = read_in_data(file)
data_allorders = combine_data(data_allorders, data, dict)
# Name the results dir that this is going into:
results_allorders_dir = os.path.join(data_dir, 'RESULTS', incl_excl,
'B0S_6', 'ALL_ORDERS', sep_av,
transform)
# Now plot the data
plot_data(data_allorders, results_allorders_dir, roi_name, colors,
shapes)
def Q_n_b0s(data_dir, incl_excl_list, sep_av_list, transform_list, roi_list, ec_b0_list, colors, shapes):
"""
Q_ec_vol_n6 asks the question:
"How does the number of B0s you include change your measurement?"
It reads in all the necessary files from a series of results_files and collapses
across all of them so they can be plotted together
Inputs: data_dir
incl_excl_list
sep_av_list
transform_list
roi_list
Output: data array
"""
#==========================================================================
import os
import numpy as np
import numpy.lib.recfunctions as rfn
from glob import glob
import itertools as it
#---------------------------------------------------------------------------
from combine_data import combine_data
from get_b0_orders import get_b0_orders
from plot_data import plot_data
from read_in_data import read_in_data
#==========================================================================
print ' Question: How does the number of B0s change your measurement'
# Find all the results files in all the b0_order folders
for incl_excl, sep_av, transform, roi_name, ec_b0 in it.product(incl_excl_list, sep_av_list, transform_list, roi_list, ec_b0_list):
# Start off with an empty data array
data_allorders_allb0s = None
for n_b0s in range(1,7):
b0_orders = get_b0_orders(np.int(n_b0s))
b0_orders = [ order for order in b0_orders if order[:2] == ec_b0 ]
for b0_order in b0_orders:
glob_string = os.path.join(data_dir, 'RESULTS', incl_excl, 'B0S_{}'.format(n_b0s),
'B0_ORDER_{}'.format(b0_order), sep_av, transform, '{}_FA_MD_vol.txt'.format(roi_name))
files = glob(glob_string)
dict = { 'b0_order': b0_order, 'n_b0s' : n_b0s }
# Read in every file and combine them
for file in files:
data = read_in_data(file)
data_allorders_allb0s = combine_data(data_allorders_allb0s, data, dict)
# Name the results dir that this is going into:
results_allorders_allb0s_dir = os.path.join(data_dir, 'RESULTS', incl_excl, 'ALL_B0S',
'B0_{}'.format(ec_b0), sep_av, transform)
# Now plot the data
plot_data(data_allorders_allb0s, results_allorders_allb0s_dir, roi_name, colors, shapes)
# Now do the same thing, but with REALLY all the B0s
# Find all the results files in all the b0_order folders
for incl_excl, sep_av, transform, roi_name in it.product(incl_excl_list, sep_av_list, transform_list, roi_list):
# Start off with an empty data array
data_allorders_allb0s = None
for n_b0s in range(1,7):
b0_orders = get_b0_orders(np.int(n_b0s))
for b0_order in b0_orders:
glob_string = os.path.join(data_dir, 'RESULTS', incl_excl, 'B0S_{}'.format(n_b0s),
'B0_ORDER_{}'.format(b0_order), sep_av, transform, '{}_FA_MD_vol.txt'.format(roi_name))
files = glob(glob_string)
dict = { 'b0_order': b0_order, 'n_b0s' : n_b0s }
# Read in every file and combine them
for file in files:
data = read_in_data(file)
data_allorders_allb0s = combine_data(data_allorders_allb0s, data, dict)
# Name the results dir that this is going into:
results_allorders_allb0s_dir = os.path.join(data_dir, 'RESULTS', incl_excl, 'ALL_B0S',
'ALL_B0S', sep_av, transform)
# Now plot the data
plot_data(data_allorders_allb0s, results_allorders_allb0s_dir, roi_name, colors, shapes)
