def threaded_function():
global memories
global done
start = timeit.timeit()
while not done:
#cpus.append(psutil.cpu_percent())
memories.append(psutil.virtual_memory()._asdict())
end = timeit.timeit()
if(end - start>5):
time.sleep(5);
start=time.timeit()
print("done")
def ap():
_time_st[s] = timeit()
return ()
def Esol_series_bessel(M, e, N=2):
E = M
for n in xrange(1, N + 1):
E += (2. / n) * jn(n, e) * sin(n * M)
return E
# # Comparación de Soluciones
# In[256]:
e = 0.6
Ms = linspace(0, 2 * pi, 100)
t1 = timeit()
# Numeric solution
Ens = array([Esol_numeric(M, e) for M in Ms])
t2 = timeit()
print "Numeric:", (t2 - t1) * 1E6
t1 = t2
# Analytic solution
Eas = array([Esol_analytic(M, e) for M in Ms])
t2 = timeit()
print "Analytic:", (t2 - t1) * 1E6
t1 = t2
# Fourier series solution
Efs = array([Esol_series_fourier(M, e) for M in Ms])
def Esol_series_bessel(M,e,N=2):
E=M
for n in xrange(1,N+1):
E+=(2./n)*jn(n,e)*sin(n*M)
return E
# # Comparación de Soluciones
# In[256]:
e=0.6
Ms=linspace(0,2*pi,100)
t1=timeit()
# Numeric solution
Ens=array([Esol_numeric(M,e) for M in Ms])
t2=timeit();print "Numeric:",(t2-t1)*1E6;t1=t2
# Analytic solution
Eas=array([Esol_analytic(M,e) for M in Ms])
t2=timeit();print "Analytic:",(t2-t1)*1E6;t1=t2
# Fourier series solution
Efs=array([Esol_series_fourier(M,e) for M in Ms])
t2=timeit();print "Fourier:",(t2-t1)*1E6;t1=t2
# Bessel series solution
Ebs=array([Esol_series_bessel(M,e,N=7) for M in Ms])
def wrapper(*args, **kwargs):
start = timeit()
rv = func(*args, **kwargs)
end = timeit()
print(start - end)
return rv
def convert(data_dir, output_filepath, append_to_existing=False):
"""
Converts TransXchange formatted schedule data into GTFS feed.
data_dir : str
Data directory containing one or multiple TransXchange .xml files.
output_filepath : str
Full filepath to the output GTFS zip-file, e.g. '/home/myuser/data/my_gtfs.zip'
append_to_existing : bool (default is False)
Flag for appending to existing gtfs-database. This might be useful if you have
TransXchange .xml files distributed into multiple directories (e.g. separate files for
train data, tube data and bus data) and you want to merge all those datasets into a single
GTFS feed.
"""
# Total start
tot_start_t = timeit()
# Filepath for temporary gtfs db
target_dir = os.path.dirname(output_filepath)
gtfs_db = os.path.join(target_dir, "gtfs.db")
# If append to database is false remove previous gtfs-database if it exists
if append_to_existing == False:
if os.path.exists(gtfs_db):
os.remove(gtfs_db)
# NAPTAN stops
naptan_stops_fp = get_path("naptan_stops")
# Retrieve all TransXChange files
files = glob.glob(os.path.join(data_dir, "*.xml"))
# Iterate over files
print("Populating database ..")
# Limit the processed files by file size (in MB)
# Files with lower filesize than below will be processed
file_size_limit = 1000
# Create workers
workers = create_workers(input_files=files,
file_size_limit=file_size_limit,
stops_fp=naptan_stops_fp,
gtfs_db=gtfs_db)
# Create Pool
pool = multiprocessing.Pool()
# Generate GTFS info to the database in parallel
pool.map(process_files, workers)
# Print information about the total time
tot_end_t = timeit()
tot_duration = (tot_end_t - tot_start_t) / 60
print("===========================================================")
print("It took %s minutes in total." % round(tot_duration, 1))
# Generate output dictionary
gtfs_data = generate_gtfs_export(gtfs_db)
# Export to disk
save_to_gtfs_zip(output_zip_fp=output_filepath, gtfs_data=gtfs_data)
def process_files(parallel):
# Get files from input instance
files = parallel.input_files
file_size_limit = parallel.file_size_limit
naptan_stops_fp = parallel.stops_fp
gtfs_db = parallel.gtfs_db
for idx, fp in enumerate(files):
# Filesize
size = round((os.path.getsize(fp) / 1000000), 1)
if file_size_limit < size:
continue
print(
"================================================================="
)
print("[%s / %s] Processing TransXChange file: %s" %
(idx, len(files), os.path.basename(fp)))
print("Size: %s MB" % size)
# Log start time
start_t = timeit()
data = untangle.parse(fp)
# Parse stops
stop_data = get_stops(data, naptan_stops_fp=naptan_stops_fp)
# Parse agency
agency = get_agency(data)
# Parse GTFS info containing data about trips, calendar, stop_times and calendar_dates
gtfs_info = get_gtfs_info(data)
# Parse stop_times
stop_times = get_stop_times(gtfs_info)
# Parse trips
trips = get_trips(gtfs_info)
# Parse calendar
calendar = get_calendar(gtfs_info)
# Parse calendar_dates
calendar_dates = get_calendar_dates(gtfs_info)
# Parse routes
routes = get_routes(gtfs_info=gtfs_info, data=data)
# Initialize database connection
conn = sqlite3.connect(gtfs_db)
# Only export data into db if there exists valid stop_times data
if len(stop_times) > 0:
stop_times.to_sql(name='stop_times',
con=conn,
index=False,
if_exists='append')
stop_data.to_sql(name='stops',
con=conn,
index=False,
if_exists='append')
routes.to_sql(name='routes',
con=conn,
index=False,
if_exists='append')
agency.to_sql(name='agency',
con=conn,
index=False,
if_exists='append')
trips.to_sql(name='trips',
con=conn,
index=False,
if_exists='append')
calendar.to_sql(name='calendar',
con=conn,
index=False,
if_exists='append')
if calendar_dates is not None:
calendar_dates.to_sql(name='calendar_dates',
con=conn,
index=False,
if_exists='append')
else:
print(
"UserWarning: File %s did not contain valid stop_sequence data, skipping."
% (os.path.basename(fp)))
# Close connection
conn.close()
# Log end time and parse duration
end_t = timeit()
duration = (end_t - start_t) / 60
print("It took %s minutes." % round(duration, 1))
def process_files(parallel):
# Get files from input instance
files = parallel.input_files
file_size_limit = parallel.file_size_limit
gtfs_db = parallel.gtfs_db
for idx, path in enumerate(files):
# If type is string, it is a direct filepath to XML
if isinstance(path, str):
data, file_size, xml_name = read_unpacked_xml(path)
# If the type is dictionary contents are in a zip
elif isinstance(path, dict):
# If the type of value is a string the file can be read directly
# from the given Zipfile path, with following structure:
# {"transxchange_name.xml" : "/home/data/myzipfile.zip"}
if isinstance(list(path.values())[0], str):
data, file_size, xml_name = read_xml_inside_zip(path)
# If the type of value is a dictionary the xml-file
# is in a ZipFile which is inside another ZipFile.
# In such cases, the path stucture is:
# {"outermost_zipfile_path.zip": {"inner_zipfile.zip": "transxchange.xml"}}
elif isinstance(list(path.values())[0], dict):
data, file_size, xml_name = read_xml_inside_nested_zip(path)
else:
raise ValueError(
"Something is wrong with the input xml-file paths.")
else:
raise ValueError(
"Something is wrong with the input xml-file paths.")
# Filesize
size = round((file_size / 1000000), 1)
if file_size_limit < size:
continue
print(
"================================================================="
)
print("[%s / %s] Processing TransXChange file: %s" %
(idx, len(files), xml_name))
print("Size: %s MB" % size)
# Log start time
start_t = timeit()
# Parse stops
stop_data = get_stops(data)
if stop_data is None:
print("Did not found any valid stops. Skipping..")
continue
# Parse agency
agency = get_agency(data)
# Parse GTFS info containing data about trips, calendar, stop_times and calendar_dates
gtfs_info = get_gtfs_info(data)
# Parse stop_times
stop_times = get_stop_times(gtfs_info)
# Parse trips
trips = get_trips(gtfs_info)
# Parse calendar
calendar = get_calendar(gtfs_info)
# Parse calendar_dates
calendar_dates = get_calendar_dates(gtfs_info)
# Parse routes
routes = get_routes(gtfs_info=gtfs_info, data=data)
# Initialize database connection
conn = sqlite3.connect(gtfs_db)
# Only export data into db if there exists valid stop_times data
if len(stop_times) > 0:
stop_times.to_sql(name='stop_times',
con=conn,
index=False,
if_exists='append')
stop_data.to_sql(name='stops',
con=conn,
index=False,
if_exists='append')
routes.to_sql(name='routes',
con=conn,
index=False,
if_exists='append')
agency.to_sql(name='agency',
con=conn,
index=False,
if_exists='append')
trips.to_sql(name='trips',
con=conn,
index=False,
if_exists='append')
calendar.to_sql(name='calendar',
con=conn,
index=False,
if_exists='append')
if calendar_dates is not None:
calendar_dates.to_sql(name='calendar_dates',
con=conn,
index=False,
if_exists='append')
else:
print(
"UserWarning: File %s did not contain valid stop_sequence data, skipping."
% (xml_name))
# Close connection
conn.close()
# Log end time and parse duration
end_t = timeit()
duration = (end_t - start_t) / 60
print("It took %s minutes." % round(duration, 1))
def convert(input_filepath,
output_filepath,
append_to_existing=False,
worker_cnt=None,
file_size_limit=2000):
"""
Converts TransXchange formatted schedule data into GTFS feed.
input_filepath : str
File path to data directory or a ZipFile containing one or multiple TransXchange .xml files.
Also nested ZipFiles are supported (i.e. a ZipFile with ZipFile(s) containing .xml files.)
output_filepath : str
Full filepath to the output GTFS zip-file, e.g. '/home/myuser/data/my_gtfs.zip'
append_to_existing : bool (default is False)
Flag for appending to existing gtfs-database. This might be useful if you have
TransXchange .xml files distributed into multiple directories (e.g. separate files for
train data, tube data and bus data) and you want to merge all those datasets into a single
GTFS feed.
worker_cnt : int
Number of workers to distribute the conversion process. By default the number of CPUs is used.
file_size_limit : int
File size limit (in megabytes) can be used to skip larger-than-memory XML-files (should not happen).
"""
# Total start
tot_start_t = timeit()
# Filepath for temporary gtfs db
target_dir = os.path.dirname(output_filepath)
gtfs_db = os.path.join(target_dir, "gtfs.db")
# If append to database is false remove previous gtfs-database if it exists
if append_to_existing == False:
if os.path.exists(gtfs_db):
os.remove(gtfs_db)
# Retrieve all TransXChange files
files = get_xml_paths(input_filepath)
# Iterate over files
print("Populating database ..")
# Create workers
workers = create_workers(input_files=files,
worker_cnt=worker_cnt,
file_size_limit=file_size_limit,
gtfs_db=gtfs_db)
# Create Pool
pool = multiprocessing.Pool()
# Generate GTFS info to the database in parallel
pool.map(process_files, workers)
# Print information about the total time
tot_end_t = timeit()
tot_duration = (tot_end_t - tot_start_t) / 60
print("===========================================================")
print("It took %s minutes in total." % round(tot_duration, 1))
# Generate output dictionary
gtfs_data = generate_gtfs_export(gtfs_db)
# Export to disk
save_to_gtfs_zip(output_zip_fp=output_filepath, gtfs_data=gtfs_data)
def calibrate(args, jones, alphas):
# simple calibration to test if simulation went as expected.
# Note do not run on large data set
# load data
ms = table(args.ms)
time = ms.getcol('TIME')
_, tbin_idx, tbin_counts = chunkify_rows(time, args.utimes_per_chunk)
n_time = tbin_idx.size
ant1 = ms.getcol('ANTENNA1')
ant2 = ms.getcol('ANTENNA2')
n_ant = np.maximum(ant1.max(), ant2.max()) + 1
uvw = ms.getcol('UVW').astype(np.float64)
data = ms.getcol(args.out_col) # this is where we put the data
# we know it is pure Stokes I so we can solve using diagonals only
data = data[:, :, (0, 3)].astype(np.complex128)
n_row, n_freq, n_corr = data.shape
flag = ms.getcol('FLAG')
flag = flag[:, :, (0, 3)]
# get phase dir
radec0 = table(args.ms + '::FIELD').getcol('PHASE_DIR').squeeze().astype(
np.float64)
# get freqs
freq = table(args.ms + '::SPECTRAL_WINDOW').getcol('CHAN_FREQ')[0].astype(
np.float64)
assert freq.size == n_freq
# now get the model
# get source coordinates from lsm
lsm = Tigger.load(args.sky_model)
radec = []
stokes = []
spi = []
ref_freqs = []
for source in lsm.sources:
radec.append([source.pos.ra, source.pos.dec])
stokes.append([source.flux.I])
tmp_spec = source.spectrum
spi.append([tmp_spec.spi if tmp_spec is not None else 0.0])
ref_freqs.append([tmp_spec.freq0 if tmp_spec is not None else 1.0])
n_dir = len(stokes)
radec = np.asarray(radec)
lm = radec_to_lm(radec, radec0)
# get model visibilities
model = np.zeros((n_row, n_freq, n_dir, 2), dtype=np.complex)
stokes = np.asarray(stokes)
ref_freqs = np.asarray(ref_freqs)
spi = np.asarray(spi)
for d in range(n_dir):
Stokes_I = stokes[d] * (freq / ref_freqs[d])**spi[d]
model[:, :, d, 0:1] = im_to_vis(Stokes_I[None, :, None], uvw,
lm[d:d + 1], freq)
model[:, :, d, 1] = model[:, :, d, 0]
# set weights to unity
weight = np.ones_like(data, dtype=np.float64)
# initialise gains
jones0 = np.ones((n_time, n_ant, n_freq, n_dir, n_corr),
dtype=np.complex128)
# calibrate
ti = timeit()
jones_hat, jhj, jhr, k = gauss_newton(tbin_idx,
tbin_counts,
ant1,
ant2,
jones0,
data,
flag,
model,
weight,
tol=1e-5,
maxiter=100)
print("%i iterations took %fs" % (k, timeit() - ti))
# verify result
for p in range(2):
for q in range(p):
diff_true = np.angle(jones[:, p] * jones[:, q].conj())
diff_hat = np.angle(jones_hat[:, p] * jones_hat[:, q].conj())
try:
assert_array_almost_equal(diff_true, diff_hat, decimal=2)
except Exception as e:
print(e)
