def tempdir() -> str:
with tempfile.TemporaryDirectory() as name:
yield name
os.mkdir(workerrec['dbfarm'])
workerrec['proc'] = process.server(mapiport=workerrec['port'], dbname=workerrec['dbname'], dbfarm=workerrec['dbfarm'], stdin=process.PIPE, stdout=process.PIPE)
workerrec['conn'] = pymonetdb.connect(database=workerrec['dbname'], port=workerport, autocommit=True)
filename = fn_template.format(workerrec['num'])
t = threading.Thread(target=worker_load, args=[filename, workerrec, cmovies, ratings_table_def_fk])
t.start()
workerrec['loadthread'] = t
for wrec in workers:
wrec['loadthread'].join()
# Start supervisor database
supervisorport = freeport()
supervisorproc = None
workers = []
with tempfile.TemporaryDirectory() as TMPDIR:
os.mkdir(os.path.join(TMPDIR, "supervisor"))
with process.server(mapiport=supervisorport, dbname="supervisor", dbfarm=os.path.join(TMPDIR, "supervisor"), stdin=process.PIPE, stdout=process.PIPE) as supervisorproc:
supervisorconn = pymonetdb.connect(database='supervisor', port=supervisorport, autocommit=True)
supervisor_uri = "mapi:monetdb://localhost:{}/supervisor".format(supervisorport)
c = supervisorconn.cursor()
# Create the movies table and load the data
movies_filename=os.getenv("TSTDATAPATH")+"/netflix_data/movies.csv"
movies_create = "CREATE TABLE movies {}".format(MOVIES_TABLE_DEF)
c.execute(movies_create)
load_movies = "COPY INTO movies FROM '{}' USING DELIMITERS ',','\n','\"'".format(movies_filename)
c.execute(load_movies)
# Declare the ratings merge table on supervisor
mtable = "CREATE MERGE TABLE ratings {}".format(RATINGS_TABLE_DEF)
c.execute(screateq)
c.execute(sloadq)
rtable = repltable + workerrec['tpf']
rcreateq = 'create table %s %s' % (rtable, replicatedtabledef)
rloadq = 'copy into %s from \'%s\'' % (rtable, workerrec['repldata'].replace('\\', '\\\\'))
c.execute(rcreateq)
c.execute(rloadq)
ssbmpath = os.path.join(os.environ['TSTSRCBASE'], 'sql', 'benchmarks', 'ssbm', 'Tests')
ssbmdatapath = os.path.join(ssbmpath, 'SF-0.01')
masterport = freeport()
masterproc = None
workers = []
with tempfile.TemporaryDirectory() as tmpdir:
os.mkdir(os.path.join(tmpdir, 'master'))
with process.server(mapiport=masterport, dbname="master", dbfarm=os.path.join(tmpdir, 'master'), stdin = process.PIPE, stdout = process.PIPE) as masterproc:
masterconn = pymonetdb.connect(database='', port=masterport, autocommit=True)
# split lineorder table into one file for each worker
# this is as portable as an anvil
lineordertbl = os.path.join(ssbmdatapath, 'lineorder.tbl')
lineorderdir = os.path.join(tmpdir, 'lineorder')
if os.path.exists(lineorderdir):
import shutil
shutil.rmtree(lineorderdir)
if not os.path.exists(lineorderdir):
os.makedirs(lineorderdir)
inputData = open(lineordertbl, 'r').read().split('\n')
linesperslice = len(inputData) // nworkers + 1
def __init__(self):
self.logger = logging.getLogger(LB_Processor.__name__)
self._log_dir = tempfile.TemporaryDirectory(prefix='lb.processor')
self._log_dir_name = self._log_dir.name
def certificates_tempdir():
custom_filepath = '/tmp/nucypher-test-certificates-'
cert_tmpdir = tempfile.TemporaryDirectory(prefix=custom_filepath)
yield cert_tmpdir.name
cert_tmpdir.cleanup()
def setUp(self):
self.dir = tempfile.TemporaryDirectory()
def setUp(self):
self._directory = tempfile.TemporaryDirectory()
self.directory = Path(self._directory.name).resolve(strict=True).absolute()
self.file = self.directory / 'test'
self.file.touch()
def get_plugin(cuda_file):
cuda_file_base = os.path.basename(cuda_file)
cuda_file_name, cuda_file_ext = os.path.splitext(cuda_file_base)
# Already in cache?
if cuda_file in _plugin_cache:
return _plugin_cache[cuda_file]
# Setup plugin.
if verbose:
print('Setting up TensorFlow plugin "%s": ' % cuda_file_base, end='', flush=True)
try:
# Hash CUDA source.
md5 = hashlib.md5()
with open(cuda_file, 'rb') as f:
md5.update(f.read())
md5.update(b'\n')
# Hash headers included by the CUDA code by running it through the preprocessor.
if not do_not_hash_included_headers:
if verbose:
print('Preprocessing... ', end='', flush=True)
with tempfile.TemporaryDirectory() as tmp_dir:
tmp_file = os.path.join(tmp_dir, cuda_file_name + '_tmp' + cuda_file_ext)
_run_cmd(_prepare_nvcc_cli('"%s" --preprocess -o "%s" --keep --keep-dir "%s"' % (cuda_file, tmp_file, tmp_dir)))
with open(tmp_file, 'rb') as f:
bad_file_str = ('"' + cuda_file.replace('\\', '/') + '"').encode('utf-8') # __FILE__ in error check macros
good_file_str = ('"' + cuda_file_base + '"').encode('utf-8')
for ln in f:
if not ln.startswith(b'# ') and not ln.startswith(b'#line '): # ignore line number pragmas
ln = ln.replace(bad_file_str, good_file_str)
md5.update(ln)
md5.update(b'\n')
# Select compiler options.
compile_opts = ''
if os.name == 'nt':
compile_opts += '"%s"' % os.path.join(tf.sysconfig.get_lib(), 'python', '_pywrap_tensorflow_internal.lib')
elif os.name == 'posix':
compile_opts += '"%s"' % os.path.join(tf.sysconfig.get_lib(), 'python', '_pywrap_tensorflow_internal.so')
compile_opts += ' --compiler-options \'-fPIC -D_GLIBCXX_USE_CXX11_ABI=0\''
else:
assert False # not Windows or Linux, w00t?
compile_opts += ' --gpu-architecture=%s' % _get_cuda_gpu_arch_string()
compile_opts += ' --use_fast_math'
nvcc_cmd = _prepare_nvcc_cli(compile_opts)
# Hash build configuration.
md5.update(('nvcc_cmd: ' + nvcc_cmd).encode('utf-8') + b'\n')
md5.update(('tf.VERSION: ' + tf.VERSION).encode('utf-8') + b'\n')
md5.update(('cuda_cache_version_tag: ' + cuda_cache_version_tag).encode('utf-8') + b'\n')
# Compile if not already compiled.
bin_file_ext = '.dll' if os.name == 'nt' else '.so'
bin_file = os.path.join(cuda_cache_path, cuda_file_name + '_' + md5.hexdigest() + bin_file_ext)
if not os.path.isfile(bin_file):
if verbose:
print('Compiling... ', end='', flush=True)
with tempfile.TemporaryDirectory() as tmp_dir:
tmp_file = os.path.join(tmp_dir, cuda_file_name + '_tmp' + bin_file_ext)
_run_cmd(nvcc_cmd + ' "%s" --shared -o "%s" --keep --keep-dir "%s"' % (cuda_file, tmp_file, tmp_dir))
os.makedirs(cuda_cache_path, exist_ok=True)
intermediate_file = os.path.join(cuda_cache_path, cuda_file_name + '_' + uuid.uuid4().hex + '_tmp' + bin_file_ext)
shutil.copyfile(tmp_file, intermediate_file)
os.rename(intermediate_file, bin_file) # atomic
# Load.
if verbose:
print('Loading... ', end='', flush=True)
plugin = tf.load_op_library(bin_file)
# Add to cache.
_plugin_cache[cuda_file] = plugin
if verbose:
print('Done.', flush=True)
return plugin
except:
if verbose:
print('Failed!', flush=True)
raise
def setUp(self):
self.tmp_test = tempfile.TemporaryDirectory()
def test_workflow_loading_headless(self):
with tempfile.TemporaryDirectory() as temp_dir:
for wf in self.workflow_list:
yield self.start_workflow_load_project_headless, wf, temp_dir
def test_minimal_save():
signal = Signal1D([0, 1])
with tempfile.TemporaryDirectory() as tmp:
signal.save(os.path.join(tmp, 'testfile.emd'))
temp = tempfile.TemporaryFile()
print(temp)
print(temp.name)
temp = tempfile.NamedTemporaryFile()
print(temp)
print(temp.name)
temp = tempfile.NamedTemporaryFile(prefix='pre_', suffix='_suf')
print(temp.name)
temp = tempfile.TemporaryFile()
temp.write(b'foo bar')
temp.seek(0)
print(temp.read())
temp.close()
temp_dir = tempfile.TemporaryDirectory()
print(temp_dir)
secure_temp = tempfile.mkstemp(prefix="pre_", suffix="_suf")
print(secure_temp)
tempfile.tempdir = "/temp"
print(tempfile.gettempdir())
with tempfile.TemporaryFile() as fp:
fp.write(b'Hello world!')
fp.seek(0)
fp.read()
def test_hub_dir(self):
with tempfile.TemporaryDirectory('hub_dir') as dirname:
torch.hub.set_dir(dirname)
self.assertEqual(torch.hub.get_dir(), dirname)
def wrapper(*args, **kwargs):
with tempfile.TemporaryDirectory(prefix='htx_') as temp_dir:
return func(temp_dir=temp_dir, *args, **kwargs)
def test_state_with_id_handler(self):
num_cells = 20
model_type = pt_gs_k.PTGSKModel
model = self.build_model(model_type, pt_gs_k.PTGSKParameter, num_cells,
2)
cids_unspecified = api.IntVector()
cids_1 = api.IntVector([1])
cids_2 = api.IntVector([2])
model_state_12 = model.state.extract_state(
cids_unspecified) # this is how to get all states from model
model_state_1 = model.state.extract_state(
cids_1) # this is how to get only specified states from model
model_state_2 = model.state.extract_state(cids_2)
self.assertEqual(
len(model_state_1) + len(model_state_2), len(model_state_12))
self.assertGreater(len(model_state_1), 0)
self.assertGreater(len(model_state_2), 0)
for i in range(
len(model_state_1)): # verify selective extract catchment 1
self.assertEqual(model_state_1[i].id.cid, 1)
for i in range(
len(model_state_2)): # verify selective extract catchment 2
self.assertEqual(model_state_2[i].id.cid, 2)
for i in range(len(model_state_12)):
model_state_12[i].state.kirchner.q = 100 + i
model.state.apply_state(
model_state_12,
cids_unspecified) # this is how to put all states into model
ms_12 = model.state.extract_state(cids_unspecified)
for i in range(len(ms_12)):
self.assertAlmostEqual(ms_12[i].state.kirchner.q, 100 + i)
for i in range(len(model_state_2)):
model_state_2[i].state.kirchner.q = 200 + i
unapplied = model.state.apply_state(
model_state_2,
cids_2) # this is how to put a limited set of state into model
self.assertEqual(len(unapplied), 0)
ms_12 = model.state.extract_state(cids_unspecified)
for i in range(len(ms_12)):
if ms_12[i].id.cid == 1:
self.assertAlmostEqual(ms_12[i].state.kirchner.q, 100 + i)
ms_2 = model.state.extract_state(cids_2)
for i in range(len(ms_2)):
self.assertAlmostEqual(ms_2[i].state.kirchner.q, 200 + i)
# serialization support, to and from bytes
bytes = ms_2.serialize_to_bytes(
) # first make some bytes out of the state
with tempfile.TemporaryDirectory() as tmpdirname:
file_path = str(path.join(tmpdirname, "pt_gs_k_state_test.bin"))
api.byte_vector_to_file(file_path, bytes) # stash it into a file
bytes = api.byte_vector_from_file(
file_path) # get it back from the file and into ByteVector
ms_2x = pt_gs_k.deserialize_from_bytes(
bytes) # then restore it from bytes to a StateWithIdVector
self.assertIsNotNone(ms_2x)
for i in range(len(ms_2x)):
self.assertAlmostEqual(ms_2x[i].state.kirchner.q, 200 + i)
def setUp(self):
self._tempdir = tempfile.TemporaryDirectory()
self._temp_filepath = path.join(self._tempdir.name, '000.jpg.kpt')
def test_tempdirectory(self):
'''Fake TemporaryDirectory class is bound'''
with tempfile.TemporaryDirectory() as td:
with open('%s/fake_file.txt' % td, 'w') as f:
self.assertTrue(self.fs.Exists(td))
def setUp(self):
self._tempdir = tempfile.TemporaryDirectory()
self._samples_dirpath = path.abspath(
path.join(path.dirname(__file__), '..', 'samples', 'maupertuis'))
self._kapture_dirpath = path.join(self._samples_dirpath, 'kapture')
def build(l4t_path, public_sources,
# todo: xconfig=None,
arch=ARCH,
cross_prefix=tegrity.toolchain.get_cross_prefix(),
load_kconfig=None,
localversion=None,
menuconfig=None,
module_archive=None,
public_sources_sha512=None,
save_kconfig=None,
):
logger.info("Preparing to build kernel")
# set some envs
os.environ['CROSS_COMPILE'] = cross_prefix
logger.debug(f'CROSS_COMPILE: {cross_prefix}')
localversion = localversion if localversion else DEFAULT_LOCALVERSION
os.environ['LOCALVERSION'] = localversion
logger.debug(f'LOCALVERSION: {localversion}')
# set up some initial paths
logger.debug(f"Linux_for_Tegra path: {l4t_path}")
l4t_kernel_path = tegrity.utils.join_and_check(l4t_path, "kernel")
logger.debug(f"L4T kernel path: {l4t_kernel_path}")
# create a temporary folder that self destructs at the end of the context.
with tempfile.TemporaryDirectory() as tmp:
# set up a temporary rootfs folder instead of a real one just to create
# the kernel_supplements which will be installed by apply_binaries.sh
rootfs = os.path.join(tmp, 'rootfs')
logger.debug(f"creating temporary rootfs at: {rootfs}")
os.makedirs(rootfs, 0o755)
# Obtaining the Kernel Sources
_get_source(tmp, public_sources, public_sources_sha512)
# Building the kernel
# 1. set kernel out path
kernel_out = os.path.join(tmp, "kernel_out")
# 2.5 set common make arguments
make_common = [f"ARCH={arch}", f"O={kernel_out}"]
# 3. Create the initial config
config(make_common, kernel_out, load_kconfig)
os.chdir(kernel_out)
# 3.5 Customize initial configuration interactively (optional)
if menuconfig:
make_menuconfig(make_common)
# 4 Build the kernel and all modules
make_kernel(make_common)
# 5 Backup and replace old kernel with new kernel
replace_kernel(kernel_out, l4t_kernel_path)
# 6 Replace dtb folder with dts folder
replace_dtb(kernel_out, l4t_kernel_path)
# 7 Install kernel modules
make_modules_install(make_common, rootfs)
# 8 Archive modules
archive_modules(rootfs, module_archive, l4t_kernel_path)
# 8.5 Archive config
archive_kconfig(kernel_out, save_kconfig)
def test_invalid_project_config_hooks():
with tempfile.TemporaryDirectory() as tmpdir:
config = {"foo": {HOOKS_PROPERTY: {"pre": "nonexistentfile.sh"}}}
assert not check_project_configuration(config, hookdir=tmpdir)
def setUp(self):
self._tempdir = tempfile.TemporaryDirectory()
self.tempdir = Path(self._tempdir.name).resolve(strict=True).absolute()
self.existing_file = self.ensure_file(self.tempdir / 'test.py')
self.nonexistent_file = (self.tempdir / 'does_not_exist.py').absolute()
self.reloader = self.RELOADER_CLS()
def test_invalid_project_config_hooknames():
with tempfile.TemporaryDirectory() as tmpdir:
config = {"foo": {HOOKS_PROPERTY: {"blah": "value"}}}
assert not check_project_configuration(config, hookdir=tmpdir)
def temp_dir_path():
temp_dir = tempfile.TemporaryDirectory(prefix='nucypher-test-')
yield temp_dir.name
temp_dir.cleanup()
def test_invalid_project_config_nonexec_hook():
with tempfile.TemporaryDirectory() as tmpdir:
with open(os.path.join(tmpdir, "foo.sh"), 'w+') as tmpfile:
tmpfile.write("foo\n")
config = {"foo": {HOOKS_PROPERTY: {"pre": "foo.sh"}}}
assert not check_project_configuration(config, hookdir=tmpdir)
def test_pt_tf_model_equivalence(self):
if not is_torch_available():
return
import torch
import transformers
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
pt_model_class_name = model_class.__name__[2:] # Skip the "TF" at the beggining
pt_model_class = getattr(transformers, pt_model_class_name)
config.output_hidden_states = True
tf_model = model_class(config)
pt_model = pt_model_class(config)
# Check we can load pt model in tf and vice-versa with model => model functions
tf_model = transformers.load_pytorch_model_in_tf2_model(tf_model, pt_model, tf_inputs=inputs_dict)
pt_model = transformers.load_tf2_model_in_pytorch_model(pt_model, tf_model)
# Check predictions on first output (logits/hidden-states) are close enought given low-level computational differences
pt_model.eval()
pt_inputs_dict = dict(
(name, torch.from_numpy(key.numpy()).to(torch.long)) for name, key in inputs_dict.items()
)
with torch.no_grad():
pto = pt_model(**pt_inputs_dict)
tfo = tf_model(inputs_dict, training=False)
tf_hidden_states = tfo[0].numpy()
pt_hidden_states = pto[0].numpy()
pt_hidden_states[np.isnan(tf_hidden_states)] = 0
tf_hidden_states[np.isnan(tf_hidden_states)] = 0
pt_hidden_states[np.isnan(pt_hidden_states)] = 0
tf_hidden_states[np.isnan(pt_hidden_states)] = 0
max_diff = np.amax(np.abs(tf_hidden_states - pt_hidden_states))
# Debug info (remove when fixed)
if max_diff >= 2e-2:
print("===")
print(model_class)
print(config)
print(inputs_dict)
print(pt_inputs_dict)
self.assertLessEqual(max_diff, 2e-2)
# Check we can load pt model in tf and vice-versa with checkpoint => model functions
with tempfile.TemporaryDirectory() as tmpdirname:
pt_checkpoint_path = os.path.join(tmpdirname, "pt_model.bin")
torch.save(pt_model.state_dict(), pt_checkpoint_path)
tf_model = transformers.load_pytorch_checkpoint_in_tf2_model(tf_model, pt_checkpoint_path)
tf_checkpoint_path = os.path.join(tmpdirname, "tf_model.h5")
tf_model.save_weights(tf_checkpoint_path)
pt_model = transformers.load_tf2_checkpoint_in_pytorch_model(pt_model, tf_checkpoint_path)
# Check predictions on first output (logits/hidden-states) are close enought given low-level computational differences
pt_model.eval()
pt_inputs_dict = dict(
(name, torch.from_numpy(key.numpy()).to(torch.long)) for name, key in inputs_dict.items()
)
with torch.no_grad():
pto = pt_model(**pt_inputs_dict)
tfo = tf_model(inputs_dict)
tfo = tfo[0].numpy()
pto = pto[0].numpy()
tfo[np.isnan(tfo)] = 0
pto[np.isnan(pto)] = 0
max_diff = np.amax(np.abs(tfo - pto))
self.assertLessEqual(max_diff, 2e-2)
def get_positives_DE(run_date) -> pandas.DataFrame:
""" Retrieves table of positives & deaths for all German regions.
Parameters
----------
run_date : pandas.Timestamp
use the data as it was release on that day
Returns
-------
result : pandas.DataFrame
[region, date]-indexed table that has rows for every region & date combination in [2020-03-01, run_date - 1]
contains columns "positive" and "deaths" that are the number of NEW positives/deaths for each day/region
"all" region is the sum over all states.
"""
date_str = run_date.strftime('%Y-%m-%d')
with tempfile.TemporaryDirectory() as td:
fp_tempfile = pathlib.Path(td, 'data_arcgis.csv')
if run_date.date() < datetime.datetime.utcnow().date():
release_id = (run_date + pandas.DateOffset(1)).strftime('%Y-%m-%d')
release_url = f'https://github.com/ihucos/rki-covid19-data/releases/download/{release_id}/data.csv'
# For explanations of the columns, see https://www.arcgis.com/home/item.html?id=f10774f1c63e40168479a1feb6c7ca74
# the CSV is automatically released at 01 AM Berlin local time, but has one day offset to the RKI data
_log.info('Downloading German data from %s', release_url)
with open(fp_tempfile, 'wb') as file:
file.write(requests.get(release_url).content)
encoding = 'utf-8'
else:
_log.info('Downloading RKI COVID-19 dataset from ArcGIS')
from arcgis.gis import GIS
anon_gis = GIS()
features = anon_gis.content.get('dd4580c810204019a7b8eb3e0b329dd6').tables[0].query()
features.save(save_location=td, out_name='download.csv')
shutil.copy2(os.path.join(td, 'download.csv'), fp_tempfile)
encoding = 'unicode_escape' if os.name == 'nt' else 'utf-8'
if CSV_SAVEPATH:
shutil.copy2(fp_tempfile, CSV_SAVEPATH)
df = pandas.read_csv(
fp_tempfile,
usecols=['Bundesland', 'Meldedatum', 'Datenstand', 'AnzahlFall', 'AnzahlTodesfall'],
encoding=encoding
)
_log.info('Data was loaded for the following regions: %s', df.Bundesland.unique())
df.Meldedatum = pandas.to_datetime(df.Meldedatum, unit='ms')
assert len(set(df.Datenstand)) == 1
datenstand = df.Datenstand[0]
assert run_date.strftime('%d.%m.%Y') in df.Datenstand[0]
# transform to multi-indexed dataframe with required columns
_log.info('Transforming to multi-indexed dataframe')
df_sparse = df.rename(columns={
'Meldedatum': 'date',
'Bundesland': 'region',
'AnzahlFall': 'new_cases',
'AnzahlTodesfall': 'new_deaths',
}).replace(DE_REGION_CODES).groupby(['region', 'date']).sum().sort_index()
# make sure that the result has rows for every region/date combination.
_log.info('Inserting 0-counts for missing dates')
full_index = pandas.date_range(
'2020-03-01',
run_date - pandas.DateOffset(2)
# ToDo: use max(run_date-2, date in data)
#max(run_date - pandas.DateOffset(2),
)
df_full = pandas.concat({
region : df_sparse.xs(region).reindex(full_index, fill_value=0)
for region in DE_REGIONS
if region != 'all'
}, names=['region', 'date'])
# add region "all" that is the sum over all states
df_all = df_full.sum(level='date')
df_all.insert(0, column='region', value='all')
df_all = df_all.reset_index().set_index(['region', 'date'])
df_merged = pandas.concat([df_full, df_all]).sort_index()
return df_merged
def tcp_listen_python3(self, listen_ssl=False):
"""Open a TCP socket on a given port and print incoming
data to stdout."""
class DeenTcpHandler(socketserver.StreamRequestHandler):
def handle(self):
self.data = self.rfile.readline().strip()
print(self.data.decode())
class TcpServerSsl(socketserver.TCPServer):
def __init__(self,
server_address,
RequestHandlerClass,
certfile,
keyfile,
ssl_version=ssl.PROTOCOL_TLS_SERVER,
bind_and_activate=True):
socketserver.TCPServer.__init__(self, server_address,
RequestHandlerClass,
bind_and_activate)
self.certfile = certfile
self.keyfile = keyfile
self.ssl_version = ssl_version
def get_request(self):
newsocket, fromaddr = self.socket.accept()
connstream = ssl.wrap_socket(newsocket,
server_side=True,
certfile=self.certfile,
keyfile=self.keyfile,
ssl_version=self.ssl_version)
return connstream, fromaddr
server = None
if listen_ssl:
# The certificate chain and private key need to
# be available as actual files that can be opened
# with fopen(3).
with tempfile.TemporaryDirectory() as tmpdirname:
cert_chain = tmpdirname + '/cert_chain.pem'
server_key = tmpdirname + '/server_key.pem'
with open(cert_chain, 'wb') as f:
f.write(self.server_cert_pem)
with open(server_key, 'wb') as f:
f.write(self.server_key_pem)
try:
server = TcpServerSsl(self.listen_socket,
DeenTcpHandler,
cert_chain,
server_key)
except OSError as e:
self.error = e
self.log.error(self.error)
self.log.debug(self.error, exc_info=True)
return
else:
try:
server = socketserver.TCPServer(self.listen_socket,
DeenTcpHandler)
except OSError as e:
self.error = e
self.log.error(self.error)
self.log.debug(self.error, exc_info=True)
return
message = 'Listening on TCP port ' + str(self.listen_port)
if listen_ssl:
message += ' (SSL)'
print(message)
try:
server.serve_forever()
except KeyboardInterrupt:
pass
import unittest
import tempfile
from dicomml.cases.case import DicommlCase
from tests import sample_case_config
class TestDicommlCase(unittest.TestCase):
def test_create(self):
case = DicommlCase(**sample_case_config())
self.assertIsInstance(case, DicommlCase)
def test_save_load(self):
case = DicommlCase(**sample_case_config())
with tempfile.TemporaryDirectory() as temp_folder:
zipfile = case.save(path=temp_folder)
case_loaded = DicommlCase.load(zipfile)
self.assertEqual(case, case_loaded)
def test_export(self):
case = DicommlCase(**sample_case_config())
exports = case.export()
self.assertCountEqual(
list(exports.keys()),
['images', 'truth'])
self.assertEqual(exports['images'].shape, (1, 10, 120, 120))
def main():
if sys.platform.startswith("linux"):
os_ = "linux"
elif sys.platform == "darwin":
os_ = "macos"
elif sys.platform == "win32":
os_ = "windows"
else:
raise NotImplementedError(f"sys.platform '{sys.platform}' is not supported yet.")
p = argparse.ArgumentParser(description="Convert wheel to be independent of python implementation and ABI")
p.set_defaults(prog=Path(sys.argv[0]).name)
p.add_argument("WHEEL_FILE", help="Path to wheel file.")
p.add_argument(
"-w",
"--wheel-dir",
dest="WHEEL_DIR",
help=('Directory to store delocated wheels (default: "wheelhouse/")'),
default="wheelhouse/",
)
args = p.parse_args()
file = Path(args.WHEEL_FILE).resolve(strict=True)
wheelhouse = Path(args.WHEEL_DIR).resolve()
wheelhouse.mkdir(parents=True, exist_ok=True)
with tempfile.TemporaryDirectory() as tmpdir_:
tmpdir = Path(tmpdir_)
# use the platform specific repair tool first
if os_ == "linux":
subprocess.run(["auditwheel", "repair", "-w", str(tmpdir), str(file)], check=True, stdout=subprocess.PIPE)
elif os_ == "macos":
subprocess.run(
["delocate-wheel", "--require-archs", "x86_64,arm64", "-w", str(tmpdir), str(file)],
check=True,
stdout=subprocess.PIPE,
)
elif os_ == "windows":
# no specific tool, just copy
shutil.copyfile(file, tmpdir / file.name)
files = list(tmpdir.glob("*.whl"))
assert len(files) == 1, files
file = files[0]
# we need to handle macOS x86_64 & arm64 here for now, let's use additional_platforms for this.
additional_platforms = []
if os_ == "macos":
# delocate-wheel --require-archs does not seem to check executables...
with tempfile.TemporaryDirectory() as tmpdir2_:
tmpdir2 = Path(tmpdir2_)
with zipfile.ZipFile(file, 'r') as zip_ref:
zip_ref.extractall(tmpdir2)
exe = list(tmpdir2.glob("**/bin/ninja"))
assert len(exe) == 1, exe
subprocess.run(["lipo", str(exe[0]), "-verify_arch", "x86_64", "arm64"], check=True, stdout=subprocess.PIPE)
# first, get the target macOS deployment target from the wheel
match = re.match(r"^.*-macosx_(\d+)_(\d+)_.*\.whl$", file.name)
assert match is not None, f"Couldn't match on {file.name}"
target = tuple(map(int, match.groups()))
# given pip support for universal2 was added after x86_64 introduction
# let's also add x86_64 platform.
additional_platforms.append("macosx_{}_{}_x86_64".format(*target))
# given pip support for universal2 was added after arm64 introduction
# let's also add arm64 platform.
arm64_target = target
if arm64_target < (11, 0):
arm64_target = (11, 0)
additional_platforms.append("macosx_{}_{}_arm64".format(*arm64_target))
if target < (11, 0):
# They're were also issues with pip not picking up some universal2 wheels, tag twice
additional_platforms.append("macosx_11_0_universal2")
# make this a py2.py3 wheel
convert_to_generic_platform_wheel(
str(file),
out_dir=str(wheelhouse),
py2_py3=True,
additional_platforms=additional_platforms,
)
def before_scenario(context, current_scenario):
context.temporary_directory = tempfile.TemporaryDirectory()
