body_hash stringlengths 64 64 | body stringlengths 23 109k | docstring stringlengths 1 57k | path stringlengths 4 198 | name stringlengths 1 115 | repository_name stringlengths 7 111 | repository_stars float64 0 191k | lang stringclasses 1 value | body_without_docstring stringlengths 14 108k | unified stringlengths 45 133k |
|---|---|---|---|---|---|---|---|---|---|
9397005a087bcbdc622d4112a3d4bd0ab2157f3cbb4938139ec4a028ac87226c | def get_region_wise_buckets(self):
'\n Fetch all buckets in all regions.\n '
try:
buckets = self.list_existing_buckets()
if buckets:
region_wise_bucket = {}
for bucket in buckets:
region = self.get_bucket_region(bucket)
if (region in region_wise_bucket):
region_wise_bucket[region].append(bucket)
else:
region_wise_bucket[region] = [bucket]
return region_wise_bucket
else:
return {}
except Exception as e:
self.logger.error(e)
return {} | Fetch all buckets in all regions. | lib/awsLib/S3.py | get_region_wise_buckets | umang-cb/TAF | 9 | python | def get_region_wise_buckets(self):
'\n \n '
try:
buckets = self.list_existing_buckets()
if buckets:
region_wise_bucket = {}
for bucket in buckets:
region = self.get_bucket_region(bucket)
if (region in region_wise_bucket):
region_wise_bucket[region].append(bucket)
else:
region_wise_bucket[region] = [bucket]
return region_wise_bucket
else:
return {}
except Exception as e:
self.logger.error(e)
return {} | def get_region_wise_buckets(self):
'\n \n '
try:
buckets = self.list_existing_buckets()
if buckets:
region_wise_bucket = {}
for bucket in buckets:
region = self.get_bucket_region(bucket)
if (region in region_wise_bucket):
region_wise_bucket[region].append(bucket)
else:
region_wise_bucket[region] = [bucket]
return region_wise_bucket
else:
return {}
except Exception as e:
self.logger.error(e)
return {}<|docstring|>Fetch all buckets in all regions.<|endoftext|> |
3e679084fb53fdfa89a859e269c4ea76973f860d3e838ebf71726a270d52b029 | def get_bucket_region(self, bucket_name):
'\n Gets the region where the bucket is located\n '
try:
response = self.s3_client.list_buckets(Bucket=bucket_name)
if response:
if (response['LocationConstraint'] == None):
return 'us-east-1'
else:
return response['LocationConstraint']
else:
return ''
except Exception as e:
self.logger.error(e)
return '' | Gets the region where the bucket is located | lib/awsLib/S3.py | get_bucket_region | umang-cb/TAF | 9 | python | def get_bucket_region(self, bucket_name):
'\n \n '
try:
response = self.s3_client.list_buckets(Bucket=bucket_name)
if response:
if (response['LocationConstraint'] == None):
return 'us-east-1'
else:
return response['LocationConstraint']
else:
return
except Exception as e:
self.logger.error(e)
return | def get_bucket_region(self, bucket_name):
'\n \n '
try:
response = self.s3_client.list_buckets(Bucket=bucket_name)
if response:
if (response['LocationConstraint'] == None):
return 'us-east-1'
else:
return response['LocationConstraint']
else:
return
except Exception as e:
self.logger.error(e)
return <|docstring|>Gets the region where the bucket is located<|endoftext|> |
98a7bb25266ddd849cd20252a4e4269054151428ca5ee335b597f9b3a280a90f | def upload_file(self, bucket_name, source_path, destination_path):
'\n Uploads a file to bucket specified.\n :param bucket_name: name of the bucket where file has to be uploaded.\n :param source_path: path of the file to be uploaded.\n :param destination_path: path relative to aws bucket. If only file name is specified\n then file will be loaded in root folder relative to bucket.\n :return: True/False\n '
try:
response = self.s3_resource.Bucket(bucket_name).upload_file(source_path, destination_path)
if (not response):
return True
else:
return False
except Exception as e:
self.logger.error(e)
return False | Uploads a file to bucket specified.
:param bucket_name: name of the bucket where file has to be uploaded.
:param source_path: path of the file to be uploaded.
:param destination_path: path relative to aws bucket. If only file name is specified
then file will be loaded in root folder relative to bucket.
:return: True/False | lib/awsLib/S3.py | upload_file | umang-cb/TAF | 9 | python | def upload_file(self, bucket_name, source_path, destination_path):
'\n Uploads a file to bucket specified.\n :param bucket_name: name of the bucket where file has to be uploaded.\n :param source_path: path of the file to be uploaded.\n :param destination_path: path relative to aws bucket. If only file name is specified\n then file will be loaded in root folder relative to bucket.\n :return: True/False\n '
try:
response = self.s3_resource.Bucket(bucket_name).upload_file(source_path, destination_path)
if (not response):
return True
else:
return False
except Exception as e:
self.logger.error(e)
return False | def upload_file(self, bucket_name, source_path, destination_path):
'\n Uploads a file to bucket specified.\n :param bucket_name: name of the bucket where file has to be uploaded.\n :param source_path: path of the file to be uploaded.\n :param destination_path: path relative to aws bucket. If only file name is specified\n then file will be loaded in root folder relative to bucket.\n :return: True/False\n '
try:
response = self.s3_resource.Bucket(bucket_name).upload_file(source_path, destination_path)
if (not response):
return True
else:
return False
except Exception as e:
self.logger.error(e)
return False<|docstring|>Uploads a file to bucket specified.
:param bucket_name: name of the bucket where file has to be uploaded.
:param source_path: path of the file to be uploaded.
:param destination_path: path relative to aws bucket. If only file name is specified
then file will be loaded in root folder relative to bucket.
:return: True/False<|endoftext|> |
eaf06910785f20123b4b878be63d75effbcbdee74f11428b43c9db0c5ee5c00b | def upload_large_file(self, bucket_name, source_path, destination_path, multipart_threshold=((1024 * 1024) * 8), max_concurrency=10, multipart_chunksize=((1024 * 1024) * 8), use_threads=True):
'\n Uploads a large file to bucket specified.\n :param bucket_name: name of the bucket where file has to be uploaded.\n :param source_path: path of the file to be uploaded.\n :param destination_path: path relative to aws bucket. If only file name is specified\n then file will be loaded in root folder relative to bucket.\n :param multipart_threshold: The transfer size threshold.\n :param max_concurrency: The maximum number of threads that will be\n making requests to perform a transfer. If ``use_threads`` is\n set to ``False``, the value provided is ignored as the transfer\n will only ever use the main thread.\n :param multipart_chunksize: The partition size of each part for a\n multipart transfer.\n :param use_threads: If True, threads will be used when performing\n S3 transfers. If False, no threads will be used in\n performing transfers\n :return: True/False\n '
'\n WARNING : Please use this function if you want to upload a large file only (ex - file above 10 MB, \n again this value is only subjective), as this API call to AWS is charged extra.\n '
try:
config = TransferConfig(multipart_threshold=multipart_threshold, max_concurrency=max_concurrency, multipart_chunksize=multipart_chunksize, use_threads=use_threads)
response = self.s3_resource.Bucket(bucket_name).upload_file(source_path, destination_path, Config=config)
if (not response):
return True
else:
return False
except Exception as e:
self.logger.error(e)
return False | Uploads a large file to bucket specified.
:param bucket_name: name of the bucket where file has to be uploaded.
:param source_path: path of the file to be uploaded.
:param destination_path: path relative to aws bucket. If only file name is specified
then file will be loaded in root folder relative to bucket.
:param multipart_threshold: The transfer size threshold.
:param max_concurrency: The maximum number of threads that will be
making requests to perform a transfer. If ``use_threads`` is
set to ``False``, the value provided is ignored as the transfer
will only ever use the main thread.
:param multipart_chunksize: The partition size of each part for a
multipart transfer.
:param use_threads: If True, threads will be used when performing
S3 transfers. If False, no threads will be used in
performing transfers
:return: True/False | lib/awsLib/S3.py | upload_large_file | umang-cb/TAF | 9 | python | def upload_large_file(self, bucket_name, source_path, destination_path, multipart_threshold=((1024 * 1024) * 8), max_concurrency=10, multipart_chunksize=((1024 * 1024) * 8), use_threads=True):
'\n Uploads a large file to bucket specified.\n :param bucket_name: name of the bucket where file has to be uploaded.\n :param source_path: path of the file to be uploaded.\n :param destination_path: path relative to aws bucket. If only file name is specified\n then file will be loaded in root folder relative to bucket.\n :param multipart_threshold: The transfer size threshold.\n :param max_concurrency: The maximum number of threads that will be\n making requests to perform a transfer. If ``use_threads`` is\n set to ``False``, the value provided is ignored as the transfer\n will only ever use the main thread.\n :param multipart_chunksize: The partition size of each part for a\n multipart transfer.\n :param use_threads: If True, threads will be used when performing\n S3 transfers. If False, no threads will be used in\n performing transfers\n :return: True/False\n '
'\n WARNING : Please use this function if you want to upload a large file only (ex - file above 10 MB, \n again this value is only subjective), as this API call to AWS is charged extra.\n '
try:
config = TransferConfig(multipart_threshold=multipart_threshold, max_concurrency=max_concurrency, multipart_chunksize=multipart_chunksize, use_threads=use_threads)
response = self.s3_resource.Bucket(bucket_name).upload_file(source_path, destination_path, Config=config)
if (not response):
return True
else:
return False
except Exception as e:
self.logger.error(e)
return False | def upload_large_file(self, bucket_name, source_path, destination_path, multipart_threshold=((1024 * 1024) * 8), max_concurrency=10, multipart_chunksize=((1024 * 1024) * 8), use_threads=True):
'\n Uploads a large file to bucket specified.\n :param bucket_name: name of the bucket where file has to be uploaded.\n :param source_path: path of the file to be uploaded.\n :param destination_path: path relative to aws bucket. If only file name is specified\n then file will be loaded in root folder relative to bucket.\n :param multipart_threshold: The transfer size threshold.\n :param max_concurrency: The maximum number of threads that will be\n making requests to perform a transfer. If ``use_threads`` is\n set to ``False``, the value provided is ignored as the transfer\n will only ever use the main thread.\n :param multipart_chunksize: The partition size of each part for a\n multipart transfer.\n :param use_threads: If True, threads will be used when performing\n S3 transfers. If False, no threads will be used in\n performing transfers\n :return: True/False\n '
'\n WARNING : Please use this function if you want to upload a large file only (ex - file above 10 MB, \n again this value is only subjective), as this API call to AWS is charged extra.\n '
try:
config = TransferConfig(multipart_threshold=multipart_threshold, max_concurrency=max_concurrency, multipart_chunksize=multipart_chunksize, use_threads=use_threads)
response = self.s3_resource.Bucket(bucket_name).upload_file(source_path, destination_path, Config=config)
if (not response):
return True
else:
return False
except Exception as e:
self.logger.error(e)
return False<|docstring|>Uploads a large file to bucket specified.
:param bucket_name: name of the bucket where file has to be uploaded.
:param source_path: path of the file to be uploaded.
:param destination_path: path relative to aws bucket. If only file name is specified
then file will be loaded in root folder relative to bucket.
:param multipart_threshold: The transfer size threshold.
:param max_concurrency: The maximum number of threads that will be
making requests to perform a transfer. If ``use_threads`` is
set to ``False``, the value provided is ignored as the transfer
will only ever use the main thread.
:param multipart_chunksize: The partition size of each part for a
multipart transfer.
:param use_threads: If True, threads will be used when performing
S3 transfers. If False, no threads will be used in
performing transfers
:return: True/False<|endoftext|> |
5be83905dbac7f4adec2eda8683042ea20d668266e206c498fae8ae227868315 | def perform(self, data):
'\n Parses the given request data and returns a matching response header.\n '
key = self._build_web_socket_accept_from_request_header(data.decode('utf-8'))
return self._build_response_header(key) | Parses the given request data and returns a matching response header. | WebSocket/Handshake.py | perform | Cottin/BrowserREPL-for-Sublime | 32 | python | def perform(self, data):
'\n \n '
key = self._build_web_socket_accept_from_request_header(data.decode('utf-8'))
return self._build_response_header(key) | def perform(self, data):
'\n \n '
key = self._build_web_socket_accept_from_request_header(data.decode('utf-8'))
return self._build_response_header(key)<|docstring|>Parses the given request data and returns a matching response header.<|endoftext|> |
ee71dd7d48306988994792e91b41c09d50a40b12eb055b656b10a070b73ffd83 | def _build_web_socket_accept_from_request_header(self, header):
'\n Parses the response header and builds a sec web socket accept.\n '
search_term = 'Sec-WebSocket-Key: '
start = (header.find(search_term) + len(search_term))
end = header.find('\r\n', start)
key = header[start:end]
guid = '258EAFA5-E914-47DA-95CA-C5AB0DC85B11'
key = (key + guid).encode('utf-8')
sha1 = hashlib.sha1(key).digest()
return base64.b64encode(sha1) | Parses the response header and builds a sec web socket accept. | WebSocket/Handshake.py | _build_web_socket_accept_from_request_header | Cottin/BrowserREPL-for-Sublime | 32 | python | def _build_web_socket_accept_from_request_header(self, header):
'\n \n '
search_term = 'Sec-WebSocket-Key: '
start = (header.find(search_term) + len(search_term))
end = header.find('\r\n', start)
key = header[start:end]
guid = '258EAFA5-E914-47DA-95CA-C5AB0DC85B11'
key = (key + guid).encode('utf-8')
sha1 = hashlib.sha1(key).digest()
return base64.b64encode(sha1) | def _build_web_socket_accept_from_request_header(self, header):
'\n \n '
search_term = 'Sec-WebSocket-Key: '
start = (header.find(search_term) + len(search_term))
end = header.find('\r\n', start)
key = header[start:end]
guid = '258EAFA5-E914-47DA-95CA-C5AB0DC85B11'
key = (key + guid).encode('utf-8')
sha1 = hashlib.sha1(key).digest()
return base64.b64encode(sha1)<|docstring|>Parses the response header and builds a sec web socket accept.<|endoftext|> |
32654773c14170ddf2b497565dceb7a95869d6b430b6cf6a46da460e93b04eaa | def _build_response_header(self, key):
'\n Builds the response header containing the given key.\n '
return str(((((('HTTP/1.1 101 Switching Protocols\r\n' + 'Upgrade: websocket\r\n') + 'Connection: Upgrade\r\n') + 'Sec-WebSocket-Accept: ') + key.decode('utf-8')) + '\r\n\r\n')) | Builds the response header containing the given key. | WebSocket/Handshake.py | _build_response_header | Cottin/BrowserREPL-for-Sublime | 32 | python | def _build_response_header(self, key):
'\n \n '
return str(((((('HTTP/1.1 101 Switching Protocols\r\n' + 'Upgrade: websocket\r\n') + 'Connection: Upgrade\r\n') + 'Sec-WebSocket-Accept: ') + key.decode('utf-8')) + '\r\n\r\n')) | def _build_response_header(self, key):
'\n \n '
return str(((((('HTTP/1.1 101 Switching Protocols\r\n' + 'Upgrade: websocket\r\n') + 'Connection: Upgrade\r\n') + 'Sec-WebSocket-Accept: ') + key.decode('utf-8')) + '\r\n\r\n'))<|docstring|>Builds the response header containing the given key.<|endoftext|> |
b3fe5ebca447393db9dc6aebf147e7c3a7f0319d3220edf6c0c7ab4a76e8359f | @group.command('get')
@click.argument('id', type=int)
@click.pass_context
def get(ctx, id):
'Gets a single record from the table.'
record = model.get(id)
if (not record):
ctx.fail(click.style(f'No record found with id "{id}".', fg='red'))
click.echo(record.to_json()) | Gets a single record from the table. | rfidsecuritysvc/cli/guest.py | get | bcurnow/rfid-security-svc | 0 | python | @group.command('get')
@click.argument('id', type=int)
@click.pass_context
def get(ctx, id):
record = model.get(id)
if (not record):
ctx.fail(click.style(f'No record found with id "{id}".', fg='red'))
click.echo(record.to_json()) | @group.command('get')
@click.argument('id', type=int)
@click.pass_context
def get(ctx, id):
record = model.get(id)
if (not record):
ctx.fail(click.style(f'No record found with id "{id}".', fg='red'))
click.echo(record.to_json())<|docstring|>Gets a single record from the table.<|endoftext|> |
0307e9c17897a35ee853097b1d412805d9c60eec2d9c149cecdb56beff7bd256 | @group.command('list')
def list():
'List all the records in the table.'
for i in model.list():
click.echo(i.to_json()) | List all the records in the table. | rfidsecuritysvc/cli/guest.py | list | bcurnow/rfid-security-svc | 0 | python | @group.command('list')
def list():
for i in model.list():
click.echo(i.to_json()) | @group.command('list')
def list():
for i in model.list():
click.echo(i.to_json())<|docstring|>List all the records in the table.<|endoftext|> |
0587dcc4d6f624e7c278758ce8724e5814b135c91b086fe48f2d26e2dfa20a35 | @group.command('create')
@click.argument('first_name')
@click.argument('last_name')
@click.argument('sound', type=int, required=False)
@click.argument('color', type=int, required=False)
@click.pass_context
def create(ctx, first_name, last_name, sound, color):
'Manually adds a record to the table.'
try:
model.create(first_name, last_name, sound, color)
ctx.invoke(list)
except exception.DuplicateGuestError:
ctx.fail(click.style(f'Record with first_name "{first_name}" and last_name "{last_name}" already exists.', fg='red')) | Manually adds a record to the table. | rfidsecuritysvc/cli/guest.py | create | bcurnow/rfid-security-svc | 0 | python | @group.command('create')
@click.argument('first_name')
@click.argument('last_name')
@click.argument('sound', type=int, required=False)
@click.argument('color', type=int, required=False)
@click.pass_context
def create(ctx, first_name, last_name, sound, color):
try:
model.create(first_name, last_name, sound, color)
ctx.invoke(list)
except exception.DuplicateGuestError:
ctx.fail(click.style(f'Record with first_name "{first_name}" and last_name "{last_name}" already exists.', fg='red')) | @group.command('create')
@click.argument('first_name')
@click.argument('last_name')
@click.argument('sound', type=int, required=False)
@click.argument('color', type=int, required=False)
@click.pass_context
def create(ctx, first_name, last_name, sound, color):
try:
model.create(first_name, last_name, sound, color)
ctx.invoke(list)
except exception.DuplicateGuestError:
ctx.fail(click.style(f'Record with first_name "{first_name}" and last_name "{last_name}" already exists.', fg='red'))<|docstring|>Manually adds a record to the table.<|endoftext|> |
f618e2a779cda06c22ac560b655eaa282ef5551662697d46559179fbe83b0d38 | @group.command('delete')
@click.argument('id', type=int)
@click.pass_context
def delete(ctx, id):
'Manually deletes a record from the table.'
click.echo(click.style(f'{model.delete(id)} record(s) deleted.', bg='green', fg='black'))
ctx.invoke(list) | Manually deletes a record from the table. | rfidsecuritysvc/cli/guest.py | delete | bcurnow/rfid-security-svc | 0 | python | @group.command('delete')
@click.argument('id', type=int)
@click.pass_context
def delete(ctx, id):
click.echo(click.style(f'{model.delete(id)} record(s) deleted.', bg='green', fg='black'))
ctx.invoke(list) | @group.command('delete')
@click.argument('id', type=int)
@click.pass_context
def delete(ctx, id):
click.echo(click.style(f'{model.delete(id)} record(s) deleted.', bg='green', fg='black'))
ctx.invoke(list)<|docstring|>Manually deletes a record from the table.<|endoftext|> |
756701c45e61b45cfddc2409751ce904757c55e95afab8b5fb83e39f32d74bd0 | @group.command('update')
@click.argument('id', type=int)
@click.argument('first_name')
@click.argument('last_name')
@click.argument('sound', type=int, required=False)
@click.argument('color', type=int, required=False)
@click.pass_context
def update(ctx, id, first_name, last_name, sound, color):
'Manually updates a record in the table.'
try:
model.update(id, first_name, last_name, sound, color)
click.echo(click.style('Record updated.', bg='green', fg='black'))
ctx.invoke(list)
except exception.GuestNotFoundError:
ctx.fail(click.style(f'Record with id "{id}" does not exist.', fg='red')) | Manually updates a record in the table. | rfidsecuritysvc/cli/guest.py | update | bcurnow/rfid-security-svc | 0 | python | @group.command('update')
@click.argument('id', type=int)
@click.argument('first_name')
@click.argument('last_name')
@click.argument('sound', type=int, required=False)
@click.argument('color', type=int, required=False)
@click.pass_context
def update(ctx, id, first_name, last_name, sound, color):
try:
model.update(id, first_name, last_name, sound, color)
click.echo(click.style('Record updated.', bg='green', fg='black'))
ctx.invoke(list)
except exception.GuestNotFoundError:
ctx.fail(click.style(f'Record with id "{id}" does not exist.', fg='red')) | @group.command('update')
@click.argument('id', type=int)
@click.argument('first_name')
@click.argument('last_name')
@click.argument('sound', type=int, required=False)
@click.argument('color', type=int, required=False)
@click.pass_context
def update(ctx, id, first_name, last_name, sound, color):
try:
model.update(id, first_name, last_name, sound, color)
click.echo(click.style('Record updated.', bg='green', fg='black'))
ctx.invoke(list)
except exception.GuestNotFoundError:
ctx.fail(click.style(f'Record with id "{id}" does not exist.', fg='red'))<|docstring|>Manually updates a record in the table.<|endoftext|> |
e0232aecbd67cc7d4490a3e598b4252f88f783b737a276f9898880eb733c16d4 | def setStyleSheet(stylesheetname):
'Set stylesheet from the _stylesheets resource (from https://github.com/Alexhuszagh/BreezeStyleSheets).\n\n NOT USED BECAUSE THIS IS UNUSABLE!\n '
if (stylesheetname == 'dark'):
ss = qdarkstyle.load_stylesheet(palette=qdarkstyle.DarkPalette)
elif (stylesheetname == 'light'):
ss = qdarkstyle.load_stylesheet(palette=qdarkstyle.LightPalette)
else:
ss = ''
qw.QApplication.instance().setStyleSheet(ss)
settings = qc.QSettings()
settings.setValue('theme', stylesheetname) | Set stylesheet from the _stylesheets resource (from https://github.com/Alexhuszagh/BreezeStyleSheets).
NOT USED BECAUSE THIS IS UNUSABLE! | argos/utility.py | setStyleSheet | subhacom/argos | 1 | python | def setStyleSheet(stylesheetname):
'Set stylesheet from the _stylesheets resource (from https://github.com/Alexhuszagh/BreezeStyleSheets).\n\n NOT USED BECAUSE THIS IS UNUSABLE!\n '
if (stylesheetname == 'dark'):
ss = qdarkstyle.load_stylesheet(palette=qdarkstyle.DarkPalette)
elif (stylesheetname == 'light'):
ss = qdarkstyle.load_stylesheet(palette=qdarkstyle.LightPalette)
else:
ss =
qw.QApplication.instance().setStyleSheet(ss)
settings = qc.QSettings()
settings.setValue('theme', stylesheetname) | def setStyleSheet(stylesheetname):
'Set stylesheet from the _stylesheets resource (from https://github.com/Alexhuszagh/BreezeStyleSheets).\n\n NOT USED BECAUSE THIS IS UNUSABLE!\n '
if (stylesheetname == 'dark'):
ss = qdarkstyle.load_stylesheet(palette=qdarkstyle.DarkPalette)
elif (stylesheetname == 'light'):
ss = qdarkstyle.load_stylesheet(palette=qdarkstyle.LightPalette)
else:
ss =
qw.QApplication.instance().setStyleSheet(ss)
settings = qc.QSettings()
settings.setValue('theme', stylesheetname)<|docstring|>Set stylesheet from the _stylesheets resource (from https://github.com/Alexhuszagh/BreezeStyleSheets).
NOT USED BECAUSE THIS IS UNUSABLE!<|endoftext|> |
daf772d391934f3ddd7119145d9d5dcbf114daea403da3841e227d83a40802bd | def init():
'Initialize logging and Qt settings.'
qc.QCoreApplication.setOrganizationName('NIH')
qc.QCoreApplication.setOrganizationDomain('nih.gov')
qc.QCoreApplication.setApplicationName('Argos')
settings = qc.QSettings()
logging.basicConfig(stream=sys.stdout, format='%(asctime)s p=%(processName)s[%(process)d] t=%(threadName)s[%(thread)d] %(filename)s#%(lineno)d:%(funcName)s: %(message)s', level=logging.DEBUG)
return settings | Initialize logging and Qt settings. | argos/utility.py | init | subhacom/argos | 1 | python | def init():
qc.QCoreApplication.setOrganizationName('NIH')
qc.QCoreApplication.setOrganizationDomain('nih.gov')
qc.QCoreApplication.setApplicationName('Argos')
settings = qc.QSettings()
logging.basicConfig(stream=sys.stdout, format='%(asctime)s p=%(processName)s[%(process)d] t=%(threadName)s[%(thread)d] %(filename)s#%(lineno)d:%(funcName)s: %(message)s', level=logging.DEBUG)
return settings | def init():
qc.QCoreApplication.setOrganizationName('NIH')
qc.QCoreApplication.setOrganizationDomain('nih.gov')
qc.QCoreApplication.setApplicationName('Argos')
settings = qc.QSettings()
logging.basicConfig(stream=sys.stdout, format='%(asctime)s p=%(processName)s[%(process)d] t=%(threadName)s[%(thread)d] %(filename)s#%(lineno)d:%(funcName)s: %(message)s', level=logging.DEBUG)
return settings<|docstring|>Initialize logging and Qt settings.<|endoftext|> |
55f0b8db79084862ece37333226c378168a9d5e9407796701d3831b806e80d61 | def to_qpolygon(points, scale=1.0):
'Convert a sequence of (x, y) points into a `qg.QPolygonF`.'
return qg.QPolygonF([qc.QPointF((p0 * scale), (p1 * scale)) for (p0, p1) in points]) | Convert a sequence of (x, y) points into a `qg.QPolygonF`. | argos/utility.py | to_qpolygon | subhacom/argos | 1 | python | def to_qpolygon(points, scale=1.0):
return qg.QPolygonF([qc.QPointF((p0 * scale), (p1 * scale)) for (p0, p1) in points]) | def to_qpolygon(points, scale=1.0):
return qg.QPolygonF([qc.QPointF((p0 * scale), (p1 * scale)) for (p0, p1) in points])<|docstring|>Convert a sequence of (x, y) points into a `qg.QPolygonF`.<|endoftext|> |
843aceec090182b793f1707a2b7fdd365f4e8111ed3b590452d6b0dc66ef8b82 | def cond_bbox_overlap(ra, rb, min_iou):
'Check if IoU of axis-aligned bounding boxes overlap.\n\n Parameters\n ----------\n ra, rb: array like\n Rectangles specified as (x, y, w, h)\n min_iou: flat\n Minimum value of IoU to consider overlap.\n\n Returns\n -------\n bool\n True if `ra` and `rb` have IoU >= `min_iou`. False otherwise.\n '
return (rect_iou(ra, rb) >= min_iou) | Check if IoU of axis-aligned bounding boxes overlap.
Parameters
----------
ra, rb: array like
Rectangles specified as (x, y, w, h)
min_iou: flat
Minimum value of IoU to consider overlap.
Returns
-------
bool
True if `ra` and `rb` have IoU >= `min_iou`. False otherwise. | argos/utility.py | cond_bbox_overlap | subhacom/argos | 1 | python | def cond_bbox_overlap(ra, rb, min_iou):
'Check if IoU of axis-aligned bounding boxes overlap.\n\n Parameters\n ----------\n ra, rb: array like\n Rectangles specified as (x, y, w, h)\n min_iou: flat\n Minimum value of IoU to consider overlap.\n\n Returns\n -------\n bool\n True if `ra` and `rb` have IoU >= `min_iou`. False otherwise.\n '
return (rect_iou(ra, rb) >= min_iou) | def cond_bbox_overlap(ra, rb, min_iou):
'Check if IoU of axis-aligned bounding boxes overlap.\n\n Parameters\n ----------\n ra, rb: array like\n Rectangles specified as (x, y, w, h)\n min_iou: flat\n Minimum value of IoU to consider overlap.\n\n Returns\n -------\n bool\n True if `ra` and `rb` have IoU >= `min_iou`. False otherwise.\n '
return (rect_iou(ra, rb) >= min_iou)<|docstring|>Check if IoU of axis-aligned bounding boxes overlap.
Parameters
----------
ra, rb: array like
Rectangles specified as (x, y, w, h)
min_iou: flat
Minimum value of IoU to consider overlap.
Returns
-------
bool
True if `ra` and `rb` have IoU >= `min_iou`. False otherwise.<|endoftext|> |
53069f0e7d8dc4d9aae9e49455c9204f014c619a1fef4be8b10758496c7572b0 | def cond_minrect_overlap(ra, rb, min_iou):
'Check if IoU of minimum area (rotated) bounding rectangles is at least\n `min_iou`.\n\n Parameters\n ----------\n ra: array like\n First rectangle defined by the coordinates of four corners.\n rb: array like\n Second rectangle defined by the coordinates of four corners.\n min_iou: float\n Minimum overlap defined by intersection over union of bounding boxes.\n\n Returns\n -------\n bool\n True if area of overlap is greater or equal to `min_iou`.\n '
(area_i, _) = cv2.intersectConvexConvex(ra, rb)
area_u = ((cv2.contourArea(ra) + cv2.contourArea(rb)) - area_i)
return (area_i >= (min_iou * area_u)) | Check if IoU of minimum area (rotated) bounding rectangles is at least
`min_iou`.
Parameters
----------
ra: array like
First rectangle defined by the coordinates of four corners.
rb: array like
Second rectangle defined by the coordinates of four corners.
min_iou: float
Minimum overlap defined by intersection over union of bounding boxes.
Returns
-------
bool
True if area of overlap is greater or equal to `min_iou`. | argos/utility.py | cond_minrect_overlap | subhacom/argos | 1 | python | def cond_minrect_overlap(ra, rb, min_iou):
'Check if IoU of minimum area (rotated) bounding rectangles is at least\n `min_iou`.\n\n Parameters\n ----------\n ra: array like\n First rectangle defined by the coordinates of four corners.\n rb: array like\n Second rectangle defined by the coordinates of four corners.\n min_iou: float\n Minimum overlap defined by intersection over union of bounding boxes.\n\n Returns\n -------\n bool\n True if area of overlap is greater or equal to `min_iou`.\n '
(area_i, _) = cv2.intersectConvexConvex(ra, rb)
area_u = ((cv2.contourArea(ra) + cv2.contourArea(rb)) - area_i)
return (area_i >= (min_iou * area_u)) | def cond_minrect_overlap(ra, rb, min_iou):
'Check if IoU of minimum area (rotated) bounding rectangles is at least\n `min_iou`.\n\n Parameters\n ----------\n ra: array like\n First rectangle defined by the coordinates of four corners.\n rb: array like\n Second rectangle defined by the coordinates of four corners.\n min_iou: float\n Minimum overlap defined by intersection over union of bounding boxes.\n\n Returns\n -------\n bool\n True if area of overlap is greater or equal to `min_iou`.\n '
(area_i, _) = cv2.intersectConvexConvex(ra, rb)
area_u = ((cv2.contourArea(ra) + cv2.contourArea(rb)) - area_i)
return (area_i >= (min_iou * area_u))<|docstring|>Check if IoU of minimum area (rotated) bounding rectangles is at least
`min_iou`.
Parameters
----------
ra: array like
First rectangle defined by the coordinates of four corners.
rb: array like
Second rectangle defined by the coordinates of four corners.
min_iou: float
Minimum overlap defined by intersection over union of bounding boxes.
Returns
-------
bool
True if area of overlap is greater or equal to `min_iou`.<|endoftext|> |
0e4069cf6319e20be1e4a437d9cdc9715c7f522c9f419a017a3beb7d3609cb2e | def cond_proximity(points_a, points_b, min_dist):
'Check if the proximity of two arrays of points is more than `min_dist`.\n\n To take the shape of the object into account, I use the following measure\n of distance:\n scale the distance between centres of mass by the geometric mean of the\n square roots of the second moments.\n\n (x1 - x2) / sqrt(sigma_1_x * sigma_2_x)\n (y1 - y2) / sqrt(sigma_1_y * sigma_2_y)\n\n Parameters\n ----------\n points_a: array like\n Sequence of points\n points_b: array like\n Sequence of points\n min_dist: float\n Minimum distance.\n\n Returns\n -------\n bool\n `True` if the centres of mass (mean position) of `points_a` and\n `points_b` are closer than `min_dist`, `False` otherwise.\n '
sigma = (np.std(points_a, axis=0) * np.std(points_b, axis=0))
dx2 = (((np.mean(points_a[(:, 0)]) - np.mean(points_b[(:, 0)])) ** 2) / sigma[0])
dy2 = (((np.mean(points_a[(:, 1)]) - np.mean(points_b[(:, 1)])) ** 2) / sigma[1])
return ((dx2 + dy2) < (min_dist ** 2)) | Check if the proximity of two arrays of points is more than `min_dist`.
To take the shape of the object into account, I use the following measure
of distance:
scale the distance between centres of mass by the geometric mean of the
square roots of the second moments.
(x1 - x2) / sqrt(sigma_1_x * sigma_2_x)
(y1 - y2) / sqrt(sigma_1_y * sigma_2_y)
Parameters
----------
points_a: array like
Sequence of points
points_b: array like
Sequence of points
min_dist: float
Minimum distance.
Returns
-------
bool
`True` if the centres of mass (mean position) of `points_a` and
`points_b` are closer than `min_dist`, `False` otherwise. | argos/utility.py | cond_proximity | subhacom/argos | 1 | python | def cond_proximity(points_a, points_b, min_dist):
'Check if the proximity of two arrays of points is more than `min_dist`.\n\n To take the shape of the object into account, I use the following measure\n of distance:\n scale the distance between centres of mass by the geometric mean of the\n square roots of the second moments.\n\n (x1 - x2) / sqrt(sigma_1_x * sigma_2_x)\n (y1 - y2) / sqrt(sigma_1_y * sigma_2_y)\n\n Parameters\n ----------\n points_a: array like\n Sequence of points\n points_b: array like\n Sequence of points\n min_dist: float\n Minimum distance.\n\n Returns\n -------\n bool\n `True` if the centres of mass (mean position) of `points_a` and\n `points_b` are closer than `min_dist`, `False` otherwise.\n '
sigma = (np.std(points_a, axis=0) * np.std(points_b, axis=0))
dx2 = (((np.mean(points_a[(:, 0)]) - np.mean(points_b[(:, 0)])) ** 2) / sigma[0])
dy2 = (((np.mean(points_a[(:, 1)]) - np.mean(points_b[(:, 1)])) ** 2) / sigma[1])
return ((dx2 + dy2) < (min_dist ** 2)) | def cond_proximity(points_a, points_b, min_dist):
'Check if the proximity of two arrays of points is more than `min_dist`.\n\n To take the shape of the object into account, I use the following measure\n of distance:\n scale the distance between centres of mass by the geometric mean of the\n square roots of the second moments.\n\n (x1 - x2) / sqrt(sigma_1_x * sigma_2_x)\n (y1 - y2) / sqrt(sigma_1_y * sigma_2_y)\n\n Parameters\n ----------\n points_a: array like\n Sequence of points\n points_b: array like\n Sequence of points\n min_dist: float\n Minimum distance.\n\n Returns\n -------\n bool\n `True` if the centres of mass (mean position) of `points_a` and\n `points_b` are closer than `min_dist`, `False` otherwise.\n '
sigma = (np.std(points_a, axis=0) * np.std(points_b, axis=0))
dx2 = (((np.mean(points_a[(:, 0)]) - np.mean(points_b[(:, 0)])) ** 2) / sigma[0])
dy2 = (((np.mean(points_a[(:, 1)]) - np.mean(points_b[(:, 1)])) ** 2) / sigma[1])
return ((dx2 + dy2) < (min_dist ** 2))<|docstring|>Check if the proximity of two arrays of points is more than `min_dist`.
To take the shape of the object into account, I use the following measure
of distance:
scale the distance between centres of mass by the geometric mean of the
square roots of the second moments.
(x1 - x2) / sqrt(sigma_1_x * sigma_2_x)
(y1 - y2) / sqrt(sigma_1_y * sigma_2_y)
Parameters
----------
points_a: array like
Sequence of points
points_b: array like
Sequence of points
min_dist: float
Minimum distance.
Returns
-------
bool
`True` if the centres of mass (mean position) of `points_a` and
`points_b` are closer than `min_dist`, `False` otherwise.<|endoftext|> |
9162c9d0949fafaa8ccca8e1b854819d01093d37771bc71d88741aae66309892 | def cv2qimage(frame: np.ndarray, copy: bool=False) -> qg.QImage:
'Convert BGR/gray/bw frame from array into QImage".\n\n OpenCV reads images into 2D or 3D matrix. This function converts it into\n Qt QImage.\n\n Parameters\n ----------\n frame: numpy.ndarray\n Input image data as a 2D (black and white, gray() or 3D (color, OpenCV\n reads images in BGR instead of RGB format) array.\n copy: bool, default False\n If True Make a copy of the image data.\n\n Returns\n -------\n qg.QImage\n Converted image.\n '
if ((len(frame.shape) == 3) and (frame.shape[2] == 3)):
img = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
(h, w, c) = img.shape
qimg = qg.QImage(img.tobytes(), w, h, (w * c), qg.QImage.Format_RGB888)
elif (len(frame.shape) == 2):
(h, w) = frame.shape
qimg = qg.QImage(frame.tobytes(), w, h, (w * 1), qg.QImage.Format_Grayscale8)
if copy:
return qimg.copy()
return qimg | Convert BGR/gray/bw frame from array into QImage".
OpenCV reads images into 2D or 3D matrix. This function converts it into
Qt QImage.
Parameters
----------
frame: numpy.ndarray
Input image data as a 2D (black and white, gray() or 3D (color, OpenCV
reads images in BGR instead of RGB format) array.
copy: bool, default False
If True Make a copy of the image data.
Returns
-------
qg.QImage
Converted image. | argos/utility.py | cv2qimage | subhacom/argos | 1 | python | def cv2qimage(frame: np.ndarray, copy: bool=False) -> qg.QImage:
'Convert BGR/gray/bw frame from array into QImage".\n\n OpenCV reads images into 2D or 3D matrix. This function converts it into\n Qt QImage.\n\n Parameters\n ----------\n frame: numpy.ndarray\n Input image data as a 2D (black and white, gray() or 3D (color, OpenCV\n reads images in BGR instead of RGB format) array.\n copy: bool, default False\n If True Make a copy of the image data.\n\n Returns\n -------\n qg.QImage\n Converted image.\n '
if ((len(frame.shape) == 3) and (frame.shape[2] == 3)):
img = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
(h, w, c) = img.shape
qimg = qg.QImage(img.tobytes(), w, h, (w * c), qg.QImage.Format_RGB888)
elif (len(frame.shape) == 2):
(h, w) = frame.shape
qimg = qg.QImage(frame.tobytes(), w, h, (w * 1), qg.QImage.Format_Grayscale8)
if copy:
return qimg.copy()
return qimg | def cv2qimage(frame: np.ndarray, copy: bool=False) -> qg.QImage:
'Convert BGR/gray/bw frame from array into QImage".\n\n OpenCV reads images into 2D or 3D matrix. This function converts it into\n Qt QImage.\n\n Parameters\n ----------\n frame: numpy.ndarray\n Input image data as a 2D (black and white, gray() or 3D (color, OpenCV\n reads images in BGR instead of RGB format) array.\n copy: bool, default False\n If True Make a copy of the image data.\n\n Returns\n -------\n qg.QImage\n Converted image.\n '
if ((len(frame.shape) == 3) and (frame.shape[2] == 3)):
img = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
(h, w, c) = img.shape
qimg = qg.QImage(img.tobytes(), w, h, (w * c), qg.QImage.Format_RGB888)
elif (len(frame.shape) == 2):
(h, w) = frame.shape
qimg = qg.QImage(frame.tobytes(), w, h, (w * 1), qg.QImage.Format_Grayscale8)
if copy:
return qimg.copy()
return qimg<|docstring|>Convert BGR/gray/bw frame from array into QImage".
OpenCV reads images into 2D or 3D matrix. This function converts it into
Qt QImage.
Parameters
----------
frame: numpy.ndarray
Input image data as a 2D (black and white, gray() or 3D (color, OpenCV
reads images in BGR instead of RGB format) array.
copy: bool, default False
If True Make a copy of the image data.
Returns
-------
qg.QImage
Converted image.<|endoftext|> |
4257d44bcaeff862fce5adfe1e4b8754e818df60f1a812108c1b399d5a6c8107 | def match_bboxes(id_bboxes: dict, new_bboxes: np.ndarray, boxtype: OutlineStyle, metric: DistanceMetric=DistanceMetric.euclidean, max_dist: float=10000) -> Tuple[(Dict[(int, int)], Set[int], Set[int])]:
'Match the rectangular bounding boxes in `new_bboxes` to the closest\n object in the `id_bboxes` dictionary.\n\n Parameters\n ----------\n id_bboxes: dict[int, np.ndarray]\n Mapping ids to bounding boxes\n new_bboxes: np.ndarray\n Array of new bounding boxes to be matched to those in ``id_bboxes``.\n boxtype: {OutlineStyle.bbox, OutlineStyle.minrect}\n Type of bounding box to match.\n max_dist: int, default 10000\n Anything that is more than this distance from all of the bboxes in\n ``id_bboxes`` are put in the unmatched list\n metric: {DistanceMetric.euclidean, DistanceMetric.iou}\n `DistanceMetric.euclidean` for Euclidean distance between centers of the\n boxes. `DistanceMetric.iou` for area of inetersection over union of the\n boxes,\n\n Returns\n -------\n matched: dict[int, int]\n Mapping keys in ``id_bboxes`` to bbox indices in ``new_bboxes`` that are\n closest.\n new_unmatched: set[int]\n Set of indices into `bboxes` that did not match anything in\n ``id_bboxes``\n old_unmatched: set[int]\n Set of keys in ``id_bboxes`` whose corresponding bbox values did not\n match anything in ``bboxes``.\n '
if (len(id_bboxes) == 0):
return ({}, set(range(len(new_bboxes))), {})
labels = list(id_bboxes.keys())
bboxes = np.array(np.rint(list(id_bboxes.values())), dtype=np.int_)
dist_matrix = pairwise_distance(new_bboxes, bboxes, boxtype=boxtype, metric=metric)
(row_ind, col_ind) = optimize.linear_sum_assignment(dist_matrix)
if (metric == DistanceMetric.euclidean):
max_dist *= max_dist
result = [(row, labels[col], (labels[col], row)) for (row, col) in zip(row_ind, col_ind) if (dist_matrix[(row, col)] < max_dist)]
if (len(result) > 0):
(good_rows, good_cols, matched) = zip(*result)
good_rows = set(good_rows)
good_cols = set(good_cols)
matched = dict(matched)
new_unmatched = (set(range(len(new_bboxes))) - good_rows)
old_unmatched = (set(id_bboxes.keys()) - good_cols)
else:
matched = {}
new_unmatched = set(range(len(new_bboxes)))
old_unmatched = set(id_bboxes.keys())
return (matched, new_unmatched, old_unmatched) | Match the rectangular bounding boxes in `new_bboxes` to the closest
object in the `id_bboxes` dictionary.
Parameters
----------
id_bboxes: dict[int, np.ndarray]
Mapping ids to bounding boxes
new_bboxes: np.ndarray
Array of new bounding boxes to be matched to those in ``id_bboxes``.
boxtype: {OutlineStyle.bbox, OutlineStyle.minrect}
Type of bounding box to match.
max_dist: int, default 10000
Anything that is more than this distance from all of the bboxes in
``id_bboxes`` are put in the unmatched list
metric: {DistanceMetric.euclidean, DistanceMetric.iou}
`DistanceMetric.euclidean` for Euclidean distance between centers of the
boxes. `DistanceMetric.iou` for area of inetersection over union of the
boxes,
Returns
-------
matched: dict[int, int]
Mapping keys in ``id_bboxes`` to bbox indices in ``new_bboxes`` that are
closest.
new_unmatched: set[int]
Set of indices into `bboxes` that did not match anything in
``id_bboxes``
old_unmatched: set[int]
Set of keys in ``id_bboxes`` whose corresponding bbox values did not
match anything in ``bboxes``. | argos/utility.py | match_bboxes | subhacom/argos | 1 | python | def match_bboxes(id_bboxes: dict, new_bboxes: np.ndarray, boxtype: OutlineStyle, metric: DistanceMetric=DistanceMetric.euclidean, max_dist: float=10000) -> Tuple[(Dict[(int, int)], Set[int], Set[int])]:
'Match the rectangular bounding boxes in `new_bboxes` to the closest\n object in the `id_bboxes` dictionary.\n\n Parameters\n ----------\n id_bboxes: dict[int, np.ndarray]\n Mapping ids to bounding boxes\n new_bboxes: np.ndarray\n Array of new bounding boxes to be matched to those in ``id_bboxes``.\n boxtype: {OutlineStyle.bbox, OutlineStyle.minrect}\n Type of bounding box to match.\n max_dist: int, default 10000\n Anything that is more than this distance from all of the bboxes in\n ``id_bboxes`` are put in the unmatched list\n metric: {DistanceMetric.euclidean, DistanceMetric.iou}\n `DistanceMetric.euclidean` for Euclidean distance between centers of the\n boxes. `DistanceMetric.iou` for area of inetersection over union of the\n boxes,\n\n Returns\n -------\n matched: dict[int, int]\n Mapping keys in ``id_bboxes`` to bbox indices in ``new_bboxes`` that are\n closest.\n new_unmatched: set[int]\n Set of indices into `bboxes` that did not match anything in\n ``id_bboxes``\n old_unmatched: set[int]\n Set of keys in ``id_bboxes`` whose corresponding bbox values did not\n match anything in ``bboxes``.\n '
if (len(id_bboxes) == 0):
return ({}, set(range(len(new_bboxes))), {})
labels = list(id_bboxes.keys())
bboxes = np.array(np.rint(list(id_bboxes.values())), dtype=np.int_)
dist_matrix = pairwise_distance(new_bboxes, bboxes, boxtype=boxtype, metric=metric)
(row_ind, col_ind) = optimize.linear_sum_assignment(dist_matrix)
if (metric == DistanceMetric.euclidean):
max_dist *= max_dist
result = [(row, labels[col], (labels[col], row)) for (row, col) in zip(row_ind, col_ind) if (dist_matrix[(row, col)] < max_dist)]
if (len(result) > 0):
(good_rows, good_cols, matched) = zip(*result)
good_rows = set(good_rows)
good_cols = set(good_cols)
matched = dict(matched)
new_unmatched = (set(range(len(new_bboxes))) - good_rows)
old_unmatched = (set(id_bboxes.keys()) - good_cols)
else:
matched = {}
new_unmatched = set(range(len(new_bboxes)))
old_unmatched = set(id_bboxes.keys())
return (matched, new_unmatched, old_unmatched) | def match_bboxes(id_bboxes: dict, new_bboxes: np.ndarray, boxtype: OutlineStyle, metric: DistanceMetric=DistanceMetric.euclidean, max_dist: float=10000) -> Tuple[(Dict[(int, int)], Set[int], Set[int])]:
'Match the rectangular bounding boxes in `new_bboxes` to the closest\n object in the `id_bboxes` dictionary.\n\n Parameters\n ----------\n id_bboxes: dict[int, np.ndarray]\n Mapping ids to bounding boxes\n new_bboxes: np.ndarray\n Array of new bounding boxes to be matched to those in ``id_bboxes``.\n boxtype: {OutlineStyle.bbox, OutlineStyle.minrect}\n Type of bounding box to match.\n max_dist: int, default 10000\n Anything that is more than this distance from all of the bboxes in\n ``id_bboxes`` are put in the unmatched list\n metric: {DistanceMetric.euclidean, DistanceMetric.iou}\n `DistanceMetric.euclidean` for Euclidean distance between centers of the\n boxes. `DistanceMetric.iou` for area of inetersection over union of the\n boxes,\n\n Returns\n -------\n matched: dict[int, int]\n Mapping keys in ``id_bboxes`` to bbox indices in ``new_bboxes`` that are\n closest.\n new_unmatched: set[int]\n Set of indices into `bboxes` that did not match anything in\n ``id_bboxes``\n old_unmatched: set[int]\n Set of keys in ``id_bboxes`` whose corresponding bbox values did not\n match anything in ``bboxes``.\n '
if (len(id_bboxes) == 0):
return ({}, set(range(len(new_bboxes))), {})
labels = list(id_bboxes.keys())
bboxes = np.array(np.rint(list(id_bboxes.values())), dtype=np.int_)
dist_matrix = pairwise_distance(new_bboxes, bboxes, boxtype=boxtype, metric=metric)
(row_ind, col_ind) = optimize.linear_sum_assignment(dist_matrix)
if (metric == DistanceMetric.euclidean):
max_dist *= max_dist
result = [(row, labels[col], (labels[col], row)) for (row, col) in zip(row_ind, col_ind) if (dist_matrix[(row, col)] < max_dist)]
if (len(result) > 0):
(good_rows, good_cols, matched) = zip(*result)
good_rows = set(good_rows)
good_cols = set(good_cols)
matched = dict(matched)
new_unmatched = (set(range(len(new_bboxes))) - good_rows)
old_unmatched = (set(id_bboxes.keys()) - good_cols)
else:
matched = {}
new_unmatched = set(range(len(new_bboxes)))
old_unmatched = set(id_bboxes.keys())
return (matched, new_unmatched, old_unmatched)<|docstring|>Match the rectangular bounding boxes in `new_bboxes` to the closest
object in the `id_bboxes` dictionary.
Parameters
----------
id_bboxes: dict[int, np.ndarray]
Mapping ids to bounding boxes
new_bboxes: np.ndarray
Array of new bounding boxes to be matched to those in ``id_bboxes``.
boxtype: {OutlineStyle.bbox, OutlineStyle.minrect}
Type of bounding box to match.
max_dist: int, default 10000
Anything that is more than this distance from all of the bboxes in
``id_bboxes`` are put in the unmatched list
metric: {DistanceMetric.euclidean, DistanceMetric.iou}
`DistanceMetric.euclidean` for Euclidean distance between centers of the
boxes. `DistanceMetric.iou` for area of inetersection over union of the
boxes,
Returns
-------
matched: dict[int, int]
Mapping keys in ``id_bboxes`` to bbox indices in ``new_bboxes`` that are
closest.
new_unmatched: set[int]
Set of indices into `bboxes` that did not match anything in
``id_bboxes``
old_unmatched: set[int]
Set of keys in ``id_bboxes`` whose corresponding bbox values did not
match anything in ``bboxes``.<|endoftext|> |
9c41626a4bf8c4719ecccfb2bba3a8ece732e1d102b05af526fbb71f75bdf923 | def reconnect(signal, newhandler=None, oldhandler=None):
'Disconnect PyQt signal from oldhandler and connect to newhandler'
while True:
try:
if (oldhandler is not None):
signal.disconnect(oldhandler)
else:
signal.disconnect()
except TypeError:
break
if (newhandler is not None):
signal.connect(newhandler) | Disconnect PyQt signal from oldhandler and connect to newhandler | argos/utility.py | reconnect | subhacom/argos | 1 | python | def reconnect(signal, newhandler=None, oldhandler=None):
while True:
try:
if (oldhandler is not None):
signal.disconnect(oldhandler)
else:
signal.disconnect()
except TypeError:
break
if (newhandler is not None):
signal.connect(newhandler) | def reconnect(signal, newhandler=None, oldhandler=None):
while True:
try:
if (oldhandler is not None):
signal.disconnect(oldhandler)
else:
signal.disconnect()
except TypeError:
break
if (newhandler is not None):
signal.connect(newhandler)<|docstring|>Disconnect PyQt signal from oldhandler and connect to newhandler<|endoftext|> |
b6e83f7a9c0e62632e3be6b6aa2ce2865cc70ae27ad48a5ca42be29c0456f0c9 | def make_color(num: int) -> Tuple[int]:
'Create a random color based on number.\n\n The provided number is passed through the murmur hash function in order\n to generate bytes which are somewhat apart from each other. The three least\n significant byte values are taken as r, g, and b.\n\n Parameters\n ----------\n num: int\n number to use as hash key\n\n Returns\n -------\n bytes[3]\n (r, g, b) values\n\n '
val = murmurhash3_32(num, positive=True).to_bytes(8, 'little')
return val[:3] | Create a random color based on number.
The provided number is passed through the murmur hash function in order
to generate bytes which are somewhat apart from each other. The three least
significant byte values are taken as r, g, and b.
Parameters
----------
num: int
number to use as hash key
Returns
-------
bytes[3]
(r, g, b) values | argos/utility.py | make_color | subhacom/argos | 1 | python | def make_color(num: int) -> Tuple[int]:
'Create a random color based on number.\n\n The provided number is passed through the murmur hash function in order\n to generate bytes which are somewhat apart from each other. The three least\n significant byte values are taken as r, g, and b.\n\n Parameters\n ----------\n num: int\n number to use as hash key\n\n Returns\n -------\n bytes[3]\n (r, g, b) values\n\n '
val = murmurhash3_32(num, positive=True).to_bytes(8, 'little')
return val[:3] | def make_color(num: int) -> Tuple[int]:
'Create a random color based on number.\n\n The provided number is passed through the murmur hash function in order\n to generate bytes which are somewhat apart from each other. The three least\n significant byte values are taken as r, g, and b.\n\n Parameters\n ----------\n num: int\n number to use as hash key\n\n Returns\n -------\n bytes[3]\n (r, g, b) values\n\n '
val = murmurhash3_32(num, positive=True).to_bytes(8, 'little')
return val[:3]<|docstring|>Create a random color based on number.
The provided number is passed through the murmur hash function in order
to generate bytes which are somewhat apart from each other. The three least
significant byte values are taken as r, g, and b.
Parameters
----------
num: int
number to use as hash key
Returns
-------
bytes[3]
(r, g, b) values<|endoftext|> |
079790c218ea818fc5969476e6c333ca37802b432ca80370527cda4429797a5c | def get_cmap_color(num, maxnum, cmap):
'Get rgb based on specified colormap `cmap` for index `num` where the\n total range of values is (0, maxnum].\n\n Parameters\n ----------\n num: real number\n Position into colormap.\n maxnum: real number\n Normalize `num` by this value.\n cmap: str\n Name of colormap\n\n Returns\n -------\n tuple: (r, g, b)\n The red, green and blue value for the color at position `num`/`maxnum`\n in the (0, 1) range of the colormap.\n '
rgba = cm.get_cmap(cmap)((float(num) / maxnum))
int_rgb = (max(0, min(255, floor((v * 256)))) for v in rgba[:3])
return int_rgb | Get rgb based on specified colormap `cmap` for index `num` where the
total range of values is (0, maxnum].
Parameters
----------
num: real number
Position into colormap.
maxnum: real number
Normalize `num` by this value.
cmap: str
Name of colormap
Returns
-------
tuple: (r, g, b)
The red, green and blue value for the color at position `num`/`maxnum`
in the (0, 1) range of the colormap. | argos/utility.py | get_cmap_color | subhacom/argos | 1 | python | def get_cmap_color(num, maxnum, cmap):
'Get rgb based on specified colormap `cmap` for index `num` where the\n total range of values is (0, maxnum].\n\n Parameters\n ----------\n num: real number\n Position into colormap.\n maxnum: real number\n Normalize `num` by this value.\n cmap: str\n Name of colormap\n\n Returns\n -------\n tuple: (r, g, b)\n The red, green and blue value for the color at position `num`/`maxnum`\n in the (0, 1) range of the colormap.\n '
rgba = cm.get_cmap(cmap)((float(num) / maxnum))
int_rgb = (max(0, min(255, floor((v * 256)))) for v in rgba[:3])
return int_rgb | def get_cmap_color(num, maxnum, cmap):
'Get rgb based on specified colormap `cmap` for index `num` where the\n total range of values is (0, maxnum].\n\n Parameters\n ----------\n num: real number\n Position into colormap.\n maxnum: real number\n Normalize `num` by this value.\n cmap: str\n Name of colormap\n\n Returns\n -------\n tuple: (r, g, b)\n The red, green and blue value for the color at position `num`/`maxnum`\n in the (0, 1) range of the colormap.\n '
rgba = cm.get_cmap(cmap)((float(num) / maxnum))
int_rgb = (max(0, min(255, floor((v * 256)))) for v in rgba[:3])
return int_rgb<|docstring|>Get rgb based on specified colormap `cmap` for index `num` where the
total range of values is (0, maxnum].
Parameters
----------
num: real number
Position into colormap.
maxnum: real number
Normalize `num` by this value.
cmap: str
Name of colormap
Returns
-------
tuple: (r, g, b)
The red, green and blue value for the color at position `num`/`maxnum`
in the (0, 1) range of the colormap.<|endoftext|> |
29cea57d5853fa01f4f57192b7398bd33dfec26e4bcb0a74638a9868e6f36357 | def extract_frames(vidfile, nframes, scale=1.0, outdir='.', random=False):
'Extract `nframes` frames from `vidfile` into `outdir`'
cap = cv2.VideoCapture(vidfile)
fname = os.path.basename(vidfile)
prefix = fname.rpartition('.')[0]
frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
idx = np.arange(frame_count, dtype=int)
if (frame_count < nframes):
if random:
np.random.shuffle(idx)
idx = idx[:nframes]
else:
step = (frame_count // nframes)
idx = idx[::step]
idx = sorted(idx)
ii = 0
jj = 0
while cap.isOpened():
(ret, frame) = cap.read()
if (frame is None):
break
if (idx[jj] == ii):
size = (int((frame.shape[1] * scale)), int((frame.shape[0] * scale)))
if (scale < 1):
frame = cv2.resize(frame, size, cv2.INTER_AREA)
elif (scale > 1):
frame = cv2.resize(frame, size, cv2.INTER_CUBIC)
cv2.imwrite(os.path.join(outdir, f'{prefix}_{idx[jj]:06d}.png'), frame)
jj += 1
ii += 1
cap.release() | Extract `nframes` frames from `vidfile` into `outdir` | argos/utility.py | extract_frames | subhacom/argos | 1 | python | def extract_frames(vidfile, nframes, scale=1.0, outdir='.', random=False):
cap = cv2.VideoCapture(vidfile)
fname = os.path.basename(vidfile)
prefix = fname.rpartition('.')[0]
frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
idx = np.arange(frame_count, dtype=int)
if (frame_count < nframes):
if random:
np.random.shuffle(idx)
idx = idx[:nframes]
else:
step = (frame_count // nframes)
idx = idx[::step]
idx = sorted(idx)
ii = 0
jj = 0
while cap.isOpened():
(ret, frame) = cap.read()
if (frame is None):
break
if (idx[jj] == ii):
size = (int((frame.shape[1] * scale)), int((frame.shape[0] * scale)))
if (scale < 1):
frame = cv2.resize(frame, size, cv2.INTER_AREA)
elif (scale > 1):
frame = cv2.resize(frame, size, cv2.INTER_CUBIC)
cv2.imwrite(os.path.join(outdir, f'{prefix}_{idx[jj]:06d}.png'), frame)
jj += 1
ii += 1
cap.release() | def extract_frames(vidfile, nframes, scale=1.0, outdir='.', random=False):
cap = cv2.VideoCapture(vidfile)
fname = os.path.basename(vidfile)
prefix = fname.rpartition('.')[0]
frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
idx = np.arange(frame_count, dtype=int)
if (frame_count < nframes):
if random:
np.random.shuffle(idx)
idx = idx[:nframes]
else:
step = (frame_count // nframes)
idx = idx[::step]
idx = sorted(idx)
ii = 0
jj = 0
while cap.isOpened():
(ret, frame) = cap.read()
if (frame is None):
break
if (idx[jj] == ii):
size = (int((frame.shape[1] * scale)), int((frame.shape[0] * scale)))
if (scale < 1):
frame = cv2.resize(frame, size, cv2.INTER_AREA)
elif (scale > 1):
frame = cv2.resize(frame, size, cv2.INTER_CUBIC)
cv2.imwrite(os.path.join(outdir, f'{prefix}_{idx[jj]:06d}.png'), frame)
jj += 1
ii += 1
cap.release()<|docstring|>Extract `nframes` frames from `vidfile` into `outdir`<|endoftext|> |
722c1a6e5a3cbe11725e839f5883dae6ed2bcda2a2edea4aa31a58d10166bc0b | def points2rect(p0: np.ndarray, p1: np.ndarray) -> np.ndarray:
'Convert diagonally opposite vertices into (x, y, w, h) format\n rectangle.\n\n Returns\n -------\n np.ndarray:\n Rectangle with diagonal corners `p0` and `p1` after scaling\n by `scale`. This will work with both top-left - bottom-right\n and bottom-left - top-right diagonals.\n\n '
x = (p0[0], p1[0])
y = (p0[1], p1[1])
xleft = min(x)
w = (max(x) - xleft)
ytop = min(y)
h = (max(y) - ytop)
return np.array((xleft, ytop, w, h)) | Convert diagonally opposite vertices into (x, y, w, h) format
rectangle.
Returns
-------
np.ndarray:
Rectangle with diagonal corners `p0` and `p1` after scaling
by `scale`. This will work with both top-left - bottom-right
and bottom-left - top-right diagonals. | argos/utility.py | points2rect | subhacom/argos | 1 | python | def points2rect(p0: np.ndarray, p1: np.ndarray) -> np.ndarray:
'Convert diagonally opposite vertices into (x, y, w, h) format\n rectangle.\n\n Returns\n -------\n np.ndarray:\n Rectangle with diagonal corners `p0` and `p1` after scaling\n by `scale`. This will work with both top-left - bottom-right\n and bottom-left - top-right diagonals.\n\n '
x = (p0[0], p1[0])
y = (p0[1], p1[1])
xleft = min(x)
w = (max(x) - xleft)
ytop = min(y)
h = (max(y) - ytop)
return np.array((xleft, ytop, w, h)) | def points2rect(p0: np.ndarray, p1: np.ndarray) -> np.ndarray:
'Convert diagonally opposite vertices into (x, y, w, h) format\n rectangle.\n\n Returns\n -------\n np.ndarray:\n Rectangle with diagonal corners `p0` and `p1` after scaling\n by `scale`. This will work with both top-left - bottom-right\n and bottom-left - top-right diagonals.\n\n '
x = (p0[0], p1[0])
y = (p0[1], p1[1])
xleft = min(x)
w = (max(x) - xleft)
ytop = min(y)
h = (max(y) - ytop)
return np.array((xleft, ytop, w, h))<|docstring|>Convert diagonally opposite vertices into (x, y, w, h) format
rectangle.
Returns
-------
np.ndarray:
Rectangle with diagonal corners `p0` and `p1` after scaling
by `scale`. This will work with both top-left - bottom-right
and bottom-left - top-right diagonals.<|endoftext|> |
e318297c9e10ab5cfc40fd53917f9305f80e2a7baaf2f5601d421acc98e10767 | def rect2points(rect: np.ndarray) -> np.ndarray:
'Convert topleft, width, height format rectangle into four anti-clockwise\n vertices'
return np.vstack([rect[:2], (rect[0], (rect[1] + rect[3])), (rect[:2] + rect[2:]), ((rect[0] + rect[2]), rect[1])]) | Convert topleft, width, height format rectangle into four anti-clockwise
vertices | argos/utility.py | rect2points | subhacom/argos | 1 | python | def rect2points(rect: np.ndarray) -> np.ndarray:
'Convert topleft, width, height format rectangle into four anti-clockwise\n vertices'
return np.vstack([rect[:2], (rect[0], (rect[1] + rect[3])), (rect[:2] + rect[2:]), ((rect[0] + rect[2]), rect[1])]) | def rect2points(rect: np.ndarray) -> np.ndarray:
'Convert topleft, width, height format rectangle into four anti-clockwise\n vertices'
return np.vstack([rect[:2], (rect[0], (rect[1] + rect[3])), (rect[:2] + rect[2:]), ((rect[0] + rect[2]), rect[1])])<|docstring|>Convert topleft, width, height format rectangle into four anti-clockwise
vertices<|endoftext|> |
6103ca89e4023ad97e3ca970520f7a08aad46e9339511e96c74188b41a26f936 | def tlwh2xyrh(rect):
'Convert top-left, width, height into center, aspect ratio, height'
return np.array(((rect[0] + (rect[2] / 2.0)), (rect[1] + (rect[3] / 2.0)), (rect[2] / float(rect[3])), rect[3])) | Convert top-left, width, height into center, aspect ratio, height | argos/utility.py | tlwh2xyrh | subhacom/argos | 1 | python | def tlwh2xyrh(rect):
return np.array(((rect[0] + (rect[2] / 2.0)), (rect[1] + (rect[3] / 2.0)), (rect[2] / float(rect[3])), rect[3])) | def tlwh2xyrh(rect):
return np.array(((rect[0] + (rect[2] / 2.0)), (rect[1] + (rect[3] / 2.0)), (rect[2] / float(rect[3])), rect[3]))<|docstring|>Convert top-left, width, height into center, aspect ratio, height<|endoftext|> |
79f59dddbdf1c173a781ab6a717957dca935351571c90489ed77ba240cecfa89 | def xyrh2tlwh(rect: np.ndarray) -> np.ndarray:
'Convert centre, aspect ratio, height into top-left, width, height\n format'
w = (rect[2] * rect[3])
return np.asanyarray(((rect[0] - (w / 2.0)), (rect[1] - (rect[3] / 2.0)), w, rect[3]), dtype=int) | Convert centre, aspect ratio, height into top-left, width, height
format | argos/utility.py | xyrh2tlwh | subhacom/argos | 1 | python | def xyrh2tlwh(rect: np.ndarray) -> np.ndarray:
'Convert centre, aspect ratio, height into top-left, width, height\n format'
w = (rect[2] * rect[3])
return np.asanyarray(((rect[0] - (w / 2.0)), (rect[1] - (rect[3] / 2.0)), w, rect[3]), dtype=int) | def xyrh2tlwh(rect: np.ndarray) -> np.ndarray:
'Convert centre, aspect ratio, height into top-left, width, height\n format'
w = (rect[2] * rect[3])
return np.asanyarray(((rect[0] - (w / 2.0)), (rect[1] - (rect[3] / 2.0)), w, rect[3]), dtype=int)<|docstring|>Convert centre, aspect ratio, height into top-left, width, height
format<|endoftext|> |
2b790ef145f906d90010cba2903a414e12239485b24ca2afe436ab625776dc7c | def rect_intersection(ra: np.ndarray, rb: np.ndarray) -> np.ndarray:
'Find if two axis-aligned rectangles intersect.\n\n This runs almost 50 times faster than Polygon intersection in shapely.\n and ~5 times faster than cv2.intersectConvexConvex.\n\n Parameters\n ----------\n ra: np.ndarray\n rb: np.ndarray\n Rectangles specified as (x, y, w, h) where (x, y) is the coordinate\n of the lower left corner, w and h are width and height.\n\n Returns\n -------\n np.ndarray\n (x, y, dx, dy) specifying the overlap rectangle. If there is no\n overlap, all entries are 0.\n '
ret = np.zeros((4,), dtype=int)
(xa, ya, wa, ha) = ra
(xb, yb, wb, hb) = rb
x = max(xa, xb)
y = max(ya, yb)
dx = (min((xa + wa), (xb + wb)) - x)
dy = (min((ya + ha), (yb + hb)) - y)
if ((dx > 0) and (dy > 0)):
ret[:] = (x, y, dx, dy)
return ret | Find if two axis-aligned rectangles intersect.
This runs almost 50 times faster than Polygon intersection in shapely.
and ~5 times faster than cv2.intersectConvexConvex.
Parameters
----------
ra: np.ndarray
rb: np.ndarray
Rectangles specified as (x, y, w, h) where (x, y) is the coordinate
of the lower left corner, w and h are width and height.
Returns
-------
np.ndarray
(x, y, dx, dy) specifying the overlap rectangle. If there is no
overlap, all entries are 0. | argos/utility.py | rect_intersection | subhacom/argos | 1 | python | def rect_intersection(ra: np.ndarray, rb: np.ndarray) -> np.ndarray:
'Find if two axis-aligned rectangles intersect.\n\n This runs almost 50 times faster than Polygon intersection in shapely.\n and ~5 times faster than cv2.intersectConvexConvex.\n\n Parameters\n ----------\n ra: np.ndarray\n rb: np.ndarray\n Rectangles specified as (x, y, w, h) where (x, y) is the coordinate\n of the lower left corner, w and h are width and height.\n\n Returns\n -------\n np.ndarray\n (x, y, dx, dy) specifying the overlap rectangle. If there is no\n overlap, all entries are 0.\n '
ret = np.zeros((4,), dtype=int)
(xa, ya, wa, ha) = ra
(xb, yb, wb, hb) = rb
x = max(xa, xb)
y = max(ya, yb)
dx = (min((xa + wa), (xb + wb)) - x)
dy = (min((ya + ha), (yb + hb)) - y)
if ((dx > 0) and (dy > 0)):
ret[:] = (x, y, dx, dy)
return ret | def rect_intersection(ra: np.ndarray, rb: np.ndarray) -> np.ndarray:
'Find if two axis-aligned rectangles intersect.\n\n This runs almost 50 times faster than Polygon intersection in shapely.\n and ~5 times faster than cv2.intersectConvexConvex.\n\n Parameters\n ----------\n ra: np.ndarray\n rb: np.ndarray\n Rectangles specified as (x, y, w, h) where (x, y) is the coordinate\n of the lower left corner, w and h are width and height.\n\n Returns\n -------\n np.ndarray\n (x, y, dx, dy) specifying the overlap rectangle. If there is no\n overlap, all entries are 0.\n '
ret = np.zeros((4,), dtype=int)
(xa, ya, wa, ha) = ra
(xb, yb, wb, hb) = rb
x = max(xa, xb)
y = max(ya, yb)
dx = (min((xa + wa), (xb + wb)) - x)
dy = (min((ya + ha), (yb + hb)) - y)
if ((dx > 0) and (dy > 0)):
ret[:] = (x, y, dx, dy)
return ret<|docstring|>Find if two axis-aligned rectangles intersect.
This runs almost 50 times faster than Polygon intersection in shapely.
and ~5 times faster than cv2.intersectConvexConvex.
Parameters
----------
ra: np.ndarray
rb: np.ndarray
Rectangles specified as (x, y, w, h) where (x, y) is the coordinate
of the lower left corner, w and h are width and height.
Returns
-------
np.ndarray
(x, y, dx, dy) specifying the overlap rectangle. If there is no
overlap, all entries are 0.<|endoftext|> |
63deae5b5d49aa1e6ea915995b4b869f93d910a437976f90c70cc394bc409c7e | def rect_iou(ra: np.ndarray, rb: np.ndarray) -> float:
'Compute Intersection over Union of two axis-aligned rectangles.\n\n This is the ratio of the are of intersection to the area of the union\n of the two rectangles.\n\n Parameters\n ----------\n ra: np.ndarray\n rb: np.ndarray\n Axis aligned rectangles specified as (x, y, w, h) where (x, y) is\n the position of the lower left corner, w and h are width and height.\n\n Returns\n -------\n float\n The Intersection over Union of two rectangles.\n '
(x, y, dx, dy) = rect_intersection(ra, rb)
area_i = (dx * dy)
area_u = (((ra[2] * ra[3]) + (rb[2] * rb[3])) - area_i)
if ((area_u <= 0) or (area_i < 0)):
raise ValueError('Area not positive')
ret = ((1.0 * area_i) / area_u)
if (np.isinf(ret) or np.isnan(ret) or (ret < 0)):
raise ValueError('Invalid intersection')
return ret | Compute Intersection over Union of two axis-aligned rectangles.
This is the ratio of the are of intersection to the area of the union
of the two rectangles.
Parameters
----------
ra: np.ndarray
rb: np.ndarray
Axis aligned rectangles specified as (x, y, w, h) where (x, y) is
the position of the lower left corner, w and h are width and height.
Returns
-------
float
The Intersection over Union of two rectangles. | argos/utility.py | rect_iou | subhacom/argos | 1 | python | def rect_iou(ra: np.ndarray, rb: np.ndarray) -> float:
'Compute Intersection over Union of two axis-aligned rectangles.\n\n This is the ratio of the are of intersection to the area of the union\n of the two rectangles.\n\n Parameters\n ----------\n ra: np.ndarray\n rb: np.ndarray\n Axis aligned rectangles specified as (x, y, w, h) where (x, y) is\n the position of the lower left corner, w and h are width and height.\n\n Returns\n -------\n float\n The Intersection over Union of two rectangles.\n '
(x, y, dx, dy) = rect_intersection(ra, rb)
area_i = (dx * dy)
area_u = (((ra[2] * ra[3]) + (rb[2] * rb[3])) - area_i)
if ((area_u <= 0) or (area_i < 0)):
raise ValueError('Area not positive')
ret = ((1.0 * area_i) / area_u)
if (np.isinf(ret) or np.isnan(ret) or (ret < 0)):
raise ValueError('Invalid intersection')
return ret | def rect_iou(ra: np.ndarray, rb: np.ndarray) -> float:
'Compute Intersection over Union of two axis-aligned rectangles.\n\n This is the ratio of the are of intersection to the area of the union\n of the two rectangles.\n\n Parameters\n ----------\n ra: np.ndarray\n rb: np.ndarray\n Axis aligned rectangles specified as (x, y, w, h) where (x, y) is\n the position of the lower left corner, w and h are width and height.\n\n Returns\n -------\n float\n The Intersection over Union of two rectangles.\n '
(x, y, dx, dy) = rect_intersection(ra, rb)
area_i = (dx * dy)
area_u = (((ra[2] * ra[3]) + (rb[2] * rb[3])) - area_i)
if ((area_u <= 0) or (area_i < 0)):
raise ValueError('Area not positive')
ret = ((1.0 * area_i) / area_u)
if (np.isinf(ret) or np.isnan(ret) or (ret < 0)):
raise ValueError('Invalid intersection')
return ret<|docstring|>Compute Intersection over Union of two axis-aligned rectangles.
This is the ratio of the are of intersection to the area of the union
of the two rectangles.
Parameters
----------
ra: np.ndarray
rb: np.ndarray
Axis aligned rectangles specified as (x, y, w, h) where (x, y) is
the position of the lower left corner, w and h are width and height.
Returns
-------
float
The Intersection over Union of two rectangles.<|endoftext|> |
3c20ec57fdc3e590f284ddd05ec65bb23f8f5a1ed5fc4ae1382060dd3a0430b7 | def rect_ios(ra: np.ndarray, rb: np.ndarray) -> float:
'Compute intersection over area of smaller of two axis-aligned\n rectangles.\n\n This is the ratio of the area of intersection to the area of the smaller\n of the two rectangles.\n\n Parameters\n ----------\n ra: np.ndarray\n rb: np.ndarray\n Axis aligned rectangles specified as (x, y, w, h) where (x, y) is\n the position of the lower left corner, w and h are width and height.\n\n Returns\n -------\n float\n The Intersection over area of the smaller of two rectangles.\n '
(x, y, dx, dy) = rect_intersection(ra, rb)
area_i = (dx * dy)
area_a = (ra[2] * ra[3])
area_b = (rb[2] * rb[3])
if ((area_i < 0) or (area_a <= 0) or (area_b <= 0)):
raise ValueError('Area not positive')
ret = (area_i / min(area_a, area_b))
if (np.isinf(ret) or np.isnan(ret) or (ret < 0)):
raise ValueError('Invalid intersection')
return ret | Compute intersection over area of smaller of two axis-aligned
rectangles.
This is the ratio of the area of intersection to the area of the smaller
of the two rectangles.
Parameters
----------
ra: np.ndarray
rb: np.ndarray
Axis aligned rectangles specified as (x, y, w, h) where (x, y) is
the position of the lower left corner, w and h are width and height.
Returns
-------
float
The Intersection over area of the smaller of two rectangles. | argos/utility.py | rect_ios | subhacom/argos | 1 | python | def rect_ios(ra: np.ndarray, rb: np.ndarray) -> float:
'Compute intersection over area of smaller of two axis-aligned\n rectangles.\n\n This is the ratio of the area of intersection to the area of the smaller\n of the two rectangles.\n\n Parameters\n ----------\n ra: np.ndarray\n rb: np.ndarray\n Axis aligned rectangles specified as (x, y, w, h) where (x, y) is\n the position of the lower left corner, w and h are width and height.\n\n Returns\n -------\n float\n The Intersection over area of the smaller of two rectangles.\n '
(x, y, dx, dy) = rect_intersection(ra, rb)
area_i = (dx * dy)
area_a = (ra[2] * ra[3])
area_b = (rb[2] * rb[3])
if ((area_i < 0) or (area_a <= 0) or (area_b <= 0)):
raise ValueError('Area not positive')
ret = (area_i / min(area_a, area_b))
if (np.isinf(ret) or np.isnan(ret) or (ret < 0)):
raise ValueError('Invalid intersection')
return ret | def rect_ios(ra: np.ndarray, rb: np.ndarray) -> float:
'Compute intersection over area of smaller of two axis-aligned\n rectangles.\n\n This is the ratio of the area of intersection to the area of the smaller\n of the two rectangles.\n\n Parameters\n ----------\n ra: np.ndarray\n rb: np.ndarray\n Axis aligned rectangles specified as (x, y, w, h) where (x, y) is\n the position of the lower left corner, w and h are width and height.\n\n Returns\n -------\n float\n The Intersection over area of the smaller of two rectangles.\n '
(x, y, dx, dy) = rect_intersection(ra, rb)
area_i = (dx * dy)
area_a = (ra[2] * ra[3])
area_b = (rb[2] * rb[3])
if ((area_i < 0) or (area_a <= 0) or (area_b <= 0)):
raise ValueError('Area not positive')
ret = (area_i / min(area_a, area_b))
if (np.isinf(ret) or np.isnan(ret) or (ret < 0)):
raise ValueError('Invalid intersection')
return ret<|docstring|>Compute intersection over area of smaller of two axis-aligned
rectangles.
This is the ratio of the area of intersection to the area of the smaller
of the two rectangles.
Parameters
----------
ra: np.ndarray
rb: np.ndarray
Axis aligned rectangles specified as (x, y, w, h) where (x, y) is
the position of the lower left corner, w and h are width and height.
Returns
-------
float
The Intersection over area of the smaller of two rectangles.<|endoftext|> |
c06d778958fda6fbf798a47ab04600c58848a452a6f7482d816da2ad4e874bf3 | def pairwise_distance(new_bboxes: np.ndarray, bboxes: np.ndarray, boxtype: OutlineStyle, metric: DistanceMetric) -> np.ndarray:
'Computes the distance between all pairs of rectangles.\n\n Parameters\n ----------\n new_bboxes: np.ndarray\n Array of bounding boxes, each row as (x, y, w, h)\n bboxes: np.ndarray\n Array of bounding boxes, each row as (x, y, w, h)\n boxtype: {OutlineStyle.bbox, OulineStyle.minrect}\n OutlineStyle.bbox for axis aligned rectangle bounding box or\n OulineStyle.minrect for minimum area rotated rectangle\n metric: {DistanceMetric.euclidean, DistanceMetric.iou}\n When `DistanceMetric.euclidean`, the squared Euclidean distance is\n used (calculating square root is expensive and unnecessary. If\n `DistanceMetric.iou`, use the area of intersection divided by the\n area of union.\n\n Returns\n --------\n np.ndarray\n Row ``ii``, column ``jj`` contains the computed distance between\n ``new_bboxes[ii]`` and ``bboxes[jj]``.\n '
dist = np.zeros((new_bboxes.shape[0], bboxes.shape[0]), dtype=np.float)
if (metric == DistanceMetric.euclidean):
centers = (bboxes[(:, :2)] + (bboxes[(:, 2:)] * 0.5))
new_centers = (new_bboxes[(:, :2)] + (new_bboxes[(:, 2:)] * 0.5))
for ii in range(len(new_bboxes)):
for jj in range(len(bboxes)):
dist[(ii, jj)] = np.sum(((new_centers[ii] - centers[jj]) ** 2))
elif (metric == DistanceMetric.iou):
if (boxtype == OutlineStyle.bbox):
for ii in range(len(new_bboxes)):
for jj in range(len(bboxes)):
dist[(ii, jj)] = (1.0 - rect_iou(bboxes[jj], new_bboxes[ii]))
else:
raise NotImplementedError('Only handling axis-aligned bounding boxes')
elif ((metric == DistanceMetric.ios) and (boxtype == OutlineStyle.bbox)):
for ii in range(len(new_bboxes)):
for jj in range(len(bboxes)):
dist[(ii, jj)] = (1.0 - rect_ios(bboxes[jj], new_bboxes[ii]))
else:
raise NotImplementedError(f'Unknown metric {metric}')
return dist | Computes the distance between all pairs of rectangles.
Parameters
----------
new_bboxes: np.ndarray
Array of bounding boxes, each row as (x, y, w, h)
bboxes: np.ndarray
Array of bounding boxes, each row as (x, y, w, h)
boxtype: {OutlineStyle.bbox, OulineStyle.minrect}
OutlineStyle.bbox for axis aligned rectangle bounding box or
OulineStyle.minrect for minimum area rotated rectangle
metric: {DistanceMetric.euclidean, DistanceMetric.iou}
When `DistanceMetric.euclidean`, the squared Euclidean distance is
used (calculating square root is expensive and unnecessary. If
`DistanceMetric.iou`, use the area of intersection divided by the
area of union.
Returns
--------
np.ndarray
Row ``ii``, column ``jj`` contains the computed distance between
``new_bboxes[ii]`` and ``bboxes[jj]``. | argos/utility.py | pairwise_distance | subhacom/argos | 1 | python | def pairwise_distance(new_bboxes: np.ndarray, bboxes: np.ndarray, boxtype: OutlineStyle, metric: DistanceMetric) -> np.ndarray:
'Computes the distance between all pairs of rectangles.\n\n Parameters\n ----------\n new_bboxes: np.ndarray\n Array of bounding boxes, each row as (x, y, w, h)\n bboxes: np.ndarray\n Array of bounding boxes, each row as (x, y, w, h)\n boxtype: {OutlineStyle.bbox, OulineStyle.minrect}\n OutlineStyle.bbox for axis aligned rectangle bounding box or\n OulineStyle.minrect for minimum area rotated rectangle\n metric: {DistanceMetric.euclidean, DistanceMetric.iou}\n When `DistanceMetric.euclidean`, the squared Euclidean distance is\n used (calculating square root is expensive and unnecessary. If\n `DistanceMetric.iou`, use the area of intersection divided by the\n area of union.\n\n Returns\n --------\n np.ndarray\n Row ``ii``, column ``jj`` contains the computed distance between\n ``new_bboxes[ii]`` and ``bboxes[jj]``.\n '
dist = np.zeros((new_bboxes.shape[0], bboxes.shape[0]), dtype=np.float)
if (metric == DistanceMetric.euclidean):
centers = (bboxes[(:, :2)] + (bboxes[(:, 2:)] * 0.5))
new_centers = (new_bboxes[(:, :2)] + (new_bboxes[(:, 2:)] * 0.5))
for ii in range(len(new_bboxes)):
for jj in range(len(bboxes)):
dist[(ii, jj)] = np.sum(((new_centers[ii] - centers[jj]) ** 2))
elif (metric == DistanceMetric.iou):
if (boxtype == OutlineStyle.bbox):
for ii in range(len(new_bboxes)):
for jj in range(len(bboxes)):
dist[(ii, jj)] = (1.0 - rect_iou(bboxes[jj], new_bboxes[ii]))
else:
raise NotImplementedError('Only handling axis-aligned bounding boxes')
elif ((metric == DistanceMetric.ios) and (boxtype == OutlineStyle.bbox)):
for ii in range(len(new_bboxes)):
for jj in range(len(bboxes)):
dist[(ii, jj)] = (1.0 - rect_ios(bboxes[jj], new_bboxes[ii]))
else:
raise NotImplementedError(f'Unknown metric {metric}')
return dist | def pairwise_distance(new_bboxes: np.ndarray, bboxes: np.ndarray, boxtype: OutlineStyle, metric: DistanceMetric) -> np.ndarray:
'Computes the distance between all pairs of rectangles.\n\n Parameters\n ----------\n new_bboxes: np.ndarray\n Array of bounding boxes, each row as (x, y, w, h)\n bboxes: np.ndarray\n Array of bounding boxes, each row as (x, y, w, h)\n boxtype: {OutlineStyle.bbox, OulineStyle.minrect}\n OutlineStyle.bbox for axis aligned rectangle bounding box or\n OulineStyle.minrect for minimum area rotated rectangle\n metric: {DistanceMetric.euclidean, DistanceMetric.iou}\n When `DistanceMetric.euclidean`, the squared Euclidean distance is\n used (calculating square root is expensive and unnecessary. If\n `DistanceMetric.iou`, use the area of intersection divided by the\n area of union.\n\n Returns\n --------\n np.ndarray\n Row ``ii``, column ``jj`` contains the computed distance between\n ``new_bboxes[ii]`` and ``bboxes[jj]``.\n '
dist = np.zeros((new_bboxes.shape[0], bboxes.shape[0]), dtype=np.float)
if (metric == DistanceMetric.euclidean):
centers = (bboxes[(:, :2)] + (bboxes[(:, 2:)] * 0.5))
new_centers = (new_bboxes[(:, :2)] + (new_bboxes[(:, 2:)] * 0.5))
for ii in range(len(new_bboxes)):
for jj in range(len(bboxes)):
dist[(ii, jj)] = np.sum(((new_centers[ii] - centers[jj]) ** 2))
elif (metric == DistanceMetric.iou):
if (boxtype == OutlineStyle.bbox):
for ii in range(len(new_bboxes)):
for jj in range(len(bboxes)):
dist[(ii, jj)] = (1.0 - rect_iou(bboxes[jj], new_bboxes[ii]))
else:
raise NotImplementedError('Only handling axis-aligned bounding boxes')
elif ((metric == DistanceMetric.ios) and (boxtype == OutlineStyle.bbox)):
for ii in range(len(new_bboxes)):
for jj in range(len(bboxes)):
dist[(ii, jj)] = (1.0 - rect_ios(bboxes[jj], new_bboxes[ii]))
else:
raise NotImplementedError(f'Unknown metric {metric}')
return dist<|docstring|>Computes the distance between all pairs of rectangles.
Parameters
----------
new_bboxes: np.ndarray
Array of bounding boxes, each row as (x, y, w, h)
bboxes: np.ndarray
Array of bounding boxes, each row as (x, y, w, h)
boxtype: {OutlineStyle.bbox, OulineStyle.minrect}
OutlineStyle.bbox for axis aligned rectangle bounding box or
OulineStyle.minrect for minimum area rotated rectangle
metric: {DistanceMetric.euclidean, DistanceMetric.iou}
When `DistanceMetric.euclidean`, the squared Euclidean distance is
used (calculating square root is expensive and unnecessary. If
`DistanceMetric.iou`, use the area of intersection divided by the
area of union.
Returns
--------
np.ndarray
Row ``ii``, column ``jj`` contains the computed distance between
``new_bboxes[ii]`` and ``bboxes[jj]``.<|endoftext|> |
7e51b637c95bb695d2f45aba5b76fce7014eb9c2e5eb180af3c712f237df530a | def forward(self, x):
'\n Feed forward the model.\n \n Args:\n x (torch.Tensor): Input data.\n \n Raises:\n -\n\n Returns:\n x (torch.Tensor): Output of the feed forward execution.\n \n '
x = self.bn1(self.pool(F.relu(self.conv1(x))))
x = self.bn2(self.pool(F.relu(self.conv2(x))))
x = self.bn3(self.pool(F.relu(self.conv3(x))))
x = self.bn4(self.pool(F.relu(self.conv4(x))))
x = self.bn5(self.pool(F.relu(self.conv5(x))))
x = self.bn6(self.pool(F.relu(self.conv6(x))))
x = x.view((- 1), ((512 * 4) * 4))
x = self.dropout(x)
x = F.relu(self.fc1(x))
x = self.dropout(x)
x = F.relu(self.fc2(x))
x = self.dropout(x)
x = F.relu(self.fc3(x))
x = self.dropout(x)
x = self.fc4(x)
return x | Feed forward the model.
Args:
x (torch.Tensor): Input data.
Raises:
-
Returns:
x (torch.Tensor): Output of the feed forward execution. | project_cnn/cnn.py | forward | vsaveris/deep-learning | 0 | python | def forward(self, x):
'\n Feed forward the model.\n \n Args:\n x (torch.Tensor): Input data.\n \n Raises:\n -\n\n Returns:\n x (torch.Tensor): Output of the feed forward execution.\n \n '
x = self.bn1(self.pool(F.relu(self.conv1(x))))
x = self.bn2(self.pool(F.relu(self.conv2(x))))
x = self.bn3(self.pool(F.relu(self.conv3(x))))
x = self.bn4(self.pool(F.relu(self.conv4(x))))
x = self.bn5(self.pool(F.relu(self.conv5(x))))
x = self.bn6(self.pool(F.relu(self.conv6(x))))
x = x.view((- 1), ((512 * 4) * 4))
x = self.dropout(x)
x = F.relu(self.fc1(x))
x = self.dropout(x)
x = F.relu(self.fc2(x))
x = self.dropout(x)
x = F.relu(self.fc3(x))
x = self.dropout(x)
x = self.fc4(x)
return x | def forward(self, x):
'\n Feed forward the model.\n \n Args:\n x (torch.Tensor): Input data.\n \n Raises:\n -\n\n Returns:\n x (torch.Tensor): Output of the feed forward execution.\n \n '
x = self.bn1(self.pool(F.relu(self.conv1(x))))
x = self.bn2(self.pool(F.relu(self.conv2(x))))
x = self.bn3(self.pool(F.relu(self.conv3(x))))
x = self.bn4(self.pool(F.relu(self.conv4(x))))
x = self.bn5(self.pool(F.relu(self.conv5(x))))
x = self.bn6(self.pool(F.relu(self.conv6(x))))
x = x.view((- 1), ((512 * 4) * 4))
x = self.dropout(x)
x = F.relu(self.fc1(x))
x = self.dropout(x)
x = F.relu(self.fc2(x))
x = self.dropout(x)
x = F.relu(self.fc3(x))
x = self.dropout(x)
x = self.fc4(x)
return x<|docstring|>Feed forward the model.
Args:
x (torch.Tensor): Input data.
Raises:
-
Returns:
x (torch.Tensor): Output of the feed forward execution.<|endoftext|> |
d63c4e4a7766eac736902b263222214c728574cff69a606ed05f4ed38ed07a50 | def __init__(self, max_failures=None):
"Creates a new Benchmark.\n\n Args:\n max_failures: The number of story run's failures before bailing\n from executing subsequent page runs. If None, we never bail.\n "
self._expectations = None
self._max_failures = max_failures | Creates a new Benchmark.
Args:
max_failures: The number of story run's failures before bailing
from executing subsequent page runs. If None, we never bail. | telemetry/telemetry/benchmark.py | __init__ | tdresser/catapult-csm | 4 | python | def __init__(self, max_failures=None):
"Creates a new Benchmark.\n\n Args:\n max_failures: The number of story run's failures before bailing\n from executing subsequent page runs. If None, we never bail.\n "
self._expectations = None
self._max_failures = max_failures | def __init__(self, max_failures=None):
"Creates a new Benchmark.\n\n Args:\n max_failures: The number of story run's failures before bailing\n from executing subsequent page runs. If None, we never bail.\n "
self._expectations = None
self._max_failures = max_failures<|docstring|>Creates a new Benchmark.
Args:
max_failures: The number of story run's failures before bailing
from executing subsequent page runs. If None, we never bail.<|endoftext|> |
5fd62d2674a4f650614f6551fac62abde71d5bf933148d14fe09bce598a79b46 | @classmethod
def ShouldDisable(cls, possible_browser):
'Override this method to disable a benchmark under specific conditions.\n\n Supports logic too complex for simple Enabled and Disabled decorators.\n Decorators are still respected in cases where this function returns False.\n '
return False | Override this method to disable a benchmark under specific conditions.
Supports logic too complex for simple Enabled and Disabled decorators.
Decorators are still respected in cases where this function returns False. | telemetry/telemetry/benchmark.py | ShouldDisable | tdresser/catapult-csm | 4 | python | @classmethod
def ShouldDisable(cls, possible_browser):
'Override this method to disable a benchmark under specific conditions.\n\n Supports logic too complex for simple Enabled and Disabled decorators.\n Decorators are still respected in cases where this function returns False.\n '
return False | @classmethod
def ShouldDisable(cls, possible_browser):
'Override this method to disable a benchmark under specific conditions.\n\n Supports logic too complex for simple Enabled and Disabled decorators.\n Decorators are still respected in cases where this function returns False.\n '
return False<|docstring|>Override this method to disable a benchmark under specific conditions.
Supports logic too complex for simple Enabled and Disabled decorators.
Decorators are still respected in cases where this function returns False.<|endoftext|> |
75ff77d8db41f683c3a71e34a6d7a96e8346c8309e740d18703ab36708de81cb | def Run(self, finder_options):
'Do not override this method.'
return story_runner.RunBenchmark(self, finder_options) | Do not override this method. | telemetry/telemetry/benchmark.py | Run | tdresser/catapult-csm | 4 | python | def Run(self, finder_options):
return story_runner.RunBenchmark(self, finder_options) | def Run(self, finder_options):
return story_runner.RunBenchmark(self, finder_options)<|docstring|>Do not override this method.<|endoftext|> |
629f54e84d42aae4504f16e9f25bc0efcfba3c9b6a95b5dfc34acb49ca3fb67b | @classmethod
def ShouldTearDownStateAfterEachStoryRun(cls):
'Override to specify whether to tear down state after each story run.\n\n Tearing down all states after each story run, e.g., clearing profiles,\n stopping the browser, stopping local server, etc. So the browser will not be\n reused among multiple stories. This is particularly useful to get the\n startup part of launching the browser in each story.\n\n This should only be used by TimelineBasedMeasurement (TBM) benchmarks, but\n not by PageTest based benchmarks.\n '
return True | Override to specify whether to tear down state after each story run.
Tearing down all states after each story run, e.g., clearing profiles,
stopping the browser, stopping local server, etc. So the browser will not be
reused among multiple stories. This is particularly useful to get the
startup part of launching the browser in each story.
This should only be used by TimelineBasedMeasurement (TBM) benchmarks, but
not by PageTest based benchmarks. | telemetry/telemetry/benchmark.py | ShouldTearDownStateAfterEachStoryRun | tdresser/catapult-csm | 4 | python | @classmethod
def ShouldTearDownStateAfterEachStoryRun(cls):
'Override to specify whether to tear down state after each story run.\n\n Tearing down all states after each story run, e.g., clearing profiles,\n stopping the browser, stopping local server, etc. So the browser will not be\n reused among multiple stories. This is particularly useful to get the\n startup part of launching the browser in each story.\n\n This should only be used by TimelineBasedMeasurement (TBM) benchmarks, but\n not by PageTest based benchmarks.\n '
return True | @classmethod
def ShouldTearDownStateAfterEachStoryRun(cls):
'Override to specify whether to tear down state after each story run.\n\n Tearing down all states after each story run, e.g., clearing profiles,\n stopping the browser, stopping local server, etc. So the browser will not be\n reused among multiple stories. This is particularly useful to get the\n startup part of launching the browser in each story.\n\n This should only be used by TimelineBasedMeasurement (TBM) benchmarks, but\n not by PageTest based benchmarks.\n '
return True<|docstring|>Override to specify whether to tear down state after each story run.
Tearing down all states after each story run, e.g., clearing profiles,
stopping the browser, stopping local server, etc. So the browser will not be
reused among multiple stories. This is particularly useful to get the
startup part of launching the browser in each story.
This should only be used by TimelineBasedMeasurement (TBM) benchmarks, but
not by PageTest based benchmarks.<|endoftext|> |
a5acf980952e56ae225a697cdb7c369bb35071fcc07205298bd0d2097e8bcc01 | @classmethod
def ShouldTearDownStateAfterEachStorySetRun(cls):
'Override to specify whether to tear down state after each story set run.\n\n Defaults to True in order to reset the state and make individual story set\n repeats more independent of each other. The intended effect is to average\n out noise in measurements between repeats.\n\n Long running benchmarks willing to stess test the browser and have it run\n for long periods of time may switch this value to False.\n\n This should only be used by TimelineBasedMeasurement (TBM) benchmarks, but\n not by PageTest based benchmarks.\n '
return True | Override to specify whether to tear down state after each story set run.
Defaults to True in order to reset the state and make individual story set
repeats more independent of each other. The intended effect is to average
out noise in measurements between repeats.
Long running benchmarks willing to stess test the browser and have it run
for long periods of time may switch this value to False.
This should only be used by TimelineBasedMeasurement (TBM) benchmarks, but
not by PageTest based benchmarks. | telemetry/telemetry/benchmark.py | ShouldTearDownStateAfterEachStorySetRun | tdresser/catapult-csm | 4 | python | @classmethod
def ShouldTearDownStateAfterEachStorySetRun(cls):
'Override to specify whether to tear down state after each story set run.\n\n Defaults to True in order to reset the state and make individual story set\n repeats more independent of each other. The intended effect is to average\n out noise in measurements between repeats.\n\n Long running benchmarks willing to stess test the browser and have it run\n for long periods of time may switch this value to False.\n\n This should only be used by TimelineBasedMeasurement (TBM) benchmarks, but\n not by PageTest based benchmarks.\n '
return True | @classmethod
def ShouldTearDownStateAfterEachStorySetRun(cls):
'Override to specify whether to tear down state after each story set run.\n\n Defaults to True in order to reset the state and make individual story set\n repeats more independent of each other. The intended effect is to average\n out noise in measurements between repeats.\n\n Long running benchmarks willing to stess test the browser and have it run\n for long periods of time may switch this value to False.\n\n This should only be used by TimelineBasedMeasurement (TBM) benchmarks, but\n not by PageTest based benchmarks.\n '
return True<|docstring|>Override to specify whether to tear down state after each story set run.
Defaults to True in order to reset the state and make individual story set
repeats more independent of each other. The intended effect is to average
out noise in measurements between repeats.
Long running benchmarks willing to stess test the browser and have it run
for long periods of time may switch this value to False.
This should only be used by TimelineBasedMeasurement (TBM) benchmarks, but
not by PageTest based benchmarks.<|endoftext|> |
a6b1043e15350620b13c6a410fb8d6ecaa3cd208f83df7c34208c294d3a5ac79 | def SetupBenchmarkDefaultTraceRerunOptions(self, tbm_options):
'Setup tracing categories associated with default trace option.' | Setup tracing categories associated with default trace option. | telemetry/telemetry/benchmark.py | SetupBenchmarkDefaultTraceRerunOptions | tdresser/catapult-csm | 4 | python | def SetupBenchmarkDefaultTraceRerunOptions(self, tbm_options):
| def SetupBenchmarkDefaultTraceRerunOptions(self, tbm_options):
<|docstring|>Setup tracing categories associated with default trace option.<|endoftext|> |
3ed4aa6d7a31e78d1d0f790463b50401944852b3bb60bfbbbfdbecbfb8d8bb7d | def SetupBenchmarkDebugTraceRerunOptions(self, tbm_options):
'Setup tracing categories associated with debug trace option.' | Setup tracing categories associated with debug trace option. | telemetry/telemetry/benchmark.py | SetupBenchmarkDebugTraceRerunOptions | tdresser/catapult-csm | 4 | python | def SetupBenchmarkDebugTraceRerunOptions(self, tbm_options):
| def SetupBenchmarkDebugTraceRerunOptions(self, tbm_options):
<|docstring|>Setup tracing categories associated with debug trace option.<|endoftext|> |
6a69a453a5ca09a3bbd54d43b2a601f8bfa5f5509a7d27168cfa0c1e0fc35e2d | @classmethod
def ValueCanBeAddedPredicate(cls, value, is_first_result):
'Returns whether |value| can be added to the test results.\n\n Override this method to customize the logic of adding values to test\n results.\n\n Args:\n value: a value.Value instance (except failure.FailureValue,\n skip.SkipValue or trace.TraceValue which will always be added).\n is_first_result: True if |value| is the first result for its\n corresponding story.\n\n Returns:\n True if |value| should be added to the test results.\n Otherwise, it returns False.\n '
return True | Returns whether |value| can be added to the test results.
Override this method to customize the logic of adding values to test
results.
Args:
value: a value.Value instance (except failure.FailureValue,
skip.SkipValue or trace.TraceValue which will always be added).
is_first_result: True if |value| is the first result for its
corresponding story.
Returns:
True if |value| should be added to the test results.
Otherwise, it returns False. | telemetry/telemetry/benchmark.py | ValueCanBeAddedPredicate | tdresser/catapult-csm | 4 | python | @classmethod
def ValueCanBeAddedPredicate(cls, value, is_first_result):
'Returns whether |value| can be added to the test results.\n\n Override this method to customize the logic of adding values to test\n results.\n\n Args:\n value: a value.Value instance (except failure.FailureValue,\n skip.SkipValue or trace.TraceValue which will always be added).\n is_first_result: True if |value| is the first result for its\n corresponding story.\n\n Returns:\n True if |value| should be added to the test results.\n Otherwise, it returns False.\n '
return True | @classmethod
def ValueCanBeAddedPredicate(cls, value, is_first_result):
'Returns whether |value| can be added to the test results.\n\n Override this method to customize the logic of adding values to test\n results.\n\n Args:\n value: a value.Value instance (except failure.FailureValue,\n skip.SkipValue or trace.TraceValue which will always be added).\n is_first_result: True if |value| is the first result for its\n corresponding story.\n\n Returns:\n True if |value| should be added to the test results.\n Otherwise, it returns False.\n '
return True<|docstring|>Returns whether |value| can be added to the test results.
Override this method to customize the logic of adding values to test
results.
Args:
value: a value.Value instance (except failure.FailureValue,
skip.SkipValue or trace.TraceValue which will always be added).
is_first_result: True if |value| is the first result for its
corresponding story.
Returns:
True if |value| should be added to the test results.
Otherwise, it returns False.<|endoftext|> |
6ae0d6a0e40c308ebbd9c421c068d6f21aa07c21aee071c360e7b7757754bd76 | def CustomizeBrowserOptions(self, options):
'Add browser options that are required by this benchmark.' | Add browser options that are required by this benchmark. | telemetry/telemetry/benchmark.py | CustomizeBrowserOptions | tdresser/catapult-csm | 4 | python | def CustomizeBrowserOptions(self, options):
| def CustomizeBrowserOptions(self, options):
<|docstring|>Add browser options that are required by this benchmark.<|endoftext|> |
5cb77061689bbe1373429100ef98442e1dc23806f029ff1449e3fe40ccfe20a9 | def GetBugComponents(self):
"Returns a GenericSet Diagnostic containing the benchmark's Monorail\n component.\n\n Returns:\n GenericSet Diagnostic with the benchmark's bug component name\n "
benchmark_component = decorators.GetComponent(self)
component_diagnostic_value = ([benchmark_component] if benchmark_component else [])
return histogram.GenericSet(component_diagnostic_value) | Returns a GenericSet Diagnostic containing the benchmark's Monorail
component.
Returns:
GenericSet Diagnostic with the benchmark's bug component name | telemetry/telemetry/benchmark.py | GetBugComponents | tdresser/catapult-csm | 4 | python | def GetBugComponents(self):
"Returns a GenericSet Diagnostic containing the benchmark's Monorail\n component.\n\n Returns:\n GenericSet Diagnostic with the benchmark's bug component name\n "
benchmark_component = decorators.GetComponent(self)
component_diagnostic_value = ([benchmark_component] if benchmark_component else [])
return histogram.GenericSet(component_diagnostic_value) | def GetBugComponents(self):
"Returns a GenericSet Diagnostic containing the benchmark's Monorail\n component.\n\n Returns:\n GenericSet Diagnostic with the benchmark's bug component name\n "
benchmark_component = decorators.GetComponent(self)
component_diagnostic_value = ([benchmark_component] if benchmark_component else [])
return histogram.GenericSet(component_diagnostic_value)<|docstring|>Returns a GenericSet Diagnostic containing the benchmark's Monorail
component.
Returns:
GenericSet Diagnostic with the benchmark's bug component name<|endoftext|> |
cc5ccae8c05c03041076feeeb98a1a0ade2fe3bc63f9b481ba2075e3676d9805 | def GetOwners(self):
"Returns a Generic Diagnostic containing the benchmark's owners' emails\n in a list.\n\n Returns:\n Diagnostic with a list of the benchmark's owners' emails\n "
return histogram.GenericSet((decorators.GetEmails(self) or [])) | Returns a Generic Diagnostic containing the benchmark's owners' emails
in a list.
Returns:
Diagnostic with a list of the benchmark's owners' emails | telemetry/telemetry/benchmark.py | GetOwners | tdresser/catapult-csm | 4 | python | def GetOwners(self):
"Returns a Generic Diagnostic containing the benchmark's owners' emails\n in a list.\n\n Returns:\n Diagnostic with a list of the benchmark's owners' emails\n "
return histogram.GenericSet((decorators.GetEmails(self) or [])) | def GetOwners(self):
"Returns a Generic Diagnostic containing the benchmark's owners' emails\n in a list.\n\n Returns:\n Diagnostic with a list of the benchmark's owners' emails\n "
return histogram.GenericSet((decorators.GetEmails(self) or []))<|docstring|>Returns a Generic Diagnostic containing the benchmark's owners' emails
in a list.
Returns:
Diagnostic with a list of the benchmark's owners' emails<|endoftext|> |
b05ca62487cd660107a7dc1f9adb4c64574ddb9c2f6f8f9258a9fdf650d11b3f | @decorators.Deprecated(2017, 7, 29, 'Use CreateCoreTimelineBasedMeasurementOptions instead.')
def CreateTimelineBasedMeasurementOptions(self):
'See CreateCoreTimelineBasedMeasurementOptions.'
return self.CreateCoreTimelineBasedMeasurementOptions() | See CreateCoreTimelineBasedMeasurementOptions. | telemetry/telemetry/benchmark.py | CreateTimelineBasedMeasurementOptions | tdresser/catapult-csm | 4 | python | @decorators.Deprecated(2017, 7, 29, 'Use CreateCoreTimelineBasedMeasurementOptions instead.')
def CreateTimelineBasedMeasurementOptions(self):
return self.CreateCoreTimelineBasedMeasurementOptions() | @decorators.Deprecated(2017, 7, 29, 'Use CreateCoreTimelineBasedMeasurementOptions instead.')
def CreateTimelineBasedMeasurementOptions(self):
return self.CreateCoreTimelineBasedMeasurementOptions()<|docstring|>See CreateCoreTimelineBasedMeasurementOptions.<|endoftext|> |
64c1af7336b67175c5b3025e77ac1caf6bf0a88f3faff197c2a9c49d3bff18d7 | def CreateCoreTimelineBasedMeasurementOptions(self):
'Return the base TimelineBasedMeasurementOptions for this Benchmark.\n\n Additional chrome and atrace categories can be appended when running the\n benchmark with the --extra-chrome-categories and --extra-atrace-categories\n flags.\n\n Override this method to configure a TimelineBasedMeasurement benchmark. If\n this is not a TimelineBasedMeasurement benchmark, override CreatePageTest\n for PageTest tests. Do not override both methods.\n '
return timeline_based_measurement.Options() | Return the base TimelineBasedMeasurementOptions for this Benchmark.
Additional chrome and atrace categories can be appended when running the
benchmark with the --extra-chrome-categories and --extra-atrace-categories
flags.
Override this method to configure a TimelineBasedMeasurement benchmark. If
this is not a TimelineBasedMeasurement benchmark, override CreatePageTest
for PageTest tests. Do not override both methods. | telemetry/telemetry/benchmark.py | CreateCoreTimelineBasedMeasurementOptions | tdresser/catapult-csm | 4 | python | def CreateCoreTimelineBasedMeasurementOptions(self):
'Return the base TimelineBasedMeasurementOptions for this Benchmark.\n\n Additional chrome and atrace categories can be appended when running the\n benchmark with the --extra-chrome-categories and --extra-atrace-categories\n flags.\n\n Override this method to configure a TimelineBasedMeasurement benchmark. If\n this is not a TimelineBasedMeasurement benchmark, override CreatePageTest\n for PageTest tests. Do not override both methods.\n '
return timeline_based_measurement.Options() | def CreateCoreTimelineBasedMeasurementOptions(self):
'Return the base TimelineBasedMeasurementOptions for this Benchmark.\n\n Additional chrome and atrace categories can be appended when running the\n benchmark with the --extra-chrome-categories and --extra-atrace-categories\n flags.\n\n Override this method to configure a TimelineBasedMeasurement benchmark. If\n this is not a TimelineBasedMeasurement benchmark, override CreatePageTest\n for PageTest tests. Do not override both methods.\n '
return timeline_based_measurement.Options()<|docstring|>Return the base TimelineBasedMeasurementOptions for this Benchmark.
Additional chrome and atrace categories can be appended when running the
benchmark with the --extra-chrome-categories and --extra-atrace-categories
flags.
Override this method to configure a TimelineBasedMeasurement benchmark. If
this is not a TimelineBasedMeasurement benchmark, override CreatePageTest
for PageTest tests. Do not override both methods.<|endoftext|> |
93374d170e4df7453d061296ffac3c5eb2ac7d1c2ed299cf59d5990a1e3b2afe | def _GetTimelineBasedMeasurementOptions(self, options):
'Return all timeline based measurements for the curren benchmark run.\n\n This includes the benchmark-configured measurements in\n CreateCoreTimelineBasedMeasurementOptions as well as the user-flag-\n configured options from --extra-chrome-categories and\n --extra-atrace-categories.\n '
tbm_options = None
assert (not (class_util.IsMethodOverridden(Benchmark, self.__class__, 'CreateTimelineBasedMeasurementOptions') and class_util.IsMethodOverridden(Benchmark, self.__class__, 'CreateCoreTimelineBasedMeasurementOptions'))), 'Benchmarks should override CreateCoreTimelineBasedMeasurementOptions and NOT also CreateTimelineBasedMeasurementOptions.'
if class_util.IsMethodOverridden(Benchmark, self.__class__, 'CreateCoreTimelineBasedMeasurementOptions'):
tbm_options = self.CreateCoreTimelineBasedMeasurementOptions()
else:
tbm_options = self.CreateTimelineBasedMeasurementOptions()
if (options and options.extra_chrome_categories):
assert tbm_options.config.enable_chrome_trace, 'This benchmark does not support Chrome tracing.'
tbm_options.config.chrome_trace_config.category_filter.AddFilterString(options.extra_chrome_categories)
if (options and options.extra_atrace_categories):
tbm_options.config.enable_atrace_trace = True
categories = tbm_options.config.atrace_config.categories
if (type(categories) != list):
categories = categories.split(',')
for category in options.extra_atrace_categories.split(','):
if (category not in categories):
categories.append(category)
tbm_options.config.atrace_config.categories = categories
return tbm_options | Return all timeline based measurements for the curren benchmark run.
This includes the benchmark-configured measurements in
CreateCoreTimelineBasedMeasurementOptions as well as the user-flag-
configured options from --extra-chrome-categories and
--extra-atrace-categories. | telemetry/telemetry/benchmark.py | _GetTimelineBasedMeasurementOptions | tdresser/catapult-csm | 4 | python | def _GetTimelineBasedMeasurementOptions(self, options):
'Return all timeline based measurements for the curren benchmark run.\n\n This includes the benchmark-configured measurements in\n CreateCoreTimelineBasedMeasurementOptions as well as the user-flag-\n configured options from --extra-chrome-categories and\n --extra-atrace-categories.\n '
tbm_options = None
assert (not (class_util.IsMethodOverridden(Benchmark, self.__class__, 'CreateTimelineBasedMeasurementOptions') and class_util.IsMethodOverridden(Benchmark, self.__class__, 'CreateCoreTimelineBasedMeasurementOptions'))), 'Benchmarks should override CreateCoreTimelineBasedMeasurementOptions and NOT also CreateTimelineBasedMeasurementOptions.'
if class_util.IsMethodOverridden(Benchmark, self.__class__, 'CreateCoreTimelineBasedMeasurementOptions'):
tbm_options = self.CreateCoreTimelineBasedMeasurementOptions()
else:
tbm_options = self.CreateTimelineBasedMeasurementOptions()
if (options and options.extra_chrome_categories):
assert tbm_options.config.enable_chrome_trace, 'This benchmark does not support Chrome tracing.'
tbm_options.config.chrome_trace_config.category_filter.AddFilterString(options.extra_chrome_categories)
if (options and options.extra_atrace_categories):
tbm_options.config.enable_atrace_trace = True
categories = tbm_options.config.atrace_config.categories
if (type(categories) != list):
categories = categories.split(',')
for category in options.extra_atrace_categories.split(','):
if (category not in categories):
categories.append(category)
tbm_options.config.atrace_config.categories = categories
return tbm_options | def _GetTimelineBasedMeasurementOptions(self, options):
'Return all timeline based measurements for the curren benchmark run.\n\n This includes the benchmark-configured measurements in\n CreateCoreTimelineBasedMeasurementOptions as well as the user-flag-\n configured options from --extra-chrome-categories and\n --extra-atrace-categories.\n '
tbm_options = None
assert (not (class_util.IsMethodOverridden(Benchmark, self.__class__, 'CreateTimelineBasedMeasurementOptions') and class_util.IsMethodOverridden(Benchmark, self.__class__, 'CreateCoreTimelineBasedMeasurementOptions'))), 'Benchmarks should override CreateCoreTimelineBasedMeasurementOptions and NOT also CreateTimelineBasedMeasurementOptions.'
if class_util.IsMethodOverridden(Benchmark, self.__class__, 'CreateCoreTimelineBasedMeasurementOptions'):
tbm_options = self.CreateCoreTimelineBasedMeasurementOptions()
else:
tbm_options = self.CreateTimelineBasedMeasurementOptions()
if (options and options.extra_chrome_categories):
assert tbm_options.config.enable_chrome_trace, 'This benchmark does not support Chrome tracing.'
tbm_options.config.chrome_trace_config.category_filter.AddFilterString(options.extra_chrome_categories)
if (options and options.extra_atrace_categories):
tbm_options.config.enable_atrace_trace = True
categories = tbm_options.config.atrace_config.categories
if (type(categories) != list):
categories = categories.split(',')
for category in options.extra_atrace_categories.split(','):
if (category not in categories):
categories.append(category)
tbm_options.config.atrace_config.categories = categories
return tbm_options<|docstring|>Return all timeline based measurements for the curren benchmark run.
This includes the benchmark-configured measurements in
CreateCoreTimelineBasedMeasurementOptions as well as the user-flag-
configured options from --extra-chrome-categories and
--extra-atrace-categories.<|endoftext|> |
822dc929c75e6d936303d8164c5cbdd089010993f40ef92e8809f1a01a277e86 | def CreatePageTest(self, options):
'Return the PageTest for this Benchmark.\n\n Override this method for PageTest tests.\n Override, CreateCoreTimelineBasedMeasurementOptions to configure\n TimelineBasedMeasurement tests. Do not override both methods.\n\n Args:\n options: a browser_options.BrowserFinderOptions instance\n Returns:\n |test()| if |test| is a PageTest class.\n Otherwise, a TimelineBasedMeasurement instance.\n '
is_page_test = issubclass(self.test, legacy_page_test.LegacyPageTest)
is_tbm = (self.test == timeline_based_measurement.TimelineBasedMeasurement)
if ((not is_page_test) and (not is_tbm)):
raise TypeError(('"%s" is not a PageTest or a TimelineBasedMeasurement.' % self.test.__name__))
if is_page_test:
return self.test()
opts = self._GetTimelineBasedMeasurementOptions(options)
self.SetupTraceRerunOptions(options, opts)
return timeline_based_measurement.TimelineBasedMeasurement(opts) | Return the PageTest for this Benchmark.
Override this method for PageTest tests.
Override, CreateCoreTimelineBasedMeasurementOptions to configure
TimelineBasedMeasurement tests. Do not override both methods.
Args:
options: a browser_options.BrowserFinderOptions instance
Returns:
|test()| if |test| is a PageTest class.
Otherwise, a TimelineBasedMeasurement instance. | telemetry/telemetry/benchmark.py | CreatePageTest | tdresser/catapult-csm | 4 | python | def CreatePageTest(self, options):
'Return the PageTest for this Benchmark.\n\n Override this method for PageTest tests.\n Override, CreateCoreTimelineBasedMeasurementOptions to configure\n TimelineBasedMeasurement tests. Do not override both methods.\n\n Args:\n options: a browser_options.BrowserFinderOptions instance\n Returns:\n |test()| if |test| is a PageTest class.\n Otherwise, a TimelineBasedMeasurement instance.\n '
is_page_test = issubclass(self.test, legacy_page_test.LegacyPageTest)
is_tbm = (self.test == timeline_based_measurement.TimelineBasedMeasurement)
if ((not is_page_test) and (not is_tbm)):
raise TypeError(('"%s" is not a PageTest or a TimelineBasedMeasurement.' % self.test.__name__))
if is_page_test:
return self.test()
opts = self._GetTimelineBasedMeasurementOptions(options)
self.SetupTraceRerunOptions(options, opts)
return timeline_based_measurement.TimelineBasedMeasurement(opts) | def CreatePageTest(self, options):
'Return the PageTest for this Benchmark.\n\n Override this method for PageTest tests.\n Override, CreateCoreTimelineBasedMeasurementOptions to configure\n TimelineBasedMeasurement tests. Do not override both methods.\n\n Args:\n options: a browser_options.BrowserFinderOptions instance\n Returns:\n |test()| if |test| is a PageTest class.\n Otherwise, a TimelineBasedMeasurement instance.\n '
is_page_test = issubclass(self.test, legacy_page_test.LegacyPageTest)
is_tbm = (self.test == timeline_based_measurement.TimelineBasedMeasurement)
if ((not is_page_test) and (not is_tbm)):
raise TypeError(('"%s" is not a PageTest or a TimelineBasedMeasurement.' % self.test.__name__))
if is_page_test:
return self.test()
opts = self._GetTimelineBasedMeasurementOptions(options)
self.SetupTraceRerunOptions(options, opts)
return timeline_based_measurement.TimelineBasedMeasurement(opts)<|docstring|>Return the PageTest for this Benchmark.
Override this method for PageTest tests.
Override, CreateCoreTimelineBasedMeasurementOptions to configure
TimelineBasedMeasurement tests. Do not override both methods.
Args:
options: a browser_options.BrowserFinderOptions instance
Returns:
|test()| if |test| is a PageTest class.
Otherwise, a TimelineBasedMeasurement instance.<|endoftext|> |
174a8e0f885cb59701a70e6d9c6583c509cb45232308ee9a57a722b05952cebb | def CreateStorySet(self, options):
'Creates the instance of StorySet used to run the benchmark.\n\n Can be overridden by subclasses.\n '
del options
if (not self.page_set):
raise NotImplementedError('This test has no "page_set" attribute.')
return self.page_set() | Creates the instance of StorySet used to run the benchmark.
Can be overridden by subclasses. | telemetry/telemetry/benchmark.py | CreateStorySet | tdresser/catapult-csm | 4 | python | def CreateStorySet(self, options):
'Creates the instance of StorySet used to run the benchmark.\n\n Can be overridden by subclasses.\n '
del options
if (not self.page_set):
raise NotImplementedError('This test has no "page_set" attribute.')
return self.page_set() | def CreateStorySet(self, options):
'Creates the instance of StorySet used to run the benchmark.\n\n Can be overridden by subclasses.\n '
del options
if (not self.page_set):
raise NotImplementedError('This test has no "page_set" attribute.')
return self.page_set()<|docstring|>Creates the instance of StorySet used to run the benchmark.
Can be overridden by subclasses.<|endoftext|> |
92b8d21cd9417ab984be8db1a6cbcdb65378408e02421767e918cabfb60f058d | def InitializeExpectations(self):
'Returns StoryExpectation object.\n\n This is a wrapper for GetExpectations. The user overrides GetExpectatoins\n in the benchmark class to have it use the correct expectations. This is what\n story_runner.py uses to get the expectations.\n '
if (not self._expectations):
self._expectations = self.GetExpectations()
return self._expectations | Returns StoryExpectation object.
This is a wrapper for GetExpectations. The user overrides GetExpectatoins
in the benchmark class to have it use the correct expectations. This is what
story_runner.py uses to get the expectations. | telemetry/telemetry/benchmark.py | InitializeExpectations | tdresser/catapult-csm | 4 | python | def InitializeExpectations(self):
'Returns StoryExpectation object.\n\n This is a wrapper for GetExpectations. The user overrides GetExpectatoins\n in the benchmark class to have it use the correct expectations. This is what\n story_runner.py uses to get the expectations.\n '
if (not self._expectations):
self._expectations = self.GetExpectations()
return self._expectations | def InitializeExpectations(self):
'Returns StoryExpectation object.\n\n This is a wrapper for GetExpectations. The user overrides GetExpectatoins\n in the benchmark class to have it use the correct expectations. This is what\n story_runner.py uses to get the expectations.\n '
if (not self._expectations):
self._expectations = self.GetExpectations()
return self._expectations<|docstring|>Returns StoryExpectation object.
This is a wrapper for GetExpectations. The user overrides GetExpectatoins
in the benchmark class to have it use the correct expectations. This is what
story_runner.py uses to get the expectations.<|endoftext|> |
50ebc9f767c89afba63885c56534ac140da4e6c2b0a504e83b10b26db32ebbc1 | def GetExpectations(self):
'Returns a StoryExpectation object.\n\n This object is used to determine what stories are disabled. This needs to be\n overridden by the subclass. It defaults to an empty expectations object.\n '
return expectations.StoryExpectations() | Returns a StoryExpectation object.
This object is used to determine what stories are disabled. This needs to be
overridden by the subclass. It defaults to an empty expectations object. | telemetry/telemetry/benchmark.py | GetExpectations | tdresser/catapult-csm | 4 | python | def GetExpectations(self):
'Returns a StoryExpectation object.\n\n This object is used to determine what stories are disabled. This needs to be\n overridden by the subclass. It defaults to an empty expectations object.\n '
return expectations.StoryExpectations() | def GetExpectations(self):
'Returns a StoryExpectation object.\n\n This object is used to determine what stories are disabled. This needs to be\n overridden by the subclass. It defaults to an empty expectations object.\n '
return expectations.StoryExpectations()<|docstring|>Returns a StoryExpectation object.
This object is used to determine what stories are disabled. This needs to be
overridden by the subclass. It defaults to an empty expectations object.<|endoftext|> |
39d7d43fd0b0afd7854c2a6b2a95b38c4f61eef7de499355e7b0a042a332e61f | def _dct(self, x, y, u, v, n):
' calculate discrete cosine transformation '
a = tf.math.cos((((((2 * x) + 1) * u) * math.pi) / (2 * n)))
b = tf.math.cos((((((2 * y) + 1) * v) * math.pi) / (2 * n)))
return (a * b) | calculate discrete cosine transformation | noise/dct.py | _dct | marco-willi/HiDDeN-tensorflow | 0 | python | def _dct(self, x, y, u, v, n):
' '
a = tf.math.cos((((((2 * x) + 1) * u) * math.pi) / (2 * n)))
b = tf.math.cos((((((2 * y) + 1) * v) * math.pi) / (2 * n)))
return (a * b) | def _dct(self, x, y, u, v, n):
' '
a = tf.math.cos((((((2 * x) + 1) * u) * math.pi) / (2 * n)))
b = tf.math.cos((((((2 * y) + 1) * v) * math.pi) / (2 * n)))
return (a * b)<|docstring|>calculate discrete cosine transformation<|endoftext|> |
ec782feb3b2aeb26a58072239867258f606fdf1460b65bb664c0aecaa28116b6 | def _dct_kernel(self, n, normalize):
' Build DCT 2D Convolutional Kernels '
full_kernel = ((n * n), (n * n))
G = np.zeros(shape=full_kernel)
for x in range(0, n):
for y in range(0, n):
for u in range(0, n):
for v in range(0, n):
val = self._dct(x, y, u, v, n)
if normalize:
val *= self._normalize(u, v)
x_coord = ((n * u) + v)
y_coord = ((n * x) + y)
G[(x_coord, y_coord)] = val
G = tf.cast(tf.Variable(G), tf.float32)
G_filter = tf.reshape(G, shape=((n * n), n, n))
G_filter_conv = tf.transpose(G_filter, perm=[1, 2, 0])
G_filter_conv = tf.expand_dims(G_filter_conv, 2)
return G_filter_conv | Build DCT 2D Convolutional Kernels | noise/dct.py | _dct_kernel | marco-willi/HiDDeN-tensorflow | 0 | python | def _dct_kernel(self, n, normalize):
' '
full_kernel = ((n * n), (n * n))
G = np.zeros(shape=full_kernel)
for x in range(0, n):
for y in range(0, n):
for u in range(0, n):
for v in range(0, n):
val = self._dct(x, y, u, v, n)
if normalize:
val *= self._normalize(u, v)
x_coord = ((n * u) + v)
y_coord = ((n * x) + y)
G[(x_coord, y_coord)] = val
G = tf.cast(tf.Variable(G), tf.float32)
G_filter = tf.reshape(G, shape=((n * n), n, n))
G_filter_conv = tf.transpose(G_filter, perm=[1, 2, 0])
G_filter_conv = tf.expand_dims(G_filter_conv, 2)
return G_filter_conv | def _dct_kernel(self, n, normalize):
' '
full_kernel = ((n * n), (n * n))
G = np.zeros(shape=full_kernel)
for x in range(0, n):
for y in range(0, n):
for u in range(0, n):
for v in range(0, n):
val = self._dct(x, y, u, v, n)
if normalize:
val *= self._normalize(u, v)
x_coord = ((n * u) + v)
y_coord = ((n * x) + y)
G[(x_coord, y_coord)] = val
G = tf.cast(tf.Variable(G), tf.float32)
G_filter = tf.reshape(G, shape=((n * n), n, n))
G_filter_conv = tf.transpose(G_filter, perm=[1, 2, 0])
G_filter_conv = tf.expand_dims(G_filter_conv, 2)
return G_filter_conv<|docstring|>Build DCT 2D Convolutional Kernels<|endoftext|> |
7cf8167c04e082aac4064119b06dbffb380389ba6abe2b81253578b928bb7997 | def _mask_filters(self, res_channel, mask):
' Mask filters according to mask '
mask = tf.reshape(mask, shape=(res_channel.shape[(- 1)],))
mask = tf.cast(mask, tf.float32)
return tf.multiply(res_channel, mask) | Mask filters according to mask | noise/dct.py | _mask_filters | marco-willi/HiDDeN-tensorflow | 0 | python | def _mask_filters(self, res_channel, mask):
' '
mask = tf.reshape(mask, shape=(res_channel.shape[(- 1)],))
mask = tf.cast(mask, tf.float32)
return tf.multiply(res_channel, mask) | def _mask_filters(self, res_channel, mask):
' '
mask = tf.reshape(mask, shape=(res_channel.shape[(- 1)],))
mask = tf.cast(mask, tf.float32)
return tf.multiply(res_channel, mask)<|docstring|>Mask filters according to mask<|endoftext|> |
de2c70aaa28d3d34c5f5d6044db74df2c4a20f5dfdbc91e173709b224e28bda6 | def __call__(self, inputs, masks=None):
'\n Args:\n inputs: tensor (batch, x, y, n x n, c)\n masks: list of c (n x n) binary masks\n '
n_channels = inputs.shape[(- 1)]
if (masks is not None):
assert (len(masks) == n_channels), 'length of masks ({}) must equal n_channels ({})'.format(len(masks), n_channels)
res = list()
splits = tf.split(inputs, n_channels, (- 1))
for (i, split) in enumerate(splits):
res_channel = super(DCT2D, self).__call__(split)
if (masks is not None):
res_channel = self._mask_filters(res_channel, masks[i])
res.append(res_channel)
return tf.concat(res, (- 1)) | Args:
inputs: tensor (batch, x, y, n x n, c)
masks: list of c (n x n) binary masks | noise/dct.py | __call__ | marco-willi/HiDDeN-tensorflow | 0 | python | def __call__(self, inputs, masks=None):
'\n Args:\n inputs: tensor (batch, x, y, n x n, c)\n masks: list of c (n x n) binary masks\n '
n_channels = inputs.shape[(- 1)]
if (masks is not None):
assert (len(masks) == n_channels), 'length of masks ({}) must equal n_channels ({})'.format(len(masks), n_channels)
res = list()
splits = tf.split(inputs, n_channels, (- 1))
for (i, split) in enumerate(splits):
res_channel = super(DCT2D, self).__call__(split)
if (masks is not None):
res_channel = self._mask_filters(res_channel, masks[i])
res.append(res_channel)
return tf.concat(res, (- 1)) | def __call__(self, inputs, masks=None):
'\n Args:\n inputs: tensor (batch, x, y, n x n, c)\n masks: list of c (n x n) binary masks\n '
n_channels = inputs.shape[(- 1)]
if (masks is not None):
assert (len(masks) == n_channels), 'length of masks ({}) must equal n_channels ({})'.format(len(masks), n_channels)
res = list()
splits = tf.split(inputs, n_channels, (- 1))
for (i, split) in enumerate(splits):
res_channel = super(DCT2D, self).__call__(split)
if (masks is not None):
res_channel = self._mask_filters(res_channel, masks[i])
res.append(res_channel)
return tf.concat(res, (- 1))<|docstring|>Args:
inputs: tensor (batch, x, y, n x n, c)
masks: list of c (n x n) binary masks<|endoftext|> |
d4c8b303c540695dd315f2badb30d4ac09827d6f58b13cba03b00c2743997c95 | def __init__(self, path: str):
'Initializes Dotfile class.'
self.path = Path(path)
self.local_base = 'dotfiles'
self.absolute = self._get_absolute(self.path)
self.category = self._get_path_category(self.path)
self.factory = DotfileHandlerFactory() | Initializes Dotfile class. | handlers/dotfile_handler.py | __init__ | tomislavperich/nomad | 0 | python | def __init__(self, path: str):
self.path = Path(path)
self.local_base = 'dotfiles'
self.absolute = self._get_absolute(self.path)
self.category = self._get_path_category(self.path)
self.factory = DotfileHandlerFactory() | def __init__(self, path: str):
self.path = Path(path)
self.local_base = 'dotfiles'
self.absolute = self._get_absolute(self.path)
self.category = self._get_path_category(self.path)
self.factory = DotfileHandlerFactory()<|docstring|>Initializes Dotfile class.<|endoftext|> |
b9308d241608bfcf34912c3e2b58bcecfd290df3037b805d9269f1a6565c0f70 | def _get_absolute(self, path: Path) -> Path:
'Resolves given path to absolute.\n\n Args:\n path: Path to be resolved.\n\n Returns:\n Path: resolved, absolute Path.\n '
return path.expanduser().absolute() | Resolves given path to absolute.
Args:
path: Path to be resolved.
Returns:
Path: resolved, absolute Path. | handlers/dotfile_handler.py | _get_absolute | tomislavperich/nomad | 0 | python | def _get_absolute(self, path: Path) -> Path:
'Resolves given path to absolute.\n\n Args:\n path: Path to be resolved.\n\n Returns:\n Path: resolved, absolute Path.\n '
return path.expanduser().absolute() | def _get_absolute(self, path: Path) -> Path:
'Resolves given path to absolute.\n\n Args:\n path: Path to be resolved.\n\n Returns:\n Path: resolved, absolute Path.\n '
return path.expanduser().absolute()<|docstring|>Resolves given path to absolute.
Args:
path: Path to be resolved.
Returns:
Path: resolved, absolute Path.<|endoftext|> |
41036983710b76e93935139e71a60a2c505b43df5bdd45d86fb1eecab11c8bcb | def _get_path_type(self, path: Path) -> str:
'Determines path type.\n\n Determines whether the path is a file or a directory.\n\n Args:\n path: Path to the dotfile.\n\n Returns:\n str: A string indicating path type.\n '
if path.is_dir():
return 'dir'
elif path.is_file():
return 'file'
else:
raise FileNotFoundError(f'File {path} not found') | Determines path type.
Determines whether the path is a file or a directory.
Args:
path: Path to the dotfile.
Returns:
str: A string indicating path type. | handlers/dotfile_handler.py | _get_path_type | tomislavperich/nomad | 0 | python | def _get_path_type(self, path: Path) -> str:
'Determines path type.\n\n Determines whether the path is a file or a directory.\n\n Args:\n path: Path to the dotfile.\n\n Returns:\n str: A string indicating path type.\n '
if path.is_dir():
return 'dir'
elif path.is_file():
return 'file'
else:
raise FileNotFoundError(f'File {path} not found') | def _get_path_type(self, path: Path) -> str:
'Determines path type.\n\n Determines whether the path is a file or a directory.\n\n Args:\n path: Path to the dotfile.\n\n Returns:\n str: A string indicating path type.\n '
if path.is_dir():
return 'dir'
elif path.is_file():
return 'file'
else:
raise FileNotFoundError(f'File {path} not found')<|docstring|>Determines path type.
Determines whether the path is a file or a directory.
Args:
path: Path to the dotfile.
Returns:
str: A string indicating path type.<|endoftext|> |
399801753458cb20dc810294a6a1f0bf814448e73cd77bde5c1d7cc5c9f999ad | def _get_path_category(self, path: Path) -> str:
'Determines path category.\n\n Determines path category for placing files locally.\n\n Args:\n path: Path str to determine category of.\n\n Returns:\n str: Category in which file belongs.\n '
if str(path).startswith('/'):
return 'global'
elif str(path).startswith('~'):
return 'local'
return 'custom' | Determines path category.
Determines path category for placing files locally.
Args:
path: Path str to determine category of.
Returns:
str: Category in which file belongs. | handlers/dotfile_handler.py | _get_path_category | tomislavperich/nomad | 0 | python | def _get_path_category(self, path: Path) -> str:
'Determines path category.\n\n Determines path category for placing files locally.\n\n Args:\n path: Path str to determine category of.\n\n Returns:\n str: Category in which file belongs.\n '
if str(path).startswith('/'):
return 'global'
elif str(path).startswith('~'):
return 'local'
return 'custom' | def _get_path_category(self, path: Path) -> str:
'Determines path category.\n\n Determines path category for placing files locally.\n\n Args:\n path: Path str to determine category of.\n\n Returns:\n str: Category in which file belongs.\n '
if str(path).startswith('/'):
return 'global'
elif str(path).startswith('~'):
return 'local'
return 'custom'<|docstring|>Determines path category.
Determines path category for placing files locally.
Args:
path: Path str to determine category of.
Returns:
str: Category in which file belongs.<|endoftext|> |
ea17f4763927a62e42190ee67b04f1ea64e787d38a1f197e3070423bda34f999 | def _get_local_dest(self, path: Path) -> Path:
'Gets local destination for copying.\n\n Gets local destination based on source path.\n\n Args:\n path: Path to build destination path from.\n\n Returns:\n str: Path pointing to local destination.\n '
dest = ''
if str(path).startswith('~'):
path = path.relative_to('~')
if (self.category == 'global'):
dest = f'{self.local_base}/global/{path}'
elif (self.category == 'local'):
dest = f'{self.local_base}/local/{path}'
else:
dest = f'{self.local_base}/custom/{path}'
return Path(dest) | Gets local destination for copying.
Gets local destination based on source path.
Args:
path: Path to build destination path from.
Returns:
str: Path pointing to local destination. | handlers/dotfile_handler.py | _get_local_dest | tomislavperich/nomad | 0 | python | def _get_local_dest(self, path: Path) -> Path:
'Gets local destination for copying.\n\n Gets local destination based on source path.\n\n Args:\n path: Path to build destination path from.\n\n Returns:\n str: Path pointing to local destination.\n '
dest =
if str(path).startswith('~'):
path = path.relative_to('~')
if (self.category == 'global'):
dest = f'{self.local_base}/global/{path}'
elif (self.category == 'local'):
dest = f'{self.local_base}/local/{path}'
else:
dest = f'{self.local_base}/custom/{path}'
return Path(dest) | def _get_local_dest(self, path: Path) -> Path:
'Gets local destination for copying.\n\n Gets local destination based on source path.\n\n Args:\n path: Path to build destination path from.\n\n Returns:\n str: Path pointing to local destination.\n '
dest =
if str(path).startswith('~'):
path = path.relative_to('~')
if (self.category == 'global'):
dest = f'{self.local_base}/global/{path}'
elif (self.category == 'local'):
dest = f'{self.local_base}/local/{path}'
else:
dest = f'{self.local_base}/custom/{path}'
return Path(dest)<|docstring|>Gets local destination for copying.
Gets local destination based on source path.
Args:
path: Path to build destination path from.
Returns:
str: Path pointing to local destination.<|endoftext|> |
04fd5c28098981d21b976189fd5999ffd8abc36fd7f73c4ae5da40c87acc5afe | def _get_local_src(self, path: Path) -> Path:
'Gets local source path for copying.\n\n Gets local source path based on passed source path.\n\n Args:\n path: Path to build local source path from.\n\n Returns:\n str: Path pointing to local source.\n '
src = ''
if str(path).startswith('~'):
path = Path(str(path).replace('~/', ''))
if (self.category == 'global'):
src = f'{self.local_base}/global{path}'
elif (self.category == 'local'):
src = f'{self.local_base}/local/{path}'
else:
src = f'{self.local_base}/custom/{path}'
return Path(src) | Gets local source path for copying.
Gets local source path based on passed source path.
Args:
path: Path to build local source path from.
Returns:
str: Path pointing to local source. | handlers/dotfile_handler.py | _get_local_src | tomislavperich/nomad | 0 | python | def _get_local_src(self, path: Path) -> Path:
'Gets local source path for copying.\n\n Gets local source path based on passed source path.\n\n Args:\n path: Path to build local source path from.\n\n Returns:\n str: Path pointing to local source.\n '
src =
if str(path).startswith('~'):
path = Path(str(path).replace('~/', ))
if (self.category == 'global'):
src = f'{self.local_base}/global{path}'
elif (self.category == 'local'):
src = f'{self.local_base}/local/{path}'
else:
src = f'{self.local_base}/custom/{path}'
return Path(src) | def _get_local_src(self, path: Path) -> Path:
'Gets local source path for copying.\n\n Gets local source path based on passed source path.\n\n Args:\n path: Path to build local source path from.\n\n Returns:\n str: Path pointing to local source.\n '
src =
if str(path).startswith('~'):
path = Path(str(path).replace('~/', ))
if (self.category == 'global'):
src = f'{self.local_base}/global{path}'
elif (self.category == 'local'):
src = f'{self.local_base}/local/{path}'
else:
src = f'{self.local_base}/custom/{path}'
return Path(src)<|docstring|>Gets local source path for copying.
Gets local source path based on passed source path.
Args:
path: Path to build local source path from.
Returns:
str: Path pointing to local source.<|endoftext|> |
fb9e53da6997cce51e771d8128e10662a634172611edf77c99990b23e390e4c5 | def update(self) -> None:
'Fetches dotfiles from given path'
destination = self._get_local_dest(self.path)
try:
path_type = self._get_path_type(self.absolute)
handler = self.factory.get_handler(path_type)
handler.update(self.absolute, destination)
except Exception as e:
print(f'[!] Skipping {self.path}: {e}') | Fetches dotfiles from given path | handlers/dotfile_handler.py | update | tomislavperich/nomad | 0 | python | def update(self) -> None:
destination = self._get_local_dest(self.path)
try:
path_type = self._get_path_type(self.absolute)
handler = self.factory.get_handler(path_type)
handler.update(self.absolute, destination)
except Exception as e:
print(f'[!] Skipping {self.path}: {e}') | def update(self) -> None:
destination = self._get_local_dest(self.path)
try:
path_type = self._get_path_type(self.absolute)
handler = self.factory.get_handler(path_type)
handler.update(self.absolute, destination)
except Exception as e:
print(f'[!] Skipping {self.path}: {e}')<|docstring|>Fetches dotfiles from given path<|endoftext|> |
c8957070e3e0443a3296d401c8289fbe82d199f851c18db6a8ceaf5296281123 | def bootstrap(self, backup: bool, overwrite: bool) -> None:
'Bootstraps dotfiles to given path.'
src = self._get_local_src(self.path)
try:
path_type = self._get_path_type(src)
handler = self.factory.get_handler(path_type)
handler.bootstrap(src, self.absolute, backup, overwrite)
except Exception as e:
print(f'[!] Skipping {self.path}: {e}') | Bootstraps dotfiles to given path. | handlers/dotfile_handler.py | bootstrap | tomislavperich/nomad | 0 | python | def bootstrap(self, backup: bool, overwrite: bool) -> None:
src = self._get_local_src(self.path)
try:
path_type = self._get_path_type(src)
handler = self.factory.get_handler(path_type)
handler.bootstrap(src, self.absolute, backup, overwrite)
except Exception as e:
print(f'[!] Skipping {self.path}: {e}') | def bootstrap(self, backup: bool, overwrite: bool) -> None:
src = self._get_local_src(self.path)
try:
path_type = self._get_path_type(src)
handler = self.factory.get_handler(path_type)
handler.bootstrap(src, self.absolute, backup, overwrite)
except Exception as e:
print(f'[!] Skipping {self.path}: {e}')<|docstring|>Bootstraps dotfiles to given path.<|endoftext|> |
ed10c555baa875ac2a4f7283f5c79b74fd9e5df622c6aa89314f6e3fbc0a988f | def is_matrix_spd(matrix: np.ndarray) -> bool:
'\n Mengembalikan True jika matriks\n input adalah definit positif simetris.\n Mengembalikan False sebaliknya.\n >>> import numpy as np\n >>> dimension = 3\n >>> set_matrix = create_spd_matrix(dimension)\n >>> is_matrix_spd(set_matrix)\n True\n '
assert (np.shape(matrix)[0] == np.shape(matrix)[1])
if (np.allclose(matrix, matrix.T) is False):
return False
(eigen_value, _) = np.linalg.eigh(matrix)
return bool(np.all((eigen_value > 0))) | Mengembalikan True jika matriks
input adalah definit positif simetris.
Mengembalikan False sebaliknya.
>>> import numpy as np
>>> dimension = 3
>>> set_matrix = create_spd_matrix(dimension)
>>> is_matrix_spd(set_matrix)
True | implementation/linear_algebra/conjugate_gradient.py | is_matrix_spd | reskimulud/Python | 79 | python | def is_matrix_spd(matrix: np.ndarray) -> bool:
'\n Mengembalikan True jika matriks\n input adalah definit positif simetris.\n Mengembalikan False sebaliknya.\n >>> import numpy as np\n >>> dimension = 3\n >>> set_matrix = create_spd_matrix(dimension)\n >>> is_matrix_spd(set_matrix)\n True\n '
assert (np.shape(matrix)[0] == np.shape(matrix)[1])
if (np.allclose(matrix, matrix.T) is False):
return False
(eigen_value, _) = np.linalg.eigh(matrix)
return bool(np.all((eigen_value > 0))) | def is_matrix_spd(matrix: np.ndarray) -> bool:
'\n Mengembalikan True jika matriks\n input adalah definit positif simetris.\n Mengembalikan False sebaliknya.\n >>> import numpy as np\n >>> dimension = 3\n >>> set_matrix = create_spd_matrix(dimension)\n >>> is_matrix_spd(set_matrix)\n True\n '
assert (np.shape(matrix)[0] == np.shape(matrix)[1])
if (np.allclose(matrix, matrix.T) is False):
return False
(eigen_value, _) = np.linalg.eigh(matrix)
return bool(np.all((eigen_value > 0)))<|docstring|>Mengembalikan True jika matriks
input adalah definit positif simetris.
Mengembalikan False sebaliknya.
>>> import numpy as np
>>> dimension = 3
>>> set_matrix = create_spd_matrix(dimension)
>>> is_matrix_spd(set_matrix)
True<|endoftext|> |
56db85872710d8f7f9713dc4cc45b3b4d5792ba73338c60cde7f82cd88123e22 | def create_spd_matrix(dimension: int) -> Any:
'\n Mengembalikan matriks definit positif\n simetris yang diberi dimensi.\n '
random_matrix = np.random.randn(dimension, dimension)
spd_matrix = np.dot(random_matrix, random_matrix.T)
assert is_matrix_spd(spd_matrix)
return spd_matrix | Mengembalikan matriks definit positif
simetris yang diberi dimensi. | implementation/linear_algebra/conjugate_gradient.py | create_spd_matrix | reskimulud/Python | 79 | python | def create_spd_matrix(dimension: int) -> Any:
'\n Mengembalikan matriks definit positif\n simetris yang diberi dimensi.\n '
random_matrix = np.random.randn(dimension, dimension)
spd_matrix = np.dot(random_matrix, random_matrix.T)
assert is_matrix_spd(spd_matrix)
return spd_matrix | def create_spd_matrix(dimension: int) -> Any:
'\n Mengembalikan matriks definit positif\n simetris yang diberi dimensi.\n '
random_matrix = np.random.randn(dimension, dimension)
spd_matrix = np.dot(random_matrix, random_matrix.T)
assert is_matrix_spd(spd_matrix)
return spd_matrix<|docstring|>Mengembalikan matriks definit positif
simetris yang diberi dimensi.<|endoftext|> |
2bd3bfcb05fcd6b744b3fbc600736b8214683fffdd214ea36d9ab1bff3e14635 | def conjugate_gradient(spd_matrix, load_vector, max_iterations=1000, tol=1e-08):
'\n return solusi linear sistem np.dot(spd_matrix, x) = b\n >>> import numpy as np\n >>> spd_matrix_1= np.array([\n ... [8.73256573, -5.02034289, -2.68709226],\n ... [-5.02034289, 3.78188322, 0.91980451],\n ... [-2.68709226, 0.91980451, 1.94746467]])\n >>> b = np.array([\n ... [-5.80872761],\n ... [ 3.23807431],\n ... [ 1.95381422]])\n >>> conjugate_gradient(spd_matrix_1, b)\n array([[-0.63114139],\n [-0.01561498],\n [ 0.13979294]])\n '
assert (np.shape(spd_matrix)[0] == np.shape(spd_matrix)[1])
assert (np.shape(load_vector)[0] == np.shape(spd_matrix)[0])
assert is_matrix_spd(spd_matrix)
x0 = np.zeros((np.shape(load_vector)[0], 1))
r0 = np.copy(load_vector)
p0 = np.copy(r0)
error_residual = 1000000000.0
error_x_solution = 1000000000.0
error = 1000000000.0
iterations = 0
while (error > tol):
w = np.dot(spd_matrix, p0)
alpha = (np.dot(r0.T, r0) / np.dot(p0.T, w))
x = (x0 + (alpha * p0))
r = (r0 - (alpha * w))
beta = (np.dot(r.T, r) / np.dot(r0.T, r0))
p = (r + (beta * p0))
error_residual = np.linalg.norm((r - r0))
error_x_solution = np.linalg.norm((x - x0))
error = np.maximum(error_residual, error_x_solution)
x0 = np.copy(x)
r0 = np.copy(r)
p0 = np.copy(p)
iterations += 1
if (iterations > max_iterations):
break
return x | return solusi linear sistem np.dot(spd_matrix, x) = b
>>> import numpy as np
>>> spd_matrix_1= np.array([
... [8.73256573, -5.02034289, -2.68709226],
... [-5.02034289, 3.78188322, 0.91980451],
... [-2.68709226, 0.91980451, 1.94746467]])
>>> b = np.array([
... [-5.80872761],
... [ 3.23807431],
... [ 1.95381422]])
>>> conjugate_gradient(spd_matrix_1, b)
array([[-0.63114139],
[-0.01561498],
[ 0.13979294]]) | implementation/linear_algebra/conjugate_gradient.py | conjugate_gradient | reskimulud/Python | 79 | python | def conjugate_gradient(spd_matrix, load_vector, max_iterations=1000, tol=1e-08):
'\n return solusi linear sistem np.dot(spd_matrix, x) = b\n >>> import numpy as np\n >>> spd_matrix_1= np.array([\n ... [8.73256573, -5.02034289, -2.68709226],\n ... [-5.02034289, 3.78188322, 0.91980451],\n ... [-2.68709226, 0.91980451, 1.94746467]])\n >>> b = np.array([\n ... [-5.80872761],\n ... [ 3.23807431],\n ... [ 1.95381422]])\n >>> conjugate_gradient(spd_matrix_1, b)\n array([[-0.63114139],\n [-0.01561498],\n [ 0.13979294]])\n '
assert (np.shape(spd_matrix)[0] == np.shape(spd_matrix)[1])
assert (np.shape(load_vector)[0] == np.shape(spd_matrix)[0])
assert is_matrix_spd(spd_matrix)
x0 = np.zeros((np.shape(load_vector)[0], 1))
r0 = np.copy(load_vector)
p0 = np.copy(r0)
error_residual = 1000000000.0
error_x_solution = 1000000000.0
error = 1000000000.0
iterations = 0
while (error > tol):
w = np.dot(spd_matrix, p0)
alpha = (np.dot(r0.T, r0) / np.dot(p0.T, w))
x = (x0 + (alpha * p0))
r = (r0 - (alpha * w))
beta = (np.dot(r.T, r) / np.dot(r0.T, r0))
p = (r + (beta * p0))
error_residual = np.linalg.norm((r - r0))
error_x_solution = np.linalg.norm((x - x0))
error = np.maximum(error_residual, error_x_solution)
x0 = np.copy(x)
r0 = np.copy(r)
p0 = np.copy(p)
iterations += 1
if (iterations > max_iterations):
break
return x | def conjugate_gradient(spd_matrix, load_vector, max_iterations=1000, tol=1e-08):
'\n return solusi linear sistem np.dot(spd_matrix, x) = b\n >>> import numpy as np\n >>> spd_matrix_1= np.array([\n ... [8.73256573, -5.02034289, -2.68709226],\n ... [-5.02034289, 3.78188322, 0.91980451],\n ... [-2.68709226, 0.91980451, 1.94746467]])\n >>> b = np.array([\n ... [-5.80872761],\n ... [ 3.23807431],\n ... [ 1.95381422]])\n >>> conjugate_gradient(spd_matrix_1, b)\n array([[-0.63114139],\n [-0.01561498],\n [ 0.13979294]])\n '
assert (np.shape(spd_matrix)[0] == np.shape(spd_matrix)[1])
assert (np.shape(load_vector)[0] == np.shape(spd_matrix)[0])
assert is_matrix_spd(spd_matrix)
x0 = np.zeros((np.shape(load_vector)[0], 1))
r0 = np.copy(load_vector)
p0 = np.copy(r0)
error_residual = 1000000000.0
error_x_solution = 1000000000.0
error = 1000000000.0
iterations = 0
while (error > tol):
w = np.dot(spd_matrix, p0)
alpha = (np.dot(r0.T, r0) / np.dot(p0.T, w))
x = (x0 + (alpha * p0))
r = (r0 - (alpha * w))
beta = (np.dot(r.T, r) / np.dot(r0.T, r0))
p = (r + (beta * p0))
error_residual = np.linalg.norm((r - r0))
error_x_solution = np.linalg.norm((x - x0))
error = np.maximum(error_residual, error_x_solution)
x0 = np.copy(x)
r0 = np.copy(r)
p0 = np.copy(p)
iterations += 1
if (iterations > max_iterations):
break
return x<|docstring|>return solusi linear sistem np.dot(spd_matrix, x) = b
>>> import numpy as np
>>> spd_matrix_1= np.array([
... [8.73256573, -5.02034289, -2.68709226],
... [-5.02034289, 3.78188322, 0.91980451],
... [-2.68709226, 0.91980451, 1.94746467]])
>>> b = np.array([
... [-5.80872761],
... [ 3.23807431],
... [ 1.95381422]])
>>> conjugate_gradient(spd_matrix_1, b)
array([[-0.63114139],
[-0.01561498],
[ 0.13979294]])<|endoftext|> |
ad95a5993472ee314211e221c4d69dccffa1e154072e4f7c109f4adeef8279b6 | def testing_conjugate_gradient() -> None:
'\n >>> testing_conjugate_gradient()\n '
dimension = 3
spd_matrix = create_spd_matrix(dimension)
x_true = np.random.randn(dimension, 1)
b = np.dot(spd_matrix, x_true)
x_numpy = np.linalg.solve(spd_matrix, b)
x_conjugate_gradient = conjugate_gradient(spd_matrix, b)
assert (np.linalg.norm((x_numpy - x_true)) <= 1e-06)
assert (np.linalg.norm((x_conjugate_gradient - x_true)) <= 1e-06) | >>> testing_conjugate_gradient() | implementation/linear_algebra/conjugate_gradient.py | testing_conjugate_gradient | reskimulud/Python | 79 | python | def testing_conjugate_gradient() -> None:
'\n \n '
dimension = 3
spd_matrix = create_spd_matrix(dimension)
x_true = np.random.randn(dimension, 1)
b = np.dot(spd_matrix, x_true)
x_numpy = np.linalg.solve(spd_matrix, b)
x_conjugate_gradient = conjugate_gradient(spd_matrix, b)
assert (np.linalg.norm((x_numpy - x_true)) <= 1e-06)
assert (np.linalg.norm((x_conjugate_gradient - x_true)) <= 1e-06) | def testing_conjugate_gradient() -> None:
'\n \n '
dimension = 3
spd_matrix = create_spd_matrix(dimension)
x_true = np.random.randn(dimension, 1)
b = np.dot(spd_matrix, x_true)
x_numpy = np.linalg.solve(spd_matrix, b)
x_conjugate_gradient = conjugate_gradient(spd_matrix, b)
assert (np.linalg.norm((x_numpy - x_true)) <= 1e-06)
assert (np.linalg.norm((x_conjugate_gradient - x_true)) <= 1e-06)<|docstring|>>>> testing_conjugate_gradient()<|endoftext|> |
67a58621ee17448f1e63bc14b6c02f17319ef350d6f2d8cc4bda1e82dc7af658 | def smallest_size_at_least(height, width, resize_min):
'Computes new shape with the smallest side equal to `smallest_side`.\n\n Computes new shape with the smallest side equal to `smallest_side` while\n preserving the original aspect ratio.\n\n Args:\n height: an int32 scalar tensor indicating the current height.\n width: an int32 scalar tensor indicating the current width.\n resize_min: A python integer or scalar `Tensor` indicating the size of\n the smallest side after resize.\n\n Returns:\n new_height: an int32 scalar tensor indicating the new height.\n new_width: an int32 scalar tensor indicating the new width.\n '
resize_min = tf.cast(resize_min, tf.float32)
(height, width) = (tf.cast(height, tf.float32), tf.cast(width, tf.float32))
smaller_dim = tf.minimum(height, width)
scale_ratio = (resize_min / smaller_dim)
new_height = tf.cast(tf.round((height * scale_ratio)), tf.int32)
new_width = tf.cast(tf.round((width * scale_ratio)), tf.int32)
return (new_height, new_width) | Computes new shape with the smallest side equal to `smallest_side`.
Computes new shape with the smallest side equal to `smallest_side` while
preserving the original aspect ratio.
Args:
height: an int32 scalar tensor indicating the current height.
width: an int32 scalar tensor indicating the current width.
resize_min: A python integer or scalar `Tensor` indicating the size of
the smallest side after resize.
Returns:
new_height: an int32 scalar tensor indicating the new height.
new_width: an int32 scalar tensor indicating the new width. | dataset/preprocess_dataset.py | smallest_size_at_least | bolide2006/r329_aipu | 0 | python | def smallest_size_at_least(height, width, resize_min):
'Computes new shape with the smallest side equal to `smallest_side`.\n\n Computes new shape with the smallest side equal to `smallest_side` while\n preserving the original aspect ratio.\n\n Args:\n height: an int32 scalar tensor indicating the current height.\n width: an int32 scalar tensor indicating the current width.\n resize_min: A python integer or scalar `Tensor` indicating the size of\n the smallest side after resize.\n\n Returns:\n new_height: an int32 scalar tensor indicating the new height.\n new_width: an int32 scalar tensor indicating the new width.\n '
resize_min = tf.cast(resize_min, tf.float32)
(height, width) = (tf.cast(height, tf.float32), tf.cast(width, tf.float32))
smaller_dim = tf.minimum(height, width)
scale_ratio = (resize_min / smaller_dim)
new_height = tf.cast(tf.round((height * scale_ratio)), tf.int32)
new_width = tf.cast(tf.round((width * scale_ratio)), tf.int32)
return (new_height, new_width) | def smallest_size_at_least(height, width, resize_min):
'Computes new shape with the smallest side equal to `smallest_side`.\n\n Computes new shape with the smallest side equal to `smallest_side` while\n preserving the original aspect ratio.\n\n Args:\n height: an int32 scalar tensor indicating the current height.\n width: an int32 scalar tensor indicating the current width.\n resize_min: A python integer or scalar `Tensor` indicating the size of\n the smallest side after resize.\n\n Returns:\n new_height: an int32 scalar tensor indicating the new height.\n new_width: an int32 scalar tensor indicating the new width.\n '
resize_min = tf.cast(resize_min, tf.float32)
(height, width) = (tf.cast(height, tf.float32), tf.cast(width, tf.float32))
smaller_dim = tf.minimum(height, width)
scale_ratio = (resize_min / smaller_dim)
new_height = tf.cast(tf.round((height * scale_ratio)), tf.int32)
new_width = tf.cast(tf.round((width * scale_ratio)), tf.int32)
return (new_height, new_width)<|docstring|>Computes new shape with the smallest side equal to `smallest_side`.
Computes new shape with the smallest side equal to `smallest_side` while
preserving the original aspect ratio.
Args:
height: an int32 scalar tensor indicating the current height.
width: an int32 scalar tensor indicating the current width.
resize_min: A python integer or scalar `Tensor` indicating the size of
the smallest side after resize.
Returns:
new_height: an int32 scalar tensor indicating the new height.
new_width: an int32 scalar tensor indicating the new width.<|endoftext|> |
a7ae15b1c6e25d6748c1a69cec955f72803ace5684400043e05bd92a86945e8f | def resize_image(image, height, width, method='BILINEAR'):
'Simple wrapper around tf.resize_images.\n\n This is primarily to make sure we use the same `ResizeMethod` and other\n details each time.\n\n Args:\n image: A 3-D image `Tensor`.\n height: The target height for the resized image.\n width: The target width for the resized image.\n\n Returns:\n resized_image: A 3-D tensor containing the resized image. The first two\n dimensions have the shape [height, width].\n '
resize_func = (tf.image.ResizeMethod.NEAREST_NEIGHBOR if (method == 'NEAREST') else tf.image.ResizeMethod.BILINEAR)
return tf.image.resize_images(image, [height, width], method=resize_func, align_corners=False) | Simple wrapper around tf.resize_images.
This is primarily to make sure we use the same `ResizeMethod` and other
details each time.
Args:
image: A 3-D image `Tensor`.
height: The target height for the resized image.
width: The target width for the resized image.
Returns:
resized_image: A 3-D tensor containing the resized image. The first two
dimensions have the shape [height, width]. | dataset/preprocess_dataset.py | resize_image | bolide2006/r329_aipu | 0 | python | def resize_image(image, height, width, method='BILINEAR'):
'Simple wrapper around tf.resize_images.\n\n This is primarily to make sure we use the same `ResizeMethod` and other\n details each time.\n\n Args:\n image: A 3-D image `Tensor`.\n height: The target height for the resized image.\n width: The target width for the resized image.\n\n Returns:\n resized_image: A 3-D tensor containing the resized image. The first two\n dimensions have the shape [height, width].\n '
resize_func = (tf.image.ResizeMethod.NEAREST_NEIGHBOR if (method == 'NEAREST') else tf.image.ResizeMethod.BILINEAR)
return tf.image.resize_images(image, [height, width], method=resize_func, align_corners=False) | def resize_image(image, height, width, method='BILINEAR'):
'Simple wrapper around tf.resize_images.\n\n This is primarily to make sure we use the same `ResizeMethod` and other\n details each time.\n\n Args:\n image: A 3-D image `Tensor`.\n height: The target height for the resized image.\n width: The target width for the resized image.\n\n Returns:\n resized_image: A 3-D tensor containing the resized image. The first two\n dimensions have the shape [height, width].\n '
resize_func = (tf.image.ResizeMethod.NEAREST_NEIGHBOR if (method == 'NEAREST') else tf.image.ResizeMethod.BILINEAR)
return tf.image.resize_images(image, [height, width], method=resize_func, align_corners=False)<|docstring|>Simple wrapper around tf.resize_images.
This is primarily to make sure we use the same `ResizeMethod` and other
details each time.
Args:
image: A 3-D image `Tensor`.
height: The target height for the resized image.
width: The target width for the resized image.
Returns:
resized_image: A 3-D tensor containing the resized image. The first two
dimensions have the shape [height, width].<|endoftext|> |
cc4dd2d1c3afe57dceba709c14567bb6ca56b9801700d9e4451b6491d601bc48 | def aspect_preserving_resize(image, resize_min, channels=3, method='BILINEAR'):
'Resize images preserving the original aspect ratio.\n\n Args:\n image: A 3-D image `Tensor`.\n resize_min: A python integer or scalar `Tensor` indicating the size of\n the smallest side after resize.\n\n Returns:\n resized_image: A 3-D tensor containing the resized image.\n '
shape = tf.shape(image)
(height, width) = (shape[0], shape[1])
(new_height, new_width) = smallest_size_at_least(height, width, resize_min)
return resize_image(image, new_height, new_width, method) | Resize images preserving the original aspect ratio.
Args:
image: A 3-D image `Tensor`.
resize_min: A python integer or scalar `Tensor` indicating the size of
the smallest side after resize.
Returns:
resized_image: A 3-D tensor containing the resized image. | dataset/preprocess_dataset.py | aspect_preserving_resize | bolide2006/r329_aipu | 0 | python | def aspect_preserving_resize(image, resize_min, channels=3, method='BILINEAR'):
'Resize images preserving the original aspect ratio.\n\n Args:\n image: A 3-D image `Tensor`.\n resize_min: A python integer or scalar `Tensor` indicating the size of\n the smallest side after resize.\n\n Returns:\n resized_image: A 3-D tensor containing the resized image.\n '
shape = tf.shape(image)
(height, width) = (shape[0], shape[1])
(new_height, new_width) = smallest_size_at_least(height, width, resize_min)
return resize_image(image, new_height, new_width, method) | def aspect_preserving_resize(image, resize_min, channels=3, method='BILINEAR'):
'Resize images preserving the original aspect ratio.\n\n Args:\n image: A 3-D image `Tensor`.\n resize_min: A python integer or scalar `Tensor` indicating the size of\n the smallest side after resize.\n\n Returns:\n resized_image: A 3-D tensor containing the resized image.\n '
shape = tf.shape(image)
(height, width) = (shape[0], shape[1])
(new_height, new_width) = smallest_size_at_least(height, width, resize_min)
return resize_image(image, new_height, new_width, method)<|docstring|>Resize images preserving the original aspect ratio.
Args:
image: A 3-D image `Tensor`.
resize_min: A python integer or scalar `Tensor` indicating the size of
the smallest side after resize.
Returns:
resized_image: A 3-D tensor containing the resized image.<|endoftext|> |
c310ea0577d8785bcf5b7961cfa0d754ab325588584bf72e938cd98ef17db505 | def central_crop(image, crop_height, crop_width, channels=3):
'Performs central crops of the given image list.\n\n Args:\n image: a 3-D image tensor\n crop_height: the height of the image following the crop.\n crop_width: the width of the image following the crop.\n\n Returns:\n 3-D tensor with cropped image.\n '
shape = tf.shape(image)
(height, width) = (shape[0], shape[1])
amount_to_be_cropped_h = (height - crop_height)
crop_top = (amount_to_be_cropped_h // 2)
amount_to_be_cropped_w = (width - crop_width)
crop_left = (amount_to_be_cropped_w // 2)
size_assertion = tf.Assert(tf.logical_and(tf.greater_equal(height, crop_height), tf.greater_equal(width, crop_width)), ['Crop size greater than the image size.'])
with tf.control_dependencies([size_assertion]):
if (channels == 1):
image = tf.squeeze(image)
crop_start = [crop_top, crop_left]
crop_shape = [crop_height, crop_width]
elif (channels >= 3):
crop_start = [crop_top, crop_left, 0]
crop_shape = [crop_height, crop_width, (- 1)]
image = tf.slice(image, crop_start, crop_shape)
return tf.reshape(image, [crop_height, crop_width, (- 1)]) | Performs central crops of the given image list.
Args:
image: a 3-D image tensor
crop_height: the height of the image following the crop.
crop_width: the width of the image following the crop.
Returns:
3-D tensor with cropped image. | dataset/preprocess_dataset.py | central_crop | bolide2006/r329_aipu | 0 | python | def central_crop(image, crop_height, crop_width, channels=3):
'Performs central crops of the given image list.\n\n Args:\n image: a 3-D image tensor\n crop_height: the height of the image following the crop.\n crop_width: the width of the image following the crop.\n\n Returns:\n 3-D tensor with cropped image.\n '
shape = tf.shape(image)
(height, width) = (shape[0], shape[1])
amount_to_be_cropped_h = (height - crop_height)
crop_top = (amount_to_be_cropped_h // 2)
amount_to_be_cropped_w = (width - crop_width)
crop_left = (amount_to_be_cropped_w // 2)
size_assertion = tf.Assert(tf.logical_and(tf.greater_equal(height, crop_height), tf.greater_equal(width, crop_width)), ['Crop size greater than the image size.'])
with tf.control_dependencies([size_assertion]):
if (channels == 1):
image = tf.squeeze(image)
crop_start = [crop_top, crop_left]
crop_shape = [crop_height, crop_width]
elif (channels >= 3):
crop_start = [crop_top, crop_left, 0]
crop_shape = [crop_height, crop_width, (- 1)]
image = tf.slice(image, crop_start, crop_shape)
return tf.reshape(image, [crop_height, crop_width, (- 1)]) | def central_crop(image, crop_height, crop_width, channels=3):
'Performs central crops of the given image list.\n\n Args:\n image: a 3-D image tensor\n crop_height: the height of the image following the crop.\n crop_width: the width of the image following the crop.\n\n Returns:\n 3-D tensor with cropped image.\n '
shape = tf.shape(image)
(height, width) = (shape[0], shape[1])
amount_to_be_cropped_h = (height - crop_height)
crop_top = (amount_to_be_cropped_h // 2)
amount_to_be_cropped_w = (width - crop_width)
crop_left = (amount_to_be_cropped_w // 2)
size_assertion = tf.Assert(tf.logical_and(tf.greater_equal(height, crop_height), tf.greater_equal(width, crop_width)), ['Crop size greater than the image size.'])
with tf.control_dependencies([size_assertion]):
if (channels == 1):
image = tf.squeeze(image)
crop_start = [crop_top, crop_left]
crop_shape = [crop_height, crop_width]
elif (channels >= 3):
crop_start = [crop_top, crop_left, 0]
crop_shape = [crop_height, crop_width, (- 1)]
image = tf.slice(image, crop_start, crop_shape)
return tf.reshape(image, [crop_height, crop_width, (- 1)])<|docstring|>Performs central crops of the given image list.
Args:
image: a 3-D image tensor
crop_height: the height of the image following the crop.
crop_width: the width of the image following the crop.
Returns:
3-D tensor with cropped image.<|endoftext|> |
3a1ff86ccbe563ab787b43a78ada8d16a1fefbef6b032b1a10ce5aadcae55203 | def _block_diag(arrays):
' Create block-diagonal matrix from `arrays`. '
result = None
for arr in arrays:
arr[(arr == (- 0))] = 0
if (result is None):
result = arr
else:
(r_rows, r_cols) = result.shape
(a_rows, a_cols) = arr.shape
result = np.vstack((np.hstack((result, np.zeros((r_rows, a_cols)))), np.hstack((np.zeros((a_rows, r_cols)), arr))))
return result | Create block-diagonal matrix from `arrays`. | openmdao.lib/src/openmdao/lib/geometry/stl_group.py | _block_diag | mjfwest/OpenMDAO-Framework | 69 | python | def _block_diag(arrays):
' '
result = None
for arr in arrays:
arr[(arr == (- 0))] = 0
if (result is None):
result = arr
else:
(r_rows, r_cols) = result.shape
(a_rows, a_cols) = arr.shape
result = np.vstack((np.hstack((result, np.zeros((r_rows, a_cols)))), np.hstack((np.zeros((a_rows, r_cols)), arr))))
return result | def _block_diag(arrays):
' '
result = None
for arr in arrays:
arr[(arr == (- 0))] = 0
if (result is None):
result = arr
else:
(r_rows, r_cols) = result.shape
(a_rows, a_cols) = arr.shape
result = np.vstack((np.hstack((result, np.zeros((r_rows, a_cols)))), np.hstack((np.zeros((a_rows, r_cols)), arr))))
return result<|docstring|>Create block-diagonal matrix from `arrays`.<|endoftext|> |
ff79cbf4e166fa28984c0b436788c4b62d15f24b47a0cb7cfa7c3c62be996ff6 | def _build_io(self):
" returns a dictionary of io sets key'd to component names"
self.comp_param_count = {}
params = []
for comp in self._comps:
name = comp.name
if isinstance(comp, Body):
val = comp.delta_C[(:, 0)]
meta = {'value': val, 'iotype': 'in', 'shape': val.shape, 'desc': 'axial location of control points for the ffd'}
tup = ((name, 'X'), meta)
params.append(tup)
n_X = val.shape[0]
val = comp.delta_C[(:, 1)]
meta = {'value': val, 'iotype': 'in', 'shape': val.shape, 'desc': 'radial location of control points for the ffd'}
tup = ((name, 'R'), meta)
params.append(tup)
n_R = val.shape[0]
self.comp_param_count[comp] = (n_X, n_R)
else:
val = comp.delta_Cc[(:, 0)]
meta = {'value': val, 'iotype': 'in', 'shape': val.shape, 'desc': 'axial location of the control points for the centerline of the shell'}
tup = ((name, 'X'), meta)
params.append(tup)
n_X = val.shape[0]
val = comp.delta_Cc[(:, 1)]
meta = {'value': val, 'iotype': 'in', 'shape': val.shape, 'desc': 'radial location of the control points for the centerline of the shell'}
tup = ((name, 'R'), meta)
params.append(tup)
n_R = val.shape[0]
val = comp.delta_Ct[(:, 1)]
meta = {'value': val, 'iotype': 'in', 'shape': val.shape, 'desc': 'thickness of the shell at each axial station'}
tup = ((name, 'thickness'), meta)
params.append(tup)
n_T = val.shape[0]
self.comp_param_count[comp] = (n_X, n_R, n_T)
points = []
triangles = []
i_offset = 0
n_controls = 0
for comp in self._comps:
n_controls += sum(self.comp_param_count[comp])
if isinstance(comp, Body):
points.extend(comp.stl.points)
size = len(points)
triangles.extend((comp.stl.triangles + i_offset))
i_offset = size
else:
points.extend(comp.outer_stl.points)
size = len(points)
triangles.extend((comp.outer_stl.triangles + i_offset))
i_offset = size
points.extend(comp.inner_stl.points)
size = len(points)
triangles.extend((comp.inner_stl.triangles + i_offset))
i_offset = size
self.points = np.array(points)
self.n_controls = n_controls
self.n_points = len(points)
self.triangles = np.array(triangles)
self.n_triangles = len(triangles)
return params | returns a dictionary of io sets key'd to component names | openmdao.lib/src/openmdao/lib/geometry/stl_group.py | _build_io | mjfwest/OpenMDAO-Framework | 69 | python | def _build_io(self):
" "
self.comp_param_count = {}
params = []
for comp in self._comps:
name = comp.name
if isinstance(comp, Body):
val = comp.delta_C[(:, 0)]
meta = {'value': val, 'iotype': 'in', 'shape': val.shape, 'desc': 'axial location of control points for the ffd'}
tup = ((name, 'X'), meta)
params.append(tup)
n_X = val.shape[0]
val = comp.delta_C[(:, 1)]
meta = {'value': val, 'iotype': 'in', 'shape': val.shape, 'desc': 'radial location of control points for the ffd'}
tup = ((name, 'R'), meta)
params.append(tup)
n_R = val.shape[0]
self.comp_param_count[comp] = (n_X, n_R)
else:
val = comp.delta_Cc[(:, 0)]
meta = {'value': val, 'iotype': 'in', 'shape': val.shape, 'desc': 'axial location of the control points for the centerline of the shell'}
tup = ((name, 'X'), meta)
params.append(tup)
n_X = val.shape[0]
val = comp.delta_Cc[(:, 1)]
meta = {'value': val, 'iotype': 'in', 'shape': val.shape, 'desc': 'radial location of the control points for the centerline of the shell'}
tup = ((name, 'R'), meta)
params.append(tup)
n_R = val.shape[0]
val = comp.delta_Ct[(:, 1)]
meta = {'value': val, 'iotype': 'in', 'shape': val.shape, 'desc': 'thickness of the shell at each axial station'}
tup = ((name, 'thickness'), meta)
params.append(tup)
n_T = val.shape[0]
self.comp_param_count[comp] = (n_X, n_R, n_T)
points = []
triangles = []
i_offset = 0
n_controls = 0
for comp in self._comps:
n_controls += sum(self.comp_param_count[comp])
if isinstance(comp, Body):
points.extend(comp.stl.points)
size = len(points)
triangles.extend((comp.stl.triangles + i_offset))
i_offset = size
else:
points.extend(comp.outer_stl.points)
size = len(points)
triangles.extend((comp.outer_stl.triangles + i_offset))
i_offset = size
points.extend(comp.inner_stl.points)
size = len(points)
triangles.extend((comp.inner_stl.triangles + i_offset))
i_offset = size
self.points = np.array(points)
self.n_controls = n_controls
self.n_points = len(points)
self.triangles = np.array(triangles)
self.n_triangles = len(triangles)
return params | def _build_io(self):
" "
self.comp_param_count = {}
params = []
for comp in self._comps:
name = comp.name
if isinstance(comp, Body):
val = comp.delta_C[(:, 0)]
meta = {'value': val, 'iotype': 'in', 'shape': val.shape, 'desc': 'axial location of control points for the ffd'}
tup = ((name, 'X'), meta)
params.append(tup)
n_X = val.shape[0]
val = comp.delta_C[(:, 1)]
meta = {'value': val, 'iotype': 'in', 'shape': val.shape, 'desc': 'radial location of control points for the ffd'}
tup = ((name, 'R'), meta)
params.append(tup)
n_R = val.shape[0]
self.comp_param_count[comp] = (n_X, n_R)
else:
val = comp.delta_Cc[(:, 0)]
meta = {'value': val, 'iotype': 'in', 'shape': val.shape, 'desc': 'axial location of the control points for the centerline of the shell'}
tup = ((name, 'X'), meta)
params.append(tup)
n_X = val.shape[0]
val = comp.delta_Cc[(:, 1)]
meta = {'value': val, 'iotype': 'in', 'shape': val.shape, 'desc': 'radial location of the control points for the centerline of the shell'}
tup = ((name, 'R'), meta)
params.append(tup)
n_R = val.shape[0]
val = comp.delta_Ct[(:, 1)]
meta = {'value': val, 'iotype': 'in', 'shape': val.shape, 'desc': 'thickness of the shell at each axial station'}
tup = ((name, 'thickness'), meta)
params.append(tup)
n_T = val.shape[0]
self.comp_param_count[comp] = (n_X, n_R, n_T)
points = []
triangles = []
i_offset = 0
n_controls = 0
for comp in self._comps:
n_controls += sum(self.comp_param_count[comp])
if isinstance(comp, Body):
points.extend(comp.stl.points)
size = len(points)
triangles.extend((comp.stl.triangles + i_offset))
i_offset = size
else:
points.extend(comp.outer_stl.points)
size = len(points)
triangles.extend((comp.outer_stl.triangles + i_offset))
i_offset = size
points.extend(comp.inner_stl.points)
size = len(points)
triangles.extend((comp.inner_stl.triangles + i_offset))
i_offset = size
self.points = np.array(points)
self.n_controls = n_controls
self.n_points = len(points)
self.triangles = np.array(triangles)
self.n_triangles = len(triangles)
return params<|docstring|>returns a dictionary of io sets key'd to component names<|endoftext|> |
f7e5f6d23caf396cb9460746104596114c189b19f69055fea78af7f0019beaa3 | def deform(self, **kwargs):
' deforms the geometry applying the new locations for the control points, given by body name'
for (name, delta_C) in kwargs.iteritems():
i = self._i_comps[name]
comp = self._comps[i]
if isinstance(comp, Body):
comp.deform(delta_C)
else:
comp.deform(*delta_C)
self.list_parameters() | deforms the geometry applying the new locations for the control points, given by body name | openmdao.lib/src/openmdao/lib/geometry/stl_group.py | deform | mjfwest/OpenMDAO-Framework | 69 | python | def deform(self, **kwargs):
' '
for (name, delta_C) in kwargs.iteritems():
i = self._i_comps[name]
comp = self._comps[i]
if isinstance(comp, Body):
comp.deform(delta_C)
else:
comp.deform(*delta_C)
self.list_parameters() | def deform(self, **kwargs):
' '
for (name, delta_C) in kwargs.iteritems():
i = self._i_comps[name]
comp = self._comps[i]
if isinstance(comp, Body):
comp.deform(delta_C)
else:
comp.deform(*delta_C)
self.list_parameters()<|docstring|>deforms the geometry applying the new locations for the control points, given by body name<|endoftext|> |
c5db9bc63ae298da32a9ba56af4ddce26a2cb526e5d556ce3d39261ee7c37ecc | def _build_ascii_stl(self, facets):
'returns a list of ascii lines for the stl file '
lines = ['solid ffd_geom']
for facet in facets:
lines.append(ASCII_FACET.format(face=facet))
lines.append('endsolid ffd_geom')
return lines | returns a list of ascii lines for the stl file | openmdao.lib/src/openmdao/lib/geometry/stl_group.py | _build_ascii_stl | mjfwest/OpenMDAO-Framework | 69 | python | def _build_ascii_stl(self, facets):
' '
lines = ['solid ffd_geom']
for facet in facets:
lines.append(ASCII_FACET.format(face=facet))
lines.append('endsolid ffd_geom')
return lines | def _build_ascii_stl(self, facets):
' '
lines = ['solid ffd_geom']
for facet in facets:
lines.append(ASCII_FACET.format(face=facet))
lines.append('endsolid ffd_geom')
return lines<|docstring|>returns a list of ascii lines for the stl file<|endoftext|> |
af9fc9e8e7c236fe488e3f9954d6d4e61cd81fcb76b6496ca7f435abd094325e | def _build_binary_stl(self, facets):
'returns a string of binary binary data for the stl file'
lines = [struct.pack(BINARY_HEADER, b'Binary STL Writer', len(facets))]
for facet in facets:
facet = list(facet)
facet.append(0)
lines.append(struct.pack(BINARY_FACET, *facet))
return lines | returns a string of binary binary data for the stl file | openmdao.lib/src/openmdao/lib/geometry/stl_group.py | _build_binary_stl | mjfwest/OpenMDAO-Framework | 69 | python | def _build_binary_stl(self, facets):
lines = [struct.pack(BINARY_HEADER, b'Binary STL Writer', len(facets))]
for facet in facets:
facet = list(facet)
facet.append(0)
lines.append(struct.pack(BINARY_FACET, *facet))
return lines | def _build_binary_stl(self, facets):
lines = [struct.pack(BINARY_HEADER, b'Binary STL Writer', len(facets))]
for facet in facets:
facet = list(facet)
facet.append(0)
lines.append(struct.pack(BINARY_FACET, *facet))
return lines<|docstring|>returns a string of binary binary data for the stl file<|endoftext|> |
0e778363d2721445b57cf5c63aaf783969b7ba8acfc16b547a2b04ce42313777 | def writeSTL(self, file_name, ascii=False):
'outputs an STL file'
facets = []
for comp in self._comps:
if isinstance(comp, Body):
facets.extend(comp.stl.get_facets())
else:
facets.extend(comp.outer_stl.get_facets())
facets.extend(comp.inner_stl.get_facets())
f = open(file_name, 'w')
if ascii:
lines = self._build_ascii_stl(facets)
f.write('\n'.join(lines))
else:
data = self._build_binary_stl(facets)
f.write(''.join(data))
f.close() | outputs an STL file | openmdao.lib/src/openmdao/lib/geometry/stl_group.py | writeSTL | mjfwest/OpenMDAO-Framework | 69 | python | def writeSTL(self, file_name, ascii=False):
facets = []
for comp in self._comps:
if isinstance(comp, Body):
facets.extend(comp.stl.get_facets())
else:
facets.extend(comp.outer_stl.get_facets())
facets.extend(comp.inner_stl.get_facets())
f = open(file_name, 'w')
if ascii:
lines = self._build_ascii_stl(facets)
f.write('\n'.join(lines))
else:
data = self._build_binary_stl(facets)
f.write(.join(data))
f.close() | def writeSTL(self, file_name, ascii=False):
facets = []
for comp in self._comps:
if isinstance(comp, Body):
facets.extend(comp.stl.get_facets())
else:
facets.extend(comp.outer_stl.get_facets())
facets.extend(comp.inner_stl.get_facets())
f = open(file_name, 'w')
if ascii:
lines = self._build_ascii_stl(facets)
f.write('\n'.join(lines))
else:
data = self._build_binary_stl(facets)
f.write(.join(data))
f.close()<|docstring|>outputs an STL file<|endoftext|> |
216a23bd1431c8cf120baf9c515f620ae9752bc52b2c421a64be8d68d45ad77e | def writeFEPOINT(self, stream):
'writes out a new FEPOINT file with the given name, using the supplied points.\n derivs is of size (3,len(points),len(control_points)), giving matricies of\n X,Y,Z drivatives\n\n jacobian should have a shape of (len(points),len(control_points))'
self.provideJ()
lines = ['TITLE = "FFD_geom"']
var_line = 'VARIABLES = "X" "Y" "Z" "ID" '
deriv_X_names = []
deriv_R_names = []
deriv_T_names = []
deriv_tmpl = string.Template('"dx_d${name}_${type}$i" "dy_d${name}_${type}$i" "dz_d${name}_${type}$i"')
for comp in self._comps:
if isinstance(comp, Body):
deriv_X_names.extend([deriv_tmpl.substitute({'name': comp.name, 'i': str(i), 'type': 'X'}) for i in xrange(0, comp.n_controls)])
deriv_R_names.extend([deriv_tmpl.substitute({'name': comp.name, 'i': str(i), 'type': 'R'}) for i in xrange(0, comp.n_controls)])
else:
deriv_X_names.extend([deriv_tmpl.substitute({'name': comp.name, 'i': str(i), 'type': 'X'}) for i in xrange(0, comp.n_c_controls)])
deriv_R_names.extend([deriv_tmpl.substitute({'name': comp.name, 'i': str(i), 'type': 'R'}) for i in xrange(0, comp.n_c_controls)])
deriv_T_names.extend([deriv_tmpl.substitute({'name': comp.name, 'i': str(i), 'type': 'T'}) for i in xrange(0, comp.n_t_controls)])
var_line += ' '.join(deriv_X_names)
var_line += ' '.join(deriv_R_names)
var_line += ' '.join(deriv_T_names)
lines.append(var_line)
lines.append(('ZONE T = group0, I = %d, J = %d, F=FEPOINT' % (self.n_points, self.n_triangles)))
nx = (3 * self.dXqdC.shape[1])
nr = (3 * self.dYqdCr.shape[1])
nt = (3 * self.dYqdCt.shape[1])
j_cols = ((nx + nr) + nt)
for (i, p) in enumerate(self.points):
line = ('%.8f %.8f %.8f %d ' % (p[0], p[1], p[2], (i + 1)))
deriv_values = np.zeros((j_cols,))
deriv_values[:nx:3] = self.dXqdC[i]
deriv_values[(nx + 1):(nx + nr):3] = self.dYqdCr[i]
deriv_values[(nx + 2):(nx + nr):3] = self.dZqdCr[i]
deriv_values[((nx + nr) + 1)::3] = self.dYqdCt[i]
deriv_values[((nx + nr) + 2)::3] = self.dZqdCt[i]
line += ' '.join(np.char.mod('%.8f', deriv_values))
lines.append(line)
for tri in self.triangles:
line = ('%d %d %d %d' % ((tri[0] + 1), (tri[1] + 1), (tri[2] + 1), (tri[2] + 1)))
lines.append(line)
needs_close = False
if isinstance(stream, basestring):
stream = open(stream, 'w')
needs_close = True
((print >> stream), '\n'.join(lines))
if needs_close:
stream.close() | writes out a new FEPOINT file with the given name, using the supplied points.
derivs is of size (3,len(points),len(control_points)), giving matricies of
X,Y,Z drivatives
jacobian should have a shape of (len(points),len(control_points)) | openmdao.lib/src/openmdao/lib/geometry/stl_group.py | writeFEPOINT | mjfwest/OpenMDAO-Framework | 69 | python | def writeFEPOINT(self, stream):
'writes out a new FEPOINT file with the given name, using the supplied points.\n derivs is of size (3,len(points),len(control_points)), giving matricies of\n X,Y,Z drivatives\n\n jacobian should have a shape of (len(points),len(control_points))'
self.provideJ()
lines = ['TITLE = "FFD_geom"']
var_line = 'VARIABLES = "X" "Y" "Z" "ID" '
deriv_X_names = []
deriv_R_names = []
deriv_T_names = []
deriv_tmpl = string.Template('"dx_d${name}_${type}$i" "dy_d${name}_${type}$i" "dz_d${name}_${type}$i"')
for comp in self._comps:
if isinstance(comp, Body):
deriv_X_names.extend([deriv_tmpl.substitute({'name': comp.name, 'i': str(i), 'type': 'X'}) for i in xrange(0, comp.n_controls)])
deriv_R_names.extend([deriv_tmpl.substitute({'name': comp.name, 'i': str(i), 'type': 'R'}) for i in xrange(0, comp.n_controls)])
else:
deriv_X_names.extend([deriv_tmpl.substitute({'name': comp.name, 'i': str(i), 'type': 'X'}) for i in xrange(0, comp.n_c_controls)])
deriv_R_names.extend([deriv_tmpl.substitute({'name': comp.name, 'i': str(i), 'type': 'R'}) for i in xrange(0, comp.n_c_controls)])
deriv_T_names.extend([deriv_tmpl.substitute({'name': comp.name, 'i': str(i), 'type': 'T'}) for i in xrange(0, comp.n_t_controls)])
var_line += ' '.join(deriv_X_names)
var_line += ' '.join(deriv_R_names)
var_line += ' '.join(deriv_T_names)
lines.append(var_line)
lines.append(('ZONE T = group0, I = %d, J = %d, F=FEPOINT' % (self.n_points, self.n_triangles)))
nx = (3 * self.dXqdC.shape[1])
nr = (3 * self.dYqdCr.shape[1])
nt = (3 * self.dYqdCt.shape[1])
j_cols = ((nx + nr) + nt)
for (i, p) in enumerate(self.points):
line = ('%.8f %.8f %.8f %d ' % (p[0], p[1], p[2], (i + 1)))
deriv_values = np.zeros((j_cols,))
deriv_values[:nx:3] = self.dXqdC[i]
deriv_values[(nx + 1):(nx + nr):3] = self.dYqdCr[i]
deriv_values[(nx + 2):(nx + nr):3] = self.dZqdCr[i]
deriv_values[((nx + nr) + 1)::3] = self.dYqdCt[i]
deriv_values[((nx + nr) + 2)::3] = self.dZqdCt[i]
line += ' '.join(np.char.mod('%.8f', deriv_values))
lines.append(line)
for tri in self.triangles:
line = ('%d %d %d %d' % ((tri[0] + 1), (tri[1] + 1), (tri[2] + 1), (tri[2] + 1)))
lines.append(line)
needs_close = False
if isinstance(stream, basestring):
stream = open(stream, 'w')
needs_close = True
((print >> stream), '\n'.join(lines))
if needs_close:
stream.close() | def writeFEPOINT(self, stream):
'writes out a new FEPOINT file with the given name, using the supplied points.\n derivs is of size (3,len(points),len(control_points)), giving matricies of\n X,Y,Z drivatives\n\n jacobian should have a shape of (len(points),len(control_points))'
self.provideJ()
lines = ['TITLE = "FFD_geom"']
var_line = 'VARIABLES = "X" "Y" "Z" "ID" '
deriv_X_names = []
deriv_R_names = []
deriv_T_names = []
deriv_tmpl = string.Template('"dx_d${name}_${type}$i" "dy_d${name}_${type}$i" "dz_d${name}_${type}$i"')
for comp in self._comps:
if isinstance(comp, Body):
deriv_X_names.extend([deriv_tmpl.substitute({'name': comp.name, 'i': str(i), 'type': 'X'}) for i in xrange(0, comp.n_controls)])
deriv_R_names.extend([deriv_tmpl.substitute({'name': comp.name, 'i': str(i), 'type': 'R'}) for i in xrange(0, comp.n_controls)])
else:
deriv_X_names.extend([deriv_tmpl.substitute({'name': comp.name, 'i': str(i), 'type': 'X'}) for i in xrange(0, comp.n_c_controls)])
deriv_R_names.extend([deriv_tmpl.substitute({'name': comp.name, 'i': str(i), 'type': 'R'}) for i in xrange(0, comp.n_c_controls)])
deriv_T_names.extend([deriv_tmpl.substitute({'name': comp.name, 'i': str(i), 'type': 'T'}) for i in xrange(0, comp.n_t_controls)])
var_line += ' '.join(deriv_X_names)
var_line += ' '.join(deriv_R_names)
var_line += ' '.join(deriv_T_names)
lines.append(var_line)
lines.append(('ZONE T = group0, I = %d, J = %d, F=FEPOINT' % (self.n_points, self.n_triangles)))
nx = (3 * self.dXqdC.shape[1])
nr = (3 * self.dYqdCr.shape[1])
nt = (3 * self.dYqdCt.shape[1])
j_cols = ((nx + nr) + nt)
for (i, p) in enumerate(self.points):
line = ('%.8f %.8f %.8f %d ' % (p[0], p[1], p[2], (i + 1)))
deriv_values = np.zeros((j_cols,))
deriv_values[:nx:3] = self.dXqdC[i]
deriv_values[(nx + 1):(nx + nr):3] = self.dYqdCr[i]
deriv_values[(nx + 2):(nx + nr):3] = self.dZqdCr[i]
deriv_values[((nx + nr) + 1)::3] = self.dYqdCt[i]
deriv_values[((nx + nr) + 2)::3] = self.dZqdCt[i]
line += ' '.join(np.char.mod('%.8f', deriv_values))
lines.append(line)
for tri in self.triangles:
line = ('%d %d %d %d' % ((tri[0] + 1), (tri[1] + 1), (tri[2] + 1), (tri[2] + 1)))
lines.append(line)
needs_close = False
if isinstance(stream, basestring):
stream = open(stream, 'w')
needs_close = True
((print >> stream), '\n'.join(lines))
if needs_close:
stream.close()<|docstring|>writes out a new FEPOINT file with the given name, using the supplied points.
derivs is of size (3,len(points),len(control_points)), giving matricies of
X,Y,Z drivatives
jacobian should have a shape of (len(points),len(control_points))<|endoftext|> |
c871307e65e0083e315b3a1d7272819f622f8fdf13ed7bf2898586ab97578234 | def read_corpus():
'读取语料，每行一个json\n '
while True:
with open(corpus_path) as f:
for l in f:
(yield json.loads(l)) | 读取语料，每行一个json | simbert_sim.py | read_corpus | baokui/simbert | 0 | python | def read_corpus():
'\n '
while True:
with open(corpus_path) as f:
for l in f:
(yield json.loads(l)) | def read_corpus():
'\n '
while True:
with open(corpus_path) as f:
for l in f:
(yield json.loads(l))<|docstring|>读取语料，每行一个json<|endoftext|> |
1e7348a5eab099d2db86c8d08fb91ac1269df85f39b3d4c19ec02aa6322236b5 | def truncate(text):
'截断句子\n '
(seps, strips) = (u'\n。！？!?；;，, ', u'；;，, ')
return text_segmentate(text, (maxlen - 2), seps, strips)[0] | 截断句子 | simbert_sim.py | truncate | baokui/simbert | 0 | python | def truncate(text):
'\n '
(seps, strips) = (u'\n。！？!?；;，, ', u'；;，, ')
return text_segmentate(text, (maxlen - 2), seps, strips)[0] | def truncate(text):
'\n '
(seps, strips) = (u'\n。！？!?；;，, ', u'；;，, ')
return text_segmentate(text, (maxlen - 2), seps, strips)[0]<|docstring|>截断句子<|endoftext|> |
7e508cbc47524de233c45cbb92c0624f1eb324eed1c5e0ba6eae83e92493fcb4 | def gen_synonyms(text, n=100, k=20):
'"含义： 产生sent的n个相似句，然后返回最相似的k个。\n 做法：用seq2seq生成，并用encoder算相似度并排序。\n 效果：\n >>> gen_synonyms(u\'微信和支付宝哪个好？\')\n [\n u\'微信和支付宝，哪个好?\',\n u\'微信和支付宝哪个好\',\n u\'支付宝和微信哪个好\',\n u\'支付宝和微信哪个好啊\',\n u\'微信和支付宝那个好用？\',\n u\'微信和支付宝哪个好用\',\n u\'支付宝和微信那个更好\',\n u\'支付宝和微信哪个好用\',\n u\'微信和支付宝用起来哪个好？\',\n u\'微信和支付宝选哪个好\',\n ]\n '
r = synonyms_generator.generate(text, n)
r = [i for i in set(r) if (i != text)]
r = ([text] + r)
(X, S) = ([], [])
for t in r:
(x, s) = tokenizer.encode(t)
X.append(x)
S.append(s)
X = sequence_padding(X)
S = sequence_padding(S)
Z = encoder.predict([X, S])
Z /= ((Z ** 2).sum(axis=1, keepdims=True) ** 0.5)
argsort = np.dot(Z[1:], (- Z[0])).argsort()
return [r[(i + 1)] for i in argsort[:k]] | "含义： 产生sent的n个相似句，然后返回最相似的k个。
做法：用seq2seq生成，并用encoder算相似度并排序。
效果：
>>> gen_synonyms(u'微信和支付宝哪个好？')
[
u'微信和支付宝，哪个好?',
u'微信和支付宝哪个好',
u'支付宝和微信哪个好',
u'支付宝和微信哪个好啊',
u'微信和支付宝那个好用？',
u'微信和支付宝哪个好用',
u'支付宝和微信那个更好',
u'支付宝和微信哪个好用',
u'微信和支付宝用起来哪个好？',
u'微信和支付宝选哪个好',
] | simbert_sim.py | gen_synonyms | baokui/simbert | 0 | python | def gen_synonyms(text, n=100, k=20):
'"含义： 产生sent的n个相似句，然后返回最相似的k个。\n 做法：用seq2seq生成，并用encoder算相似度并排序。\n 效果：\n >>> gen_synonyms(u\'微信和支付宝哪个好？\')\n [\n u\'微信和支付宝，哪个好?\',\n u\'微信和支付宝哪个好\',\n u\'支付宝和微信哪个好\',\n u\'支付宝和微信哪个好啊\',\n u\'微信和支付宝那个好用？\',\n u\'微信和支付宝哪个好用\',\n u\'支付宝和微信那个更好\',\n u\'支付宝和微信哪个好用\',\n u\'微信和支付宝用起来哪个好？\',\n u\'微信和支付宝选哪个好\',\n ]\n '
r = synonyms_generator.generate(text, n)
r = [i for i in set(r) if (i != text)]
r = ([text] + r)
(X, S) = ([], [])
for t in r:
(x, s) = tokenizer.encode(t)
X.append(x)
S.append(s)
X = sequence_padding(X)
S = sequence_padding(S)
Z = encoder.predict([X, S])
Z /= ((Z ** 2).sum(axis=1, keepdims=True) ** 0.5)
argsort = np.dot(Z[1:], (- Z[0])).argsort()
return [r[(i + 1)] for i in argsort[:k]] | def gen_synonyms(text, n=100, k=20):
'"含义： 产生sent的n个相似句，然后返回最相似的k个。\n 做法：用seq2seq生成，并用encoder算相似度并排序。\n 效果：\n >>> gen_synonyms(u\'微信和支付宝哪个好？\')\n [\n u\'微信和支付宝，哪个好?\',\n u\'微信和支付宝哪个好\',\n u\'支付宝和微信哪个好\',\n u\'支付宝和微信哪个好啊\',\n u\'微信和支付宝那个好用？\',\n u\'微信和支付宝哪个好用\',\n u\'支付宝和微信那个更好\',\n u\'支付宝和微信哪个好用\',\n u\'微信和支付宝用起来哪个好？\',\n u\'微信和支付宝选哪个好\',\n ]\n '
r = synonyms_generator.generate(text, n)
r = [i for i in set(r) if (i != text)]
r = ([text] + r)
(X, S) = ([], [])
for t in r:
(x, s) = tokenizer.encode(t)
X.append(x)
S.append(s)
X = sequence_padding(X)
S = sequence_padding(S)
Z = encoder.predict([X, S])
Z /= ((Z ** 2).sum(axis=1, keepdims=True) ** 0.5)
argsort = np.dot(Z[1:], (- Z[0])).argsort()
return [r[(i + 1)] for i in argsort[:k]]<|docstring|>"含义： 产生sent的n个相似句，然后返回最相似的k个。
做法：用seq2seq生成，并用encoder算相似度并排序。
效果：
>>> gen_synonyms(u'微信和支付宝哪个好？')
[
u'微信和支付宝，哪个好?',
u'微信和支付宝哪个好',
u'支付宝和微信哪个好',
u'支付宝和微信哪个好啊',
u'微信和支付宝那个好用？',
u'微信和支付宝哪个好用',
u'支付宝和微信那个更好',
u'支付宝和微信哪个好用',
u'微信和支付宝用起来哪个好？',
u'微信和支付宝选哪个好',
]<|endoftext|> |
f59f99a5db95da73b745c5b952df56506003c1b800f44856d61f5ed2a7a13cd7 | def just_show():
'随机观察一些样本的效果\n '
S = random.sample(TrnData, k=10)
for s in S:
try:
print('###########################')
print('------------------')
print((u'原句子：%s' % s['input']))
print(u'同义句子：')
r = gen_synonyms(s['click'], 10, 10)
for rr in r:
print(rr)
print('------------------')
print((u'原句子：%s' % s['click'][0]))
print(u'同义句子：')
r = gen_synonyms(s['click'][0], 10, 10)
for rr in r:
print(rr)
except:
pass | 随机观察一些样本的效果 | simbert_sim.py | just_show | baokui/simbert | 0 | python | def just_show():
'\n '
S = random.sample(TrnData, k=10)
for s in S:
try:
print('###########################')
print('------------------')
print((u'原句子：%s' % s['input']))
print(u'同义句子：')
r = gen_synonyms(s['click'], 10, 10)
for rr in r:
print(rr)
print('------------------')
print((u'原句子：%s' % s['click'][0]))
print(u'同义句子：')
r = gen_synonyms(s['click'][0], 10, 10)
for rr in r:
print(rr)
except:
pass | def just_show():
'\n '
S = random.sample(TrnData, k=10)
for s in S:
try:
print('###########################')
print('------------------')
print((u'原句子：%s' % s['input']))
print(u'同义句子：')
r = gen_synonyms(s['click'], 10, 10)
for rr in r:
print(rr)
print('------------------')
print((u'原句子：%s' % s['click'][0]))
print(u'同义句子：')
r = gen_synonyms(s['click'][0], 10, 10)
for rr in r:
print(rr)
except:
pass<|docstring|>随机观察一些样本的效果<|endoftext|> |
b8a658d3665433788cf321b0219acf08f3dfdf610eedd6f775df0c71c33e922f | def __init__(__self__, *, vpc_id: pulumi.Input[str], assign_ipv6_address_on_creation: Optional[pulumi.Input[bool]]=None, availability_zone: Optional[pulumi.Input[str]]=None, availability_zone_id: Optional[pulumi.Input[str]]=None, cidr_block: Optional[pulumi.Input[str]]=None, enable_dns64: Optional[pulumi.Input[bool]]=None, ipv6_cidr_block: Optional[pulumi.Input[str]]=None, ipv6_native: Optional[pulumi.Input[bool]]=None, map_public_ip_on_launch: Optional[pulumi.Input[bool]]=None, outpost_arn: Optional[pulumi.Input[str]]=None, private_dns_name_options_on_launch: Optional[pulumi.Input['PrivateDnsNameOptionsOnLaunchPropertiesArgs']]=None, tags: Optional[pulumi.Input[Sequence[pulumi.Input['SubnetTagArgs']]]]=None):
'\n The set of arguments for constructing a Subnet resource.\n '
pulumi.set(__self__, 'vpc_id', vpc_id)
if (assign_ipv6_address_on_creation is not None):
pulumi.set(__self__, 'assign_ipv6_address_on_creation', assign_ipv6_address_on_creation)
if (availability_zone is not None):
pulumi.set(__self__, 'availability_zone', availability_zone)
if (availability_zone_id is not None):
pulumi.set(__self__, 'availability_zone_id', availability_zone_id)
if (cidr_block is not None):
pulumi.set(__self__, 'cidr_block', cidr_block)
if (enable_dns64 is not None):
pulumi.set(__self__, 'enable_dns64', enable_dns64)
if (ipv6_cidr_block is not None):
pulumi.set(__self__, 'ipv6_cidr_block', ipv6_cidr_block)
if (ipv6_native is not None):
pulumi.set(__self__, 'ipv6_native', ipv6_native)
if (map_public_ip_on_launch is not None):
pulumi.set(__self__, 'map_public_ip_on_launch', map_public_ip_on_launch)
if (outpost_arn is not None):
pulumi.set(__self__, 'outpost_arn', outpost_arn)
if (private_dns_name_options_on_launch is not None):
pulumi.set(__self__, 'private_dns_name_options_on_launch', private_dns_name_options_on_launch)
if (tags is not None):
pulumi.set(__self__, 'tags', tags) | The set of arguments for constructing a Subnet resource. | sdk/python/pulumi_aws_native/ec2/subnet.py | __init__ | pulumi/pulumi-aws-native | 29 | python | def __init__(__self__, *, vpc_id: pulumi.Input[str], assign_ipv6_address_on_creation: Optional[pulumi.Input[bool]]=None, availability_zone: Optional[pulumi.Input[str]]=None, availability_zone_id: Optional[pulumi.Input[str]]=None, cidr_block: Optional[pulumi.Input[str]]=None, enable_dns64: Optional[pulumi.Input[bool]]=None, ipv6_cidr_block: Optional[pulumi.Input[str]]=None, ipv6_native: Optional[pulumi.Input[bool]]=None, map_public_ip_on_launch: Optional[pulumi.Input[bool]]=None, outpost_arn: Optional[pulumi.Input[str]]=None, private_dns_name_options_on_launch: Optional[pulumi.Input['PrivateDnsNameOptionsOnLaunchPropertiesArgs']]=None, tags: Optional[pulumi.Input[Sequence[pulumi.Input['SubnetTagArgs']]]]=None):
'\n \n '
pulumi.set(__self__, 'vpc_id', vpc_id)
if (assign_ipv6_address_on_creation is not None):
pulumi.set(__self__, 'assign_ipv6_address_on_creation', assign_ipv6_address_on_creation)
if (availability_zone is not None):
pulumi.set(__self__, 'availability_zone', availability_zone)
if (availability_zone_id is not None):
pulumi.set(__self__, 'availability_zone_id', availability_zone_id)
if (cidr_block is not None):
pulumi.set(__self__, 'cidr_block', cidr_block)
if (enable_dns64 is not None):
pulumi.set(__self__, 'enable_dns64', enable_dns64)
if (ipv6_cidr_block is not None):
pulumi.set(__self__, 'ipv6_cidr_block', ipv6_cidr_block)
if (ipv6_native is not None):
pulumi.set(__self__, 'ipv6_native', ipv6_native)
if (map_public_ip_on_launch is not None):
pulumi.set(__self__, 'map_public_ip_on_launch', map_public_ip_on_launch)
if (outpost_arn is not None):
pulumi.set(__self__, 'outpost_arn', outpost_arn)
if (private_dns_name_options_on_launch is not None):
pulumi.set(__self__, 'private_dns_name_options_on_launch', private_dns_name_options_on_launch)
if (tags is not None):
pulumi.set(__self__, 'tags', tags) | def __init__(__self__, *, vpc_id: pulumi.Input[str], assign_ipv6_address_on_creation: Optional[pulumi.Input[bool]]=None, availability_zone: Optional[pulumi.Input[str]]=None, availability_zone_id: Optional[pulumi.Input[str]]=None, cidr_block: Optional[pulumi.Input[str]]=None, enable_dns64: Optional[pulumi.Input[bool]]=None, ipv6_cidr_block: Optional[pulumi.Input[str]]=None, ipv6_native: Optional[pulumi.Input[bool]]=None, map_public_ip_on_launch: Optional[pulumi.Input[bool]]=None, outpost_arn: Optional[pulumi.Input[str]]=None, private_dns_name_options_on_launch: Optional[pulumi.Input['PrivateDnsNameOptionsOnLaunchPropertiesArgs']]=None, tags: Optional[pulumi.Input[Sequence[pulumi.Input['SubnetTagArgs']]]]=None):
'\n \n '
pulumi.set(__self__, 'vpc_id', vpc_id)
if (assign_ipv6_address_on_creation is not None):
pulumi.set(__self__, 'assign_ipv6_address_on_creation', assign_ipv6_address_on_creation)
if (availability_zone is not None):
pulumi.set(__self__, 'availability_zone', availability_zone)
if (availability_zone_id is not None):
pulumi.set(__self__, 'availability_zone_id', availability_zone_id)
if (cidr_block is not None):
pulumi.set(__self__, 'cidr_block', cidr_block)
if (enable_dns64 is not None):
pulumi.set(__self__, 'enable_dns64', enable_dns64)
if (ipv6_cidr_block is not None):
pulumi.set(__self__, 'ipv6_cidr_block', ipv6_cidr_block)
if (ipv6_native is not None):
pulumi.set(__self__, 'ipv6_native', ipv6_native)
if (map_public_ip_on_launch is not None):
pulumi.set(__self__, 'map_public_ip_on_launch', map_public_ip_on_launch)
if (outpost_arn is not None):
pulumi.set(__self__, 'outpost_arn', outpost_arn)
if (private_dns_name_options_on_launch is not None):
pulumi.set(__self__, 'private_dns_name_options_on_launch', private_dns_name_options_on_launch)
if (tags is not None):
pulumi.set(__self__, 'tags', tags)<|docstring|>The set of arguments for constructing a Subnet resource.<|endoftext|> |
5393f4d8eb647a36fd4907361ce1dc12369303a9709eabff58c05c92ec331efa | @overload
def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions]=None, assign_ipv6_address_on_creation: Optional[pulumi.Input[bool]]=None, availability_zone: Optional[pulumi.Input[str]]=None, availability_zone_id: Optional[pulumi.Input[str]]=None, cidr_block: Optional[pulumi.Input[str]]=None, enable_dns64: Optional[pulumi.Input[bool]]=None, ipv6_cidr_block: Optional[pulumi.Input[str]]=None, ipv6_native: Optional[pulumi.Input[bool]]=None, map_public_ip_on_launch: Optional[pulumi.Input[bool]]=None, outpost_arn: Optional[pulumi.Input[str]]=None, private_dns_name_options_on_launch: Optional[pulumi.Input[pulumi.InputType['PrivateDnsNameOptionsOnLaunchPropertiesArgs']]]=None, tags: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['SubnetTagArgs']]]]]=None, vpc_id: Optional[pulumi.Input[str]]=None, __props__=None):
'\n Resource Type definition for AWS::EC2::Subnet\n\n :param str resource_name: The name of the resource.\n :param pulumi.ResourceOptions opts: Options for the resource.\n '
... | Resource Type definition for AWS::EC2::Subnet
:param str resource_name: The name of the resource.
:param pulumi.ResourceOptions opts: Options for the resource. | sdk/python/pulumi_aws_native/ec2/subnet.py | __init__ | pulumi/pulumi-aws-native | 29 | python | @overload
def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions]=None, assign_ipv6_address_on_creation: Optional[pulumi.Input[bool]]=None, availability_zone: Optional[pulumi.Input[str]]=None, availability_zone_id: Optional[pulumi.Input[str]]=None, cidr_block: Optional[pulumi.Input[str]]=None, enable_dns64: Optional[pulumi.Input[bool]]=None, ipv6_cidr_block: Optional[pulumi.Input[str]]=None, ipv6_native: Optional[pulumi.Input[bool]]=None, map_public_ip_on_launch: Optional[pulumi.Input[bool]]=None, outpost_arn: Optional[pulumi.Input[str]]=None, private_dns_name_options_on_launch: Optional[pulumi.Input[pulumi.InputType['PrivateDnsNameOptionsOnLaunchPropertiesArgs']]]=None, tags: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['SubnetTagArgs']]]]]=None, vpc_id: Optional[pulumi.Input[str]]=None, __props__=None):
'\n Resource Type definition for AWS::EC2::Subnet\n\n :param str resource_name: The name of the resource.\n :param pulumi.ResourceOptions opts: Options for the resource.\n '
... | @overload
def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions]=None, assign_ipv6_address_on_creation: Optional[pulumi.Input[bool]]=None, availability_zone: Optional[pulumi.Input[str]]=None, availability_zone_id: Optional[pulumi.Input[str]]=None, cidr_block: Optional[pulumi.Input[str]]=None, enable_dns64: Optional[pulumi.Input[bool]]=None, ipv6_cidr_block: Optional[pulumi.Input[str]]=None, ipv6_native: Optional[pulumi.Input[bool]]=None, map_public_ip_on_launch: Optional[pulumi.Input[bool]]=None, outpost_arn: Optional[pulumi.Input[str]]=None, private_dns_name_options_on_launch: Optional[pulumi.Input[pulumi.InputType['PrivateDnsNameOptionsOnLaunchPropertiesArgs']]]=None, tags: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['SubnetTagArgs']]]]]=None, vpc_id: Optional[pulumi.Input[str]]=None, __props__=None):
'\n Resource Type definition for AWS::EC2::Subnet\n\n :param str resource_name: The name of the resource.\n :param pulumi.ResourceOptions opts: Options for the resource.\n '
...<|docstring|>Resource Type definition for AWS::EC2::Subnet
:param str resource_name: The name of the resource.
:param pulumi.ResourceOptions opts: Options for the resource.<|endoftext|> |
40e9c9542b6ddcc67d348ce0bd2d5d79bbf7e648aa338c5c43c85cce55317580 | @overload
def __init__(__self__, resource_name: str, args: SubnetArgs, opts: Optional[pulumi.ResourceOptions]=None):
"\n Resource Type definition for AWS::EC2::Subnet\n\n :param str resource_name: The name of the resource.\n :param SubnetArgs args: The arguments to use to populate this resource's properties.\n :param pulumi.ResourceOptions opts: Options for the resource.\n "
... | Resource Type definition for AWS::EC2::Subnet
:param str resource_name: The name of the resource.
:param SubnetArgs args: The arguments to use to populate this resource's properties.
:param pulumi.ResourceOptions opts: Options for the resource. | sdk/python/pulumi_aws_native/ec2/subnet.py | __init__ | pulumi/pulumi-aws-native | 29 | python | @overload
def __init__(__self__, resource_name: str, args: SubnetArgs, opts: Optional[pulumi.ResourceOptions]=None):
"\n Resource Type definition for AWS::EC2::Subnet\n\n :param str resource_name: The name of the resource.\n :param SubnetArgs args: The arguments to use to populate this resource's properties.\n :param pulumi.ResourceOptions opts: Options for the resource.\n "
... | @overload
def __init__(__self__, resource_name: str, args: SubnetArgs, opts: Optional[pulumi.ResourceOptions]=None):
"\n Resource Type definition for AWS::EC2::Subnet\n\n :param str resource_name: The name of the resource.\n :param SubnetArgs args: The arguments to use to populate this resource's properties.\n :param pulumi.ResourceOptions opts: Options for the resource.\n "
...<|docstring|>Resource Type definition for AWS::EC2::Subnet
:param str resource_name: The name of the resource.
:param SubnetArgs args: The arguments to use to populate this resource's properties.
:param pulumi.ResourceOptions opts: Options for the resource.<|endoftext|> |
860218a028505098e8be91473c27b8e76f21a1702fc4cfafa24e5118e426df09 | @staticmethod
def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions]=None) -> 'Subnet':
"\n Get an existing Subnet resource's state with the given name, id, and optional extra\n properties used to qualify the lookup.\n\n :param str resource_name: The unique name of the resulting resource.\n :param pulumi.Input[str] id: The unique provider ID of the resource to lookup.\n :param pulumi.ResourceOptions opts: Options for the resource.\n "
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = SubnetArgs.__new__(SubnetArgs)
__props__.__dict__['assign_ipv6_address_on_creation'] = None
__props__.__dict__['availability_zone'] = None
__props__.__dict__['availability_zone_id'] = None
__props__.__dict__['cidr_block'] = None
__props__.__dict__['enable_dns64'] = None
__props__.__dict__['ipv6_cidr_block'] = None
__props__.__dict__['ipv6_cidr_blocks'] = None
__props__.__dict__['ipv6_native'] = None
__props__.__dict__['map_public_ip_on_launch'] = None
__props__.__dict__['network_acl_association_id'] = None
__props__.__dict__['outpost_arn'] = None
__props__.__dict__['private_dns_name_options_on_launch'] = None
__props__.__dict__['subnet_id'] = None
__props__.__dict__['tags'] = None
__props__.__dict__['vpc_id'] = None
return Subnet(resource_name, opts=opts, __props__=__props__) | Get an existing Subnet resource's state with the given name, id, and optional extra
properties used to qualify the lookup.
:param str resource_name: The unique name of the resulting resource.
:param pulumi.Input[str] id: The unique provider ID of the resource to lookup.
:param pulumi.ResourceOptions opts: Options for the resource. | sdk/python/pulumi_aws_native/ec2/subnet.py | get | pulumi/pulumi-aws-native | 29 | python | @staticmethod
def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions]=None) -> 'Subnet':
"\n Get an existing Subnet resource's state with the given name, id, and optional extra\n properties used to qualify the lookup.\n\n :param str resource_name: The unique name of the resulting resource.\n :param pulumi.Input[str] id: The unique provider ID of the resource to lookup.\n :param pulumi.ResourceOptions opts: Options for the resource.\n "
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = SubnetArgs.__new__(SubnetArgs)
__props__.__dict__['assign_ipv6_address_on_creation'] = None
__props__.__dict__['availability_zone'] = None
__props__.__dict__['availability_zone_id'] = None
__props__.__dict__['cidr_block'] = None
__props__.__dict__['enable_dns64'] = None
__props__.__dict__['ipv6_cidr_block'] = None
__props__.__dict__['ipv6_cidr_blocks'] = None
__props__.__dict__['ipv6_native'] = None
__props__.__dict__['map_public_ip_on_launch'] = None
__props__.__dict__['network_acl_association_id'] = None
__props__.__dict__['outpost_arn'] = None
__props__.__dict__['private_dns_name_options_on_launch'] = None
__props__.__dict__['subnet_id'] = None
__props__.__dict__['tags'] = None
__props__.__dict__['vpc_id'] = None
return Subnet(resource_name, opts=opts, __props__=__props__) | @staticmethod
def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions]=None) -> 'Subnet':
"\n Get an existing Subnet resource's state with the given name, id, and optional extra\n properties used to qualify the lookup.\n\n :param str resource_name: The unique name of the resulting resource.\n :param pulumi.Input[str] id: The unique provider ID of the resource to lookup.\n :param pulumi.ResourceOptions opts: Options for the resource.\n "
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = SubnetArgs.__new__(SubnetArgs)
__props__.__dict__['assign_ipv6_address_on_creation'] = None
__props__.__dict__['availability_zone'] = None
__props__.__dict__['availability_zone_id'] = None
__props__.__dict__['cidr_block'] = None
__props__.__dict__['enable_dns64'] = None
__props__.__dict__['ipv6_cidr_block'] = None
__props__.__dict__['ipv6_cidr_blocks'] = None
__props__.__dict__['ipv6_native'] = None
__props__.__dict__['map_public_ip_on_launch'] = None
__props__.__dict__['network_acl_association_id'] = None
__props__.__dict__['outpost_arn'] = None
__props__.__dict__['private_dns_name_options_on_launch'] = None
__props__.__dict__['subnet_id'] = None
__props__.__dict__['tags'] = None
__props__.__dict__['vpc_id'] = None
return Subnet(resource_name, opts=opts, __props__=__props__)<|docstring|>Get an existing Subnet resource's state with the given name, id, and optional extra
properties used to qualify the lookup.
:param str resource_name: The unique name of the resulting resource.
:param pulumi.Input[str] id: The unique provider ID of the resource to lookup.
:param pulumi.ResourceOptions opts: Options for the resource.<|endoftext|> |
a9cdf47e586f64d1121407f57c9c5928c4754ed33b5cf70acd553cad78e8e102 | @pytest.fixture(name='simple_sim')
def sim_fixt(tmp_path):
'Pytest fixture for basic simulation class'
dic = {'Grid': {'N': 2, 'r_min': 0, 'r_max': 1}, 'Clock': {'start_time': 0, 'end_time': 10, 'num_steps': 100}, 'Tools': {'ExampleTool': [{'custom_name': 'example'}, {'custom_name': 'example2'}]}, 'PhysicsModules': {'ExampleModule': {}}, 'Diagnostics': {'directory': f'{tmp_path}/default_output', 'clock': {}, 'ExampleDiagnostic': [{}, {}]}}
return Simulation(dic) | Pytest fixture for basic simulation class | tests/test_core.py | sim_fixt | kpf59/turbopy | 0 | python | @pytest.fixture(name='simple_sim')
def sim_fixt(tmp_path):
dic = {'Grid': {'N': 2, 'r_min': 0, 'r_max': 1}, 'Clock': {'start_time': 0, 'end_time': 10, 'num_steps': 100}, 'Tools': {'ExampleTool': [{'custom_name': 'example'}, {'custom_name': 'example2'}]}, 'PhysicsModules': {'ExampleModule': {}}, 'Diagnostics': {'directory': f'{tmp_path}/default_output', 'clock': {}, 'ExampleDiagnostic': [{}, {}]}}
return Simulation(dic) | @pytest.fixture(name='simple_sim')
def sim_fixt(tmp_path):
dic = {'Grid': {'N': 2, 'r_min': 0, 'r_max': 1}, 'Clock': {'start_time': 0, 'end_time': 10, 'num_steps': 100}, 'Tools': {'ExampleTool': [{'custom_name': 'example'}, {'custom_name': 'example2'}]}, 'PhysicsModules': {'ExampleModule': {}}, 'Diagnostics': {'directory': f'{tmp_path}/default_output', 'clock': {}, 'ExampleDiagnostic': [{}, {}]}}
return Simulation(dic)<|docstring|>Pytest fixture for basic simulation class<|endoftext|> |
ce514e70506a31ff3a1145ee3a83c0afe305b2020bd5b6f41c251bc529c59d26 | def test_simulation_init_should_create_class_instance_when_called(simple_sim, tmp_path):
'Test init method for Simulation class'
assert (simple_sim.physics_modules == [])
assert (simple_sim.compute_tools == [])
assert (simple_sim.diagnostics == [])
assert (simple_sim.grid is None)
assert (simple_sim.clock is None)
assert (simple_sim.units is None)
dic = {'Grid': {'N': 2, 'r_min': 0, 'r_max': 1}, 'Clock': {'start_time': 0, 'end_time': 10, 'num_steps': 100}, 'Tools': {'ExampleTool': [{'custom_name': 'example'}, {'custom_name': 'example2'}]}, 'PhysicsModules': {'ExampleModule': {}}, 'Diagnostics': {'directory': f'{tmp_path}/default_output', 'clock': {}, 'ExampleDiagnostic': [{}, {}]}}
assert (simple_sim.input_data == dic) | Test init method for Simulation class | tests/test_core.py | test_simulation_init_should_create_class_instance_when_called | kpf59/turbopy | 0 | python | def test_simulation_init_should_create_class_instance_when_called(simple_sim, tmp_path):
assert (simple_sim.physics_modules == [])
assert (simple_sim.compute_tools == [])
assert (simple_sim.diagnostics == [])
assert (simple_sim.grid is None)
assert (simple_sim.clock is None)
assert (simple_sim.units is None)
dic = {'Grid': {'N': 2, 'r_min': 0, 'r_max': 1}, 'Clock': {'start_time': 0, 'end_time': 10, 'num_steps': 100}, 'Tools': {'ExampleTool': [{'custom_name': 'example'}, {'custom_name': 'example2'}]}, 'PhysicsModules': {'ExampleModule': {}}, 'Diagnostics': {'directory': f'{tmp_path}/default_output', 'clock': {}, 'ExampleDiagnostic': [{}, {}]}}
assert (simple_sim.input_data == dic) | def test_simulation_init_should_create_class_instance_when_called(simple_sim, tmp_path):
assert (simple_sim.physics_modules == [])
assert (simple_sim.compute_tools == [])
assert (simple_sim.diagnostics == [])
assert (simple_sim.grid is None)
assert (simple_sim.clock is None)
assert (simple_sim.units is None)
dic = {'Grid': {'N': 2, 'r_min': 0, 'r_max': 1}, 'Clock': {'start_time': 0, 'end_time': 10, 'num_steps': 100}, 'Tools': {'ExampleTool': [{'custom_name': 'example'}, {'custom_name': 'example2'}]}, 'PhysicsModules': {'ExampleModule': {}}, 'Diagnostics': {'directory': f'{tmp_path}/default_output', 'clock': {}, 'ExampleDiagnostic': [{}, {}]}}
assert (simple_sim.input_data == dic)<|docstring|>Test init method for Simulation class<|endoftext|> |
ac3ddd15502e63e0fd74c58d2b882e59239f5eb7248fe8b28f09730d991007b4 | def test_read_grid_from_input_should_set_grid_attr_when_called(simple_sim):
'Test read_grid_from_input method in Simulation class'
simple_sim.read_grid_from_input()
assert (simple_sim.grid.num_points == 2)
assert (simple_sim.grid.r_min == 0)
assert (simple_sim.grid.r_max == 1) | Test read_grid_from_input method in Simulation class | tests/test_core.py | test_read_grid_from_input_should_set_grid_attr_when_called | kpf59/turbopy | 0 | python | def test_read_grid_from_input_should_set_grid_attr_when_called(simple_sim):
simple_sim.read_grid_from_input()
assert (simple_sim.grid.num_points == 2)
assert (simple_sim.grid.r_min == 0)
assert (simple_sim.grid.r_max == 1) | def test_read_grid_from_input_should_set_grid_attr_when_called(simple_sim):
simple_sim.read_grid_from_input()
assert (simple_sim.grid.num_points == 2)
assert (simple_sim.grid.r_min == 0)
assert (simple_sim.grid.r_max == 1)<|docstring|>Test read_grid_from_input method in Simulation class<|endoftext|> |
6385a4b251f5f8b667060a6310dced8e880a5fcbc4aec6e87505fb58d2dc76c1 | def test_gridless_simulation(tmp_path):
'Test a gridless simulation'
dic = {'Clock': {'start_time': 0, 'end_time': 10, 'num_steps': 100}, 'Tools': {'ExampleTool': [{'custom_name': 'example'}, {'custom_name': 'example2'}]}, 'PhysicsModules': {'ExampleModule': {}}, 'Diagnostics': {'directory': f'{tmp_path}/default_output', 'clock': {}, 'ExampleDiagnostic': [{}, {}]}}
with warnings.catch_warnings(record=True) as w:
sim = Simulation(dic)
sim.run()
assert (sim.clock is not None)
assert (sim.grid is None)
assert (len(w) == 1)
assert (str(w[(- 1)].message) == 'No Grid Found.') | Test a gridless simulation | tests/test_core.py | test_gridless_simulation | kpf59/turbopy | 0 | python | def test_gridless_simulation(tmp_path):
dic = {'Clock': {'start_time': 0, 'end_time': 10, 'num_steps': 100}, 'Tools': {'ExampleTool': [{'custom_name': 'example'}, {'custom_name': 'example2'}]}, 'PhysicsModules': {'ExampleModule': {}}, 'Diagnostics': {'directory': f'{tmp_path}/default_output', 'clock': {}, 'ExampleDiagnostic': [{}, {}]}}
with warnings.catch_warnings(record=True) as w:
sim = Simulation(dic)
sim.run()
assert (sim.clock is not None)
assert (sim.grid is None)
assert (len(w) == 1)
assert (str(w[(- 1)].message) == 'No Grid Found.') | def test_gridless_simulation(tmp_path):
dic = {'Clock': {'start_time': 0, 'end_time': 10, 'num_steps': 100}, 'Tools': {'ExampleTool': [{'custom_name': 'example'}, {'custom_name': 'example2'}]}, 'PhysicsModules': {'ExampleModule': {}}, 'Diagnostics': {'directory': f'{tmp_path}/default_output', 'clock': {}, 'ExampleDiagnostic': [{}, {}]}}
with warnings.catch_warnings(record=True) as w:
sim = Simulation(dic)
sim.run()
assert (sim.clock is not None)
assert (sim.grid is None)
assert (len(w) == 1)
assert (str(w[(- 1)].message) == 'No Grid Found.')<|docstring|>Test a gridless simulation<|endoftext|> |
9753007957265bd15a9565312cf39a26f82144f36ffe6a7d8158928d6f7be499 | def test_read_clock_from_input_should_set_clock_attr_when_called(simple_sim):
'Test read_clock_from_input method in Simulation class'
simple_sim.read_clock_from_input()
assert (simple_sim.clock._owner == simple_sim)
assert (simple_sim.clock.start_time == 0)
assert (simple_sim.clock.time == 0)
assert (simple_sim.clock.end_time == 10)
assert (simple_sim.clock.this_step == 0)
assert (simple_sim.clock.print_time is False)
assert (simple_sim.clock.num_steps == 100)
assert (simple_sim.clock.dt == 0.1)
dic = {'Grid': {'N': 2, 'r_min': 0, 'r_max': 1}, 'Clock': {'start_time': 0, 'end_time': 10, 'dt': 0.2, 'print_time': True}}
other_sim = Simulation(dic)
other_sim.read_clock_from_input()
assert (other_sim.clock.dt == 0.2)
assert (other_sim.clock.num_steps == 50)
assert (other_sim.clock.print_time is True) | Test read_clock_from_input method in Simulation class | tests/test_core.py | test_read_clock_from_input_should_set_clock_attr_when_called | kpf59/turbopy | 0 | python | def test_read_clock_from_input_should_set_clock_attr_when_called(simple_sim):
simple_sim.read_clock_from_input()
assert (simple_sim.clock._owner == simple_sim)
assert (simple_sim.clock.start_time == 0)
assert (simple_sim.clock.time == 0)
assert (simple_sim.clock.end_time == 10)
assert (simple_sim.clock.this_step == 0)
assert (simple_sim.clock.print_time is False)
assert (simple_sim.clock.num_steps == 100)
assert (simple_sim.clock.dt == 0.1)
dic = {'Grid': {'N': 2, 'r_min': 0, 'r_max': 1}, 'Clock': {'start_time': 0, 'end_time': 10, 'dt': 0.2, 'print_time': True}}
other_sim = Simulation(dic)
other_sim.read_clock_from_input()
assert (other_sim.clock.dt == 0.2)
assert (other_sim.clock.num_steps == 50)
assert (other_sim.clock.print_time is True) | def test_read_clock_from_input_should_set_clock_attr_when_called(simple_sim):
simple_sim.read_clock_from_input()
assert (simple_sim.clock._owner == simple_sim)
assert (simple_sim.clock.start_time == 0)
assert (simple_sim.clock.time == 0)
assert (simple_sim.clock.end_time == 10)
assert (simple_sim.clock.this_step == 0)
assert (simple_sim.clock.print_time is False)
assert (simple_sim.clock.num_steps == 100)
assert (simple_sim.clock.dt == 0.1)
dic = {'Grid': {'N': 2, 'r_min': 0, 'r_max': 1}, 'Clock': {'start_time': 0, 'end_time': 10, 'dt': 0.2, 'print_time': True}}
other_sim = Simulation(dic)
other_sim.read_clock_from_input()
assert (other_sim.clock.dt == 0.2)
assert (other_sim.clock.num_steps == 50)
assert (other_sim.clock.print_time is True)<|docstring|>Test read_clock_from_input method in Simulation class<|endoftext|> |
0b42e10385c6626de1593ca9d75aa0986cd6f3519d54a64b7fe69e9f474303db | def test_read_tools_from_input_should_set_tools_attr_when_called(simple_sim):
'Test read_tools_from_input method in Simulation class'
simple_sim.read_tools_from_input()
assert (simple_sim.compute_tools[0]._owner == simple_sim)
assert (simple_sim.compute_tools[0]._input_data == {'type': 'ExampleTool', 'custom_name': 'example'})
assert (simple_sim.compute_tools[1]._owner == simple_sim)
assert (simple_sim.compute_tools[1]._input_data == {'type': 'ExampleTool', 'custom_name': 'example2'}) | Test read_tools_from_input method in Simulation class | tests/test_core.py | test_read_tools_from_input_should_set_tools_attr_when_called | kpf59/turbopy | 0 | python | def test_read_tools_from_input_should_set_tools_attr_when_called(simple_sim):
simple_sim.read_tools_from_input()
assert (simple_sim.compute_tools[0]._owner == simple_sim)
assert (simple_sim.compute_tools[0]._input_data == {'type': 'ExampleTool', 'custom_name': 'example'})
assert (simple_sim.compute_tools[1]._owner == simple_sim)
assert (simple_sim.compute_tools[1]._input_data == {'type': 'ExampleTool', 'custom_name': 'example2'}) | def test_read_tools_from_input_should_set_tools_attr_when_called(simple_sim):
simple_sim.read_tools_from_input()
assert (simple_sim.compute_tools[0]._owner == simple_sim)
assert (simple_sim.compute_tools[0]._input_data == {'type': 'ExampleTool', 'custom_name': 'example'})
assert (simple_sim.compute_tools[1]._owner == simple_sim)
assert (simple_sim.compute_tools[1]._input_data == {'type': 'ExampleTool', 'custom_name': 'example2'})<|docstring|>Test read_tools_from_input method in Simulation class<|endoftext|> |
e8716d4bd20a853cbaf1e0c4dfccac1bfc2c7e924a74ca03eee7385606154cc9 | def test_fundamental_cycle_should_advance_clock_when_called(simple_sim):
'Test fundamental_cycle method in Simulation class'
simple_sim.read_clock_from_input()
simple_sim.fundamental_cycle()
assert (simple_sim.clock.this_step == 1)
assert (simple_sim.clock.time == 0.1) | Test fundamental_cycle method in Simulation class | tests/test_core.py | test_fundamental_cycle_should_advance_clock_when_called | kpf59/turbopy | 0 | python | def test_fundamental_cycle_should_advance_clock_when_called(simple_sim):
simple_sim.read_clock_from_input()
simple_sim.fundamental_cycle()
assert (simple_sim.clock.this_step == 1)
assert (simple_sim.clock.time == 0.1) | def test_fundamental_cycle_should_advance_clock_when_called(simple_sim):
simple_sim.read_clock_from_input()
simple_sim.fundamental_cycle()
assert (simple_sim.clock.this_step == 1)
assert (simple_sim.clock.time == 0.1)<|docstring|>Test fundamental_cycle method in Simulation class<|endoftext|> |
7baab71f04d314290fbd740cad044e2ef2ed6b7aba2a0dd75a4e6de1c8fee11d | def test_run_should_run_simulation_while_clock_is_running(simple_sim):
'Test run method in Simulation class'
simple_sim.run()
assert (simple_sim.clock.this_step == 100)
assert (simple_sim.clock.time == 10) | Test run method in Simulation class | tests/test_core.py | test_run_should_run_simulation_while_clock_is_running | kpf59/turbopy | 0 | python | def test_run_should_run_simulation_while_clock_is_running(simple_sim):
simple_sim.run()
assert (simple_sim.clock.this_step == 100)
assert (simple_sim.clock.time == 10) | def test_run_should_run_simulation_while_clock_is_running(simple_sim):
simple_sim.run()
assert (simple_sim.clock.this_step == 100)
assert (simple_sim.clock.time == 10)<|docstring|>Test run method in Simulation class<|endoftext|> |
7c761f8c98641eb8b2ffae88013fbb44b0c612fc83c046cdb41f86328d3a609e | def test_turn_back_should_turn_back_time_when_called(simple_sim):
'Test fundamental_cycle method in Simulation class'
simple_sim.read_clock_from_input()
simple_sim.fundamental_cycle()
assert (simple_sim.clock.this_step == 1)
assert (simple_sim.clock.time == 0.1)
simple_sim.clock.turn_back()
assert (simple_sim.clock.this_step == 0)
assert (simple_sim.clock.time == 0) | Test fundamental_cycle method in Simulation class | tests/test_core.py | test_turn_back_should_turn_back_time_when_called | kpf59/turbopy | 0 | python | def test_turn_back_should_turn_back_time_when_called(simple_sim):
simple_sim.read_clock_from_input()
simple_sim.fundamental_cycle()
assert (simple_sim.clock.this_step == 1)
assert (simple_sim.clock.time == 0.1)
simple_sim.clock.turn_back()
assert (simple_sim.clock.this_step == 0)
assert (simple_sim.clock.time == 0) | def test_turn_back_should_turn_back_time_when_called(simple_sim):
simple_sim.read_clock_from_input()
simple_sim.fundamental_cycle()
assert (simple_sim.clock.this_step == 1)
assert (simple_sim.clock.time == 0.1)
simple_sim.clock.turn_back()
assert (simple_sim.clock.this_step == 0)
assert (simple_sim.clock.time == 0)<|docstring|>Test fundamental_cycle method in Simulation class<|endoftext|> |
f157c4c9e9b1926bcbea6e5c1162010f564f9d906f3980c1240860cfaaa4305b | def test_read_modules_from_input_should_set_modules_attr_when_called(simple_sim):
'Test read_modules_from_input method in Simulation calss'
simple_sim.read_modules_from_input()
assert (simple_sim.physics_modules[0]._owner == simple_sim)
assert (simple_sim.physics_modules[0]._input_data == {'name': 'ExampleModule'}) | Test read_modules_from_input method in Simulation calss | tests/test_core.py | test_read_modules_from_input_should_set_modules_attr_when_called | kpf59/turbopy | 0 | python | def test_read_modules_from_input_should_set_modules_attr_when_called(simple_sim):
simple_sim.read_modules_from_input()
assert (simple_sim.physics_modules[0]._owner == simple_sim)
assert (simple_sim.physics_modules[0]._input_data == {'name': 'ExampleModule'}) | def test_read_modules_from_input_should_set_modules_attr_when_called(simple_sim):
simple_sim.read_modules_from_input()
assert (simple_sim.physics_modules[0]._owner == simple_sim)
assert (simple_sim.physics_modules[0]._input_data == {'name': 'ExampleModule'})<|docstring|>Test read_modules_from_input method in Simulation calss<|endoftext|> |
c582456a7cb6727d4ca58c557abeca02ebd9d416b66c367549499ee06224411c | def test_default_diagnostic_filename_is_generated_if_no_name_specified(simple_sim, tmp_path):
'Test read_diagnostic_from_input method in Simulation class'
simple_sim.read_diagnostics_from_input()
input_data = simple_sim.diagnostics[0]._input_data
assert (input_data['directory'] == str(Path(f'{tmp_path}/default_output')))
assert (input_data['filename'] == str((Path(f'{tmp_path}/default_output') / Path('clock0.out')))) | Test read_diagnostic_from_input method in Simulation class | tests/test_core.py | test_default_diagnostic_filename_is_generated_if_no_name_specified | kpf59/turbopy | 0 | python | def test_default_diagnostic_filename_is_generated_if_no_name_specified(simple_sim, tmp_path):
simple_sim.read_diagnostics_from_input()
input_data = simple_sim.diagnostics[0]._input_data
assert (input_data['directory'] == str(Path(f'{tmp_path}/default_output')))
assert (input_data['filename'] == str((Path(f'{tmp_path}/default_output') / Path('clock0.out')))) | def test_default_diagnostic_filename_is_generated_if_no_name_specified(simple_sim, tmp_path):
simple_sim.read_diagnostics_from_input()
input_data = simple_sim.diagnostics[0]._input_data
assert (input_data['directory'] == str(Path(f'{tmp_path}/default_output')))
assert (input_data['filename'] == str((Path(f'{tmp_path}/default_output') / Path('clock0.out'))))<|docstring|>Test read_diagnostic_from_input method in Simulation class<|endoftext|> |
ec8bf6b1776371d50dcd65efc62dbd843dcb27dda5380eda6350653725957d33 | def test_default_diagnostic_filename_increments_for_multiple_diagnostics(simple_sim, tmp_path):
'Test read_diagnostic_from_input method in Simulation class'
simple_sim.read_diagnostics_from_input()
assert (simple_sim.diagnostics[0]._input_data['directory'] == str(Path(f'{tmp_path}/default_output')))
assert (simple_sim.diagnostics[0]._input_data['filename'] == str((Path(f'{tmp_path}/default_output') / Path('clock0.out'))))
input_data = simple_sim.diagnostics[2]._input_data
assert (input_data['directory'] == str(Path(f'{tmp_path}/default_output')))
assert (input_data['filename'] == str((Path(f'{tmp_path}/default_output') / Path('ExampleDiagnostic1.out')))) | Test read_diagnostic_from_input method in Simulation class | tests/test_core.py | test_default_diagnostic_filename_increments_for_multiple_diagnostics | kpf59/turbopy | 0 | python | def test_default_diagnostic_filename_increments_for_multiple_diagnostics(simple_sim, tmp_path):
simple_sim.read_diagnostics_from_input()
assert (simple_sim.diagnostics[0]._input_data['directory'] == str(Path(f'{tmp_path}/default_output')))
assert (simple_sim.diagnostics[0]._input_data['filename'] == str((Path(f'{tmp_path}/default_output') / Path('clock0.out'))))
input_data = simple_sim.diagnostics[2]._input_data
assert (input_data['directory'] == str(Path(f'{tmp_path}/default_output')))
assert (input_data['filename'] == str((Path(f'{tmp_path}/default_output') / Path('ExampleDiagnostic1.out')))) | def test_default_diagnostic_filename_increments_for_multiple_diagnostics(simple_sim, tmp_path):
simple_sim.read_diagnostics_from_input()
assert (simple_sim.diagnostics[0]._input_data['directory'] == str(Path(f'{tmp_path}/default_output')))
assert (simple_sim.diagnostics[0]._input_data['filename'] == str((Path(f'{tmp_path}/default_output') / Path('clock0.out'))))
input_data = simple_sim.diagnostics[2]._input_data
assert (input_data['directory'] == str(Path(f'{tmp_path}/default_output')))
assert (input_data['filename'] == str((Path(f'{tmp_path}/default_output') / Path('ExampleDiagnostic1.out'))))<|docstring|>Test read_diagnostic_from_input method in Simulation class<|endoftext|> |
c410c9e7bd30ce1e65e3fb203df5b65d1409dcd42cd01b7ecd63972072b62cb5 | @pytest.fixture(name='simple_grid')
def grid_conf():
'Pytest fixture for grid configuration dictionary'
grid = {'N': 8, 'r_min': 0, 'r_max': 0.1}
return Grid(grid) | Pytest fixture for grid configuration dictionary | tests/test_core.py | grid_conf | kpf59/turbopy | 0 | python | @pytest.fixture(name='simple_grid')
def grid_conf():
grid = {'N': 8, 'r_min': 0, 'r_max': 0.1}
return Grid(grid) | @pytest.fixture(name='simple_grid')
def grid_conf():
grid = {'N': 8, 'r_min': 0, 'r_max': 0.1}
return Grid(grid)<|docstring|>Pytest fixture for grid configuration dictionary<|endoftext|> |
b5532f3e01c51dca5485ed938eeb3589408e45e4973c6df734e676125af8e33e | def test_grid_init(simple_grid):
'Test initialization of the Grid class'
assert (simple_grid.r_min == 0.0)
assert (simple_grid.r_max == 0.1) | Test initialization of the Grid class | tests/test_core.py | test_grid_init | kpf59/turbopy | 0 | python | def test_grid_init(simple_grid):
assert (simple_grid.r_min == 0.0)
assert (simple_grid.r_max == 0.1) | def test_grid_init(simple_grid):
assert (simple_grid.r_min == 0.0)
assert (simple_grid.r_max == 0.1)<|docstring|>Test initialization of the Grid class<|endoftext|> |
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