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afdf1d513ec7d49ebe9b649c6f83a84147d0805b | jeremiahwander/sample-metadata | models/models/sequence.py | [
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] | Python | from_db | <not_specific> | def from_db(d: Dict):
"""Take DB mapping object, and return SampleSequencing"""
type_ = d.pop('type')
status = d.pop('status')
meta = d.pop('meta', None)
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type_ = SequenceType(type_)
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6c867ba7aefcd3c0f9d83b942898af98abbbe65f | jeremiahwander/sample-metadata | test/test_joint_calling_workflow.py | [
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"""
Add 3 samples: one with fastq input, one with CRAM input, one with GVCF input.
:param test_run_id: to suffix sample names for uniqueness
"""
s1 = NewSample(
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... |
Add 3 samples: one with fastq input, one with CRAM input, one with GVCF input.
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6c867ba7aefcd3c0f9d83b942898af98abbbe65f | jeremiahwander/sample-metadata | test/test_joint_calling_workflow.py | [
"MIT"
] | Python | _submit_analyses | null | def _submit_analyses(samples: List, output_project: str, a_type: str):
"""
Add or update analyses. Iterate over completed analyses,
and submit next-step analyses
"""
if a_type in ['gvcf', 'cram']:
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if a_type == 'gvcf':
cram_analysis = aapi.get_la... |
Add or update analyses. Iterate over completed analyses,
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c418ef6cd60b254af636ac049a8378fca2261c40 | jeremiahwander/sample-metadata | api/utils/exceptions.py | [
"MIT"
] | Python | determine_code_from_error | <not_specific> | def determine_code_from_error(e):
"""From error / exception, determine appropriate http code"""
if isinstance(e, NotFoundError):
return 404
if isinstance(e, ValueError):
# HTTP Bad Request
return 400
if isinstance(e, Forbidden):
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9bc413c9c84751a1697097d23aaa6125205faba8 | jeremiahwander/sample-metadata | db/backup/backup.py | [
"MIT"
] | Python | perform_backup | <not_specific> | def perform_backup():
"""Completes a backup of the databases within a local mariadb instance
and uploads this backup to GCS."""
# Logging: Any log with `severity >= ERROR` get's logged to #software-alerts
logging_client = logging.Client()
log_name = 'backup_log'
logger = logging_client.logger(l... | Completes a backup of the databases within a local mariadb instance
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logging_client = logging.Client()
log_name = 'backup_log'
logger = logging_client.logger(log_name)
utc_now = pytz.utc.localize(datetime.utcnow())
timestamp_str = utc_now.strftime('%d_%m_%Y_%H-%M-%S')
tmp_dir = f'backup_{timestamp_str}'
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dcf4c600a84d616ff40e11b299f4981e597f4814 | jeremiahwander/sample-metadata | api/utils/db.py | [
"MIT"
] | Python | authenticate | Optional[str] | def authenticate(
token: Optional[HTTPAuthorizationCredentials] = Depends(auth),
) -> Optional[str]:
"""If a token is provided, return the email, else return None"""
if token:
return email_from_id_token(token.credentials)
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5af71b8d36975d5ce07147b2e6cb1adb702eebee | jeremiahwander/sample-metadata | db/python/tables/project.py | [
"MIT"
] | Python | _read_secret | <not_specific> | def _read_secret(self, project_id: str, secret_name: str):
"""Reads the latest version of a GCP Secret Manager secret.
Returns None if the secret doesn't exist."""
secret_manager = self._get_secret_manager_client()
secret_path = secret_manager.secret_path(project_id, secret_name)
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secret_path = secret_manager.secret_path(project_id, secret_name)
response = secret_manager.access_secret_version(
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5af71b8d36975d5ce07147b2e6cb1adb702eebee | jeremiahwander/sample-metadata | db/python/tables/project.py | [
"MIT"
] | Python | check_access_to_project_ids | bool | async def check_access_to_project_ids(
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user: str,
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raise_exception=True,
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"""Check user has access to list of project_ids"""
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5af71b8d36975d5ce07147b2e6cb1adb702eebee | jeremiahwander/sample-metadata | db/python/tables/project.py | [
"MIT"
] | Python | check_access_to_project_id | bool | async def check_access_to_project_id(
self, user: str, project_id: ProjectId, readonly: bool, raise_exception=True
) -> bool:
"""Check whether a user has access to project_id"""
if self.allow_full_access:
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5af71b8d36975d5ce07147b2e6cb1adb702eebee | jeremiahwander/sample-metadata | db/python/tables/project.py | [
"MIT"
] | Python | ensure_project_id_cache_is_filled | null | async def ensure_project_id_cache_is_filled(self):
"""(CACHED) Get map of project names to project IDs"""
if (
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5114ad3a40063d7f764d53b67353ee28355ef83b | jeremiahwander/sample-metadata | scripts/parse_vcgs_manifest.py | [
"MIT"
] | Python | sequence_meta_map | <not_specific> | def sequence_meta_map():
"""Columns that will be put into sequence.meta"""
fields = [
Columns.LIBRARY_ID,
Columns.LIBRARY_STRATEGY,
Columns.LIBRARY_SOURCE,
Columns.LIBRARY_SELECTION,
Columns.LIBRARY_LAYOUT,
Columns.PLATFORM,
... | Columns that will be put into sequence.meta | Columns that will be put into sequence.meta | [
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"sequence",
".",
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] | def sequence_meta_map():
fields = [
Columns.LIBRARY_ID,
Columns.LIBRARY_STRATEGY,
Columns.LIBRARY_SOURCE,
Columns.LIBRARY_SELECTION,
Columns.LIBRARY_LAYOUT,
Columns.PLATFORM,
Columns.INSTRUMENT_MODEL,
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} |
0c097f4d6d2bbfffb40499628e58feeb485f48af | jeremiahwander/sample-metadata | db/python/tables/participant.py | [
"MIT"
] | Python | create_participant | int | async def create_participant(
self,
external_id: str,
reported_sex: int = None,
reported_gender: str = None,
karyotype: str = None,
meta: Dict = None,
author: str = None,
project: ProjectId = None,
) -> int:
"""
Create a new sample, and... |
Create a new sample, and add it to database
| Create a new sample, and add it to database | [
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self,
external_id: str,
reported_sex: int = None,
reported_gender: str = None,
karyotype: str = None,
meta: Dict = None,
author: str = None,
project: ProjectId = None,
) -> int:
_query = f"""
INSERT INTO participan... | [
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{
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0c097f4d6d2bbfffb40499628e58feeb485f48af | jeremiahwander/sample-metadata | db/python/tables/participant.py | [
"MIT"
] | Python | update_participants | null | async def update_participants(
self,
participant_ids: List[int],
reported_sexes: List[int] = None,
reported_genders: List[str] = None,
karyotypes: List[str] = None,
metas: List[Dict] = None,
author=None,
):
"""
Update many participants, expects... |
Update many participants, expects that all lists contain the same number of values.
You can't update selective fields on selective samples, if you provide metas, this
function will update EVERY participant with the provided meta values.
| Update many participants, expects that all lists contain the same number of values.
You can't update selective fields on selective samples, if you provide metas, this
function will update EVERY participant with the provided meta values. | [
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reported_genders: List[str] = None,
karyotypes: List[str] = None,
metas: List[Dict] = None,
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_author = author or self.author
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0c097f4d6d2bbfffb40499628e58feeb485f48af | jeremiahwander/sample-metadata | db/python/tables/participant.py | [
"MIT"
] | Python | update_many_participant_external_ids | <not_specific> | async def update_many_participant_external_ids(
self, internal_to_external_id: Dict[int, str]
):
"""Update many participant external_ids through the {internal: external} map"""
_query = 'UPDATE participant SET external_id = :external_id WHERE id = :participant_id'
mapped_values = [
... | Update many participant external_ids through the {internal: external} map | Update many participant external_ids through the {internal: external} map | [
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] | async def update_many_participant_external_ids(
self, internal_to_external_id: Dict[int, str]
):
_query = 'UPDATE participant SET external_id = :external_id WHERE id = :participant_id'
mapped_values = [
{'participant_id': k, 'external_id': v}
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37a05e8560dd94b574b02f8588298646544b8b76 | jeremiahwander/sample-metadata | sample_metadata/parser/generic_parser.py | [
"MIT"
] | Python | file_path | str | def file_path(self, filename: str) -> str:
"""
Get complete filepath of filename:
- Includes gs://{bucket} if relevant
- Includes path_prefix decided early on
"""
if filename.startswith('gs://'):
return filename
if self.client and not filename.startsw... |
Get complete filepath of filename:
- Includes gs://{bucket} if relevant
- Includes path_prefix decided early on
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Includes path_prefix decided early on | [
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if filename.startswith('gs://'):
return filename
if self.client and not filename.startswith('/'):
return os.path.join(
'gs://', self.default_bucket or '', self.path_prefix or '', filename or ''
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37a05e8560dd94b574b02f8588298646544b8b76 | jeremiahwander/sample-metadata | sample_metadata/parser/generic_parser.py | [
"MIT"
] | Python | file_contents | Optional[str] | def file_contents(self, filename) -> Optional[str]:
"""Get contents of file (decoded as utf8)"""
path = self.file_path(filename)
if path.startswith('gs://'):
blob = self.get_blob(path)
try:
retval = blob.download_as_string()
if isinstance(r... | Get contents of file (decoded as utf8) | Get contents of file (decoded as utf8) | [
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] | def file_contents(self, filename) -> Optional[str]:
path = self.file_path(filename)
if path.startswith('gs://'):
blob = self.get_blob(path)
try:
retval = blob.download_as_string()
if isinstance(retval, bytes):
retval = retval.de... | [
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37a05e8560dd94b574b02f8588298646544b8b76 | jeremiahwander/sample-metadata | sample_metadata/parser/generic_parser.py | [
"MIT"
] | Python | file_size | <not_specific> | def file_size(self, filename):
"""Get size of file in bytes"""
path = self.file_path(filename)
if path.startswith('gs://'):
blob = self.get_blob(filename)
return blob.size
return os.path.getsize(path) | Get size of file in bytes | Get size of file in bytes | [
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] | def file_size(self, filename):
path = self.file_path(filename)
if path.startswith('gs://'):
blob = self.get_blob(filename)
return blob.size
return os.path.getsize(path) | [
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37a05e8560dd94b574b02f8588298646544b8b76 | jeremiahwander/sample-metadata | sample_metadata/parser/generic_parser.py | [
"MIT"
] | Python | parse_manifest | Union[Dict[str, str], Tuple[List, Dict, Dict, Dict, Dict]] | def parse_manifest( # pylint: disable=too-many-branches
self, file_pointer, delimiter=',', confirm=False, dry_run=False
) -> Union[Dict[str, str], Tuple[List, Dict, Dict, Dict, Dict]]:
"""
Parse manifest from iterable (file pointer / String.IO)
Returns a dict mapping external sampl... |
Parse manifest from iterable (file pointer / String.IO)
Returns a dict mapping external sample ID to CPG sample ID
| Parse manifest from iterable (file pointer / String.IO)
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sample_map = defaultdict(list)
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for row in reader:
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37a05e8560dd94b574b02f8588298646544b8b76 | jeremiahwander/sample-metadata | sample_metadata/parser/generic_parser.py | [
"MIT"
] | Python | create_file_object | Dict[str, Any] | def create_file_object(
self,
filename: str,
secondary_files: List[Dict[str, Any]] = None,
) -> Dict[str, Any]:
"""Takes filename, returns formed CWL dictionary"""
checksum = None
if not self.skip_checking_gcs_objects:
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) -> Dict[str, Any]:
checksum = None
if not self.skip_checking_gcs_objects:
md5_filename = self.file_path(filename + '.md5')
if self.file_exists(md5_filename):
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37a05e8560dd94b574b02f8588298646544b8b76 | jeremiahwander/sample-metadata | sample_metadata/parser/generic_parser.py | [
"MIT"
] | Python | create_secondary_file_objects_by_potential_pattern | List[Dict[str, Any]] | def create_secondary_file_objects_by_potential_pattern(
self, filename, potential_secondary_patterns: List[str]
) -> List[Dict[str, Any]]:
"""
Take a base filename and potential secondary patterns:
- Try each secondary pattern, see if it works
- If it works, create a CWL file... |
Take a base filename and potential secondary patterns:
- Try each secondary pattern, see if it works
- If it works, create a CWL file object
- return a list of those secondary file objects that exist
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b02bade433efb6d0fd7721d08d9e2b04fedcb5ac | jeremiahwander/sample-metadata | scripts/parse_nagim.py | [
"MIT"
] | Python | transfer | null | def transfer(self, hbatch):
"""
Search files in buckets using search patterns and copy to CPG upload buckets
"""
for region, buckets in SRC_BUCKETS[NAMESPACE].items():
for bucket in buckets:
for ending, pattern in self.search_pattern_by_ending.items():
... |
Search files in buckets using search patterns and copy to CPG upload buckets
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for region, buckets in SRC_BUCKETS[NAMESPACE].items():
for bucket in buckets:
for ending, pattern in self.search_pattern_by_ending.items():
_add_batch_job(
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b02bade433efb6d0fd7721d08d9e2b04fedcb5ac | jeremiahwander/sample-metadata | scripts/parse_nagim.py | [
"MIT"
] | Python | transfer | <not_specific> | def transfer(
tmp_dir,
use_batch: bool,
dry_run: bool,
):
"""
Transfer data from the Terra workspaces to the GCP bucket. Must be run with
a personal account, because the read permissions to Terra buckets match
to the Terra user emails for whom the workspace is sharred, so Hail service
ac... |
Transfer data from the Terra workspaces to the GCP bucket. Must be run with
a personal account, because the read permissions to Terra buckets match
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acounts won't work here.
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if not tmp_dir:
tmp_dir = tempfile.gettempdir()
if use_batch:
hbatch = setup_batch(
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keep_scratch=False,
tmp_bucket=f'cpg-{NAGIM_PROJ_ID}-{NAMESPACE}-tmp',
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b02bade433efb6d0fd7721d08d9e2b04fedcb5ac | jeremiahwander/sample-metadata | scripts/parse_nagim.py | [
"MIT"
] | Python | _find_upload_files | null | def _find_upload_files(samples: List[Sample], tmp_dir, overwrite=False):
"""
Populate fields for each sample and verify that every sample has an expected
set of files.
"""
sample_by_sid = {s.nagim_id: s for s in samples}
# Find files
for source_name in SOURCES_TO_PROCESS:
source = S... |
Populate fields for each sample and verify that every sample has an expected
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] | def _find_upload_files(samples: List[Sample], tmp_dir, overwrite=False):
sample_by_sid = {s.nagim_id: s for s in samples}
for source_name in SOURCES_TO_PROCESS:
source = SOURCES[source_name]
for ending in source.search_pattern_by_ending:
paths = _cache_bucket_ls(
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b02bade433efb6d0fd7721d08d9e2b04fedcb5ac | jeremiahwander/sample-metadata | scripts/parse_nagim.py | [
"MIT"
] | Python | parse | null | def parse(
tmp_dir,
confirm: bool,
dry_run: bool,
overwrite_multiqc: bool,
skip_checking_objects: bool,
):
"""
Assuming the data is transferred to the CPG bucket, populate the SM projects.
"""
if not tmp_dir:
tmp_dir = tempfile.gettempdir()
samples = _parse_sample_projec... |
Assuming the data is transferred to the CPG bucket, populate the SM projects.
| Assuming the data is transferred to the CPG bucket, populate the SM projects. | [
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] | def parse(
tmp_dir,
confirm: bool,
dry_run: bool,
overwrite_multiqc: bool,
skip_checking_objects: bool,
):
if not tmp_dir:
tmp_dir = tempfile.gettempdir()
samples = _parse_sample_project_map(SAMPLE_TO_PROJECT_TSV_PATH)
_find_upload_files(samples, tmp_dir)
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b02bade433efb6d0fd7721d08d9e2b04fedcb5ac | jeremiahwander/sample-metadata | scripts/parse_nagim.py | [
"MIT"
] | Python | _run_multiqc | str | def _run_multiqc(
samples: List[Sample],
html_fpath: str,
json_fpath: str,
overwrite: bool = False,
) -> str:
"""
Runs MultiQC on QC files from Picard and VerifyBAMID.
Generates an HTML report and puts in into nagim web bucket.
Generates a JSON with metrics, extracts useful metrics int... |
Runs MultiQC on QC files from Picard and VerifyBAMID.
Generates an HTML report and puts in into nagim web bucket.
Generates a JSON with metrics, extracts useful metrics into another JSON
indexed by sample, and returns path to this JSON.
| Runs MultiQC on QC files from Picard and VerifyBAMID.
Generates an HTML report and puts in into nagim web bucket.
Generates a JSON with metrics, extracts useful metrics into another JSON
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samples: List[Sample],
html_fpath: str,
json_fpath: str,
overwrite: bool = False,
) -> str:
tmp_bucket = f'gs://cpg-{NAGIM_PROJ_ID}-{NAMESPACE}-tmp/qc'
row_by_sample_json_path = f'{tmp_bucket}/parsed-qc.json'
if can_reuse(row_by_sample_json_path, overwrite):
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b02bade433efb6d0fd7721d08d9e2b04fedcb5ac | jeremiahwander/sample-metadata | scripts/parse_nagim.py | [
"MIT"
] | Python | _get_sm_proj_id | <not_specific> | def _get_sm_proj_id(proj: str, namespace='main'):
"""
Matching the project ID to a sample-metadata project.
"""
if proj == 'csiro-als': # We don't have a project for ALS yet
proj = 'nagim'
if namespace != 'main':
proj = f'{proj}-test'
return proj |
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if proj == 'csiro-als':
proj = 'nagim'
if namespace != 'main':
proj = f'{proj}-test'
return proj | [
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b02bade433efb6d0fd7721d08d9e2b04fedcb5ac | jeremiahwander/sample-metadata | scripts/parse_nagim.py | [
"MIT"
] | Python | _fix_sample_ids | null | def _fix_sample_ids(samples: List[Sample], namespace: str = 'main'):
"""
Some samples processed with Terra use CPG IDs, so checking if we already
have them in the SMDB, and fixing the external IDs.
"""
sm_proj_ids = [_get_sm_proj_id(proj, namespace) for proj in PROJECT_ID_MAP.values()]
sapi = Sa... |
Some samples processed with Terra use CPG IDs, so checking if we already
have them in the SMDB, and fixing the external IDs.
| Some samples processed with Terra use CPG IDs, so checking if we already
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sm_proj_ids = [_get_sm_proj_id(proj, namespace) for proj in PROJECT_ID_MAP.values()]
sapi = SampleApi()
sm_sample_dicts = sapi.get_samples(
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b02bade433efb6d0fd7721d08d9e2b04fedcb5ac | jeremiahwander/sample-metadata | scripts/parse_nagim.py | [
"MIT"
] | Python | _parse_sample_project_map | List[Sample] | def _parse_sample_project_map(tsv_path: str) -> List[Sample]:
"""
Initialize list of Sample object and set project IDs.
"""
sample_by_nagim_id = {}
df = pd.read_csv(tsv_path, sep='\t', header=None, names=['nagim_id', 'proj'])
for (nagim_id, proj) in zip(df.nagim_id, df.proj):
if proj in ... |
Initialize list of Sample object and set project IDs.
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sample_by_nagim_id = {}
df = pd.read_csv(tsv_path, sep='\t', header=None, names=['nagim_id', 'proj'])
for (nagim_id, proj) in zip(df.nagim_id, df.proj):
if proj in PROJECT_ID_MAP.values():
cpg_proj = proj
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b02bade433efb6d0fd7721d08d9e2b04fedcb5ac | jeremiahwander/sample-metadata | scripts/parse_nagim.py | [
"MIT"
] | Python | _add_batch_job | <not_specific> | def _add_batch_job(cmd: str, hbatch, job_name: str):
"""
Add cmd as a Batch job.
"""
j = hbatch.new_job(job_name)
j.cpu(32)
j.memory('lowmem')
j.image('australia-southeast1-docker.pkg.dev/cpg-common/images/aspera:v1')
j.command('export GOOGLE_APPLICATION_CREDENTIALS=/gsa-key/key.json')
... |
Add cmd as a Batch job.
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j = hbatch.new_job(job_name)
j.cpu(32)
j.memory('lowmem')
j.image('australia-southeast1-docker.pkg.dev/cpg-common/images/aspera:v1')
j.command('export GOOGLE_APPLICATION_CREDENTIALS=/gsa-key/key.json')
j.command(
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5d4ba2c56c95ef8f18da522929011831b749ae45 | jeremiahwander/sample-metadata | api/routes/family.py | [
"MIT"
] | Python | update_family | <not_specific> | async def update_family(
family: FamilyUpdateModel, connection: Connection = get_projectless_db_connection
):
"""Update information for a single family"""
family_layer = FamilyLayer(connection)
return {
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id_=family.id,
external_... | Update information for a single family | Update information for a single family | [
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family: FamilyUpdateModel, connection: Connection = get_projectless_db_connection
):
family_layer = FamilyLayer(connection)
return {
'success': await family_layer.update_family(
id_=family.id,
external_id=family.external_id,
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c396249acd6225595569e7d92d3fbb577f1e1aca | jeremiahwander/sample-metadata | models/models/analysis.py | [
"MIT"
] | Python | from_db | <not_specific> | def from_db(**kwargs):
"""
Convert from db keys, mainly converting id to id_
"""
analysis_type = kwargs.pop('type', None)
status = kwargs.pop('status', None)
timestamp_completed = kwargs.pop('timestamp_completed', None)
meta = kwargs.get('meta')
if meta a... |
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analysis_type = kwargs.pop('type', None)
status = kwargs.pop('status', None)
timestamp_completed = kwargs.pop('timestamp_completed', None)
meta = kwargs.get('meta')
if meta and isinstance(meta, str):
meta = json.loads(meta)
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fd5a076600a7e75b121610809501e723531dc99c | jeremiahwander/sample-metadata | scripts/arbitrary_sm.py | [
"MIT"
] | Python | run_sm | <not_specific> | def run_sm(
api_name: str, method_name: str, args: List[str] = None, kwargs: dict = None
):
"""
Use the sample metadata API based on:
:param api_name: pure name of API, eg: 'analysis'
:param method_name: name of method in snake case
:param args: positional args of endpoint
:param kwargs: key... |
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):
api_class_name = api_name.title() + 'Api'
api = getattr(sample_metadata.api, api_class_name)
api_instance = api()
response = getattr(api_instance, method_name)(*(args or []), **(kwargs or {}))
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c0c0a20dc113fc7c88658962d2561b8e707a4812 | jeremiahwander/sample-metadata | scripts/parse_tobwgs_csv.py | [
"MIT"
] | Python | find_gvcf | Optional[str] | def find_gvcf(self, sample_id: str, cpg_id: Optional[str] = None) -> Optional[str]:
"""
Find GVCF for the sample.
"""
extension = 'g.vcf.gz'
search_locations = [f'gs://cpg-tob-wgs-main-upload/{sample_id}.{extension}']
if cpg_id:
# Sample was added before and... |
Find GVCF for the sample.
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extension = 'g.vcf.gz'
search_locations = [f'gs://cpg-tob-wgs-main-upload/{sample_id}.{extension}']
if cpg_id:
search_locations += [
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c0c0a20dc113fc7c88658962d2561b8e707a4812 | jeremiahwander/sample-metadata | scripts/parse_tobwgs_csv.py | [
"MIT"
] | Python | find_cram | Optional[str] | def find_cram(self, sample_id: str, cpg_id: Optional[str] = None) -> Optional[str]:
"""
Find CRAM for the sample.
"""
extension = 'cram'
search_locations = [f'gs://cpg-tob-wgs-main-upload/{sample_id}.{extension}']
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# Sample was added before and CPG... |
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extension = 'cram'
search_locations = [f'gs://cpg-tob-wgs-main-upload/{sample_id}.{extension}']
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ca69d0ed6b2dcf2b96e6159a3ff810170702785f | jeremiahwander/sample-metadata | scripts/create_test_subset.py | [
"MIT"
] | Python | main | null | def main(
project: str,
samples_n: Optional[int],
families_n: Optional[int],
):
"""
Script creates a test subset for a given project.
A new project with a prefix -test is created, and for any files in sample/meta,
sequence/meta, or analysis/output a copy in the -test namespace is created.
... |
Script creates a test subset for a given project.
A new project with a prefix -test is created, and for any files in sample/meta,
sequence/meta, or analysis/output a copy in the -test namespace is created.
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ca69d0ed6b2dcf2b96e6159a3ff810170702785f | jeremiahwander/sample-metadata | scripts/create_test_subset.py | [
"MIT"
] | Python | export_ped_file | List[str] | def export_ped_file( # pylint: disable=invalid-name
project: str,
replace_with_participant_external_ids: bool = False,
replace_with_family_external_ids: bool = False,
) -> List[str]:
"""
Generates a PED file for the project, returs PED file lines in a list
"""
route = f'/api/v1/family/{proj... |
Generates a PED file for the project, returs PED file lines in a list
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) -> List[str]:
route = f'/api/v1/family/{project}/pedigree'
opts = []
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2d7a280799a10d06db8729e4d01cc572e913825a | jeremiahwander/sample-metadata | sample_metadata/parser/generic_metadata_parser.py | [
"MIT"
] | Python | file_path | str | def file_path(self, filename: str) -> str:
"""
Get complete filepath of filename:
- Includes gs://{bucket} if relevant
- Includes path_prefix decided early on
"""
if filename in self.filename_map:
return self.filename_map[filename]
return super().file... |
Get complete filepath of filename:
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2d7a280799a10d06db8729e4d01cc572e913825a | jeremiahwander/sample-metadata | sample_metadata/parser/generic_metadata_parser.py | [
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] | Python | from_manifest_path | <not_specific> | def from_manifest_path(
self,
manifest: str,
confirm=False,
delimiter=None,
dry_run=False,
):
"""Parse manifest from path, and return result of parsing manifest"""
_delimiter = delimiter or GenericMetadataParser.guess_delimiter_from_filename(
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delimiter=None,
dry_run=False,
):
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59a661356ed8df76fc2212db59c8d121d61d090e | jeremiahwander/sample-metadata | test/test_add_samples_for_joint_calling.py | [
"MIT"
] | Python | _add_samples | <not_specific> | def _add_samples(run_id: str, project: str):
"""
Add 3 samples: one with fastq input, one with CRAM input, one with GVCF input.
:param run_id: to suffix sample names for uniqueness
"""
s1 = NewSample(
external_id=f'NA12878-from-fq-{run_id}',
type=SampleType('blood'),
meta={
... |
Add 3 samples: one with fastq input, one with CRAM input, one with GVCF input.
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479f52dddde3f2ec5e418e5b565fe1eb9b2b93bc | jeremiahwander/sample-metadata | db/python/layers/sequence.py | [
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] | Python | insert_many_sequencing | None | async def insert_many_sequencing(
self, sequencing: List[SampleSequencing], author=None, check_project_ids=True
) -> None:
"""Insert many sequencing, returning no IDs"""
if check_project_ids:
sample_ids = set(int(s.sample_id) for s in sequencing)
st = SampleTable(self... | Insert many sequencing, returning no IDs | Insert many sequencing, returning no IDs | [
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self, sequencing: List[SampleSequencing], author=None, check_project_ids=True
) -> None:
if check_project_ids:
sample_ids = set(int(s.sample_id) for s in sequencing)
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479f52dddde3f2ec5e418e5b565fe1eb9b2b93bc | jeremiahwander/sample-metadata | db/python/layers/sequence.py | [
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] | Python | insert_sequencing | int | async def insert_sequencing(
self,
sample_id,
sequence_type: SequenceType,
status: SequenceStatus,
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author=None,
check_project_id=True,
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status: SequenceStatus,
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479f52dddde3f2ec5e418e5b565fe1eb9b2b93bc | jeremiahwander/sample-metadata | db/python/layers/sequence.py | [
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] | Python | update_sequencing_status_from_internal_sample_id | <not_specific> | async def update_sequencing_status_from_internal_sample_id(
self, sample_id: int, status: SequenceStatus
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"""Update the sequencing status from the internal sample id"""
# check project ID in first one
seq_id = self.get_latest_sequence_id_for_sample_id(sample_id)
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seq_id = self.get_latest_sequence_id_for_sample_id(sample_id)
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479f52dddde3f2ec5e418e5b565fe1eb9b2b93bc | jeremiahwander/sample-metadata | db/python/layers/sequence.py | [
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] | Python | update_sequencing_status_from_external_sample_id | <not_specific> | async def update_sequencing_status_from_external_sample_id(
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Update the sequencing status from the external sample id,
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be78036e058d0877ee5fe7f9599c7a713b9b10c7 | dweindl/fides | fides/stepback.py | [
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Reference trust region step that will be reflected
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be78036e058d0877ee5fe7f9599c7a713b9b10c7 | dweindl/fides | fides/stepback.py | [
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be78036e058d0877ee5fe7f9599c7a713b9b10c7 | dweindl/fides | fides/stepback.py | [
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delta: float,
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5bb7efa78ec4e44815228e61ffa1eb37105edf68 | dweindl/fides | fides/hessian_approximation.py | [
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550fe3f14bf36c4d2229f24dcf796503790650b2 | dweindl/fides | fides/trust_region.py | [
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scaling: csc_matrix,
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434a1c51dc4d9a4d576e5e297ed8b95bfadbb171 | dweindl/fides | fides/minimize.py | [
"BSD-3-Clause"
] | Python | track_minimum | None | def track_minimum(self,
x_new: np.ndarray,
fval_new: float,
grad_new: np.ndarray) -> None:
"""
Function that tracks the optimization variables that have minimal
function value independent of whether the step is accepted or not.
... |
Function that tracks the optimization variables that have minimal
function value independent of whether the step is accepted or not.
:param x_new:
:param fval_new:
:param grad_new:
:return:
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434a1c51dc4d9a4d576e5e297ed8b95bfadbb171 | dweindl/fides | fides/minimize.py | [
"BSD-3-Clause"
] | Python | update | None | def update(self,
step: Step,
x_new: np.ndarray,
fval_new: float,
grad_new: np.ndarray,
hess_new: Optional[np.ndarray] = None) -> None:
"""
Update self according to employed step
:param step:
Employed step
... |
Update self according to employed step
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:param x_new:
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:param grad_new:
Objective function gradient at x_new
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fval_new: float,
grad_new: np.ndarray,
hess_new: Optional[np.ndarray] = None) -> None:
if self.hessian_update is not None:
self.hessian_update.update(step.s + step.s0,
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434a1c51dc4d9a4d576e5e297ed8b95bfadbb171 | dweindl/fides | fides/minimize.py | [
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] | Python | check_convergence | None | def check_convergence(self, step: Step, fval: float,
grad: np.ndarray) -> None:
"""
Check whether optimization has converged.
:param step:
update to optimization variables
:param fval:
updated objective function value
:param gr... |
Check whether optimization has converged.
:param step:
update to optimization variables
:param fval:
updated objective function value
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updated objective function gradient
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grad: np.ndarray) -> None:
converged = False
fatol = self.get_option(Options.FATOL)
frtol = self.get_option(Options.FRTOL)
xtol = self.get_option(Options.XTOL)
gatol = self.get_option(Options.GATOL)
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434a1c51dc4d9a4d576e5e297ed8b95bfadbb171 | dweindl/fides | fides/minimize.py | [
"BSD-3-Clause"
] | Python | check_continue | bool | def check_continue(self) -> bool:
"""
Checks whether minimization should continue based on convergence,
iteration count and remaining computational budget
:return:
flag indicating whether minimization should continue
"""
if self.converged:
return... |
Checks whether minimization should continue based on convergence,
iteration count and remaining computational budget
:return:
flag indicating whether minimization should continue
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if self.converged:
return False
maxiter = self.get_option(Options.MAXITER)
if self.iteration >= maxiter:
self.exitflag = ExitFlag.MAXITER
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434a1c51dc4d9a4d576e5e297ed8b95bfadbb171 | dweindl/fides | fides/minimize.py | [
"BSD-3-Clause"
] | Python | make_non_degenerate | None | def make_non_degenerate(self, eps=1e2 * np.spacing(1)) -> None:
"""
Ensures that x is non-degenerate, this should only be necessary for
initial points.
:param eps: degeneracy threshold
"""
if np.min(np.abs(self.ub - self.x)) < eps or \
np.min(np.abs(self.... |
Ensures that x is non-degenerate, this should only be necessary for
initial points.
:param eps: degeneracy threshold
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if np.min(np.abs(self.ub - self.x)) < eps or \
np.min(np.abs(self.x - self.lb)) < eps:
upperi = (self.ub - self.x) < eps
loweri = (self.x - self.lb) < eps
self.x[upperi] = self.x[upperi] - eps
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... |
434a1c51dc4d9a4d576e5e297ed8b95bfadbb171 | dweindl/fides | fides/minimize.py | [
"BSD-3-Clause"
] | Python | log_step | null | def log_step(self, accepted: bool, step: Step, fval: float):
"""
Prints diagnostic information about the current step to the log
:param accepted:
flag indicating whether the current step was accepted
:param step:
proposal step
:param fval:
new... |
Prints diagnostic information about the current step to the log
:param accepted:
flag indicating whether the current step was accepted
:param step:
proposal step
:param fval:
new fval if step is accepted
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normdx = norm(step.s + step.s0)
iterspaces = max(len(str(self.get_option(Options.MAXITER))), 5) - \
len(str(self.iteration))
steptypespaces = 4 - len(step.type)
reflspaces, trunspaces = [
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434a1c51dc4d9a4d576e5e297ed8b95bfadbb171 | dweindl/fides | fides/minimize.py | [
"BSD-3-Clause"
] | Python | log_step_initial | null | def log_step_initial(self):
"""
Prints diagnostic information about the initial step to the log
"""
iterspaces = max(len(str(self.get_option(Options.MAXITER))), 5) - \
len(str(self.iteration))
self.logger.info(
f'{" " * iterspaces}{self.iteration}'
... |
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iterspaces = max(len(str(self.get_option(Options.MAXITER))), 5) - \
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self.logger.info(
f'{" " * iterspaces}{self.iteration}'
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} |
434a1c51dc4d9a4d576e5e297ed8b95bfadbb171 | dweindl/fides | fides/minimize.py | [
"BSD-3-Clause"
] | Python | log_header | null | def log_header(self):
"""
Prints the header for diagnostic information, should complement
:py:func:`Optimizer.log_step`.
"""
iterspaces = len(str(self.get_option(Options.MAXITER))) - 5
self.logger.info(
f'{" " * iterspaces} iter '
f'| fval |... |
Prints the header for diagnostic information, should complement
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iterspaces = len(str(self.get_option(Options.MAXITER))) - 5
self.logger.info(
f'{" " * iterspaces} iter '
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434a1c51dc4d9a4d576e5e297ed8b95bfadbb171 | dweindl/fides | fides/minimize.py | [
"BSD-3-Clause"
] | Python | check_finite | null | def check_finite(self,
grad: Optional[np.ndarray] = None,
hess: Optional[np.ndarray] = None):
"""
Checks whether objective function value, gradient and Hessian (
approximation) have finite values and optimization can continue.
:param grad:
... |
Checks whether objective function value, gradient and Hessian (
approximation) have finite values and optimization can continue.
:param grad:
gradient to be checked for finiteness, if not provided, current
one will be checked
:param hess:
Hessian (a... | Checks whether objective function value, gradient and Hessian (
approximation) have finite values and optimization can continue. | [
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grad: Optional[np.ndarray] = None,
hess: Optional[np.ndarray] = None):
if self.iteration == 0:
pointstr = 'at initial point.'
else:
pointstr = f'at iteration {self.iteration}.'
if grad is None:
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2b8e031f931e2339538d029cb74ef1d19bf91934 | dweindl/fides | tests/test_subproblem.py | [
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1907acc366ea1368948d32302fd60bfd153cc8aa | dweindl/fides | fides/subproblem.py | [
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e5f2361479fbd51aaa76f53e7992319c0b2b5706 | charnley/optimize_gamess_parameters | bin/nodes.py | [
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cab88485243f5b624c12e078d8b20fd9a3aae8d1 | xuecan/ganggu | ganggu/forms.py | [
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4bbdecac431688b40bf237871868b89cff3ce856 | xuecan/ganggu | ganggu/httpkit.py | [
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4bbdecac431688b40bf237871868b89cff3ce856 | xuecan/ganggu | ganggu/httpkit.py | [
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bcf73818eaed2c33e7e0ed98dba80cae7fb705f2 | cloudrainstar/efai_clock | apollo.py | [
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a58e51cf1119307431b8c84860dcfbf197c70a63 | cloudrainstar/efai_clock | run.py | [
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a58e51cf1119307431b8c84860dcfbf197c70a63 | cloudrainstar/efai_clock | run.py | [
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logging.info(f"Command: /reminder triggered by {update.effective_chat.id}")
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a58e51cf1119307431b8c84860dcfbf197c70a63 | cloudrainstar/efai_clock | run.py | [
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"""Command handler: /autolog <on/off> - set autolog on or off."""
logging.info(f"Command: /autolog triggered by {update.effective_chat.id}")
if len(context.args) != 1:
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a58e51cf1119307431b8c84860dcfbf197c70a63 | cloudrainstar/efai_clock | run.py | [
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"""Handle callback: a clock in/out callback using job queue."""
u = context.job.context
clock_string = "clockin_"
if out:
clock_string = "clockout_"
logging.info(f"{clock_string}: {str(u.userid)}")
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clock_string = "clockin_"
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clock_string = "clockout_"
logging.info(f"{clock_string}: {str(u.userid)}")
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a58e51cf1119307431b8c84860dcfbf197c70a63 | cloudrainstar/efai_clock | run.py | [
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ses = apollodb.UserQuery(apollodb.Session())
us = ses.get_reminder()
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clock_string = "clockout_"
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''' Calculates and returns the nth fibonacci number'''
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5bd17e13501edc6baf65632db30f28ef39afd437 | Liulinghzi/DeepCTR | mydeepctr/models/pnn.py | [
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"""Constructs a `BertConfig` from a Python dictionary of parameters."""
config = PNNConfig(vocab_size=None)
for (key, value) in six.iteritems(json_object):
config.__dict__[key] = value
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ac8c3b99628ebaf434a58f5123f0346068562384 | Liulinghzi/DeepCTR | deepctr/models/deepfm.py | [
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l2_reg_linear=0.00001, l2_reg_embedding=0.00001, l2_reg_dnn=0, init_std=0.0001, seed=1024, dnn_dropout=0,
dnn_activation='relu', dnn_use_bn=False, task='binary'):
"""Instantiates... | Instantiates the DeepFM Network architecture.
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:param dnn_feature_columns: An iterable containing all the features used by deep part of the model.
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l2_reg_linear=0.00001, l2_reg_embedding=0.00001, l2_reg_dnn=0, init_std=0.0001, seed=1024, dnn_dropout=0,
dnn_activation='relu', dnn_use_bn=False, task='binary'):
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f3fe7666c2127e81b45f67a86f5350166dfd75a2 | Liulinghzi/DeepCTR | mydeepctr/models/wdl.py | [
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] | Python | from_dict | <not_specific> | def from_dict(cls, json_object):
"""Constructs a `BertConfig` from a Python dictionary of parameters."""
config = WDLConfig(vocab_size=None)
for (key, value) in six.iteritems(json_object):
config.__dict__[key] = value
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7b044b0d37f21b0fd839f9a2b324954326c5c70a | Liulinghzi/DeepCTR | mydeepctr/models/ffm.py | [
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"""Constructs a `BertConfig` from a Python dictionary of parameters."""
config = FFMConfig(vocab_size=None)
for (key, value) in six.iteritems(json_object):
config.__dict__[key] = value
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0971a129579a314d25aedd3be2664e024bbc6aed | Liulinghzi/DeepCTR | mydeepctr/models/mlr.py | [
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"""Constructs a `BertConfig` from a Python dictionary of parameters."""
config = MLRConfig(vocab_size=None)
for (key, value) in six.iteritems(json_object):
config.__dict__[key] = value
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3efc380b4b0be2c881c58de1fd8ea80d03dcf2c2 | Liulinghzi/DeepCTR | datapreprocess/tfrecord.py | [
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"""Decodes a record to a TensorFlow example."""
example = tf.parse_single_example(record, name_to_features)
# tf.Example only supports tf.int64, but the TPU only supports tf.int32.
# So cast all int64 to int32.
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example = tf.parse_single_example(record, name_to_features)
for name in list(example.keys()):
t = example[name]
if t.dtype == tf.int64:
t = tf.to_int32(t)
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6854d58ceb605c1dcbd8977f279023c6662184c8 | Liulinghzi/DeepCTR | deepctr/models/pnn.py | [
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seed=1024, dnn_dropout=0, dnn_activation='relu', use_inner=True, use_outter=False, kernel_type='mat',
task='binary'):
"""Instantiates the Product-based Neural Network architecture.
:p... | Instantiates the Product-based Neural Network architecture.
:param dnn_feature_columns: An iterable containing all the features used by deep part of the model.
:param dnn_hidden_units: list,list of positive integer or empty list, the layer number and units in each layer of deep net
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seed=1024, dnn_dropout=0, dnn_activation='relu', use_inner=True, use_outter=False, kernel_type='mat',
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ba067fb56adeb90823767824a020df2c7ea01886 | Liulinghzi/DeepCTR | deepctr/models/wdl.py | [
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] | Python | WDL | <not_specific> | def WDL(linear_feature_columns, dnn_feature_columns, dnn_hidden_units=(128, 128), l2_reg_linear=1e-5,
l2_reg_embedding=1e-5, l2_reg_dnn=0, init_std=0.0001, seed=1024, dnn_dropout=0, dnn_activation='relu',
task='binary'):
"""Instantiates the Wide&Deep Learning architecture.
:param linear_feature... | Instantiates the Wide&Deep Learning architecture.
:param linear_feature_columns: An iterable containing all the features used by linear part of the model.
:param dnn_feature_columns: An iterable containing all the features used by deep part of the model.
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features = build_input_features(
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67c318a06e7e9c0c709ec6b234b9a95005f13263 | Liulinghzi/DeepCTR | mydeepctr/examples/fm/model.py | [
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] | Python | create_optimizer | <not_specific> | def create_optimizer(loss, init_lr):
"""Creates an optimizer training op."""
global_step = tf.train.get_or_create_global_step()
optimizer = tf.train.AdamOptimizer(init_lr)
# optimizer = tf.train.FtrlOptimizer(init_lr)
tvars = tf.trainable_variables()
grads = tf.gradients(loss, tvars)
# Thi... | Creates an optimizer training op. | Creates an optimizer training op. | [
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global_step = tf.train.get_or_create_global_step()
optimizer = tf.train.AdamOptimizer(init_lr)
tvars = tf.trainable_variables()
grads = tf.gradients(loss, tvars)
(grads, _) = tf.clip_by_global_norm(grads, clip_norm=1.0)
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64dbb9b52f5efe2ec927def6b600c2c328bd2ca2 | Liulinghzi/DeepCTR | mydeepctr/models/fm.py | [
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] | Python | from_dict | <not_specific> | def from_dict(cls, json_object):
"""Constructs a `BertConfig` from a Python dictionary of parameters."""
config = FMConfig(vocab_size=None)
for (key, value) in six.iteritems(json_object):
config.__dict__[key] = value
return config | Constructs a `BertConfig` from a Python dictionary of parameters. | Constructs a `BertConfig` from a Python dictionary of parameters. | [
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4df59cb9d804319f6b46b47f65bd6d34d715d1cb | Liulinghzi/DeepCTR | mydeepctr/models/xdeepfm.py | [
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"""Constructs a `BertConfig` from a Python dictionary of parameters."""
config = XDeepFMConfig(vocab_size=None)
for (key, value) in six.iteritems(json_object):
config.__dict__[key] = value
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7e20102e9de1a192f978524ab12fc314ef723f84 | Liulinghzi/DeepCTR | mydeepctr/models/dcn.py | [
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"""Constructs a `BertConfig` from a Python dictionary of parameters."""
config = DCNConfig(vocab_size=None)
for (key, value) in six.iteritems(json_object):
config.__dict__[key] = value
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3c8ef3bd40ce6291b42bbdaa0bf767755a8a3529 | Liulinghzi/DeepCTR | mydeepctr/models/lr.py | [
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"""Constructs a `BertConfig` from a Python dictionary of parameters."""
config = LRConfig(vocab_size=None)
for (key, value) in six.iteritems(json_object):
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c470383c0227dd3850bc3b5457b6331465ecb0c7 | tienthanh-le/API1 | profiles_api/models.py | [
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] | Python | create_superuser | <not_specific> | def create_superuser(self, email, name, password):
"""Create and save a new superuser with given details"""
user = self.create_user(email, name, password)
user.is_superuser = True # is_superuser created by PermissionsMixin
user.is_staff = True
user.save(using=self._db)
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user = self.create_user(email, name, password)
user.is_superuser = True
user.is_staff = True
user.save(using=self._db)
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9fa8052bf5281a3352eba4cd73e7a58ba8a5f7d4 | vighneshvnkt/data-ductus-challenge | ParanthesisTreePrinter.py | [
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] | Python | paranthesesChecker | <not_specific> | def paranthesesChecker(tree):
'''
Check if given tree has equal number of closing and opening square brackets using the paranthesesStack
input : tree as string
output : boolean value indicating paranthesesCheck
'''
paranthesesStack = []
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9fa8052bf5281a3352eba4cd73e7a58ba8a5f7d4 | vighneshvnkt/data-ductus-challenge | ParanthesisTreePrinter.py | [
"MIT"
] | Python | tabs | <not_specific> | def tabs(count):
'''
Return String with n number of tabs
input : number of tabs needed
output : string with tabs = count
'''
emptySpaces = ''
if(count <= 0):
return emptySpaces
while(count > 0):
emptySpaces = emptySpaces + ' '
count = count - 1
return emptySpaces |
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7c54cfbf4c6d99b9abcffe5c4852a33a936bc7fa | Vaidic/Udacity-Deep-Reinforcement-Learning-Nanodegree | coursework/lab-taxi/agent.py | [
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] | Python | select_action | <not_specific> | def select_action(self, state, i_episode, num_episodes):
""" Given the state, select an action.
Params
======
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Returns
=======
- action: an integer, compatible with the task's action space
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7c54cfbf4c6d99b9abcffe5c4852a33a936bc7fa | Vaidic/Udacity-Deep-Reinforcement-Learning-Nanodegree | coursework/lab-taxi/agent.py | [
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] | Python | step | null | def step(self, state, action, reward, next_state, done):
""" Update the agent's knowledge, using the most recently sampled tuple.
Params
======
- state: the previous state of the environment
- action: the agent's previous choice of action
- reward: last reward received
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======
- state: the previous state of the environment
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- reward: last reward received
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62bab8caf9c34fe17fc02cbc65373e935cfd010d | Vaidic/Udacity-Deep-Reinforcement-Learning-Nanodegree | projects/p3_collab-compet/agent.py | [
"Apache-2.0"
] | Python | act | <not_specific> | def act(self, state, add_noise=True):
"""Returns actions for given state as per current policy."""
state = torch.from_numpy(state).float().to(device)
self.actor_local.eval()
with torch.no_grad():
action = self.actor_local(state).cpu().data.numpy()
self.actor_local.tra... | Returns actions for given state as per current policy. | Returns actions for given state as per current policy. | [
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action = self.actor_local(state).cpu().data.numpy()
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a1dea3953fd089b91e5f2e41b5ecc57343a92fa0 | JakubCzech/ROS_Modbus_Mobile_Robot | modbus_sterring/src/agv.py | [
"MIT"
] | Python | check_all_info | <not_specific> | def check_all_info(self):
"""
Check if all the information are available
"""
base_data_struct = self.client.read_holding_registers(self.__BASE_DATA_STRUCT_ADDR,13)
manual_control_struct = self.client.read_holding_registers(self.MANUAL_CONTROL_STRUCT,7)
SYSTEM_TI... |
Check if all the information are available
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base_data_struct = self.client.read_holding_registers(self.__BASE_DATA_STRUCT_ADDR,13)
manual_control_struct = self.client.read_holding_registers(self.MANUAL_CONTROL_STRUCT,7)
SYSTEM_TICK_MS = self.client.read_holding_registers(self.__SYSTEM_TICK_MS_ADDR,2)
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a1dea3953fd089b91e5f2e41b5ecc57343a92fa0 | JakubCzech/ROS_Modbus_Mobile_Robot | modbus_sterring/src/agv.py | [
"MIT"
] | Python | update_time | <not_specific> | def update_time(self):
"""
Update the time of the robot
"""
try:
SYSTEM_TICK_MS = self.client.read_holding_registers(self.__SYSTEM_TICK_MS_ADDR,2)
if(SYSTEM_TICK_MS):
self.__ROBOT_TIME = SYSTEM_TICK_MS[0]
return True
... |
Update the time of the robot
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try:
SYSTEM_TICK_MS = self.client.read_holding_registers(self.__SYSTEM_TICK_MS_ADDR,2)
if(SYSTEM_TICK_MS):
self.__ROBOT_TIME = SYSTEM_TICK_MS[0]
return True
else:
self.log_error("Time error")
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00d8067ecd72003472d0b552650efbcc012e403d | neilferg/matlab2cpp | conftest.py | [
"BSD-3-Clause"
] | Python | workspace | null | def workspace(workspace_folder, doctest_namespace):
"""Fill temporary folder for each test."""
# move data to workspace:
source = os.path.join(os.path.dirname(inspect.stack()[0][1]), "test", "data")
if os.path.isdir(workspace_folder):
shutil.rmtree(workspace_folder)
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shutil.copytree(source, workspace_folder)
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1a9aa6f116020180ebd30617e5a5310750c6ffd8 | Steinbeck-Lab/DECIMER-Image_Transformer | DECIMER/config.py | [
"MIT"
] | Python | central_square_image | <not_specific> | def central_square_image(im):
"""
This function takes a Pillow Image object and will add white padding
so that the image has a square shape with the width/height of the longest side
of the original image.
___
im: PIL.Image
___
output: PIL.Image
"""
max_wh = int(1.2 * max(im.size)... |
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___
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1a9aa6f116020180ebd30617e5a5310750c6ffd8 | Steinbeck-Lab/DECIMER-Image_Transformer | DECIMER/config.py | [
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] | Python | delete_empty_borders | <not_specific> | def delete_empty_borders(im):
"""This function takes a Pillow Image object, converts it to grayscale and
deletes white space at the borders.
___
im: PIL.Image
___
output: PIL.Image
"""
im = np.asarray(im.convert("L"))
mask = im > 200
rows = np.flatnonzero((~mask).sum(axis=1))
... | This function takes a Pillow Image object, converts it to grayscale and
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cols = np.flatnonzero((~mask).sum(axis=0))
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"outlier_params": [],
"others": []
} |
1a9aa6f116020180ebd30617e5a5310750c6ffd8 | Steinbeck-Lab/DECIMER-Image_Transformer | DECIMER/config.py | [
"MIT"
] | Python | PIL_im_to_BytesIO | <not_specific> | def PIL_im_to_BytesIO(im):
"""
Convert pillow image to io.BytesIO object
___
im: PIL.Image
___
Output: io.BytesIO object with the image data
"""
output = io.BytesIO()
im.save(output, format="PNG")
return output |
Convert pillow image to io.BytesIO object
___
im: PIL.Image
___
Output: io.BytesIO object with the image data
| Convert pillow image to io.BytesIO object
im: PIL.Image
Output: io.BytesIO object with the image data | [
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1a9aa6f116020180ebd30617e5a5310750c6ffd8 | Steinbeck-Lab/DECIMER-Image_Transformer | DECIMER/config.py | [
"MIT"
] | Python | remove_transparent | <not_specific> | def remove_transparent(image_path: str):
"""
Removes the transparent layer from a PNG image with an alpha channel
___
image_path (str): path of input image
___
Output: PIL.Image
"""
png = Image.open(image_path).convert("RGBA")
background = Image.new("RGBA", png.size, (255, 255, 255))... |
Removes the transparent layer from a PNG image with an alpha channel
___
image_path (str): path of input image
___
Output: PIL.Image
| Removes the transparent layer from a PNG image with an alpha channel
image_path (str): path of input image
Output: PIL.Image | [
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png = Image.open(image_path).convert("RGBA")
background = Image.new("RGBA", png.size, (255, 255, 255))
alpha_composite = Image.alpha_composite(background, png)
return alpha_composite | [
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} |
1a9aa6f116020180ebd30617e5a5310750c6ffd8 | Steinbeck-Lab/DECIMER-Image_Transformer | DECIMER/config.py | [
"MIT"
] | Python | initialize_encoder_config | null | def initialize_encoder_config(
self,
image_embedding_dim,
preprocessing_fn,
backbone_fn,
image_shape,
do_permute=False,
pretrained_weights=None,
):
"""This functions initializes the Efficient-Net V2 encoder with user defined
configurations.
... | This functions initializes the Efficient-Net V2 encoder with user defined
configurations.
Args:
image_embedding_dim (int): Embedding dimention of the input image
preprocessing_fn (method): Efficient Net preprocessing function for input image
backbone_fn (method): Cal... | This functions initializes the Efficient-Net V2 encoder with user defined
configurations. | [
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] | def initialize_encoder_config(
self,
image_embedding_dim,
preprocessing_fn,
backbone_fn,
image_shape,
do_permute=False,
pretrained_weights=None,
):
self.encoder_config = dict(
image_embedding_dim=image_embedding_dim,
preprocessi... | [
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"returns": [],
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"params": [
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{
"identifier": "image_embedding_dim",
"type": null,
"docstring": "Embedding dimenti... |
1a9aa6f116020180ebd30617e5a5310750c6ffd8 | Steinbeck-Lab/DECIMER-Image_Transformer | DECIMER/config.py | [
"MIT"
] | Python | initialize_transformer_config | null | def initialize_transformer_config(
self,
vocab_len,
max_len,
n_transformer_layers,
transformer_d_dff,
transformer_n_heads,
image_embedding_dim,
dropout_rate=0.1,
):
"""This functions initializes the Transformer model as decoder with user define... | This functions initializes the Transformer model as decoder with user defined
configurations.
Args:
vocab_len (int): Total number of words in the input vocabulary
max_len (int): Maximum length of the string found on the training dataset
n_transformer_layers (int): N... | This functions initializes the Transformer model as decoder with user defined
configurations. | [
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] | def initialize_transformer_config(
self,
vocab_len,
max_len,
n_transformer_layers,
transformer_d_dff,
transformer_n_heads,
image_embedding_dim,
dropout_rate=0.1,
):
self.transformer_config = dict(
num_layers=n_transformer_layers,
... | [
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"t... | {
"returns": [],
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"params": [
{
"identifier": "self",
"type": null,
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"docstring_tokens": [],
"default": null,
"is_optional": null
},
{
"identifier": "vocab_len",
"type": null,
"docstring": "Total number of words in th... |
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