index int64 0 731k | package stringlengths 2 98 ⌀ | name stringlengths 1 76 | docstring stringlengths 0 281k ⌀ | code stringlengths 4 8.19k | signature stringlengths 2 42.8k ⌀ | embed_func_code listlengths 768 768 |
|---|---|---|---|---|---|---|
720,056 | ibm_platform_services.user_management_v1 | get_user_profile |
Get user profile.
Retrieve a user's profile by the user's IAM ID in your account. You can use the
IAM service token or a user token for authorization. To use this method, the
requesting user or service ID must have at least the viewer, editor, or
administrator role on the User ... | def get_user_profile(
self,
account_id: str,
iam_id: str,
*,
include_activity: str = None,
**kwargs,
) -> DetailedResponse:
"""
Get user profile.
Retrieve a user's profile by the user's IAM ID in your account. You can use the
IAM service token or a user token for authorization. T... | (self, account_id: str, iam_id: str, *, include_activity: Optional[str] = None, **kwargs) -> ibm_cloud_sdk_core.detailed_response.DetailedResponse | [
-0.048188719898462296,
-0.028666306287050247,
-0.00007957504567457363,
-0.037115730345249176,
-0.00879183690994978,
-0.0424400232732296,
0.009843192063272,
-0.030981218442320824,
-0.0598018504679203,
-0.0434817336499691,
0.027894670143723488,
0.0012973144184798002,
0.0007583742844872177,
0... |
720,057 | ibm_platform_services.user_management_v1 | get_user_settings |
Get user settings.
Retrieve a user's settings by the user's IAM ID. You can use the IAM service token
or a user token for authorization. To use this method, the requesting user or
service ID must have the viewer, editor, or administrator role on the User
Management service. <br... | def get_user_settings(
self,
account_id: str,
iam_id: str,
**kwargs,
) -> DetailedResponse:
"""
Get user settings.
Retrieve a user's settings by the user's IAM ID. You can use the IAM service token
or a user token for authorization. To use this method, the requesting user or
service ... | (self, account_id: str, iam_id: str, **kwargs) -> ibm_cloud_sdk_core.detailed_response.DetailedResponse | [
-0.023026717826724052,
-0.02194036915898323,
0.01933119259774685,
-0.029932795092463493,
0.010039028711616993,
0.005577238276600838,
-0.040233708918094635,
-0.04962287098169327,
-0.040621690452098846,
0.019981062039732933,
0.01869102194905281,
-0.0011372716398909688,
-0.0016064870869740844,
... |
720,058 | ibm_platform_services.user_management_v1 | invite_users |
Invite users to an account.
Invite users to the account. You must use a user token for authorization. Service
IDs can't invite users to the account. To use this method, the requesting user
must have the editor or administrator role on the User Management service. For
more infor... | def invite_users(
self,
account_id: str,
*,
users: List['InviteUser'] = None,
iam_policy: List['InviteUserIamPolicy'] = None,
access_groups: List[str] = None,
**kwargs,
) -> DetailedResponse:
"""
Invite users to an account.
Invite users to the account. You must use a user token f... | (self, account_id: str, *, users: Optional[List[ibm_platform_services.user_management_v1.InviteUser]] = None, iam_policy: Optional[List[ibm_platform_services.user_management_v1.InviteUserIamPolicy]] = None, access_groups: Optional[List[str]] = None, **kwargs) -> ibm_cloud_sdk_core.detailed_response.DetailedResponse | [
-0.015974054113030434,
-0.06740345805883408,
0.0025460224132984877,
-0.042852796614170074,
-0.04021228849887848,
-0.02622365392744541,
-0.019944893196225166,
-0.06050992012023926,
-0.06550874561071396,
0.03033558651804924,
0.027332263067364693,
-0.06498467177152634,
0.03259311988949776,
0.... |
720,059 | ibm_platform_services.user_management_v1 | list_users |
List users.
Retrieve users in the account. You can use the IAM service token or a user token
for authorization. To use this method, the requesting user or service ID must have
at least the viewer, editor, or administrator role on the User Management service.
If unrestricted vie... | def list_users(
self,
account_id: str,
*,
limit: int = None,
include_settings: bool = None,
search: str = None,
start: str = None,
user_id: str = None,
**kwargs,
) -> DetailedResponse:
"""
List users.
Retrieve users in the account. You can use the IAM service token or a u... | (self, account_id: str, *, limit: Optional[int] = None, include_settings: Optional[bool] = None, search: Optional[str] = None, start: Optional[str] = None, user_id: Optional[str] = None, **kwargs) -> ibm_cloud_sdk_core.detailed_response.DetailedResponse | [
-0.03809461370110512,
-0.03495527803897858,
0.0005615836125798523,
-0.034613169729709625,
-0.007018222939223051,
0.01685379631817341,
-0.05026959255337715,
-0.09039672464132309,
-0.0718827024102211,
-0.028193635866045952,
0.016350697726011276,
-0.03783300146460533,
0.011158722452819347,
0.... |
720,061 | ibm_platform_services.user_management_v1 | remove_user |
Remove user from account.
Remove users from an account by user's IAM ID. You must use a user token for
authorization. Service IDs can't remove users from an account. To use this method,
the requesting user must have the editor or administrator role on the User
Management servic... | def remove_user(
self,
account_id: str,
iam_id: str,
*,
include_activity: str = None,
**kwargs,
) -> DetailedResponse:
"""
Remove user from account.
Remove users from an account by user's IAM ID. You must use a user token for
authorization. Service IDs can't remove users from an ... | (self, account_id: str, iam_id: str, *, include_activity: Optional[str] = None, **kwargs) -> ibm_cloud_sdk_core.detailed_response.DetailedResponse | [
-0.0181991346180439,
0.0006709444569423795,
0.020657407119870186,
-0.07327233254909515,
-0.015374106355011463,
-0.050632454454898834,
-0.03637844696640968,
0.005362599156796932,
-0.05788831785321236,
-0.001947783282957971,
0.023413049057126045,
-0.03451491892337799,
0.0029885834082961082,
... |
720,069 | ibm_platform_services.user_management_v1 | update_user_profile |
Partially update user profile.
Partially update a user's profile by user's IAM ID. You can use the IAM service
token or a user token for authorization. To use this method, the requesting user
or service ID must have at least the editor or administrator role on the User
Manageme... | def update_user_profile(
self,
account_id: str,
iam_id: str,
*,
firstname: str = None,
lastname: str = None,
state: str = None,
email: str = None,
phonenumber: str = None,
altphonenumber: str = None,
photo: str = None,
include_activity: str = None,
**kwargs,
) -> Deta... | (self, account_id: str, iam_id: str, *, firstname: Optional[str] = None, lastname: Optional[str] = None, state: Optional[str] = None, email: Optional[str] = None, phonenumber: Optional[str] = None, altphonenumber: Optional[str] = None, photo: Optional[str] = None, include_activity: Optional[str] = None, **kwargs) -> ib... | [
-0.03273744136095047,
-0.01095631904900074,
-0.030248746275901794,
-0.03973815217614174,
-0.047952864319086075,
-0.06648654490709305,
-0.025979526340961456,
-0.046698398888111115,
-0.048640795052051544,
-0.04155914857983589,
0.016864435747265816,
-0.026363957673311234,
-0.014739940874278545,... |
720,070 | ibm_platform_services.user_management_v1 | update_user_settings |
Partially update user settings.
Update a user's settings by the user's IAM ID. You can use the IAM service token
or a user token for authorization. To fully use this method, the user or service
ID must have the editor or administrator role on the User Management service.
Withou... | def update_user_settings(
self,
account_id: str,
iam_id: str,
*,
language: str = None,
notification_language: str = None,
allowed_ip_addresses: str = None,
self_manage: bool = None,
**kwargs,
) -> DetailedResponse:
"""
Partially update user settings.
Update a user's setti... | (self, account_id: str, iam_id: str, *, language: Optional[str] = None, notification_language: Optional[str] = None, allowed_ip_addresses: Optional[str] = None, self_manage: Optional[bool] = None, **kwargs) -> ibm_cloud_sdk_core.detailed_response.DetailedResponse | [
-0.0015062179882079363,
-0.018545614555478096,
-0.009498494677245617,
-0.02092023938894272,
-0.020272614434361458,
-0.030732734128832817,
-0.04262547567486763,
-0.05200622230768204,
-0.06068046763539314,
0.028377734124660492,
0.02447236143052578,
-0.04376372694969177,
-0.00484737241640687,
... |
720,071 | ibm_platform_services.user_management_v1 | v3_remove_user |
Remove user from account (Asynchronous).
Remove users from an account by using the user's IAM ID. You must use a user token
for authorization. Service IDs can't remove users from an account. If removing the
user fails it will set the user's state to ERROR_WHILE_DELETING. To use this
... | def v3_remove_user(
self,
account_id: str,
iam_id: str,
**kwargs,
) -> DetailedResponse:
"""
Remove user from account (Asynchronous).
Remove users from an account by using the user's IAM ID. You must use a user token
for authorization. Service IDs can't remove users from an account. If r... | (self, account_id: str, iam_id: str, **kwargs) -> ibm_cloud_sdk_core.detailed_response.DetailedResponse | [
-0.02220134064555168,
0.024085089564323425,
-0.008962228894233704,
-0.06589280813932419,
0.007410057820379734,
-0.02462330460548401,
-0.04444112256169319,
0.0031115515157580376,
-0.0417884960770607,
0.002823222428560257,
0.043480027467012405,
-0.015185331925749779,
-0.01788601465523243,
-0... |
720,078 | ibm_platform_services.common | get_sdk_headers |
Get the request headers to be sent in requests by the SDK
| def get_sdk_headers(service_name, service_version, operation_id):
# pylint: disable=unused-argument
"""
Get the request headers to be sent in requests by the SDK
"""
headers = {}
headers[HEADER_NAME_USER_AGENT] = get_user_agent()
return headers
| (service_name, service_version, operation_id) | [
-0.05029295012354851,
-0.05420341715216637,
-0.059417370706796646,
-0.0729953795671463,
-0.02101876027882099,
0.003937622997909784,
-0.018556613475084305,
0.02049374394118786,
0.02297399379312992,
0.007155611179769039,
0.015705231577157974,
-0.04989466071128845,
0.04754113778471947,
-0.020... |
720,095 | textwrap3 | TextWrapper |
Object for wrapping/filling text. The public interface consists of
the wrap() and fill() methods; the other methods are just there for
subclasses to override in order to tweak the default behaviour.
If you want to completely replace the main wrapping algorithm,
you'll probably have to override _wr... | class TextWrapper:
"""
Object for wrapping/filling text. The public interface consists of
the wrap() and fill() methods; the other methods are just there for
subclasses to override in order to tweak the default behaviour.
If you want to completely replace the main wrapping algorithm,
you'll pro... | (width=70, initial_indent='', subsequent_indent='', expand_tabs=True, replace_whitespace=True, fix_sentence_endings=False, break_long_words=True, drop_whitespace=True, break_on_hyphens=True, tabsize=8, max_lines=None, placeholder=' [...]') | [
0.03564799204468727,
-0.011681380681693554,
0.00489587290212512,
0.022550687193870544,
-0.001246742787770927,
-0.026777639985084534,
-0.01014572661370039,
0.004914092365652323,
-0.0008348491392098367,
-0.00464600371196866,
-0.019375266507267952,
0.02573651820421219,
-0.026444479823112488,
... |
720,096 | textwrap3 | __init__ | null | def __init__(self,
width=70,
initial_indent="",
subsequent_indent="",
expand_tabs=True,
replace_whitespace=True,
fix_sentence_endings=False,
break_long_words=True,
drop_whitespace=True,
break_on_hyphens=... | (self, width=70, initial_indent='', subsequent_indent='', expand_tabs=True, replace_whitespace=True, fix_sentence_endings=False, break_long_words=True, drop_whitespace=True, break_on_hyphens=True, tabsize=8, max_lines=None, placeholder=' [...]') | [
0.0077306064777076244,
0.03222671151161194,
0.013414209708571434,
-0.04814985767006874,
-0.03628448769450188,
-0.034672245383262634,
-0.004755205940455198,
-0.02632119692862034,
-0.0008876384235918522,
0.018549831584095955,
-0.037280816584825516,
0.012888872995972633,
-0.01641225442290306,
... |
720,098 | textwrap3 | _handle_long_word | _handle_long_word(chunks : [string],
cur_line : [string],
cur_len : int, width : int)
Handle a chunk of text (most likely a word, not whitespace) that
is too long to fit in any line.
| def _handle_long_word(self, reversed_chunks, cur_line, cur_len, width):
"""_handle_long_word(chunks : [string],
cur_line : [string],
cur_len : int, width : int)
Handle a chunk of text (most likely a word, not whitespace) that
is too long to fit in any line.
... | (self, reversed_chunks, cur_line, cur_len, width) | [
0.0036506301257759333,
0.053742848336696625,
-0.04137816280126572,
0.0810844823718071,
0.019522272050380707,
-0.010361955501139164,
-0.0033415129873901606,
0.005807919427752495,
0.034394726157188416,
-0.04702064022421837,
-0.003742059227079153,
-0.027985988184809685,
-0.019731253385543823,
... |
720,099 | textwrap3 | _munge_whitespace | _munge_whitespace(text : string) -> string
Munge whitespace in text: expand tabs and convert all other
whitespace characters to spaces. Eg. " foo\tbar\n\nbaz"
becomes " foo bar baz".
| def _munge_whitespace(self, text):
"""_munge_whitespace(text : string) -> string
Munge whitespace in text: expand tabs and convert all other
whitespace characters to spaces. Eg. " foo\\tbar\\n\\nbaz"
becomes " foo bar baz".
"""
if self.expand_tabs:
text = text.expandtabs(self.tabsiz... | (self, text) | [
0.013998201116919518,
0.0036895922385156155,
0.07764830440282822,
0.013015545904636383,
-0.04972606152296066,
-0.06129543483257294,
-0.024955734610557556,
-0.0037475316785275936,
0.023120207712054253,
0.01660316437482834,
-0.020969491451978683,
-0.045016732066869736,
-0.026290662586688995,
... |
720,105 | textwrap3 | _translate |
Shim for Python 2 str translate, which uses an archic form of str.translate
that expects a string or buffer based table. But in Python 2,
unicode.translate is compatible with Python 3 model of a dict-based mapping
table, so wash through the unicode type, then back-map to str type.
Could alternative... | def _translate(s, mapping):
"""
Shim for Python 2 str translate, which uses an archic form of str.translate
that expects a string or buffer based table. But in Python 2,
unicode.translate is compatible with Python 3 model of a dict-based mapping
table, so wash through the unicode type, then back-map... | (s, mapping) | [
0.012150956317782402,
-0.005665476433932781,
-0.002108246786519885,
0.031346485018730164,
-0.06854481250047684,
-0.043944716453552246,
0.04129834100604057,
0.05877931788563728,
0.0427519828081131,
-0.035763319581747055,
0.009173785336315632,
0.03192421421408653,
-0.011619817465543747,
0.06... |
720,106 | textwrap3 | dedent | Remove any common leading whitespace from every line in `text`.
This can be used to make triple-quoted strings line up with the left
edge of the display, while still presenting them in the source code
in indented form.
Note that tabs and spaces are both treated as whitespace, but they
are not equa... | def dedent(text):
"""Remove any common leading whitespace from every line in `text`.
This can be used to make triple-quoted strings line up with the left
edge of the display, while still presenting them in the source code
in indented form.
Note that tabs and spaces are both treated as whitespace, ... | (text) | [
-0.03881186246871948,
0.03612884506583214,
0.03083631955087185,
0.011540648527443409,
0.005430352874100208,
-0.07064053416252136,
0.010125633329153061,
0.001485077547840774,
0.007782587315887213,
-0.013516157865524292,
-0.036239106208086014,
-0.018202248960733414,
-0.009969430044293404,
0.... |
720,110 | textwrap3 | shorten | Collapse and truncate the given text to fit in the given width.
The text first has its whitespace collapsed. If it then fits in
the *width*, it is returned as is. Otherwise, as many words
as possible are joined and then the placeholder is appended::
>>> textwrap.shorten("Hello world!", width=12... | def shorten(text, width, **kwargs):
"""Collapse and truncate the given text to fit in the given width.
The text first has its whitespace collapsed. If it then fits in
the *width*, it is returned as is. Otherwise, as many words
as possible are joined and then the placeholder is appended::
>>>... | (text, width, **kwargs) | [
-0.0035299682058393955,
-0.028805602341890335,
0.06404781341552734,
0.010043942369520664,
0.012997002340853214,
-0.03582575172185898,
0.015216219238936901,
-0.07688567042350769,
0.08530277758836746,
0.013598225079476833,
-0.04052942991256714,
-0.031953174620866776,
-0.007860092446208,
-0.0... |
720,114 | dagster_spark.types | SparkOpError | null | class SparkOpError(Exception):
pass
| null | [
0.014678414911031723,
-0.05707988888025284,
0.015120894648134708,
0.07168172299861908,
-0.0007573211332783103,
-0.034887827932834625,
-0.04101447016000748,
0.03099060244858265,
0.0658954456448555,
-0.018822407349944115,
0.03921051323413849,
0.006233008112758398,
0.05449308454990387,
-0.047... |
720,117 | dagster_spark.utils | construct_spark_shell_command | Constructs the spark-submit command for a Spark job. | def construct_spark_shell_command(
application_jar,
main_class,
master_url=None,
spark_conf=None,
deploy_mode=None,
application_arguments=None,
spark_home=None,
):
"""Constructs the spark-submit command for a Spark job."""
check.opt_str_param(master_url, "master_url")
check.str_p... | (application_jar, main_class, master_url=None, spark_conf=None, deploy_mode=None, application_arguments=None, spark_home=None) | [
0.05039892718195915,
-0.002004502806812525,
-0.02814219519495964,
-0.033822786062955856,
0.03261216729879379,
-0.026633581146597862,
-0.002551608718931675,
0.00728232879191637,
0.04239023104310036,
-0.016389895230531693,
0.007626888807862997,
-0.024417219683527946,
0.002162814373150468,
-0... |
720,118 | dagster_spark.ops | create_spark_op | null | def create_spark_op(
name, main_class, description=None, required_resource_keys=frozenset(["spark"])
):
check.str_param(name, "name")
check.str_param(main_class, "main_class")
check.opt_str_param(description, "description", "A parameterized Spark job.")
check.set_param(required_resource_keys, "requi... | (name, main_class, description=None, required_resource_keys=frozenset({'spark'})) | [
0.05099339410662651,
-0.05531792342662811,
-0.011532075703144073,
-0.02861396223306656,
0.029298678040504456,
-0.022992074489593506,
-0.01937028206884861,
0.02189292386174202,
0.039821699261665344,
-0.031352829188108444,
-0.02906443364918232,
-0.008176061324775219,
0.012324905954301357,
-0... |
720,119 | dagster_spark.configs | define_spark_config | Spark configuration.
See the Spark documentation for reference:
https://spark.apache.org/docs/latest/submitting-applications.html
| def define_spark_config():
"""Spark configuration.
See the Spark documentation for reference:
https://spark.apache.org/docs/latest/submitting-applications.html
"""
master_url = Field(
StringSource,
description="The master URL for the cluster (e.g. spark://23.195.26.187:7077)",
... | () | [
0.0616898350417614,
-0.030651045963168144,
-0.02679300867021084,
-0.004735305439680815,
0.0353621169924736,
0.023090066388249397,
-0.040441546589136124,
-0.01209756638854742,
-0.009213731624186039,
0.0022331546060740948,
-0.027355235069990158,
-0.03687431290745735,
0.008859916590154171,
0.... |
720,125 | pansi | ANSI | null | class ANSI(Mapping, object):
def __init__(self, **codes):
self.__codes = dict(codes)
def __getitem__(self, key):
return self.__codes[key]
def __len__(self):
return len(self.__codes) # pragma: no cover
def __iter__(self):
return iter(self.__codes) # pragma: no cov... | (**codes) | [
0.06067344918847084,
-0.0550776943564415,
-0.02963285520672798,
-0.04071878269314766,
0.0011558837722986937,
-0.02456500381231308,
-0.018810881301760674,
0.00000756967756387894,
0.10114587843418121,
-0.054620180279016495,
-0.029615258798003197,
0.02139759622514248,
0.02386113628745079,
0.0... |
720,127 | pansi | __dir__ | null | def __dir__(self):
return list(self.__codes) # pragma: no cover
| (self) | [
0.018364867195487022,
-0.02116968296468258,
0.008251668885350227,
-0.025760900229215622,
0.03723059222102165,
-0.027931293472647667,
0.0013481482164934278,
-0.018715469166636467,
0.028114940971136093,
-0.027931293472647667,
-0.032338861376047134,
-0.03178791329264641,
0.014616764150559902,
... |
720,129 | pansi | __getattr__ | null | def __getattr__(self, name):
try:
return self.__codes[name]
except KeyError:
raise AttributeError(name)
| (self, name) | [
0.07627719640731812,
-0.07226964086294174,
0.013692491687834263,
0.0009507515351288021,
-0.021958081051707268,
-0.053434114903211594,
0.02015467919409275,
-0.0162055641412735,
0.07200247049331665,
-0.022792989388108253,
-0.030924992635846138,
0.06569056212902069,
0.058543749153614044,
0.01... |
720,130 | pansi | __getitem__ | null | def __getitem__(self, key):
return self.__codes[key]
| (self, key) | [
0.05580427125096321,
-0.06789075583219528,
-0.05024382099509239,
-0.001266292529180646,
0.01208648644387722,
-0.020710177719593048,
-0.01134564820677042,
-0.021192971616983414,
0.11527111381292343,
-0.09069859236478806,
-0.03762459754943848,
0.026736771687865257,
0.054206058382987976,
-0.0... |
720,131 | pansi | __init__ | null | def __init__(self, **codes):
self.__codes = dict(codes)
| (self, **codes) | [
0.018293261528015137,
-0.025326529517769814,
0.014658277854323387,
-0.014337047003209591,
-0.04551336169242859,
-0.01643350161612034,
-0.04534429311752319,
0.02945181168615818,
0.0711442157626152,
-0.021336501464247704,
-0.019899416714906693,
0.07398457080125809,
0.006999454461038113,
0.02... |
720,132 | pansi | __iter__ | null | def __iter__(self):
return iter(self.__codes) # pragma: no cover
| (self) | [
0.039030443876981735,
-0.059832945466041565,
-0.05598825961351395,
-0.011705700308084488,
-0.03978564962744713,
-0.030276913195848465,
-0.015318675898015499,
0.016477230936288834,
0.09714701026678085,
-0.021265927702188492,
-0.00808414164930582,
0.007672210689634085,
0.014280267059803009,
... |
720,133 | pansi | __len__ | null | def __len__(self):
return len(self.__codes) # pragma: no cover
| (self) | [
-0.006300262175500393,
-0.03816857933998108,
0.0010755301918834448,
0.028756923973560333,
0.008963091298937798,
-0.01473731268197298,
-0.024467037990689278,
-0.026473334059119225,
0.059471212327480316,
-0.0735316053032875,
-0.0014557887334376574,
-0.002438548021018505,
0.02631022036075592,
... |
720,148 | pansi | RGB | null | class RGB(object):
def __init__(self, bg=False):
if bg:
self.__template = "\x1b[48;2;%s;%s;%sm"
else:
self.__template = "\x1b[38;2;%s;%s;%sm"
def __getitem__(self, code):
if len(code) == 4 and code[0] == "#":
# rgb[#XXX]
r = int(code[1], ... | (bg=False) | [
0.03373674675822258,
-0.08049199730157852,
-0.05898650363087654,
-0.009783080779016018,
-0.0006294437916949391,
-0.017185194417834282,
0.0072197020053863525,
0.04420147463679314,
0.03519604727625847,
-0.026152219623327255,
0.0575656034052372,
0.018433282151818275,
0.07150577008724213,
0.02... |
720,149 | pansi | __getitem__ | null | def __getitem__(self, code):
if len(code) == 4 and code[0] == "#":
# rgb[#XXX]
r = int(code[1], 16) * 17
g = int(code[2], 16) * 17
b = int(code[3], 16) * 17
elif len(code) == 7 and code[0] == "#":
# rgb[#XXXXXX]
r = int(code[1:3], 16)
g = int(code[3:5], 16... | (self, code) | [
0.05244096368551254,
-0.06251858174800873,
-0.03191247209906578,
-0.025175394490361214,
0.020043272525072098,
-0.005920603405684233,
0.0027363544795662165,
0.006111891474574804,
0.051470525562763214,
-0.031501900404691696,
0.06050306186079979,
0.029654337093234062,
0.06472073495388031,
0.0... |
720,150 | pansi | __init__ | null | def __init__(self, bg=False):
if bg:
self.__template = "\x1b[48;2;%s;%s;%sm"
else:
self.__template = "\x1b[38;2;%s;%s;%sm"
| (self, bg=False) | [
0.04172753170132637,
-0.05776869133114815,
0.039435938000679016,
-0.029123764485120773,
-0.028234489262104034,
-0.012518259696662426,
-0.029568402096629143,
0.062146659940481186,
0.019564056769013405,
-0.002138749696314335,
0.027328113093972206,
0.04343767836689949,
-0.014980868436396122,
... |
720,151 | poetrip.pipfile | PipFile | null | class PipFile:
_SOURCE_DEFAULT: Dict[str, str] = {
"url": "https://pypi.org/simple",
"verify_ssl": True,
"name": "pypi"
}
def __init__(
self,
source: dict = None,
requires: dict = None,
packages: dict = None,
dev_packages:... | (source: dict = None, requires: dict = None, packages: dict = None, dev_packages: dict = None) | [
0.019170226529240608,
-0.038396645337343216,
-0.014038187451660633,
-0.050533730536699295,
0.03680459037423134,
-0.06034828722476959,
-0.01889864169061184,
0.04794897884130478,
0.01694134809076786,
-0.006091955583542585,
0.04742453992366791,
-0.015246277675032616,
0.007075284142047167,
0.0... |
720,152 | poetrip.pipfile | __init__ | null | def __init__(
self,
source: dict = None,
requires: dict = None,
packages: dict = None,
dev_packages: dict = None
):
self._source: dict = source or self._SOURCE_DEFAULT
self._requires: dict = requires or {}
self._packages: dict = packages or {}
self._dev_packages: ... | (self, source: Optional[dict] = None, requires: Optional[dict] = None, packages: Optional[dict] = None, dev_packages: Optional[dict] = None) | [
0.010696894489228725,
-0.01481249462813139,
0.0008747942047193646,
-0.03631627559661865,
0.0028987941332161427,
-0.04612405598163605,
-0.010211088694632053,
0.07662899792194366,
0.02764510177075863,
-0.014482513070106506,
0.004360794555395842,
0.04011106118559837,
0.0012190976412966847,
0.... |
720,153 | poetrip.pipfile | to_file | Create a Pipfile on disk. | def to_file(self, filename: str = 'Pipfile') -> None:
"""Create a Pipfile on disk."""
with open(filename, 'w') as file:
toml.dump(self.attributes, file)
| (self, filename: str = 'Pipfile') -> NoneType | [
-0.0075452630408108234,
0.0033423430286347866,
0.021540509536862373,
-0.03730906546115875,
0.04301147535443306,
-0.07061949372291565,
-0.06223972886800766,
0.024635110050439835,
0.011126655153930187,
-0.0005229202215559781,
0.005880611017346382,
-0.05052196979522705,
0.007393140811473131,
... |
720,154 | poetrip.pyproject | PyProject | null | class PyProject:
def __init__(
self,
infos: dict = None,
dependencies: dict = None,
dev_dependencies: dict = None
):
self._infos = infos or {}
self._dependencies = dependencies or {}
self._dev_dependencies = dev_dependencies or {}
@cl... | (infos: dict = None, dependencies: dict = None, dev_dependencies: dict = None) | [
0.0691794604063034,
-0.022826574742794037,
-0.03952571749687195,
-0.026381999254226685,
-0.014685033820569515,
-0.050116341561079025,
0.015176741406321526,
0.03716174140572548,
0.008628521114587784,
-0.0006961916224099696,
0.05185622721910477,
-0.017814941704273224,
-0.006760978139936924,
... |
720,155 | poetrip.pyproject | __init__ | null | def __init__(
self,
infos: dict = None,
dependencies: dict = None,
dev_dependencies: dict = None
):
self._infos = infos or {}
self._dependencies = dependencies or {}
self._dev_dependencies = dev_dependencies or {}
| (self, infos: Optional[dict] = None, dependencies: Optional[dict] = None, dev_dependencies: Optional[dict] = None) | [
0.00044993023038841784,
-0.004924835171550512,
0.007180727552622557,
-0.01749337837100029,
-0.013181310147047043,
-0.024347295984625816,
0.014606562443077564,
0.06924726814031601,
-0.005864412058144808,
0.023130839690566063,
0.012237193994224072,
0.04814990982413292,
-0.012391520664095879,
... |
720,156 | poetrip.pyproject | _get_requires | null | def _get_requires(self) -> dict:
python_version: str = self._dependencies.pop('python')
return {'python-version': python_version}
| (self) -> dict | [
0.05799483880400658,
0.022644251585006714,
-0.006708092987537384,
3.119463087841723e-7,
0.05831427127122879,
0.012874924577772617,
0.021863413974642754,
0.0556168369948864,
0.03389539197087288,
-0.022999176755547523,
0.0437978133559227,
-0.008868702687323093,
-0.012795066460967064,
0.02090... |
720,157 | poetrip.pyproject | to_pipfile | null | def to_pipfile(self) -> 'PipFile':
return PipFile(
requires=self._get_requires(),
packages=self._dependencies,
dev_packages=self._dev_dependencies
)
| (self) -> poetrip.pipfile.PipFile | [
0.0012288384605199099,
-0.02802993170917034,
-0.0075040762312710285,
-0.06996447592973709,
-0.007117837201803923,
-0.05274924263358116,
0.014888602308928967,
0.04782009497284889,
-0.011651549488306046,
0.0071040429174900055,
0.06154078245162964,
-0.023229530081152916,
-0.01277348306030035,
... |
720,161 | sphinx_rtd_theme | config_initiated | null | def config_initiated(app, config):
theme_options = config.html_theme_options or {}
if theme_options.get('canonical_url'):
logger.warning(
_('The canonical_url option is deprecated, use the html_baseurl option from Sphinx instead.')
)
| (app, config) | [
-0.024816833436489105,
-0.03104894608259201,
0.06563252210617065,
-0.06827419996261597,
0.024426164105534554,
0.049782492220401764,
0.012250288389623165,
-0.001609187456779182,
0.016994135454297066,
0.019347457215189934,
-0.030993137508630753,
-0.0063995434902608395,
0.03063967451453209,
0... |
720,162 | sphinx_rtd_theme | extend_html_context | null | def extend_html_context(app, pagename, templatename, context, doctree):
# Add ``sphinx_version_info`` tuple for use in Jinja templates
context['sphinx_version_info'] = sphinx_version
| (app, pagename, templatename, context, doctree) | [
0.025548521429300308,
-0.051097042858600616,
0.05237264931201935,
-0.023307103663682938,
0.04264161363244057,
0.030632713809609413,
0.0065966942347586155,
-0.006291460245847702,
0.010751517489552498,
0.0025284290313720703,
0.023653339594602585,
-0.09526938199996948,
-0.0012835763627663255,
... |
720,164 | sphinx_rtd_theme | get_html_theme_path | Return list of HTML theme paths. | def get_html_theme_path():
"""Return list of HTML theme paths."""
cur_dir = path.abspath(path.dirname(path.dirname(__file__)))
return cur_dir
| () | [
-0.012514631263911724,
-0.0415026918053627,
0.04857505485415459,
-0.05809686705470085,
0.03587929904460907,
0.02440827339887619,
0.03153238445520401,
0.0303249079734087,
-0.02665073052048683,
0.05430193990468979,
0.05961483716964722,
-0.009996180422604084,
0.01368760783225298,
0.0163181815... |
720,166 | sphinx_rtd_theme | setup | null | def setup(app):
if python_version[0] < 3:
logger.error("Python 2 is not supported with sphinx_rtd_theme, update to Python 3.")
app.require_sphinx('5.0')
if app.config.html4_writer:
logger.error("'html4_writer' is not supported with sphinx_rtd_theme.")
# Since Sphinx 6, jquery isn't bun... | (app) | [
0.016336478292942047,
-0.032766468822956085,
0.043763432651758194,
-0.024967599660158157,
0.0498603917658329,
0.07331310212612152,
-0.006087605841457844,
0.02971798926591873,
-0.02984890528023243,
0.03961151838302612,
0.05191764608025551,
-0.010585513897240162,
0.11109179258346558,
0.04911... |
720,171 | sqlparse | format | Format *sql* according to *options*.
Available options are documented in :ref:`formatting`.
In addition to the formatting options this function accepts the
keyword "encoding" which determines the encoding of the statement.
:returns: The formatted SQL statement as string.
| def format(sql, encoding=None, **options):
"""Format *sql* according to *options*.
Available options are documented in :ref:`formatting`.
In addition to the formatting options this function accepts the
keyword "encoding" which determines the encoding of the statement.
:returns: The formatted SQL ... | (sql, encoding=None, **options) | [
0.0017238357104361057,
-0.0034632517490535975,
0.03785538673400879,
0.008261946961283684,
-0.008809479884803295,
-0.009579585865139961,
-0.039173025637865067,
-0.03888813033699989,
-0.03575428947806358,
-0.03587893024086952,
-0.034988634288311005,
-0.05559008568525314,
-0.033777832984924316,... |
720,175 | sqlparse | parse | Parse sql and return a list of statements.
:param sql: A string containing one or more SQL statements.
:param encoding: The encoding of the statement (optional).
:returns: A tuple of :class:`~sqlparse.sql.Statement` instances.
| def parse(sql, encoding=None):
"""Parse sql and return a list of statements.
:param sql: A string containing one or more SQL statements.
:param encoding: The encoding of the statement (optional).
:returns: A tuple of :class:`~sqlparse.sql.Statement` instances.
"""
return tuple(parsestream(sql, ... | (sql, encoding=None) | [
-0.03415549919009209,
0.008247369900345802,
-0.0067270551808178425,
0.02793073281645775,
-0.015149327926337719,
0.062032416462898254,
-0.0037850895896553993,
0.043124906718730927,
-0.02968873642385006,
-0.024002132937312126,
-0.03975241258740425,
0.0321822315454483,
-0.04025469720363617,
0... |
720,176 | sqlparse | parsestream | Parses sql statements from file-like object.
:param stream: A file-like object.
:param encoding: The encoding of the stream contents (optional).
:returns: A generator of :class:`~sqlparse.sql.Statement` instances.
| def parsestream(stream, encoding=None):
"""Parses sql statements from file-like object.
:param stream: A file-like object.
:param encoding: The encoding of the stream contents (optional).
:returns: A generator of :class:`~sqlparse.sql.Statement` instances.
"""
stack = engine.FilterStack()
s... | (stream, encoding=None) | [
-0.024484828114509583,
0.012207855470478535,
-0.0010205610888078809,
0.013106380589306355,
-0.026022689417004585,
0.03165575489401817,
-0.002499024849385023,
0.039224106818437576,
-0.01174131315201521,
-0.0012300731614232063,
-0.03794543445110321,
0.02531423605978489,
-0.056434329599142075,
... |
720,177 | sqlparse | split | Split *sql* into single statements.
:param sql: A string containing one or more SQL statements.
:param encoding: The encoding of the statement (optional).
:param strip_semicolon: If True, remove trainling semicolons
(default: False).
:returns: A list of strings.
| def split(sql, encoding=None, strip_semicolon=False):
"""Split *sql* into single statements.
:param sql: A string containing one or more SQL statements.
:param encoding: The encoding of the statement (optional).
:param strip_semicolon: If True, remove trainling semicolons
(default: False).
... | (sql, encoding=None, strip_semicolon=False) | [
-0.034251559525728226,
0.01598465070128441,
-0.019319577142596245,
0.0310670118778944,
-0.02443254180252552,
0.04419441521167755,
0.014896596781909466,
0.049961984157562256,
-0.04387596249580383,
-0.005179309751838446,
-0.02100030891597271,
-0.01146436482667923,
-0.033791568130254745,
-0.0... |
720,181 | mldesigner._get_io_context | IOContext | Component IO context, includes outputs information
and support operations on them (e.g. mark early available output ready).
You can use `get_io_context` to get this during runtime.
| class IOContext:
"""Component IO context, includes outputs information
and support operations on them (e.g. mark early available output ready).
You can use `get_io_context` to get this during runtime.
"""
def __init__(self):
self._outputs = OutputContext()
@property
def outputs(se... | () | [
0.011004097759723663,
-0.05636937543749809,
-0.05136428773403168,
-0.06140996515750885,
0.06311382353305817,
0.021795213222503662,
-0.039756741374731064,
0.02404927834868431,
0.03398846089839935,
-0.05615639314055443,
0.04004071652889252,
-0.013071803376078606,
-0.02229217253625393,
0.0403... |
720,182 | mldesigner._get_io_context | __init__ | null | def __init__(self):
self._outputs = OutputContext()
| (self) | [
-0.005463908426463604,
-0.06788677722215652,
-0.0046092187985777855,
-0.045664846897125244,
-0.001889256527647376,
0.023844096809625626,
-0.05920033901929855,
0.07179392874240875,
0.020617207512259483,
-0.009253323078155518,
0.012680803425610065,
0.024820884689688683,
-0.00734335370361805,
... |
720,183 | mldesigner._input_output | Input | Define an input of a component.
Default to be a uri_folder Input.
:param type: The type of the data input. Possible values include:
'uri_folder', 'uri_file', 'mltable', 'mlflow_model', 'custom_model',
'integer', 'number', 'string', 'boolean'
:type type: str
... | class Input(_IOBase):
"""Define an input of a component.
Default to be a uri_folder Input.
:param type: The type of the data input. Possible values include:
'uri_folder', 'uri_file', 'mltable', 'mlflow_model', 'custom_model',
'integer', 'number', 'string', '... | (*, type: str = 'uri_folder', path: str = None, mode: str = None, min: Union[int, float] = None, max: Union[int, float] = None, enum=None, optional: bool = None, description: str = None, **kwargs) | [
0.04673798754811287,
-0.059691302478313446,
-0.0373828150331974,
-0.018606187775731087,
-0.008692356757819653,
-0.021683547645807266,
-0.013852851465344429,
-0.0016227152664214373,
-0.022384239360690117,
-0.07192499190568924,
0.011277338489890099,
0.047003112733364105,
-0.03030015341937542,
... |
720,184 | mldesigner._input_output | __init__ | null | def __init__(
self,
*,
type: str = "uri_folder",
path: str = None,
mode: str = None,
min: Union[int, float] = None,
max: Union[int, float] = None,
enum=None,
optional: bool = None,
description: str = None,
**kwargs,
):
# As an annotation, it is not allowed to initialize t... | (self, *, type: str = 'uri_folder', path: Optional[str] = None, mode: Optional[str] = None, min: Union[int, float, NoneType] = None, max: Union[int, float, NoneType] = None, enum=None, optional: Optional[bool] = None, description: Optional[str] = None, **kwargs) | [
0.059765737503767014,
-0.02028662897646427,
-0.001135069178417325,
-0.04376602545380592,
0.021775390952825546,
0.00479811942204833,
0.039066556841135025,
-0.026977090165019035,
-0.06593602895736694,
-0.04369427636265755,
0.022349372506141663,
0.09714622795581818,
-0.007685959804803133,
0.0... |
720,185 | mldesigner._input_output | _normalize_self_properties | null | def _normalize_self_properties(self):
# parse value from string to it's original type. eg: "false" -> False
if self.type in IoConstants.PARAM_PARSERS:
for key in ["default", "min", "max"]:
if getattr(self, key) is not None:
origin_value = getattr(self, key)
ne... | (self) | [
0.04875735193490982,
0.01439910102635622,
0.06044771149754524,
-0.019050296396017075,
-0.03945496305823326,
-0.04679707810282707,
0.03425132855772972,
-0.004479670897126198,
-0.01869388297200203,
0.015370327979326248,
-0.023808414116501808,
0.040595486760139465,
0.0352136455476284,
0.04227... |
720,186 | mldesigner._input_output | _to_io_entity_args_dict | Convert the Input object to a kwargs dict for azure.ai.ml.entity.Input. | def _to_io_entity_args_dict(self):
"""Convert the Input object to a kwargs dict for azure.ai.ml.entity.Input."""
keys = self._IO_KEYS
result = {key: getattr(self, key, None) for key in keys}
result = {**self._kwargs, **result}
return _remove_empty_values(result)
| (self) | [
-0.03393102064728737,
-0.04346342012286186,
-0.03083163872361183,
-0.023840010166168213,
-0.03385894373059273,
-0.0217677503824234,
0.004793229512870312,
0.013721971772611141,
0.024326542392373085,
-0.026344742625951767,
0.020866766571998596,
0.03773316740989685,
-0.06696105748414993,
0.03... |
720,187 | mldesigner._input_output | Meta | This is the meta data of Inputs/Outputs. | class Meta(object):
"""This is the meta data of Inputs/Outputs."""
def __init__(
self,
type=None,
description=None,
min=None,
max=None,
**kwargs,
):
self.type = type
self.description = description
self._min = min
self._max = ma... | (type=None, description=None, min=None, max=None, **kwargs) | [
0.007816995494067669,
-0.06023630127310753,
-0.003449492622166872,
-0.042988840490579605,
0.0039154523983597755,
-0.014641798101365566,
-0.03241781145334244,
-0.004807962104678154,
-0.0069407131522893906,
-0.04198737442493439,
-0.0023286393843591213,
0.07281026244163513,
-0.04603032767772674... |
720,188 | mldesigner._input_output | __init__ | null | def __init__(
self,
type=None,
description=None,
min=None,
max=None,
**kwargs,
):
self.type = type
self.description = description
self._min = min
self._max = max
self._default = kwargs.pop("default", None)
self._kwargs = kwargs
| (self, type=None, description=None, min=None, max=None, **kwargs) | [
0.0505528599023819,
0.0238192118704319,
0.05749712884426117,
-0.035279057919979095,
-0.018979810178279877,
-0.008289049379527569,
0.007713358849287033,
-0.035279057919979095,
-0.04224132001399994,
-0.03403772413730621,
-0.016074370592832565,
0.10671870410442352,
-0.037240006029605865,
0.01... |
720,189 | mldesigner._input_output | _to_io_entity_args_dict | Convert the object to a kwargs dict for azure.ai.ml.entity.Output. | def _to_io_entity_args_dict(self):
"""Convert the object to a kwargs dict for azure.ai.ml.entity.Output."""
keys = set(Output._IO_KEYS + Input._IO_KEYS)
result = {key: getattr(self, key, None) for key in keys}
result.update(self._kwargs)
if IoConstants.PRIMITIVE_TYPE_2_STR.get(self.type) is not None... | (self) | [
-0.025662420317530632,
-0.052081845700740814,
-0.012235069647431374,
-0.030828969553112984,
-0.019947044551372528,
-0.027725255116820335,
0.019852418452501297,
0.019029177725315094,
-0.011809255927801132,
-0.03637402132153511,
0.03932633623480797,
0.04610151797533035,
-0.06862237304449081,
... |
720,190 | mldesigner._input_output | Output | Define an output of a component.
:param type: The type of the data output. Possible values include:
'uri_folder', 'uri_file', 'mltable', 'mlflow_model', 'custom_model', and user-defined types.
:type type: str
:param path: The path to which the output is pointing. Needs to point to a... | class Output(_IOBase):
"""Define an output of a component.
:param type: The type of the data output. Possible values include:
'uri_folder', 'uri_file', 'mltable', 'mlflow_model', 'custom_model', and user-defined types.
:type type: str
:param path: The path to which the output is... | (*, type: str = 'uri_folder', path=None, mode=None, description=None, early_available=None, **kwargs) | [
0.016467854380607605,
-0.08975532650947571,
-0.06370380520820618,
-0.02245890349149704,
0.03819418326020241,
0.000273774319794029,
-0.049253035336732864,
0.022197986021637917,
-0.020251145586371422,
-0.08429615199565887,
0.025188492611050606,
0.06113477796316147,
-0.012082448229193687,
0.0... |
720,191 | mldesigner._input_output | __init__ | null | def __init__(
self,
*,
type: str = "uri_folder",
path=None,
mode=None,
description=None,
early_available=None,
**kwargs,
):
# As an annotation, it is not allowed to initialize the _port_name.
# The _port_name will be updated by the annotated variable name.
self.path = path
... | (self, *, type: str = 'uri_folder', path=None, mode=None, description=None, early_available=None, **kwargs) | [
0.0293644480407238,
-0.0279412642121315,
-0.0034093386493623257,
-0.0483882874250412,
0.05055008828639984,
0.004737944807857275,
0.030499393120408058,
0.012133101932704449,
-0.030931754037737846,
-0.04067786782979965,
0.027256693691015244,
0.07508651912212372,
-0.0003797223907895386,
0.050... |
720,192 | mldesigner._input_output | _to_io_entity_args_dict | Convert the Output object to a kwargs dict for azure.ai.ml.entity.Output. | def _to_io_entity_args_dict(self):
"""Convert the Output object to a kwargs dict for azure.ai.ml.entity.Output."""
keys = self._IO_KEYS
result = {key: getattr(self, key) for key in keys}
result.update(self._kwargs)
return _remove_empty_values(result)
| (self) | [
-0.029273655265569687,
-0.04714105278253555,
-0.02703571878373623,
-0.04053553193807602,
-0.01561141386628151,
-0.008220807649195194,
-0.007345485966652632,
0.02864198014140129,
0.02364271879196167,
-0.03515726327896118,
0.027216197922825813,
0.04371195659041405,
-0.06789611279964447,
0.02... |
720,193 | mldesigner._input_output | ready | Mark early available output ready. | def ready(self) -> None:
"""Mark early available output ready."""
execute_logger = _LoggerFactory.get_logger("execute", target_stdout=True)
# validate
if self._ready is True:
execute_logger.warning(
"Output '%s' has already been marked as ready, ignore current operation.", self._port... | (self) -> NoneType | [
-0.007032960187643766,
-0.04287128522992134,
-0.08870998024940491,
-0.013031230308115482,
0.06184709817171097,
0.045370157808065414,
-0.0305135790258646,
-0.00655954098328948,
-0.014036636799573898,
-0.049704138189554214,
0.05848924070596695,
0.05208587646484375,
0.06383838504552841,
0.003... |
720,194 | mldesigner._get_io_context | OutputContext | Component outputs context, output can be accessed with `.<name>`. | class OutputContext:
"""Component outputs context, output can be accessed with `.<name>`."""
def __init__(self):
self._outputs: typing.Dict[str, Output] = dict()
def __setattr__(self, name: str, value: str):
if name == "_outputs":
super(OutputContext, self).__setattr__(name, va... | () | [
0.026260223239660263,
-0.04964311048388481,
-0.034637268632650375,
-0.08449891209602356,
0.045636698603630066,
0.009119144640862942,
-0.03520181030035019,
0.01416813675314188,
0.04716641828417778,
-0.07368159294128418,
0.02159821428358555,
0.059185661375522614,
-0.0214525256305933,
0.02263... |
720,195 | mldesigner._get_io_context | __getattr__ | null | def __getattr__(self, name: str) -> Output:
if name == "_outputs":
return super(OutputContext, self).__getattribute__(name)
if name not in self._outputs.keys():
error_message = f"Output {name!r} not found, please check the spelling of the name."
raise UserErrorException(error_message)
... | (self, name: str) -> mldesigner._input_output.Output | [
0.027005787938833237,
-0.1022498607635498,
0.0011890024179592729,
-0.05140316113829613,
0.00877296831458807,
-0.030692346394062042,
-0.016772106289863586,
0.021980242803692818,
0.04208242520689964,
-0.013963714241981506,
0.020102182403206825,
0.057385124266147614,
-0.007277477066963911,
0.... |
720,196 | mldesigner._get_io_context | __init__ | null | def __init__(self):
self._outputs: typing.Dict[str, Output] = dict()
| (self) | [
-0.014557728543877602,
-0.029187479987740517,
-0.0423857681453228,
-0.06269636005163193,
-0.02513616345822811,
0.018816111609339714,
-0.05441367253661156,
0.07512039691209793,
0.0361737497150898,
-0.04397027939558029,
0.009732160717248917,
0.08268285542726517,
-0.024397924542427063,
0.0466... |
720,197 | mldesigner._get_io_context | __setattr__ | null | def __setattr__(self, name: str, value: str):
if name == "_outputs":
super(OutputContext, self).__setattr__(name, value)
else:
# note: we cannot know the Output type now, so specify `string` here;
# and we cannot validate value type, neither.
self._outputs[name] = Output(ty... | (self, name: str, value: str) | [
0.043597638607025146,
-0.02901790849864483,
-0.014508954249322414,
-0.05223223567008972,
0.002169707790017128,
0.009006167761981487,
-0.01779116317629814,
0.06295470148324966,
0.025089874863624573,
-0.0444115549325943,
-0.002934966702014208,
0.046888694167137146,
0.0008570456993766129,
0.0... |
720,198 | mldesigner._get_root_pipeline_context | PipelineContext | Pipeline context, including root pipeline job name and init and/or execution stage information.
You can use `get_root_pipeline_context` to get this during pipeline runtime.
:param root_job_name: Root pipeline job name.
:type root_job_name: str
:param initialization_stage: Initialization stage informat... | class PipelineContext:
"""Pipeline context, including root pipeline job name and init and/or execution stage information.
You can use `get_root_pipeline_context` to get this during pipeline runtime.
:param root_job_name: Root pipeline job name.
:type root_job_name: str
:param initialization_stage:... | (root_job_name: str, initialization_stage: Optional[mldesigner._get_root_pipeline_context.PipelineStage], execution_stage: Optional[mldesigner._get_root_pipeline_context.PipelineStage]) | [
0.024013979360461235,
-0.06591368466615677,
-0.06156202033162117,
-0.013374369591474533,
0.0019849459640681744,
0.00326624047011137,
0.0077301859855651855,
0.0168576929718256,
0.04623140022158623,
-0.005379690323024988,
-0.006312902085483074,
0.039364561438560486,
-0.02131914347410202,
-0.... |
720,199 | mldesigner._get_root_pipeline_context | __init__ | null | def __init__(
self,
root_job_name: str,
initialization_stage: typing.Optional[PipelineStage],
execution_stage: typing.Optional[PipelineStage],
):
self.root_job_name = root_job_name
self.stages = {
STAGE_INIT: initialization_stage,
STAGE_EXECUTION: execution_stage,
}
| (self, root_job_name: str, initialization_stage: Optional[mldesigner._get_root_pipeline_context.PipelineStage], execution_stage: Optional[mldesigner._get_root_pipeline_context.PipelineStage]) | [
0.002294476144015789,
-0.0375761017203331,
-0.08030066639184952,
0.003834933042526245,
-0.03980454057455063,
-0.0021215800661593676,
-0.0013855707366019487,
0.04272456467151642,
0.015243679285049438,
-0.022015444934368134,
0.007372100371867418,
0.04122612997889519,
-0.010489034466445446,
-... |
720,200 | mldesigner._get_root_pipeline_context | _from_job_properties | null | @staticmethod
def _from_job_properties(properties: typing.Dict) -> "PipelineContext":
try:
root_job_name = properties["rootRunId"]
stages = json.loads(properties["properties"]["azureml.pipelines.stages"])
init_stage = PipelineStage._from_stage(stages.get(STAGE_INIT))
execution_stage ... | (properties: Dict) -> mldesigner._get_root_pipeline_context.PipelineContext | [
0.018272198736667633,
-0.049923159182071686,
-0.05149712786078453,
-0.029804568737745285,
-0.037795502692461014,
-0.021954888477921486,
0.011542459949851036,
0.06094096228480339,
0.016940375789999962,
0.005332333967089653,
-0.021672381088137627,
0.09120965003967285,
-0.04487837478518486,
0... |
720,201 | mldesigner._get_root_pipeline_context | PipelineStage | Pipeline stage, valid stages are "Initialization" and "Execution".
:param start_time: Stage start time, and you can get this in the string format of ISO 8601
by calling `pipeline_stage.start_time.isoformat()`.
:type start_time: datetime.datetime
:param end_time: Stage end time, similar to start_tim... | class PipelineStage:
"""Pipeline stage, valid stages are "Initialization" and "Execution".
:param start_time: Stage start time, and you can get this in the string format of ISO 8601
by calling `pipeline_stage.start_time.isoformat()`.
:type start_time: datetime.datetime
:param end_time: Stage en... | (start_time: str, end_time: str, status: str) | [
0.0245375894010067,
-0.03127521649003029,
-0.1085444912314415,
0.019618934020400047,
-0.03634236007928848,
-0.057464733719825745,
-0.026950513944029808,
0.014561072923243046,
0.017651472240686417,
-0.028379708528518677,
0.004644880071282387,
0.05245327204465866,
0.03159075230360031,
0.0177... |
720,202 | mldesigner._get_root_pipeline_context | __init__ | null | def __init__(self, start_time: str, end_time: str, status: str):
self.start_time = self._parse_time(start_time)
self.end_time = self._parse_time(end_time)
self.status = status
| (self, start_time: str, end_time: str, status: str) | [
0.020660532638430595,
0.04993106424808502,
-0.06742767989635468,
0.013373157009482384,
-0.05155624449253082,
-0.05442624166607857,
-0.019830653443932533,
0.07517322152853012,
0.007300343364477158,
-0.03488950431346893,
-0.014868668280541897,
0.03506239503622055,
0.026608001440763474,
0.030... |
720,203 | mldesigner._get_root_pipeline_context | _from_stage | null | @staticmethod
def _from_stage(stage: typing.Optional[typing.Dict[str, str]]) -> typing.Optional["PipelineStage"]:
if stage is None:
return None
return PipelineStage(stage["StartTime"], stage["EndTime"], stage["Status"])
| (stage: Optional[Dict[str, str]]) -> Optional[mldesigner._get_root_pipeline_context.PipelineStage] | [
0.009462298825383186,
-0.05002520978450775,
-0.10276413708925247,
-0.02253098413348198,
-0.05777358263731003,
-0.04716866835951805,
0.025048311799764633,
0.06523630023002625,
0.03318946063518524,
-0.008502678945660591,
-0.04652594402432442,
0.061808452010154724,
-0.025048311799764633,
-0.0... |
720,204 | mldesigner._get_root_pipeline_context | _parse_time | null | @staticmethod
def _parse_time(time_string: str) -> datetime.datetime:
# %f for 6 digits, but backend may return different digit ms
ms_start_index, ms_end_index = time_string.index("."), time_string.index("+")
ms = time_string[ms_start_index + 1 : ms_end_index]
normalized_ms = ms.ljust(6, "0")[:6]
no... | (time_string: str) -> datetime.datetime | [
0.00010713069787016138,
0.0413549542427063,
-0.031251829117536545,
0.056245751678943634,
-0.04176963493227959,
-0.08172975480556488,
0.015399725176393986,
0.10864628851413727,
0.041468046605587006,
-0.05843225121498108,
0.022618936374783516,
0.023165559396147728,
-0.04180733114480972,
0.04... |
720,205 | mldesigner._utils | check_main_package | null | def check_main_package(logger=None):
if logger is None:
logger = _LoggerFactory.get_logger("mldesigner")
version = get_package_version("azure-ai-ml")
target_version = "1.2.0"
version_to_check = pkg_resources.parse_version(target_version)
msg = (
f"Mldesigner requires azure-ai-ml >= {... | (logger=None) | [
0.05382188409566879,
-0.013239356689155102,
0.01673477329313755,
0.006596187595278025,
0.051754701882600784,
-0.03134598582983017,
0.04352356493473053,
0.004056843463331461,
-0.022344350814819336,
0.014038040302693844,
-0.00764857092872262,
0.0035940767265856266,
0.010899683460593224,
-0.0... |
720,219 | mldesigner._component | command_component | Return a decorator which is used to declare a component with @command_component.
A component is a reusable unit in an Azure Machine Learning workspace.
With the decorator @command_component, a function could be registered as a component in the workspace.
Then the component could be used to construct an Azu... | def command_component(
func=None,
*,
name=None,
version=None,
display_name=None,
description=None,
is_deterministic=None,
tags=None,
environment: Union[str, dict, PathLike, "Environment"] = None,
distribution: Union[dict, "PyTorchDistribution", "MpiDistribution", "TensorFlowDistr... | (func=None, *, name=None, version=None, display_name=None, description=None, is_deterministic=None, tags=None, environment: Union[str, dict, os.PathLike, ForwardRef('Environment')] = None, distribution: Union[dict, ForwardRef('PyTorchDistribution'), ForwardRef('MpiDistribution'), ForwardRef('TensorFlowDistribution')] =... | [
0.036622799932956696,
-0.04750645160675049,
0.008776037953794003,
-0.021455783396959305,
0.04957025498151779,
0.028211822733283043,
-0.07320666313171387,
-0.021183203905820847,
-0.029964109882712364,
-0.029574712738394737,
0.04174337536096573,
0.021027445793151855,
-0.0514783039689064,
-0.... |
720,220 | mldesigner._compile._compile | compile | Compile sdk-defined components/pipelines to yaml files, or build yaml components/pipelines with snapshot.
A component can be defined through sdk using @mldesigner.command_component decorator, and a pipeline can be
defined using @dsl.pipeline decorator. Such components or pipelines can be compiled into yaml fil... | def compile(
source: Union[str, FunctionType],
*,
name=None,
output=None,
ignore_file=None,
debug=False,
**kwargs,
):
"""Compile sdk-defined components/pipelines to yaml files, or build yaml components/pipelines with snapshot.
A component can be defined through sdk using @mldesigner... | (source: Union[str, function], *, name=None, output=None, ignore_file=None, debug=False, **kwargs) | [
-0.031012777239084244,
-0.08073395490646362,
-0.03741680830717087,
-0.04137435555458069,
-0.029681602492928505,
0.000817368330899626,
-0.05846375972032547,
0.032919593155384064,
-0.004758050665259361,
-0.008184926584362984,
0.01779097132384777,
0.02192840725183487,
-0.004312826786190271,
0... |
720,221 | mldesigner._execute._execute | execute | Execute a mldesigner component node.
A mldesigner component node can be generated by calling the @command_component decorated function. The necessary
inputs and outputs are passed during the calling process. Returned result will be a dictionary that contains
function execution result, output file/folder pa... | def execute(source: Union[ExecutorBase, "Command", "Parallel"]):
"""Execute a mldesigner component node.
A mldesigner component node can be generated by calling the @command_component decorated function. The necessary
inputs and outputs are passed during the calling process. Returned result will be a dicti... | (source: Union[mldesigner._component_executor.ExecutorBase, ForwardRef('Command'), ForwardRef('Parallel')]) | [
0.03898714482784271,
-0.05077223479747772,
-0.03961716592311859,
0.010923442430794239,
0.023977836593985558,
0.006161229219287634,
-0.0635950043797493,
0.0024181667249649763,
-0.0015889485366642475,
-0.03053745999932289,
0.05410763621330261,
-0.0005718246102333069,
-0.007750177755951881,
0... |
720,222 | mldesigner._generate._generate_package | generate | For a set of source assets, generate a python module which contains component consumption functions and import
it for use.
Supported source types:
- components: component consumption functions
:param source: List[source_identifier], dict[module_relative_path, List[source_identifier]] or str
... | def generate(
*,
source: Union[list, dict, str],
package_name: str = None,
force_regenerate: bool = False,
**kwargs,
) -> None:
"""For a set of source assets, generate a python module which contains component consumption functions and import
it for use.
Supported source types:
- ... | (*, source: Union[list, dict, str], package_name: Optional[str] = None, force_regenerate: bool = False, **kwargs) -> NoneType | [
0.008729961700737476,
-0.0464460514485836,
-0.009535659104585648,
-0.011886397376656532,
0.014151827432215214,
-0.007047477178275585,
-0.07283499091863632,
0.055545687675476074,
0.012530955485999584,
-0.042275384068489075,
0.010426664724946022,
0.025478975847363472,
-0.013677888549864292,
... |
720,223 | mldesigner._get_io_context | get_io_context | Get `IOContext` that contains component outputs information during runtime.
Outputs can be accessed via `.outputs` from `IOContext` object.
Early available output can be marked as ready with below code.
.. code-block:: python
from mldesigner import get_io_context
ctx = get... | def get_io_context() -> IOContext:
"""Get `IOContext` that contains component outputs information during runtime.
Outputs can be accessed via `.outputs` from `IOContext` object.
Early available output can be marked as ready with below code.
.. code-block:: python
from mldesigner import... | () -> mldesigner._get_io_context.IOContext | [
0.027795644477009773,
-0.0361139141023159,
-0.057596586644649506,
-0.058302152901887894,
0.06650901585817337,
0.039845991879701614,
0.004272860940545797,
0.041182857006788254,
0.016682956367731094,
-0.04757010191679001,
0.03917756304144859,
0.02218823879957199,
-0.023692212998867035,
0.032... |
720,224 | mldesigner._get_root_pipeline_context | get_root_pipeline_context | Get root pipeline job information, including init/execution stage status and start/end time.
Both init and execution stage are optional: for pipeline job without init job, init stage will be None;
for pipeline job whose init job fails, execution stage will be None. This function will only work during runtime.
... | def get_root_pipeline_context() -> PipelineContext:
"""Get root pipeline job information, including init/execution stage status and start/end time.
Both init and execution stage are optional: for pipeline job without init job, init stage will be None;
for pipeline job whose init job fails, execution stage w... | () -> mldesigner._get_root_pipeline_context.PipelineContext | [
0.05962555482983589,
-0.04670001566410065,
-0.07103044539690018,
0.045619551092386246,
0.004939617123454809,
0.01263541541993618,
-0.015826784074306488,
0.028372159227728844,
0.026071174070239067,
-0.015356581658124924,
-0.023710161447525024,
-0.007158068008720875,
-0.008318564854562283,
-... |
720,225 | mldesigner._reference_component | reference_component | Reference an existing component with a function and return a component node built with given params.
The referenced component can be defined with local yaml file or in remote with name and version.
The returned component node type are hint with function return annotation and default to Command.
If the refer... | def reference_component(path: Union[PathLike, str] = None, name=None, version=None, registry=None, **kwargs) -> _TFunc:
"""Reference an existing component with a function and return a component node built with given params.
The referenced component can be defined with local yaml file or in remote with name and ... | (path: Union[os.PathLike, str, NoneType] = None, name=None, version=None, registry=None, **kwargs) -> ~_TFunc | [
0.03283733129501343,
-0.07403253018856049,
-0.0025895542930811644,
-0.020498573780059814,
0.0013702851720154285,
0.052999213337898254,
-0.056366126984357834,
0.0034411856904625893,
0.00991259515285492,
-0.04598810523748398,
0.046819932758808136,
0.0183595921844244,
-0.03578833118081093,
-0... |
720,226 | ete3.coretype.arraytable | ArrayTable | This object is thought to work with matrix datasets (like
microarrays). It allows to load the matrix an access easily to row
and column vectors. | class ArrayTable(object):
"""This object is thought to work with matrix datasets (like
microarrays). It allows to load the matrix an access easily to row
and column vectors. """
def __repr__(self):
return "ArrayTable (%s)" %hex(self.__hash__())
def __str__(self):
return str(self.ma... | (matrix_file=None, mtype='float') | [
0.03381866216659546,
-0.039244115352630615,
-0.0020659423898905516,
0.057509809732437134,
-0.006163918413221836,
-0.07575540989637375,
-0.06514563411474228,
0.005852457135915756,
-0.04119326174259186,
-0.022385019809007645,
-0.08017615228891373,
-0.02093823254108429,
-0.02700670436024666,
... |
720,227 | ete3.coretype.arraytable | __init__ | null | def __init__(self, matrix_file=None, mtype="float"):
self.colNames = []
self.rowNames = []
self.colValues = {}
self.rowValues = {}
self.matrix = None
self.mtype = None
# If matrix file is supplied
if matrix_file is not None:
read_arraytable(matrix_file, \
... | (self, matrix_file=None, mtype='float') | [
0.03329043835401535,
-0.020929118618369102,
0.038416314870119095,
0.018042342737317085,
-0.016746995970606804,
-0.037139471620321274,
-0.03602917119860649,
0.037768639624118805,
-0.09385719895362854,
-0.0014457006473094225,
-0.0437457449734211,
0.05470068380236626,
-0.04822394624352455,
0.... |
720,228 | ete3.coretype.arraytable | __repr__ | null | def __repr__(self):
return "ArrayTable (%s)" %hex(self.__hash__())
| (self) | [
0.012702533975243568,
-0.017423013225197792,
0.02951347827911377,
-0.0012754937633872032,
0.010857941582798958,
-0.04229147359728813,
-0.054063327610492706,
0.013574523851275444,
-0.03093884512782097,
-0.03783091530203819,
-0.02406354621052742,
-0.004217409063130617,
0.00010847461089724675,
... |
720,229 | ete3.coretype.arraytable | __str__ | null | def __str__(self):
return str(self.matrix)
| (self) | [
0.014381252229213715,
-0.022817818447947502,
0.05547286942601204,
0.03612414002418518,
-0.013843786902725697,
-0.05410477891564369,
-0.02553771622478962,
0.02392532117664814,
0.033941708505153656,
-0.009478922933340073,
-0.03255733102560043,
-0.017866628244519234,
-0.02982114627957344,
0.0... |
720,230 | ete3.coretype.arraytable | _link_names2matrix | Synchronize curent column and row names to the given matrix | def _link_names2matrix(self, m):
""" Synchronize curent column and row names to the given matrix"""
if len(self.rowNames) != m.shape[0]:
raise ValueError("Expecting matrix with %d rows" % m.size[0])
if len(self.colNames) != m.shape[1]:
raise ValueError("Expecting matrix with %d columns" % ... | (self, m) | [
0.018347997218370438,
-0.02819111756980419,
0.0009921257151290774,
0.021869687363505363,
-0.060678694397211075,
-0.02711700275540352,
-0.002658874960616231,
0.06409472972154617,
0.0028349594213068485,
0.020637094974517822,
-0.060396961867809296,
0.008799819275736809,
0.004600205924361944,
... |
720,231 | ete3.coretype.arraytable | get_column_vector | Returns the vector associated to the given column name | def get_column_vector(self,colname):
""" Returns the vector associated to the given column name """
return self.colValues.get(colname,None)
| (self, colname) | [
0.08980327099561691,
-0.05713217705488205,
-0.004599123261868954,
0.030238498002290726,
0.01191802229732275,
-0.018362708389759064,
0.005388871766626835,
0.029275596141815186,
0.07297782599925995,
0.0028422498144209385,
-0.033701565116643906,
0.01623418740928173,
-0.05547666177153587,
-0.0... |
720,232 | ete3.coretype.arraytable | get_row_vector | Returns the vector associated to the given row name | def get_row_vector(self,rowname):
""" Returns the vector associated to the given row name """
return self.rowValues.get(rowname,None)
| (self, rowname) | [
0.09943068027496338,
-0.08261322975158691,
-0.02013404294848442,
0.018810756504535675,
-0.02800675481557846,
-0.02167508378624916,
-0.01652432046830654,
0.042144134640693665,
0.07591304928064346,
-0.004149924498051405,
-0.028542770072817802,
0.010536033660173416,
-0.053065430372953415,
-0.... |
720,233 | ete3.coretype.arraytable | get_several_column_vectors | Returns a list of vectors associated to several column names | def get_several_column_vectors(self,colnames):
""" Returns a list of vectors associated to several column names """
vectors = [self.colValues[cname] for cname in colnames]
return numpy.array(vectors)
| (self, colnames) | [
0.002970371162518859,
-0.03509688004851341,
-0.04818650335073471,
0.00410463148728013,
-0.019382378086447716,
-0.0012678944040089846,
0.0056843385100364685,
0.03311518579721451,
0.05889461562037468,
0.0526713952422142,
-0.017183391377329826,
0.020129859447479248,
-0.024093251675367355,
-0.... |
720,234 | ete3.coretype.arraytable | get_several_row_vectors | Returns a list vectors associated to several row names | def get_several_row_vectors(self,rownames):
""" Returns a list vectors associated to several row names """
vectors = [self.rowValues[rname] for rname in rownames]
return numpy.array(vectors)
| (self, rownames) | [
0.021410517394542694,
-0.06481485068798065,
-0.053629230707883835,
-0.008569354191422462,
-0.05517325550317764,
0.001903228578157723,
-0.020672814920544624,
0.048894211649894714,
0.06433448940515518,
0.05098722502589226,
-0.0007167934090830386,
0.0055027431808412075,
-0.023023169487714767,
... |
720,235 | ete3.coretype.arraytable | merge_columns | Returns a new ArrayTable object in which columns are
merged according to a given criterion.
'groups' argument must be a dictionary in which keys are the
new column names, and each value is the list of current
column names to be merged.
'grouping_criterion' must be 'min', 'max'... | def merge_columns(self, groups, grouping_criterion):
""" Returns a new ArrayTable object in which columns are
merged according to a given criterion.
'groups' argument must be a dictionary in which keys are the
new column names, and each value is the list of current
column names to be merged.
'gr... | (self, groups, grouping_criterion) | [
0.009500106796622276,
-0.015093798749148846,
-0.01526802871376276,
0.03147139772772789,
0.0007513658492825925,
-0.06345631182193756,
-0.053185924887657166,
-0.012929681688547134,
-0.036643270403146744,
-0.02457556501030922,
-0.034497492015361786,
-0.028005141764879227,
-0.0025744738522917032... |
720,236 | ete3.coretype.arraytable | remove_column | Removes the given column form the current dataset | def remove_column(self,colname):
"""Removes the given column form the current dataset """
col_value = self.colValues.pop(colname, None)
if col_value is not None:
new_indexes = list(range(len(self.colNames)))
index = self.colNames.index(colname)
self.colNames.pop(index)
new_in... | (self, colname) | [
0.01588759385049343,
0.02582511119544506,
-0.005976719316095114,
0.038364678621292114,
-0.01602080464363098,
-0.03150876611471176,
-0.019679658114910126,
0.07886072993278503,
0.042591895908117294,
-0.046108659356832504,
-0.021153857931494713,
0.014733102172613144,
-0.04060261696577072,
0.0... |
720,237 | ete3.coretype.arraytable | transpose | Returns a new ArrayTable in which current matrix is transposed. | def transpose(self):
""" Returns a new ArrayTable in which current matrix is transposed. """
transposedA = self.__class__()
transposedM = self.matrix.transpose()
transposedA.colNames = list(self.rowNames)
transposedA.rowNames = list(self.colNames)
transposedA._link_names2matrix(transposedM)
... | (self) | [
-0.010772150941193104,
-0.024090779945254326,
0.022625179961323738,
-0.013501828536391258,
-0.04836475849151611,
-0.046715959906578064,
-0.00472655612975359,
0.018457384780049324,
-0.0018755084602162242,
0.002014053286984563,
-0.027901336550712585,
-0.03358053043484688,
-0.01464682724326849,... |
720,238 | ete3.coretype.arraytable | write | null | def write(self, fname, colnames=None):
write_arraytable(self, fname, colnames=colnames)
| (self, fname, colnames=None) | [
-0.03426288440823555,
0.01562442909926176,
-0.010280597023665905,
-0.0036159639712423086,
-0.019660450518131256,
-0.032045669853687286,
-0.052347034215927124,
0.04846690967679024,
-0.06914936006069183,
-0.020491905510425568,
-0.025982975959777832,
0.03358732536435127,
0.03512898087501526,
... |
720,239 | ete3.clustering.clustertree | ClusterNode | Creates a new Cluster Tree object, which is a collection
of ClusterNode instances connected in a hierarchical way, and
representing a clustering result.
a newick file or string can be passed as the first argument. An
ArrayTable file or instance can be passed as a second argument.
Examples:
... | class ClusterNode(TreeNode):
""" Creates a new Cluster Tree object, which is a collection
of ClusterNode instances connected in a hierarchical way, and
representing a clustering result.
a newick file or string can be passed as the first argument. An
ArrayTable file or instance can be passed as a se... | (newick=None, text_array=None, fdist=<function spearman_dist at 0x7ff39b713d90>) | [
0.016363466158509254,
0.014288795180618763,
0.024713004007935524,
0.02735719457268715,
0.010230980813503265,
-0.017695732414722443,
-0.06057228520512581,
0.008090203627943993,
-0.017736410722136497,
-0.019170375540852547,
-0.03177095577120781,
0.027479233220219612,
-0.022150173783302307,
0... |
720,240 | ete3.coretype.tree | __add__ | This allows to sum two trees. | def __add__(self, value):
""" This allows to sum two trees."""
# Should a make the sum with two copies of the original trees?
if type(value) == self.__class__:
new_root = self.__class__()
new_root.add_child(self)
new_root.add_child(value)
return new_root
else:
rai... | (self, value) | [
-0.02803601138293743,
-0.05330894514918327,
0.06303497403860092,
0.03192273899912834,
-0.054193127900362015,
0.011457554996013641,
-0.058761414140462875,
0.02262038178741932,
0.05054587125778198,
0.015086394734680653,
0.021367786452174187,
-0.014478517696261406,
0.023154577240347862,
0.033... |
720,241 | ete3.coretype.tree | __and__ | This allows to execute tree&'A' to obtain the descendant node
whose name is A | def __and__(self, value):
""" This allows to execute tree&'A' to obtain the descendant node
whose name is A"""
value=str(value)
try:
first_match = next(self.iter_search_nodes(name=value))
return first_match
except StopIteration:
raise TreeError("Node not found")
| (self, value) | [
0.04360830783843994,
-0.04938517510890961,
0.02271999977529049,
-0.015384433791041374,
-0.029201362282037735,
0.006882046349346638,
0.009105611592531204,
-0.02109965868294239,
0.07118932902812958,
-0.04561612010002136,
0.016837457194924355,
0.047377362847328186,
0.039768803864717484,
0.014... |
720,242 | ete3.coretype.tree | __bool__ |
Python3's equivalent of __nonzero__
If this is not defined bool(class_instance) will call
__len__ in python3
| def __bool__(self):
"""
Python3's equivalent of __nonzero__
If this is not defined bool(class_instance) will call
__len__ in python3
"""
return True
| (self) | [
0.0310823991894722,
-0.0478786863386631,
-0.01619822531938553,
-0.009383684024214745,
-0.015069488435983658,
0.00044854680891148746,
-0.0005701598711311817,
0.00874350406229496,
-0.01052926853299141,
-0.01344376988708973,
0.0015067382482811809,
0.0013456405140459538,
0.033205099403858185,
... |
720,243 | ete3.coretype.tree | __contains__ | Check if item belongs to this node. The 'item' argument must
be a node instance or its associated name. | def __contains__(self, item):
""" Check if item belongs to this node. The 'item' argument must
be a node instance or its associated name."""
if isinstance(item, self.__class__):
return item in set(self.get_descendants())
elif type(item)==str:
return item in set([n.name for n in self.trav... | (self, item) | [
0.03317003324627876,
-0.03154900670051575,
-0.009368835017085075,
0.0030110999941825867,
-0.02407137118279934,
0.0023531029000878334,
0.022450344637036324,
0.010946285910904408,
0.05326727405190468,
0.0008350246935151517,
-0.0042987703345716,
-0.004540617112070322,
0.05870555713772774,
0.0... |
720,244 | ete3.clustering.clustertree | __init__ | null | def __init__(self, newick = None, text_array = None, \
fdist=clustvalidation.default_dist):
# Default dist is spearman_dist when scipy module is loaded
# otherwise, it is set to euclidean_dist.
# Initialize basic tree features and loads the newick (if any)
TreeNode.__init__(self, newick)
... | (self, newick=None, text_array=None, fdist=<function spearman_dist at 0x7ff39b713d90>) | [
0.03193956986069679,
0.01925525814294815,
0.05575239285826683,
0.007467818912118673,
-0.030127525329589844,
-0.022751223295927048,
-0.04623458534479141,
0.012116902507841587,
-0.0015981224132701755,
0.022659705951809883,
0.011238335631787777,
0.03657034784555435,
-0.027510127052664757,
-0.... |
720,245 | ete3.coretype.tree | __iter__ | Iterator over leaf nodes | def __iter__(self):
""" Iterator over leaf nodes"""
return self.iter_leaves()
| (self) | [
0.009892316535115242,
-0.026615867391228676,
-0.001093321479856968,
0.013956467621028423,
-0.04894275590777397,
0.009382136166095734,
-0.03936173766851425,
0.027912935242056847,
0.07090646773576736,
0.004777539521455765,
0.011198033578693867,
-0.013826761394739151,
0.008357451297342777,
0.... |
720,246 | ete3.coretype.tree | __len__ | Node len returns number of children. | def __len__(self):
"""Node len returns number of children."""
return len(self.get_leaves())
| (self) | [
-0.02354341931641102,
-0.011122350580990314,
0.025041939690709114,
0.03301739692687988,
-0.01375308632850647,
0.06633450090885162,
-0.007201222702860832,
0.011205601505935192,
0.04129255935549736,
-0.02246115356683731,
0.006722528487443924,
-0.024708934128284454,
0.04455600306391716,
0.000... |
720,248 | ete3.clustering.clustertree | __repr__ | null | def __repr__(self):
return "ClusterTree node (%s)" %hex(self.__hash__())
| (self) | [
0.02932180091738701,
-0.003941487520933151,
0.06684290617704391,
0.025508953258395195,
0.02444608137011528,
-0.009768310002982616,
-0.024513565003871918,
0.03367451950907707,
0.02235407754778862,
-0.032409194856882095,
-0.011101119220256805,
0.003005147213116288,
-0.008034814149141312,
0.0... |
720,249 | ete3.coretype.tree | __str__ | Print tree in newick format. | def __str__(self):
""" Print tree in newick format. """
return self.get_ascii(compact=DEFAULT_COMPACT, \
show_internal=DEFAULT_SHOWINTERNAL)
| (self) | [
-0.0007760486332699656,
-0.01945875957608223,
0.07173033058643341,
-0.007556235883384943,
0.034670762717723846,
-0.036263298243284225,
-0.016729887574911118,
-0.0012348348973318934,
0.07929486036300659,
-0.05328349024057388,
-0.010077746585011482,
-0.034670762717723846,
-0.003212022362276911... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.