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pennersr/django-allauth | allauth/socialaccount/providers/dataporten/provider.py | DataportenProvider.extract_common_fields | def extract_common_fields(self, data):
'''
This function extracts information from the /userinfo endpoint which
will be consumed by allauth.socialaccount.adapter.populate_user().
Look there to find which key-value pairs that should be saved in the
returned dict.
Typical ... | python | def extract_common_fields(self, data):
'''
This function extracts information from the /userinfo endpoint which
will be consumed by allauth.socialaccount.adapter.populate_user().
Look there to find which key-value pairs that should be saved in the
returned dict.
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pennersr/django-allauth | allauth/account/decorators.py | verified_email_required | def verified_email_required(function=None,
login_url=None,
redirect_field_name=REDIRECT_FIELD_NAME):
"""
Even when email verification is not mandatory during signup, there
may be circumstances during which you really want to prevent
unverified user... | python | def verified_email_required(function=None,
login_url=None,
redirect_field_name=REDIRECT_FIELD_NAME):
"""
Even when email verification is not mandatory during signup, there
may be circumstances during which you really want to prevent
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pennersr/django-allauth | allauth/socialaccount/providers/oauth2/client.py | OAuth2Client._strip_empty_keys | def _strip_empty_keys(self, params):
"""Added because the Dropbox OAuth2 flow doesn't
work when scope is passed in, which is empty.
"""
keys = [k for k, v in params.items() if v == '']
for key in keys:
del params[key] | python | def _strip_empty_keys(self, params):
"""Added because the Dropbox OAuth2 flow doesn't
work when scope is passed in, which is empty.
"""
keys = [k for k, v in params.items() if v == '']
for key in keys:
del params[key] | [
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pennersr/django-allauth | allauth/compat.py | int_to_base36 | def int_to_base36(i):
"""
Django on py2 raises ValueError on large values.
"""
if six.PY2:
char_set = '0123456789abcdefghijklmnopqrstuvwxyz'
if i < 0:
raise ValueError("Negative base36 conversion input.")
if not isinstance(i, six.integer_types):
raise Type... | python | def int_to_base36(i):
"""
Django on py2 raises ValueError on large values.
"""
if six.PY2:
char_set = '0123456789abcdefghijklmnopqrstuvwxyz'
if i < 0:
raise ValueError("Negative base36 conversion input.")
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pennersr/django-allauth | allauth/socialaccount/models.py | SocialLogin.save | def save(self, request, connect=False):
"""
Saves a new account. Note that while the account is new,
the user may be an existing one (when connecting accounts)
"""
assert not self.is_existing
user = self.user
user.save()
self.account.user = user
se... | python | def save(self, request, connect=False):
"""
Saves a new account. Note that while the account is new,
the user may be an existing one (when connecting accounts)
"""
assert not self.is_existing
user = self.user
user.save()
self.account.user = user
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pennersr/django-allauth | allauth/socialaccount/models.py | SocialLogin.lookup | def lookup(self):
"""
Lookup existing account, if any.
"""
assert not self.is_existing
try:
a = SocialAccount.objects.get(provider=self.account.provider,
uid=self.account.uid)
# Update account
a.extra_d... | python | def lookup(self):
"""
Lookup existing account, if any.
"""
assert not self.is_existing
try:
a = SocialAccount.objects.get(provider=self.account.provider,
uid=self.account.uid)
# Update account
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pennersr/django-allauth | allauth/socialaccount/providers/base.py | Provider.sociallogin_from_response | def sociallogin_from_response(self, request, response):
"""
Instantiates and populates a `SocialLogin` model based on the data
retrieved in `response`. The method does NOT save the model to the
DB.
Data for `SocialLogin` will be extracted from `response` with the
help of... | python | def sociallogin_from_response(self, request, response):
"""
Instantiates and populates a `SocialLogin` model based on the data
retrieved in `response`. The method does NOT save the model to the
DB.
Data for `SocialLogin` will be extracted from `response` with the
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pennersr/django-allauth | allauth/socialaccount/providers/eventbrite/provider.py | EventbriteProvider.extract_common_fields | def extract_common_fields(self, data):
"""Extract fields from a basic user query."""
email = None
for curr_email in data.get("emails", []):
email = email or curr_email.get("email")
if curr_email.get("verified", False) and \
curr_email.get("primary", Fa... | python | def extract_common_fields(self, data):
"""Extract fields from a basic user query."""
email = None
for curr_email in data.get("emails", []):
email = email or curr_email.get("email")
if curr_email.get("verified", False) and \
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pennersr/django-allauth | allauth/socialaccount/providers/dataporten/views.py | DataportenAdapter.complete_login | def complete_login(self, request, app, token, **kwargs):
'''
Arguments:
request - The get request to the callback URL
/accounts/dataporten/login/callback.
app - The corresponding SocialApp model instance
token - A token object with access token... | python | def complete_login(self, request, app, token, **kwargs):
'''
Arguments:
request - The get request to the callback URL
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app - The corresponding SocialApp model instance
token - A token object with access token... | [
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pennersr/django-allauth | allauth/socialaccount/providers/oauth/views.py | OAuthCallbackView.dispatch | def dispatch(self, request):
"""
View to handle final steps of OAuth based authentication where the user
gets redirected back to from the service provider
"""
login_done_url = reverse(self.adapter.provider_id + "_callback")
client = self._get_client(request, login_done_ur... | python | def dispatch(self, request):
"""
View to handle final steps of OAuth based authentication where the user
gets redirected back to from the service provider
"""
login_done_url = reverse(self.adapter.provider_id + "_callback")
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pennersr/django-allauth | allauth/account/views.py | ConfirmEmailView.login_on_confirm | def login_on_confirm(self, confirmation):
"""
Simply logging in the user may become a security issue. If you
do not take proper care (e.g. don't purge used email
confirmations), a malicious person that got hold of the link
will be able to login over and over again and the user is... | python | def login_on_confirm(self, confirmation):
"""
Simply logging in the user may become a security issue. If you
do not take proper care (e.g. don't purge used email
confirmations), a malicious person that got hold of the link
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pennersr/django-allauth | allauth/socialaccount/providers/orcid/provider.py | extract_from_dict | def extract_from_dict(data, path):
"""
Navigate `data`, a multidimensional array (list or dictionary), and returns
the object at `path`.
"""
value = data
try:
for key in path:
value = value[key]
return value
except (KeyError, IndexError, TypeError):
return... | python | def extract_from_dict(data, path):
"""
Navigate `data`, a multidimensional array (list or dictionary), and returns
the object at `path`.
"""
value = data
try:
for key in path:
value = value[key]
return value
except (KeyError, IndexError, TypeError):
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pennersr/django-allauth | allauth/socialaccount/providers/linkedin_oauth2/provider.py | _extract_email | def _extract_email(data):
"""
{'elements': [{'handle': 'urn:li:emailAddress:319371470',
'handle~': {'emailAddress': 'raymond.penners@intenct.nl'}}]}
"""
ret = ''
elements = data.get('elements', [])
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"""
{'elements': [{'handle': 'urn:li:emailAddress:319371470',
'handle~': {'emailAddress': 'raymond.penners@intenct.nl'}}]}
"""
ret = ''
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pennersr/django-allauth | allauth/socialaccount/providers/oauth/client.py | OAuthClient._get_request_token | def _get_request_token(self):
"""
Obtain a temporary request token to authorize an access token and to
sign the request to obtain the access token
"""
if self.request_token is None:
get_params = {}
if self.parameters:
get_params.update(self... | python | def _get_request_token(self):
"""
Obtain a temporary request token to authorize an access token and to
sign the request to obtain the access token
"""
if self.request_token is None:
get_params = {}
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pennersr/django-allauth | allauth/socialaccount/providers/oauth/client.py | OAuthClient.get_access_token | def get_access_token(self):
"""
Obtain the access token to access private resources at the API
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"""
if self.access_token is None:
request_token = self._get_rt_from_session()
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"""
Obtain the access token to access private resources at the API
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"""
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request_token = self._get_rt_from_session()
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pennersr/django-allauth | allauth/socialaccount/providers/oauth/client.py | OAuthClient.get_redirect | def get_redirect(self, authorization_url, extra_params):
"""
Returns a ``HttpResponseRedirect`` object to redirect the user
to the URL the OAuth provider handles authorization.
"""
request_token = self._get_request_token()
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"""
Returns a ``HttpResponseRedirect`` object to redirect the user
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"""
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pennersr/django-allauth | allauth/socialaccount/providers/oauth/client.py | OAuth._get_at_from_session | def _get_at_from_session(self):
"""
Get the saved access token for private resources from the session.
"""
try:
return self.request.session['oauth_%s_access_token'
% get_token_prefix(
self.req... | python | def _get_at_from_session(self):
"""
Get the saved access token for private resources from the session.
"""
try:
return self.request.session['oauth_%s_access_token'
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pennersr/django-allauth | allauth/socialaccount/providers/oauth/client.py | OAuth.query | def query(self, url, method="GET", params=dict(), headers=dict()):
"""
Request a API endpoint at ``url`` with ``params`` being either the
POST or GET data.
"""
access_token = self._get_at_from_session()
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"""
Request a API endpoint at ``url`` with ``params`` being either the
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"""
access_token = self._get_at_from_session()
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pennersr/django-allauth | allauth/account/utils.py | get_next_redirect_url | def get_next_redirect_url(request, redirect_field_name="next"):
"""
Returns the next URL to redirect to, if it was explicitly passed
via the request.
"""
redirect_to = get_request_param(request, redirect_field_name)
if not get_adapter(request).is_safe_url(redirect_to):
redirect_to = None... | python | def get_next_redirect_url(request, redirect_field_name="next"):
"""
Returns the next URL to redirect to, if it was explicitly passed
via the request.
"""
redirect_to = get_request_param(request, redirect_field_name)
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pennersr/django-allauth | allauth/account/utils.py | user_field | def user_field(user, field, *args):
"""
Gets or sets (optional) user model fields. No-op if fields do not exist.
"""
if not field:
return
User = get_user_model()
try:
field_meta = User._meta.get_field(field)
max_length = field_meta.max_length
except FieldDoesNotExist:... | python | def user_field(user, field, *args):
"""
Gets or sets (optional) user model fields. No-op if fields do not exist.
"""
if not field:
return
User = get_user_model()
try:
field_meta = User._meta.get_field(field)
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pennersr/django-allauth | allauth/account/utils.py | perform_login | def perform_login(request, user, email_verification,
redirect_url=None, signal_kwargs=None,
signup=False):
"""
Keyword arguments:
signup -- Indicates whether or not sending the
email is essential (during signup), or if it can be skipped (e.g. in
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redirect_url=None, signal_kwargs=None,
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"""
Keyword arguments:
signup -- Indicates whether or not sending the
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pennersr/django-allauth | allauth/account/utils.py | cleanup_email_addresses | def cleanup_email_addresses(request, addresses):
"""
Takes a list of EmailAddress instances and cleans it up, making
sure only valid ones remain, without multiple primaries etc.
Order is important: e.g. if multiple primary e-mail addresses
exist, the first one encountered will be kept as primary.
... | python | def cleanup_email_addresses(request, addresses):
"""
Takes a list of EmailAddress instances and cleans it up, making
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pennersr/django-allauth | allauth/account/utils.py | setup_user_email | def setup_user_email(request, user, addresses):
"""
Creates proper EmailAddress for the user that was just signed
up. Only sets up, doesn't do any other handling such as sending
out email confirmation mails etc.
"""
from .models import EmailAddress
assert not EmailAddress.objects.filter(use... | python | def setup_user_email(request, user, addresses):
"""
Creates proper EmailAddress for the user that was just signed
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out email confirmation mails etc.
"""
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pennersr/django-allauth | allauth/account/utils.py | send_email_confirmation | def send_email_confirmation(request, user, signup=False):
"""
E-mail verification mails are sent:
a) Explicitly: when a user signs up
b) Implicitly: when a user attempts to log in using an unverified
e-mail while EMAIL_VERIFICATION is mandatory.
Especially in case of b), we want to limit the nu... | python | def send_email_confirmation(request, user, signup=False):
"""
E-mail verification mails are sent:
a) Explicitly: when a user signs up
b) Implicitly: when a user attempts to log in using an unverified
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pennersr/django-allauth | allauth/account/utils.py | sync_user_email_addresses | def sync_user_email_addresses(user):
"""
Keep user.email in sync with user.emailaddress_set.
Under some circumstances the user.email may not have ended up as
an EmailAddress record, e.g. in the case of manually created admin
users.
"""
from .models import EmailAddress
email = user_email... | python | def sync_user_email_addresses(user):
"""
Keep user.email in sync with user.emailaddress_set.
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an EmailAddress record, e.g. in the case of manually created admin
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pennersr/django-allauth | allauth/account/utils.py | filter_users_by_email | def filter_users_by_email(email):
"""Return list of users by email address
Typically one, at most just a few in length. First we look through
EmailAddress table, than customisable User model table. Add results
together avoiding SQL joins and deduplicate.
"""
from .models import EmailAddress
... | python | def filter_users_by_email(email):
"""Return list of users by email address
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EmailAddress table, than customisable User model table. Add results
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pennersr/django-allauth | allauth/account/utils.py | user_pk_to_url_str | def user_pk_to_url_str(user):
"""
This should return a string.
"""
User = get_user_model()
if issubclass(type(User._meta.pk), models.UUIDField):
if isinstance(user.pk, six.string_types):
return user.pk
return user.pk.hex
ret = user.pk
if isinstance(ret, six.integ... | python | def user_pk_to_url_str(user):
"""
This should return a string.
"""
User = get_user_model()
if issubclass(type(User._meta.pk), models.UUIDField):
if isinstance(user.pk, six.string_types):
return user.pk
return user.pk.hex
ret = user.pk
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pennersr/django-allauth | example/example/demo/apps.py | setup_dummy_social_apps | def setup_dummy_social_apps(sender, **kwargs):
"""
`allauth` needs tokens for OAuth based providers. So let's
setup some dummy tokens
"""
from allauth.socialaccount.providers import registry
from allauth.socialaccount.models import SocialApp
from allauth.socialaccount.providers.oauth.provide... | python | def setup_dummy_social_apps(sender, **kwargs):
"""
`allauth` needs tokens for OAuth based providers. So let's
setup some dummy tokens
"""
from allauth.socialaccount.providers import registry
from allauth.socialaccount.models import SocialApp
from allauth.socialaccount.providers.oauth.provide... | [
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pennersr/django-allauth | allauth/socialaccount/fields.py | JSONField.value_from_object | def value_from_object(self, obj):
"""Return value dumped to string."""
val = super(JSONField, self).value_from_object(obj)
return self.get_prep_value(val) | python | def value_from_object(self, obj):
"""Return value dumped to string."""
val = super(JSONField, self).value_from_object(obj)
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pennersr/django-allauth | allauth/account/app_settings.py | AppSettings.EMAIL_VERIFICATION | def EMAIL_VERIFICATION(self):
"""
See e-mail verification method
"""
ret = self._setting("EMAIL_VERIFICATION",
self.EmailVerificationMethod.OPTIONAL)
# Deal with legacy (boolean based) setting
if ret is True:
ret = self.EmailVerific... | python | def EMAIL_VERIFICATION(self):
"""
See e-mail verification method
"""
ret = self._setting("EMAIL_VERIFICATION",
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# Deal with legacy (boolean based) setting
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pennersr/django-allauth | allauth/account/app_settings.py | AppSettings.PASSWORD_MIN_LENGTH | def PASSWORD_MIN_LENGTH(self):
"""
Minimum password Length
"""
from django.conf import settings
ret = None
if not settings.AUTH_PASSWORD_VALIDATORS:
ret = self._setting("PASSWORD_MIN_LENGTH", 6)
return ret | python | def PASSWORD_MIN_LENGTH(self):
"""
Minimum password Length
"""
from django.conf import settings
ret = None
if not settings.AUTH_PASSWORD_VALIDATORS:
ret = self._setting("PASSWORD_MIN_LENGTH", 6)
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blue-yonder/tsfresh | tsfresh/utilities/distribution.py | _function_with_partly_reduce | def _function_with_partly_reduce(chunk_list, map_function, kwargs):
"""
Small helper function to call a function (map_function)
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blue-yonder/tsfresh | tsfresh/utilities/distribution.py | DistributorBaseClass.partition | def partition(data, chunk_size):
"""
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data length, the last slice will be shorter than chunk_size.
:param data: The data to chunk.
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"""
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blue-yonder/tsfresh | tsfresh/utilities/distribution.py | DistributorBaseClass.calculate_best_chunk_size | def calculate_best_chunk_size(self, data_length):
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:param data_length: A length which defines how man... | python | def calculate_best_chunk_size(self, data_length):
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blue-yonder/tsfresh | tsfresh/utilities/distribution.py | DistributorBaseClass.map_reduce | def map_reduce(self, map_function, data, function_kwargs=None, chunk_size=None, data_length=None):
"""
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blue-yonder/tsfresh | tsfresh/utilities/distribution.py | MapDistributor.distribute | def distribute(self, func, partitioned_chunks, kwargs):
"""
Calculates the features in a sequential fashion by pythons map command
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:type func: callable
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blue-yonder/tsfresh | tsfresh/utilities/distribution.py | LocalDaskDistributor.distribute | def distribute(self, func, partitioned_chunks, kwargs):
"""
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blue-yonder/tsfresh | tsfresh/utilities/distribution.py | ClusterDaskDistributor.calculate_best_chunk_size | def calculate_best_chunk_size(self, data_length):
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blue-yonder/tsfresh | tsfresh/utilities/distribution.py | MultiprocessingDistributor.distribute | def distribute(self, func, partitioned_chunks, kwargs):
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blue-yonder/tsfresh | tsfresh/utilities/distribution.py | MultiprocessingDistributor.close | def close(self):
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self.pool.close()
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blue-yonder/tsfresh | tsfresh/feature_extraction/settings.py | from_columns | def from_columns(columns, columns_to_ignore=None):
"""
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blue-yonder/tsfresh | tsfresh/feature_extraction/feature_calculators.py | _roll | def _roll(a, shift):
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blue-yonder/tsfresh | tsfresh/feature_extraction/feature_calculators.py | _get_length_sequences_where | def _get_length_sequences_where(x):
"""
This method calculates the length of all sub-sequences where the array x is either True or 1.
Examples
--------
>>> x = [0,1,0,0,1,1,1,0,0,1,0,1,1]
>>> _get_length_sequences_where(x)
>>> [1, 3, 1, 2]
>>> x = [0,True,0,0,True,True,True,0,0,True,0,... | python | def _get_length_sequences_where(x):
"""
This method calculates the length of all sub-sequences where the array x is either True or 1.
Examples
--------
>>> x = [0,1,0,0,1,1,1,0,0,1,0,1,1]
>>> _get_length_sequences_where(x)
>>> [1, 3, 1, 2]
>>> x = [0,True,0,0,True,True,True,0,0,True,0,... | [
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blue-yonder/tsfresh | tsfresh/feature_extraction/feature_calculators.py | _estimate_friedrich_coefficients | def _estimate_friedrich_coefficients(x, m, r):
"""
Coefficients of polynomial :math:`h(x)`, which has been fitted to
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.. math::
\dot{x}(t) = h(x(t)) + \mathcal{N}(0,R)
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"""
Coefficients of polynomial :math:`h(x)`, which has been fitted to
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.. math::
\dot{x}(t) = h(x(t)) + \mathcal{N}(0,R)
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blue-yonder/tsfresh | tsfresh/feature_extraction/feature_calculators.py | _aggregate_on_chunks | def _aggregate_on_chunks(x, f_agg, chunk_len):
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blue-yonder/tsfresh | tsfresh/feature_extraction/feature_calculators.py | set_property | def set_property(key, value):
"""
This method returns a decorator that sets the property key of the function to value
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def decorate_func(func):
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"""
This method returns a decorator that sets the property key of the function to value
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blue-yonder/tsfresh | tsfresh/feature_extraction/feature_calculators.py | variance_larger_than_standard_deviation | def variance_larger_than_standard_deviation(x):
"""
Boolean variable denoting if the variance of x is greater than its standard deviation. Is equal to variance of x
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:param x: the time series to calculate the feature of
:type x: numpy.ndarray
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Boolean variable denoting if the variance of x is greater than its standard deviation. Is equal to variance of x
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blue-yonder/tsfresh | tsfresh/feature_extraction/feature_calculators.py | ratio_beyond_r_sigma | def ratio_beyond_r_sigma(x, r):
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Ratio of values that are more than r*std(x) (so r sigma) away from the mean of x.
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:type x: iterable
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:return type: float
"""
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"""
Ratio of values that are more than r*std(x) (so r sigma) away from the mean of x.
:param x: the time series to calculate the feature of
:type x: iterable
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blue-yonder/tsfresh | tsfresh/feature_extraction/feature_calculators.py | large_standard_deviation | def large_standard_deviation(x, r):
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Boolean variable denoting if the standard dev of x is higher
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.. math::
std(x) > r * (max(X)-min(X))
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"""
Boolean variable denoting if the standard dev of x is higher
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blue-yonder/tsfresh | tsfresh/feature_extraction/feature_calculators.py | symmetry_looking | def symmetry_looking(x, param):
"""
Boolean variable denoting if the distribution of x *looks symmetric*. This is the case if
.. math::
| mean(X)-median(X)| < r * (max(X)-min(X))
:param x: the time series to calculate the feature of
:type x: numpy.ndarray
:param r: the percentage of t... | python | def symmetry_looking(x, param):
"""
Boolean variable denoting if the distribution of x *looks symmetric*. This is the case if
.. math::
| mean(X)-median(X)| < r * (max(X)-min(X))
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blue-yonder/tsfresh | tsfresh/feature_extraction/feature_calculators.py | has_duplicate_max | def has_duplicate_max(x):
"""
Checks if the maximum value of x is observed more than once
:param x: the time series to calculate the feature of
:type x: numpy.ndarray
:return: the value of this feature
:return type: bool
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"""
Checks if the maximum value of x is observed more than once
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blue-yonder/tsfresh | tsfresh/feature_extraction/feature_calculators.py | has_duplicate_min | def has_duplicate_min(x):
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Checks if the minimal value of x is observed more than once
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"""
Checks if the minimal value of x is observed more than once
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blue-yonder/tsfresh | tsfresh/feature_extraction/feature_calculators.py | has_duplicate | def has_duplicate(x):
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Checks if any value in x occurs more than once
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blue-yonder/tsfresh | tsfresh/feature_extraction/feature_calculators.py | agg_autocorrelation | def agg_autocorrelation(x, param):
r"""
Calculates the value of an aggregation function :math:`f_{agg}` (e.g. the variance or the mean) over the
autocorrelation :math:`R(l)` for different lags. The autocorrelation :math:`R(l)` for lag :math:`l` is defined as
.. math::
R(l) = \frac{1}{(n-l)\sig... | python | def agg_autocorrelation(x, param):
r"""
Calculates the value of an aggregation function :math:`f_{agg}` (e.g. the variance or the mean) over the
autocorrelation :math:`R(l)` for different lags. The autocorrelation :math:`R(l)` for lag :math:`l` is defined as
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blue-yonder/tsfresh | tsfresh/feature_extraction/feature_calculators.py | partial_autocorrelation | def partial_autocorrelation(x, param):
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Calculates the value of the partial autocorrelation function at the given lag. The lag `k` partial autocorrelation
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blue-yonder/tsfresh | tsfresh/feature_extraction/feature_calculators.py | augmented_dickey_fuller | def augmented_dickey_fuller(x, param):
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blue-yonder/tsfresh | tsfresh/feature_extraction/feature_calculators.py | abs_energy | def abs_energy(x):
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E = \\sum_{i=1,\ldots, n} x_i^2
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:type x: numpy.ndarray
:return: the value of this feature
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blue-yonder/tsfresh | tsfresh/feature_extraction/feature_calculators.py | cid_ce | def cid_ce(x, normalize):
"""
This function calculator is an estimate for a time series complexity [1] (A more complex time series has more peaks,
valleys etc.). It calculates the value of
.. math::
\\sqrt{ \\sum_{i=0}^{n-2lag} ( x_{i} - x_{i+1})^2 }
.. rubric:: References
| [1] Bat... | python | def cid_ce(x, normalize):
"""
This function calculator is an estimate for a time series complexity [1] (A more complex time series has more peaks,
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.. math::
\\sqrt{ \\sum_{i=0}^{n-2lag} ( x_{i} - x_{i+1})^2 }
.. rubric:: References
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blue-yonder/tsfresh | tsfresh/feature_extraction/feature_calculators.py | mean_second_derivative_central | def mean_second_derivative_central(x):
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Returns the mean value of a central approximation of the second derivative
.. math::
\\frac{1}{n} \\sum_{i=1,\ldots, n-1} \\frac{1}{2} (x_{i+2} - 2 \\cdot x_{i+1} + x_i)
:param x: the time series to calculate the feature of
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"""
Returns the mean value of a central approximation of the second derivative
.. math::
\\frac{1}{n} \\sum_{i=1,\ldots, n-1} \\frac{1}{2} (x_{i+2} - 2 \\cdot x_{i+1} + x_i)
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blue-yonder/tsfresh | tsfresh/feature_extraction/feature_calculators.py | skewness | def skewness(x):
"""
Returns the sample skewness of x (calculated with the adjusted Fisher-Pearson standardized
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:param x: the time series to calculate the feature of
:type x: numpy.ndarray
:return: the value of this feature
:return type: float
"""
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"""
Returns the sample skewness of x (calculated with the adjusted Fisher-Pearson standardized
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:param x: the time series to calculate the feature of
:type x: numpy.ndarray
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blue-yonder/tsfresh | tsfresh/feature_extraction/feature_calculators.py | kurtosis | def kurtosis(x):
"""
Returns the kurtosis of x (calculated with the adjusted Fisher-Pearson standardized
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:param x: the time series to calculate the feature of
:type x: numpy.ndarray
:return: the value of this feature
:return type: float
"""
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"""
Returns the kurtosis of x (calculated with the adjusted Fisher-Pearson standardized
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:param x: the time series to calculate the feature of
:type x: numpy.ndarray
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blue-yonder/tsfresh | tsfresh/feature_extraction/feature_calculators.py | longest_strike_below_mean | def longest_strike_below_mean(x):
"""
Returns the length of the longest consecutive subsequence in x that is smaller than the mean of x
:param x: the time series to calculate the feature of
:type x: numpy.ndarray
:return: the value of this feature
:return type: float
"""
if not isinstan... | python | def longest_strike_below_mean(x):
"""
Returns the length of the longest consecutive subsequence in x that is smaller than the mean of x
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:type x: numpy.ndarray
:return: the value of this feature
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blue-yonder/tsfresh | tsfresh/feature_extraction/feature_calculators.py | count_above_mean | def count_above_mean(x):
"""
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"""
Returns the number of values in x that are higher than the mean of x
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blue-yonder/tsfresh | tsfresh/feature_extraction/feature_calculators.py | count_below_mean | def count_below_mean(x):
"""
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"""
Returns the number of values in x that are lower than the mean of x
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blue-yonder/tsfresh | tsfresh/feature_extraction/feature_calculators.py | last_location_of_maximum | def last_location_of_maximum(x):
"""
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:param x: the time series to calculate the feature of
:type x: numpy.ndarray
:return: the value of this feature
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"""
Returns the relative last location of the maximum value of x.
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:param x: the time series to calculate the feature of
:type x: numpy.ndarray
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blue-yonder/tsfresh | tsfresh/feature_extraction/feature_calculators.py | first_location_of_maximum | def first_location_of_maximum(x):
"""
Returns the first location of the maximum value of x.
The position is calculated relatively to the length of x.
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"""
Returns the first location of the maximum value of x.
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:type x: numpy.ndarray
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blue-yonder/tsfresh | tsfresh/feature_extraction/feature_calculators.py | last_location_of_minimum | def last_location_of_minimum(x):
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blue-yonder/tsfresh | tsfresh/feature_extraction/feature_calculators.py | first_location_of_minimum | def first_location_of_minimum(x):
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blue-yonder/tsfresh | tsfresh/feature_extraction/feature_calculators.py | percentage_of_reoccurring_values_to_all_values | def percentage_of_reoccurring_values_to_all_values(x):
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blue-yonder/tsfresh | tsfresh/feature_extraction/feature_calculators.py | sum_of_reoccurring_values | def sum_of_reoccurring_values(x):
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blue-yonder/tsfresh | tsfresh/feature_extraction/feature_calculators.py | sum_of_reoccurring_data_points | def sum_of_reoccurring_data_points(x):
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blue-yonder/tsfresh | tsfresh/feature_extraction/feature_calculators.py | fft_coefficient | def fft_coefficient(x, param):
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A_k = \\sum_{m=0}^{n-1} a_m \\exp \\left \\{ -2 \\pi i \\frac{m k}{n} \\right \\}, \\qquad k = 0,
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blue-yonder/tsfresh | tsfresh/feature_extraction/feature_calculators.py | number_peaks | def number_peaks(x, n):
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blue-yonder/tsfresh | tsfresh/feature_extraction/feature_calculators.py | number_cwt_peaks | def number_cwt_peaks(x, n):
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blue-yonder/tsfresh | tsfresh/feature_extraction/feature_calculators.py | linear_trend | def linear_trend(x, param):
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Calculate a linear least-squares regression for the values of the time series versus the sequence from 0 to
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This feature assumes the signal to be uniformly sampled. It will not use the time stamps to fit the model.
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"""
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blue-yonder/tsfresh | tsfresh/feature_extraction/feature_calculators.py | cwt_coefficients | def cwt_coefficients(x, param):
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.. math::
\\frac{2}{\\sqrt{3a} \\pi^{\\frac{1}{4}}} (1 - \\frac{x^2}{a^2}) exp(-\\frac{x^2}{2a^2})
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"""
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.. math::
\\frac{2}{\\sqrt{3a} \\pi^{\\frac{1}{4}}} (1 - \\frac{x^2}{a^2}) exp(-\\frac{x^2}{2a^2})
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blue-yonder/tsfresh | tsfresh/feature_extraction/feature_calculators.py | spkt_welch_density | def spkt_welch_density(x, param):
"""
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"""
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blue-yonder/tsfresh | tsfresh/feature_extraction/feature_calculators.py | ar_coefficient | def ar_coefficient(x, param):
"""
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.. math::
X_{t}=\\varphi_0 +\\sum _{{i=1}}^{k}\\varphi_{i}X_{{t-i}}+\\varepsilon_{t}
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"""
This feature calculator fits the unconditional maximum likelihood
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.. math::
X_{t}=\\varphi_0 +\\sum _{{i=1}}^{k}\\varphi_{i}X_{{t-i}}+\\varepsilon_{t}
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blue-yonder/tsfresh | tsfresh/feature_extraction/feature_calculators.py | change_quantiles | def change_quantiles(x, ql, qh, isabs, f_agg):
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First fixes a corridor given by the quantiles ql and qh of the distribution of x.
Then calculates the average, absolute value of consecutive changes of the series x inside this corridor.
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"""
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blue-yonder/tsfresh | tsfresh/feature_extraction/feature_calculators.py | time_reversal_asymmetry_statistic | def time_reversal_asymmetry_statistic(x, lag):
"""
This function calculates the value of
.. math::
\\frac{1}{n-2lag} \sum_{i=0}^{n-2lag} x_{i + 2 \cdot lag}^2 \cdot x_{i + lag} - x_{i + lag} \cdot x_{i}^2
which is
.. math::
\\mathbb{E}[L^2(X)^2 \cdot L(X) - L(X) \cdot X^2]
... | python | def time_reversal_asymmetry_statistic(x, lag):
"""
This function calculates the value of
.. math::
\\frac{1}{n-2lag} \sum_{i=0}^{n-2lag} x_{i + 2 \cdot lag}^2 \cdot x_{i + lag} - x_{i + lag} \cdot x_{i}^2
which is
.. math::
\\mathbb{E}[L^2(X)^2 \cdot L(X) - L(X) \cdot X^2]
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blue-yonder/tsfresh | tsfresh/feature_extraction/feature_calculators.py | c3 | def c3(x, lag):
"""
This function calculates the value of
.. math::
\\frac{1}{n-2lag} \sum_{i=0}^{n-2lag} x_{i + 2 \cdot lag}^2 \cdot x_{i + lag} \cdot x_{i}
which is
.. math::
\\mathbb{E}[L^2(X)^2 \cdot L(X) \cdot X]
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"""
This function calculates the value of
.. math::
\\frac{1}{n-2lag} \sum_{i=0}^{n-2lag} x_{i + 2 \cdot lag}^2 \cdot x_{i + lag} \cdot x_{i}
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\\mathbb{E}[L^2(X)^2 \cdot L(X) \cdot X]
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blue-yonder/tsfresh | tsfresh/feature_extraction/feature_calculators.py | binned_entropy | def binned_entropy(x, max_bins):
"""
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Then calculates the value of
.. math::
- \\sum_{k=0}^{min(max\\_bins, len(x))} p_k log(p_k) \\cdot \\mathbf{1}_{(p_k > 0)}
where :math:`p_k` is the percentage of samples in bin :math:`k`.
... | python | def binned_entropy(x, max_bins):
"""
First bins the values of x into max_bins equidistant bins.
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.. math::
- \\sum_{k=0}^{min(max\\_bins, len(x))} p_k log(p_k) \\cdot \\mathbf{1}_{(p_k > 0)}
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blue-yonder/tsfresh | tsfresh/feature_extraction/feature_calculators.py | sample_entropy | def sample_entropy(x):
"""
Calculate and return sample entropy of x.
.. rubric:: References
| [1] http://en.wikipedia.org/wiki/Sample_Entropy
| [2] https://www.ncbi.nlm.nih.gov/pubmed/10843903?dopt=Abstract
:param x: the time series to calculate the feature of
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... | python | def sample_entropy(x):
"""
Calculate and return sample entropy of x.
.. rubric:: References
| [1] http://en.wikipedia.org/wiki/Sample_Entropy
| [2] https://www.ncbi.nlm.nih.gov/pubmed/10843903?dopt=Abstract
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blue-yonder/tsfresh | tsfresh/feature_extraction/feature_calculators.py | autocorrelation | def autocorrelation(x, lag):
"""
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.. math::
\\frac{1}{(n-l)\sigma^{2}} \\sum_{t=1}^{n-l}(X_{t}-\\mu )(X_{t+l}-\\mu)
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"""
Calculates the autocorrelation of the specified lag, according to the formula [1]
.. math::
\\frac{1}{(n-l)\sigma^{2}} \\sum_{t=1}^{n-l}(X_{t}-\\mu )(X_{t+l}-\\mu)
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blue-yonder/tsfresh | tsfresh/feature_extraction/feature_calculators.py | quantile | def quantile(x, q):
"""
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:param x: the time series to calculate the feature of
:type x: numpy.ndarray
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:type q: float
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"""
Calculates the q quantile of x. This is the value of x greater than q% of the ordered values from x.
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blue-yonder/tsfresh | tsfresh/feature_extraction/feature_calculators.py | number_crossing_m | def number_crossing_m(x, m):
"""
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:param x: the time series to c... | python | def number_crossing_m(x, m):
"""
Calculates the number of crossings of x on m. A crossing is defined as two sequential values where the first value
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blue-yonder/tsfresh | tsfresh/feature_extraction/feature_calculators.py | value_count | def value_count(x, value):
"""
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:type x: numpy.ndarray
:param value: the value to be counted
:type value: int or float
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:rtype: int
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"""
Count occurrences of `value` in time series x.
:param x: the time series to calculate the feature of
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blue-yonder/tsfresh | tsfresh/feature_extraction/feature_calculators.py | range_count | def range_count(x, min, max):
"""
Count observed values within the interval [min, max).
:param x: the time series to calculate the feature of
:type x: numpy.ndarray
:param min: the inclusive lower bound of the range
:type min: int or float
:param max: the exclusive upper bound of the range
... | python | def range_count(x, min, max):
"""
Count observed values within the interval [min, max).
:param x: the time series to calculate the feature of
:type x: numpy.ndarray
:param min: the inclusive lower bound of the range
:type min: int or float
:param max: the exclusive upper bound of the range
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blue-yonder/tsfresh | tsfresh/feature_extraction/feature_calculators.py | approximate_entropy | def approximate_entropy(x, m, r):
"""
Implements a vectorized Approximate entropy algorithm.
https://en.wikipedia.org/wiki/Approximate_entropy
For short time-series this method is highly dependent on the parameters,
but should be stable for N > 2000, see:
Yentes et al. (2012) -
... | python | def approximate_entropy(x, m, r):
"""
Implements a vectorized Approximate entropy algorithm.
https://en.wikipedia.org/wiki/Approximate_entropy
For short time-series this method is highly dependent on the parameters,
but should be stable for N > 2000, see:
Yentes et al. (2012) -
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Yentes et al. (2012) -
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blue-yonder/tsfresh | tsfresh/feature_extraction/feature_calculators.py | friedrich_coefficients | def friedrich_coefficients(x, param):
"""
Coefficients of polynomial :math:`h(x)`, which has been fitted to
the deterministic dynamics of Langevin model
.. math::
\dot{x}(t) = h(x(t)) + \mathcal{N}(0,R)
as described by [1].
For short time-series this method is highly dependent on the ... | python | def friedrich_coefficients(x, param):
"""
Coefficients of polynomial :math:`h(x)`, which has been fitted to
the deterministic dynamics of Langevin model
.. math::
\dot{x}(t) = h(x(t)) + \mathcal{N}(0,R)
as described by [1].
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.. rubric:: References
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blue-yonder/tsfresh | tsfresh/feature_extraction/feature_calculators.py | max_langevin_fixed_point | def max_langevin_fixed_point(x, r, m):
"""
Largest fixed point of dynamics :math:argmax_x {h(x)=0}` estimated from polynomial :math:`h(x)`,
which has been fitted to the deterministic dynamics of Langevin model
.. math::
\dot(x)(t) = h(x(t)) + R \mathcal(N)(0,1)
as described by
Fr... | python | def max_langevin_fixed_point(x, r, m):
"""
Largest fixed point of dynamics :math:argmax_x {h(x)=0}` estimated from polynomial :math:`h(x)`,
which has been fitted to the deterministic dynamics of Langevin model
.. math::
\dot(x)(t) = h(x(t)) + R \mathcal(N)(0,1)
as described by
Fr... | [
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blue-yonder/tsfresh | tsfresh/feature_extraction/feature_calculators.py | agg_linear_trend | def agg_linear_trend(x, param):
"""
Calculates a linear least-squares regression for values of the time series that were aggregated over chunks versus
the sequence from 0 up to the number of chunks minus one.
This feature assumes the signal to be uniformly sampled. It will not use the time stamps to fi... | python | def agg_linear_trend(x, param):
"""
Calculates a linear least-squares regression for values of the time series that were aggregated over chunks versus
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This feature assumes the signal to be uniformly sampled. It will not use the time stamps to fi... | [
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blue-yonder/tsfresh | tsfresh/feature_extraction/feature_calculators.py | energy_ratio_by_chunks | def energy_ratio_by_chunks(x, param):
"""
Calculates the sum of squares of chunk i out of N chunks expressed as a ratio with the sum of squares over the whole
series.
Takes as input parameters the number num_segments of segments to divide the series into and segment_focus
which is the segment numbe... | python | def energy_ratio_by_chunks(x, param):
"""
Calculates the sum of squares of chunk i out of N chunks expressed as a ratio with the sum of squares over the whole
series.
Takes as input parameters the number num_segments of segments to divide the series into and segment_focus
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blue-yonder/tsfresh | tsfresh/feature_extraction/feature_calculators.py | linear_trend_timewise | def linear_trend_timewise(x, param):
"""
Calculate a linear least-squares regression for the values of the time series versus the sequence from 0 to
length of the time series minus one.
This feature uses the index of the time series to fit the model, which must be of a datetime
dtype.
The parame... | python | def linear_trend_timewise(x, param):
"""
Calculate a linear least-squares regression for the values of the time series versus the sequence from 0 to
length of the time series minus one.
This feature uses the index of the time series to fit the model, which must be of a datetime
dtype.
The parame... | [
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blue-yonder/tsfresh | tsfresh/transformers/feature_selector.py | FeatureSelector.fit | def fit(self, X, y):
"""
Extract the information, which of the features are relevent using the given target.
For more information, please see the :func:`~tsfresh.festure_selection.festure_selector.check_fs_sig_bh`
function. All columns in the input data sample are treated as feature. Th... | python | def fit(self, X, y):
"""
Extract the information, which of the features are relevent using the given target.
For more information, please see the :func:`~tsfresh.festure_selection.festure_selector.check_fs_sig_bh`
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blue-yonder/tsfresh | tsfresh/transformers/feature_selector.py | FeatureSelector.transform | def transform(self, X):
"""
Delete all features, which were not relevant in the fit phase.
:param X: data sample with all features, which will be reduced to only those that are relevant
:type X: pandas.DataSeries or numpy.array
:return: same data sample as X, but with only the ... | python | def transform(self, X):
"""
Delete all features, which were not relevant in the fit phase.
:param X: data sample with all features, which will be reduced to only those that are relevant
:type X: pandas.DataSeries or numpy.array
:return: same data sample as X, but with only the ... | [
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