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mitodl/django-server-status | server_status/views.py | get_redis_info | def get_redis_info():
"""Check Redis connection."""
from kombu.utils.url import _parse_url as parse_redis_url
from redis import (
StrictRedis,
ConnectionError as RedisConnectionError,
ResponseError as RedisResponseError,
)
for conf_name in ('REDIS_URL', 'BROKER_URL', 'CELERY_... | python | def get_redis_info():
"""Check Redis connection."""
from kombu.utils.url import _parse_url as parse_redis_url
from redis import (
StrictRedis,
ConnectionError as RedisConnectionError,
ResponseError as RedisResponseError,
)
for conf_name in ('REDIS_URL', 'BROKER_URL', 'CELERY_... | [
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mitodl/django-server-status | server_status/views.py | get_elasticsearch_info | def get_elasticsearch_info():
"""Check Elasticsearch connection."""
from elasticsearch import (
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if hasattr(settings, 'ELASTICSEARCH_URL'):
url = settings.ELASTICSEARCH_URL
else:
return {"status": NO_CONFIG}
sta... | python | def get_elasticsearch_info():
"""Check Elasticsearch connection."""
from elasticsearch import (
Elasticsearch,
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url = settings.ELASTICSEARCH_URL
else:
return {"status": NO_CONFIG}
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mitodl/django-server-status | server_status/views.py | get_celery_info | def get_celery_info():
"""
Check celery availability
"""
import celery
if not getattr(settings, 'USE_CELERY', False):
log.error("No celery config found. Set USE_CELERY in settings to enable.")
return {"status": NO_CONFIG}
start = datetime.now()
try:
# pylint: disable=... | python | def get_celery_info():
"""
Check celery availability
"""
import celery
if not getattr(settings, 'USE_CELERY', False):
log.error("No celery config found. Set USE_CELERY in settings to enable.")
return {"status": NO_CONFIG}
start = datetime.now()
try:
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mitodl/django-server-status | server_status/views.py | get_certificate_info | def get_certificate_info():
"""
checks app certificate expiry status
"""
if hasattr(settings, 'MIT_WS_CERTIFICATE') and settings.MIT_WS_CERTIFICATE:
mit_ws_certificate = settings.MIT_WS_CERTIFICATE
else:
return {"status": NO_CONFIG}
app_cert = OpenSSL.crypto.load_certificate(
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"""
checks app certificate expiry status
"""
if hasattr(settings, 'MIT_WS_CERTIFICATE') and settings.MIT_WS_CERTIFICATE:
mit_ws_certificate = settings.MIT_WS_CERTIFICATE
else:
return {"status": NO_CONFIG}
app_cert = OpenSSL.crypto.load_certificate(
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bitcaster-io/bitcaster | src/telebot/__init__.py | TeleBot._start | def _start(self):
'''Requests bot information based on current api_key, and sets
self.whoami to dictionary with username, first_name, and id of the
configured bot.
'''
if self.whoami is None:
me = self.get_me()
if me.get('ok', False):
self... | python | def _start(self):
'''Requests bot information based on current api_key, and sets
self.whoami to dictionary with username, first_name, and id of the
configured bot.
'''
if self.whoami is None:
me = self.get_me()
if me.get('ok', False):
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bitcaster-io/bitcaster | src/telebot/__init__.py | TeleBot.poll | def poll(self, offset=None, poll_timeout=600, cooldown=60, debug=False):
'''These should also be in the config section, but some here for
overrides
'''
if self.config['api_key'] is None:
raise ValueError('config api_key is undefined')
if offset or self.config.get('o... | python | def poll(self, offset=None, poll_timeout=600, cooldown=60, debug=False):
'''These should also be in the config section, but some here for
overrides
'''
if self.config['api_key'] is None:
raise ValueError('config api_key is undefined')
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bitcaster-io/bitcaster | src/bitcaster/utils/language.py | get_attr | def get_attr(obj, attr, default=None):
"""Recursive get object's attribute. May use dot notation.
>>> class C(object): pass
>>> a = C()
>>> a.b = C()
>>> a.b.c = 4
>>> get_attr(a, 'b.c')
4
>>> get_attr(a, 'b.c.y', None)
>>> get_attr(a, 'b.c.y', 1)
1
"""
if '.' not in a... | python | def get_attr(obj, attr, default=None):
"""Recursive get object's attribute. May use dot notation.
>>> class C(object): pass
>>> a = C()
>>> a.b = C()
>>> a.b.c = 4
>>> get_attr(a, 'b.c')
4
>>> get_attr(a, 'b.c.y', None)
>>> get_attr(a, 'b.c.y', 1)
1
"""
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bitcaster-io/bitcaster | src/bitcaster/web/templatetags/bc_assets.py | asset | def asset(path):
"""
Join the given path with the STATIC_URL setting.
Usage::
{% static path [as varname] %}
Examples::
{% static "myapp/css/base.css" %}
{% static variable_with_path %}
{% static "myapp/css/base.css" as admin_base_css %}
{% static variable_wit... | python | def asset(path):
"""
Join the given path with the STATIC_URL setting.
Usage::
{% static path [as varname] %}
Examples::
{% static "myapp/css/base.css" %}
{% static variable_with_path %}
{% static "myapp/css/base.css" as admin_base_css %}
{% static variable_wit... | [
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bitcaster-io/bitcaster | src/bitcaster/utils/wsgi.py | get_client_ip | def get_client_ip(request):
"""
Naively yank the first IP address in an X-Forwarded-For header
and assume this is correct.
Note: Don't use this in security sensitive situations since this
value may be forged from a client.
"""
try:
return request.META['HTTP_X_FORWARDED_FOR'].split('... | python | def get_client_ip(request):
"""
Naively yank the first IP address in an X-Forwarded-For header
and assume this is correct.
Note: Don't use this in security sensitive situations since this
value may be forged from a client.
"""
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bitcaster-io/bitcaster | src/tweepy/api.py | API._pack_image | def _pack_image(filename, max_size, form_field='image', f=None):
"""Pack image from file into multipart-formdata post body"""
# image must be less than 700kb in size
if f is None:
try:
if os.path.getsize(filename) > (max_size * 1024):
raise TweepEr... | python | def _pack_image(filename, max_size, form_field='image', f=None):
"""Pack image from file into multipart-formdata post body"""
# image must be less than 700kb in size
if f is None:
try:
if os.path.getsize(filename) > (max_size * 1024):
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bitcaster-io/bitcaster | src/bitcaster/web/templatetags/bitcaster.py | channel_submit_row | def channel_submit_row(context):
"""
Display the row of buttons for delete and save.
"""
change = context['change']
is_popup = context['is_popup']
save_as = context['save_as']
show_save = context.get('show_save', True)
show_save_and_continue = context.get('show_save_and_continue', True)
... | python | def channel_submit_row(context):
"""
Display the row of buttons for delete and save.
"""
change = context['change']
is_popup = context['is_popup']
save_as = context['save_as']
show_save = context.get('show_save', True)
show_save_and_continue = context.get('show_save_and_continue', True)
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bitcaster-io/bitcaster | src/bitcaster/social_auth.py | BitcasterStrategy.get_setting | def get_setting(self, name):
notfound = object()
"get configuration from 'constance.config' first "
value = getattr(config, name, notfound)
if name.endswith('_WHITELISTED_DOMAINS'):
if value:
return value.split(',')
else:
return []
... | python | def get_setting(self, name):
notfound = object()
"get configuration from 'constance.config' first "
value = getattr(config, name, notfound)
if name.endswith('_WHITELISTED_DOMAINS'):
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bitcaster-io/bitcaster | src/bitcaster/messages.py | Wrapper.debug | def debug(self, request, message, extra_tags='', fail_silently=False):
"""Add a message with the ``DEBUG`` level."""
add(self.target_name, request, constants.DEBUG, message, extra_tags=extra_tags,
fail_silently=fail_silently) | python | def debug(self, request, message, extra_tags='', fail_silently=False):
"""Add a message with the ``DEBUG`` level."""
add(self.target_name, request, constants.DEBUG, message, extra_tags=extra_tags,
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bitcaster-io/bitcaster | src/bitcaster/messages.py | Wrapper.info | def info(self, request, message, extra_tags='', fail_silently=False):
"""Add a message with the ``INFO`` level."""
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bitcaster-io/bitcaster | src/bitcaster/messages.py | Wrapper.success | def success(self, request, message, extra_tags='', fail_silently=False):
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Signup of an account.
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openvax/mhcflurry | mhcflurry/allele_encoding.py | AlleleEncoding.fixed_length_vector_encoded_sequences | def fixed_length_vector_encoded_sequences(self, vector_encoding_name):
"""
Encode alleles.
Parameters
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vector_encoding_name : string
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Encode alleles.
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vector_encoding_name : string
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openvax/mhcflurry | mhcflurry/amino_acid.py | index_encoding | def index_encoding(sequences, letter_to_index_dict):
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openvax/mhcflurry | mhcflurry/class1_neural_network.py | Class1NeuralNetwork.apply_hyperparameter_renames | def apply_hyperparameter_renames(cls, hyperparameters):
"""
Handle hyperparameter renames.
Parameters
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hyperparameters : dict
Returns
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dict : updated hyperparameters
"""
for (from_name, to_name) in cls.hyperparameter_ren... | python | def apply_hyperparameter_renames(cls, hyperparameters):
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hyperparameters : dict
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dict : updated hyperparameters
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openvax/mhcflurry | mhcflurry/class1_neural_network.py | Class1NeuralNetwork.borrow_cached_network | def borrow_cached_network(klass, network_json, network_weights):
"""
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openvax/mhcflurry | mhcflurry/class1_neural_network.py | Class1NeuralNetwork.network | def network(self, borrow=False):
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borrow : bool
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"""
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openvax/mhcflurry | mhcflurry/class1_neural_network.py | Class1NeuralNetwork.load_weights | def load_weights(self):
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openvax/mhcflurry | mhcflurry/class1_neural_network.py | Class1NeuralNetwork.predict | def predict(self, peptides, allele_encoding=None, batch_size=4096):
"""
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remains in memory. The cache is keyed in the object identity of the
EncodableSequences, not the sequences themselves.
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openvax/mhcflurry | mhcflurry/scoring.py | make_scores | def make_scores(
ic50_y,
ic50_y_pred,
sample_weight=None,
threshold_nm=500,
max_ic50=50000):
"""
Calculate AUC, F1, and Kendall Tau scores.
Parameters
-----------
ic50_y : float list
true IC50s (i.e. affinities)
ic50_y_pred : float list
p... | python | def make_scores(
ic50_y,
ic50_y_pred,
sample_weight=None,
threshold_nm=500,
max_ic50=50000):
"""
Calculate AUC, F1, and Kendall Tau scores.
Parameters
-----------
ic50_y : float list
true IC50s (i.e. affinities)
ic50_y_pred : float list
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openvax/mhcflurry | mhcflurry/encodable_sequences.py | EncodableSequences.variable_length_to_fixed_length_vector_encoding | def variable_length_to_fixed_length_vector_encoding(
self, vector_encoding_name, left_edge=4, right_edge=4, max_length=15):
"""
Encode variable-length sequences using a fixed-length encoding designed
for preserving the anchor positions of class I peptides.
The sequences must... | python | def variable_length_to_fixed_length_vector_encoding(
self, vector_encoding_name, left_edge=4, right_edge=4, max_length=15):
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openvax/mhcflurry | mhcflurry/encodable_sequences.py | EncodableSequences.sequences_to_fixed_length_index_encoded_array | def sequences_to_fixed_length_index_encoded_array(
klass, sequences, left_edge=4, right_edge=4, max_length=15):
"""
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openvax/mhcflurry | mhcflurry/ensemble_centrality.py | robust_mean | def robust_mean(log_values):
"""
Mean of values falling within the 25-75 percentiles.
Parameters
----------
log_values : 2-d numpy.array
Center is computed along the second axis (i.e. per row).
Returns
-------
center : numpy.array of length log_values.shape[1]
"""
if l... | python | def robust_mean(log_values):
"""
Mean of values falling within the 25-75 percentiles.
Parameters
----------
log_values : 2-d numpy.array
Center is computed along the second axis (i.e. per row).
Returns
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center : numpy.array of length log_values.shape[1]
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openvax/mhcflurry | mhcflurry/class1_affinity_predictor.py | Class1AffinityPredictor.neural_networks | def neural_networks(self):
"""
List of the neural networks in the ensemble.
Returns
-------
list of `Class1NeuralNetwork`
"""
result = []
for models in self.allele_to_allele_specific_models.values():
result.extend(models)
result.extend... | python | def neural_networks(self):
"""
List of the neural networks in the ensemble.
Returns
-------
list of `Class1NeuralNetwork`
"""
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openvax/mhcflurry | mhcflurry/class1_affinity_predictor.py | Class1AffinityPredictor.merge | def merge(cls, predictors):
"""
Merge the ensembles of two or more `Class1AffinityPredictor` instances.
Note: the resulting merged predictor will NOT have calibrated percentile
ranks. Call `calibrate_percentile_ranks` on it if these are needed.
Parameters
----------
... | python | def merge(cls, predictors):
"""
Merge the ensembles of two or more `Class1AffinityPredictor` instances.
Note: the resulting merged predictor will NOT have calibrated percentile
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openvax/mhcflurry | mhcflurry/class1_affinity_predictor.py | Class1AffinityPredictor.merge_in_place | def merge_in_place(self, others):
"""
Add the models present other predictors into the current predictor.
Parameters
----------
others : list of Class1AffinityPredictor
Other predictors to merge into the current predictor.
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-------
lis... | python | def merge_in_place(self, others):
"""
Add the models present other predictors into the current predictor.
Parameters
----------
others : list of Class1AffinityPredictor
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openvax/mhcflurry | mhcflurry/class1_affinity_predictor.py | Class1AffinityPredictor.percentile_ranks | def percentile_ranks(self, affinities, allele=None, alleles=None, throw=True):
"""
Return percentile ranks for the given ic50 affinities and alleles.
The 'allele' and 'alleles' argument are as in the `predict` method.
Specify one of these.
Parameters
----------
... | python | def percentile_ranks(self, affinities, allele=None, alleles=None, throw=True):
"""
Return percentile ranks for the given ic50 affinities and alleles.
The 'allele' and 'alleles' argument are as in the `predict` method.
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----------
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openvax/mhcflurry | mhcflurry/class1_affinity_predictor.py | Class1AffinityPredictor.calibrate_percentile_ranks | def calibrate_percentile_ranks(
self,
peptides=None,
num_peptides_per_length=int(1e5),
alleles=None,
bins=None):
"""
Compute the cumulative distribution of ic50 values for a set of alleles
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peptides=None,
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bins=None):
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openvax/mhcflurry | mhcflurry/class1_affinity_predictor.py | Class1AffinityPredictor.filter_networks | def filter_networks(self, predicate):
"""
Return a new Class1AffinityPredictor containing a subset of this
predictor's neural networks.
Parameters
----------
predicate : Class1NeuralNetwork -> boolean
Function specifying which neural networks to include
... | python | def filter_networks(self, predicate):
"""
Return a new Class1AffinityPredictor containing a subset of this
predictor's neural networks.
Parameters
----------
predicate : Class1NeuralNetwork -> boolean
Function specifying which neural networks to include
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openvax/mhcflurry | mhcflurry/class1_affinity_predictor.py | Class1AffinityPredictor.model_select | def model_select(
self,
score_function,
alleles=None,
min_models=1,
max_models=10000):
"""
Perform model selection using a user-specified scoring function.
Model selection is done using a "step up" variable selection procedure,
... | python | def model_select(
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score_function,
alleles=None,
min_models=1,
max_models=10000):
"""
Perform model selection using a user-specified scoring function.
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openvax/mhcflurry | mhcflurry/percent_rank_transform.py | PercentRankTransform.to_series | def to_series(self):
"""
Serialize the fit to a pandas.Series.
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Returns
-------
pandas.Series
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return pandas.Series(
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"""
Serialize the fit to a pandas.Series.
The index on the series gives the bin edges and the valeus give the CDF.
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-------
pandas.Series
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return pandas.Series(
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openvax/mhcflurry | mhcflurry/downloads.py | get_default_class1_models_dir | def get_default_class1_models_dir(test_exists=True):
"""
Return the absolute path to the default class1 models dir.
If environment variable MHCFLURRY_DEFAULT_CLASS1_MODELS is set to an
absolute path, return that path. If it's set to a relative path (i.e. does
not start with /) then return that path... | python | def get_default_class1_models_dir(test_exists=True):
"""
Return the absolute path to the default class1 models dir.
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openvax/mhcflurry | mhcflurry/downloads.py | get_current_release_downloads | def get_current_release_downloads():
"""
Return a dict of all available downloads in the current release.
The dict keys are the names of the downloads. The values are a dict
with two entries:
downloaded : bool
Whether the download is currently available locally
metadata : dict
... | python | def get_current_release_downloads():
"""
Return a dict of all available downloads in the current release.
The dict keys are the names of the downloads. The values are a dict
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downloaded : bool
Whether the download is currently available locally
metadata : dict
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downloaded : bool
Whether the download is currently available locally
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openvax/mhcflurry | mhcflurry/downloads.py | get_path | def get_path(download_name, filename='', test_exists=True):
"""
Get the local path to a file in a MHCflurry download
Parameters
-----------
download_name : string
filename : string
Relative path within the download to the file of interest
test_exists : boolean
If True (def... | python | def get_path(download_name, filename='', test_exists=True):
"""
Get the local path to a file in a MHCflurry download
Parameters
-----------
download_name : string
filename : string
Relative path within the download to the file of interest
test_exists : boolean
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openvax/mhcflurry | mhcflurry/downloads.py | configure | def configure():
"""
Setup various global variables based on environment variables.
"""
global _DOWNLOADS_DIR
global _CURRENT_RELEASE
_CURRENT_RELEASE = None
_DOWNLOADS_DIR = environ.get("MHCFLURRY_DOWNLOADS_DIR")
if not _DOWNLOADS_DIR:
metadata = get_downloads_metadata()
... | python | def configure():
"""
Setup various global variables based on environment variables.
"""
global _DOWNLOADS_DIR
global _CURRENT_RELEASE
_CURRENT_RELEASE = None
_DOWNLOADS_DIR = environ.get("MHCFLURRY_DOWNLOADS_DIR")
if not _DOWNLOADS_DIR:
metadata = get_downloads_metadata()
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openvax/mhcflurry | mhcflurry/parallelism.py | make_worker_pool | def make_worker_pool(
processes=None,
initializer=None,
initializer_kwargs_per_process=None,
max_tasks_per_worker=None):
"""
Convenience wrapper to create a multiprocessing.Pool.
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not natively supp... | python | def make_worker_pool(
processes=None,
initializer=None,
initializer_kwargs_per_process=None,
max_tasks_per_worker=None):
"""
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openvax/mhcflurry | mhcflurry/calibrate_percentile_ranks_command.py | calibrate_percentile_ranks | def calibrate_percentile_ranks(allele, predictor, peptides=None):
"""
Private helper function.
"""
global GLOBAL_DATA
if peptides is None:
peptides = GLOBAL_DATA["calibration_peptides"]
predictor.calibrate_percentile_ranks(
peptides=peptides,
alleles=[allele])
return ... | python | def calibrate_percentile_ranks(allele, predictor, peptides=None):
"""
Private helper function.
"""
global GLOBAL_DATA
if peptides is None:
peptides = GLOBAL_DATA["calibration_peptides"]
predictor.calibrate_percentile_ranks(
peptides=peptides,
alleles=[allele])
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openvax/mhcflurry | mhcflurry/common.py | set_keras_backend | def set_keras_backend(backend=None, gpu_device_nums=None, num_threads=None):
"""
Configure Keras backend to use GPU or CPU. Only tensorflow is supported.
Parameters
----------
backend : string, optional
one of 'tensorflow-default', 'tensorflow-cpu', 'tensorflow-gpu'
gpu_device_nums : l... | python | def set_keras_backend(backend=None, gpu_device_nums=None, num_threads=None):
"""
Configure Keras backend to use GPU or CPU. Only tensorflow is supported.
Parameters
----------
backend : string, optional
one of 'tensorflow-default', 'tensorflow-cpu', 'tensorflow-gpu'
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JonathanRaiman/pytreebank | pytreebank/labeled_trees.py | LabeledTree.uproot | def uproot(tree):
"""
Take a subranch of a tree and deep-copy the children
of this subbranch into a new LabeledTree
"""
uprooted = tree.copy()
uprooted.parent = None
for child in tree.all_children():
uprooted.add_general_child(child)
return upr... | python | def uproot(tree):
"""
Take a subranch of a tree and deep-copy the children
of this subbranch into a new LabeledTree
"""
uprooted = tree.copy()
uprooted.parent = None
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JonathanRaiman/pytreebank | pytreebank/labeled_trees.py | LabeledTree.copy | def copy(self):
"""
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"""
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"""
Deep Copy of a LabeledTree
"""
return LabeledTree(
udepth = self.udepth,
depth = self.depth,
text = self.text,
label = self.label,
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JonathanRaiman/pytreebank | pytreebank/labeled_trees.py | LabeledTree.add_child | def add_child(self, child):
"""
Adds a branch to the current tree.
"""
self.children.append(child)
child.parent = self
self.udepth = max([child.udepth for child in self.children]) + 1 | python | def add_child(self, child):
"""
Adds a branch to the current tree.
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JonathanRaiman/pytreebank | pytreebank/labeled_trees.py | LabeledTree.lowercase | def lowercase(self):
"""
Lowercase all strings in this tree.
Works recursively and in-place.
"""
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for child in self.children:
child.lowercase()
else:
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"""
Lowercase all strings in this tree.
Works recursively and in-place.
"""
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JonathanRaiman/pytreebank | pytreebank/labeled_trees.py | LabeledTree.inject_visualization_javascript | def inject_visualization_javascript(tree_width=1200, tree_height=400, tree_node_radius=10):
"""
In an Ipython notebook, show SST trees using the same Javascript
code as used by Jason Chuang's visualisations.
"""
from .javascript import insert_sentiment_markup
insert_senti... | python | def inject_visualization_javascript(tree_width=1200, tree_height=400, tree_node_radius=10):
"""
In an Ipython notebook, show SST trees using the same Javascript
code as used by Jason Chuang's visualisations.
"""
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JonathanRaiman/pytreebank | pytreebank/parse.py | create_tree_from_string | def create_tree_from_string(line):
"""
Parse and convert a string representation
of an example into a LabeledTree datastructure.
Arguments:
----------
line : str, string version of the tree.
Returns:
--------
LabeledTree : parsed tree.
"""
depth = 0
curr... | python | def create_tree_from_string(line):
"""
Parse and convert a string representation
of an example into a LabeledTree datastructure.
Arguments:
----------
line : str, string version of the tree.
Returns:
--------
LabeledTree : parsed tree.
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JonathanRaiman/pytreebank | pytreebank/parse.py | import_tree_corpus | def import_tree_corpus(path):
"""
Import a text file of treebank trees.
Arguments:
----------
path : str, filename for tree corpus.
Returns:
--------
list<LabeledTree> : loaded examples.
"""
tree_list = LabeledTreeCorpus()
with codecs.open(path, "r", "UTF-8") as f:
... | python | def import_tree_corpus(path):
"""
Import a text file of treebank trees.
Arguments:
----------
path : str, filename for tree corpus.
Returns:
--------
list<LabeledTree> : loaded examples.
"""
tree_list = LabeledTreeCorpus()
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JonathanRaiman/pytreebank | pytreebank/parse.py | load_sst | def load_sst(path=None,
url='http://nlp.stanford.edu/sentiment/trainDevTestTrees_PTB.zip'):
"""
Download and read in the Stanford Sentiment Treebank dataset
into a dictionary with a 'train', 'dev', and 'test' keys. The
dictionary keys point to lists of LabeledTrees.
Arguments:
----... | python | def load_sst(path=None,
url='http://nlp.stanford.edu/sentiment/trainDevTestTrees_PTB.zip'):
"""
Download and read in the Stanford Sentiment Treebank dataset
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JonathanRaiman/pytreebank | pytreebank/parse.py | LabeledTreeCorpus.labels | def labels(self):
"""
Construct a dictionary of string -> labels
Returns:
--------
OrderedDict<str, int> : string label pairs.
"""
labelings = OrderedDict()
for tree in self:
for label, line in tree.to_labeled_lines():
labe... | python | def labels(self):
"""
Construct a dictionary of string -> labels
Returns:
--------
OrderedDict<str, int> : string label pairs.
"""
labelings = OrderedDict()
for tree in self:
for label, line in tree.to_labeled_lines():
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JonathanRaiman/pytreebank | pytreebank/parse.py | LabeledTreeCorpus.to_file | def to_file(self, path, mode="w"):
"""
Save the corpus to a text file in the
original format.
Arguments:
----------
path : str, where to save the corpus.
mode : str, how to open the file.
"""
with open(path, mode=mode) as f:
fo... | python | def to_file(self, path, mode="w"):
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JonathanRaiman/pytreebank | pytreebank/treelstm.py | assign_texts | def assign_texts(node, words, next_idx=0):
"""
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to right order.
"""
if len(node.children) == 0:
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return next_idx + 1
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for child in nod... | python | def assign_texts(node, words, next_idx=0):
"""
Recursively assign the words to nodes by finding and
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JonathanRaiman/pytreebank | pytreebank/treelstm.py | read_tree | def read_tree(parents, labels, words):
"""
Take as input a list of integers for parents
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"""
trees = {}
root = None
for i in range(1, len(parents) + 1):
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"""
Take as input a list of integers for parents
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"""
trees = {}
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GiulioRossetti/ndlib | ndlib/models/opinions/CognitiveOpDynModel.py | CognitiveOpDynModel.set_initial_status | def set_initial_status(self, configuration=None):
"""
Override behaviour of methods in class DiffusionModel.
Overwrites initial status using random real values.
Generates random node profiles.
"""
super(CognitiveOpDynModel, self).set_initial_status(configuration)
... | python | def set_initial_status(self, configuration=None):
"""
Override behaviour of methods in class DiffusionModel.
Overwrites initial status using random real values.
Generates random node profiles.
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GiulioRossetti/ndlib | ndlib/models/ModelConfig.py | Configuration.add_node_configuration | def add_node_configuration(self, param_name, node_id, param_value):
"""
Set a parameter for a given node
:param param_name: parameter identifier (as specified by the chosen model)
:param node_id: node identifier
:param param_value: parameter value
"""
if param_na... | python | def add_node_configuration(self, param_name, node_id, param_value):
"""
Set a parameter for a given node
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"""
Set Nodes parameter
:param param_name: parameter identifier (as specified by the chosen model)
:param node_to_value: dictionary mapping each node a parameter value
"""
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GiulioRossetti/ndlib | ndlib/models/ModelConfig.py | Configuration.add_edge_configuration | def add_edge_configuration(self, param_name, edge, param_value):
"""
Set a parameter for a given edge
:param param_name: parameter identifier (as specified by the chosen model)
:param edge: edge identifier
:param param_value: parameter value
"""
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GiulioRossetti/ndlib | ndlib/models/ModelConfig.py | Configuration.add_edge_set_configuration | def add_edge_set_configuration(self, param_name, edge_to_value):
"""
Set Edges parameter
:param param_name: parameter identifier (as specified by the chosen model)
:param edge_to_value: dictionary mapping each edge a parameter value
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GiulioRossetti/ndlib | ndlib/utils.py | multi_runs | def multi_runs(model, execution_number=1, iteration_number=50, infection_sets=None,
nprocesses=multiprocessing.cpu_count()):
"""
Multiple executions of a given model varying the initial set of infected nodes
:param model: a configured diffusion model
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GiulioRossetti/ndlib | ndlib/utils.py | __execute | def __execute(model, iteration_number):
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"""
iterations = model.iteration_bunch(iteration_number, False)
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"""
Execute a simulation model
:param model: a configured diffusion model
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GiulioRossetti/ndlib | ndlib/models/opinions/AlgorithmicBiasModel.py | AlgorithmicBiasModel.set_initial_status | def set_initial_status(self, configuration=None):
"""
Override behaviour of methods in class DiffusionModel.
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"""
super(AlgorithmicBiasModel, self).set_initial_status(configuration)
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HearthSim/python-hearthstone | hearthstone/entities.py | Player.names | def names(self):
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scikit-hep/root_pandas | root_pandas/readwrite.py | _getgroup | def _getgroup(string, depth):
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scikit-hep/root_pandas | root_pandas/readwrite.py | filter_noexpand_columns | def filter_noexpand_columns(columns):
"""Return columns not containing and containing the noexpand prefix.
Parameters
----------
columns: sequence of str
A sequence of strings to be split
Returns
-------
Two lists, the first containing strings without the noexpand prefix, the
... | python | def filter_noexpand_columns(columns):
"""Return columns not containing and containing the noexpand prefix.
Parameters
----------
columns: sequence of str
A sequence of strings to be split
Returns
-------
Two lists, the first containing strings without the noexpand prefix, the
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scikit-hep/root_pandas | root_pandas/readwrite.py | to_root | def to_root(df, path, key='my_ttree', mode='w', store_index=True, *args, **kwargs):
"""
Write DataFrame to a ROOT file.
Parameters
----------
path: string
File path to new ROOT file (will be overwritten)
key: string
Name of tree that the DataFrame will be saved as
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"""
Write DataFrame to a ROOT file.
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path: string
File path to new ROOT file (will be overwritten)
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Name of tree that the DataFrame will be saved as
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MisterY/gnucash-portfolio | gnucash_portfolio/reports/security_info.py | SecurityInfoReport.run | def run(self, symbol: str) -> SecurityDetailsViewModel:
""" Loads the model for security details """
from pydatum import Datum
svc = self._svc
sec_agg = svc.securities.get_aggregate_for_symbol(symbol)
model = SecurityDetailsViewModel()
model.symbol = sec_agg.security.n... | python | def run(self, symbol: str) -> SecurityDetailsViewModel:
""" Loads the model for security details """
from pydatum import Datum
svc = self._svc
sec_agg = svc.securities.get_aggregate_for_symbol(symbol)
model = SecurityDetailsViewModel()
model.symbol = sec_agg.security.n... | [
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MisterY/gnucash-portfolio | gnucash_portfolio/scheduledtxaggregate.py | handle_friday | def handle_friday(next_date: Datum, period: str, mult: int, start_date: Datum):
""" Extracted the calculation for when the next_day is Friday """
assert isinstance(next_date, Datum)
assert isinstance(start_date, Datum)
# Starting from line 220.
tmp_sat = next_date.clone()
tmp_sat.add_days(1)
... | python | def handle_friday(next_date: Datum, period: str, mult: int, start_date: Datum):
""" Extracted the calculation for when the next_day is Friday """
assert isinstance(next_date, Datum)
assert isinstance(start_date, Datum)
# Starting from line 220.
tmp_sat = next_date.clone()
tmp_sat.add_days(1)
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MisterY/gnucash-portfolio | gnucash_portfolio/scheduledtxaggregate.py | ScheduledTxAggregate.get_next_occurrence | def get_next_occurrence(self) -> date:
""" Returns the next occurrence date for transaction """
result = get_next_occurrence(self.transaction)
assert isinstance(result, date)
return result | python | def get_next_occurrence(self) -> date:
""" Returns the next occurrence date for transaction """
result = get_next_occurrence(self.transaction)
assert isinstance(result, date)
return result | [
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MisterY/gnucash-portfolio | gnucash_portfolio/scheduledtxaggregate.py | ScheduledTxsAggregate.get_enabled | def get_enabled(self) -> List[ScheduledTransaction]:
""" Returns only enabled scheduled transactions """
query = (
self.query
.filter(ScheduledTransaction.enabled == True)
)
return query.all() | python | def get_enabled(self) -> List[ScheduledTransaction]:
""" Returns only enabled scheduled transactions """
query = (
self.query
.filter(ScheduledTransaction.enabled == True)
)
return query.all() | [
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MisterY/gnucash-portfolio | gnucash_portfolio/scheduledtxaggregate.py | ScheduledTxsAggregate.get_by_id | def get_by_id(self, tx_id: str) -> ScheduledTransaction:
""" Fetches a tx by id """
return self.query.filter(ScheduledTransaction.guid == tx_id).first() | python | def get_by_id(self, tx_id: str) -> ScheduledTransaction:
""" Fetches a tx by id """
return self.query.filter(ScheduledTransaction.guid == tx_id).first() | [
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MisterY/gnucash-portfolio | gnucash_portfolio/scheduledtxaggregate.py | ScheduledTxsAggregate.get_aggregate_by_id | def get_aggregate_by_id(self, tx_id: str) -> ScheduledTxAggregate:
""" Creates an aggregate for single entity """
tran = self.get_by_id(tx_id)
return self.get_aggregate_for(tran) | python | def get_aggregate_by_id(self, tx_id: str) -> ScheduledTxAggregate:
""" Creates an aggregate for single entity """
tran = self.get_by_id(tx_id)
return self.get_aggregate_for(tran) | [
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MisterY/gnucash-portfolio | gnucash_portfolio/securitiesaggregate.py | SecurityAggregate.get_avg_price_stat | def get_avg_price_stat(self) -> Decimal:
"""
Calculates the statistical average price for the security,
by averaging only the prices paid. Very simple first implementation.
"""
avg_price = Decimal(0)
price_total = Decimal(0)
price_count = 0
for account i... | python | def get_avg_price_stat(self) -> Decimal:
"""
Calculates the statistical average price for the security,
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"""
avg_price = Decimal(0)
price_total = Decimal(0)
price_count = 0
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MisterY/gnucash-portfolio | gnucash_portfolio/securitiesaggregate.py | SecurityAggregate.get_avg_price_fifo | def get_avg_price_fifo(self) -> Decimal:
"""
Calculates the average price paid for the security.
security = Commodity
Returns Decimal value.
"""
balance = self.get_quantity()
if not balance:
return Decimal(0)
paid = Decimal(0)
accounts... | python | def get_avg_price_fifo(self) -> Decimal:
"""
Calculates the average price paid for the security.
security = Commodity
Returns Decimal value.
"""
balance = self.get_quantity()
if not balance:
return Decimal(0)
paid = Decimal(0)
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MisterY/gnucash-portfolio | gnucash_portfolio/securitiesaggregate.py | SecurityAggregate.get_available_splits_for_account | def get_available_splits_for_account(self, account: Account) -> List[Split]:
""" Returns all unused splits in the account. Used for the calculation of avg.price.
The split that has been partially used will have its quantity reduced to available
quantity only. """
available_splits = []
... | python | def get_available_splits_for_account(self, account: Account) -> List[Split]:
""" Returns all unused splits in the account. Used for the calculation of avg.price.
The split that has been partially used will have its quantity reduced to available
quantity only. """
available_splits = []
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MisterY/gnucash-portfolio | gnucash_portfolio/securitiesaggregate.py | SecurityAggregate.get_num_shares | def get_num_shares(self) -> Decimal:
""" Returns the number of shares at this time """
from pydatum import Datum
today = Datum().today()
return self.get_num_shares_on(today) | python | def get_num_shares(self) -> Decimal:
""" Returns the number of shares at this time """
from pydatum import Datum
today = Datum().today()
return self.get_num_shares_on(today) | [
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MisterY/gnucash-portfolio | gnucash_portfolio/securitiesaggregate.py | SecurityAggregate.get_last_available_price | def get_last_available_price(self) -> PriceModel:
""" Finds the last available price for security. Uses PriceDb. """
price_db = PriceDbApplication()
symbol = SecuritySymbol(self.security.namespace, self.security.mnemonic)
result = price_db.get_latest_price(symbol)
return result | python | def get_last_available_price(self) -> PriceModel:
""" Finds the last available price for security. Uses PriceDb. """
price_db = PriceDbApplication()
symbol = SecuritySymbol(self.security.namespace, self.security.mnemonic)
result = price_db.get_latest_price(symbol)
return result | [
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MisterY/gnucash-portfolio | gnucash_portfolio/securitiesaggregate.py | SecurityAggregate.__get_holding_accounts_query | def __get_holding_accounts_query(self):
""" Returns all holding accounts, except Trading accounts. """
query = (
self.book.session.query(Account)
.filter(Account.commodity == self.security)
.filter(Account.type != AccountType.trading.value)
)
# generic... | python | def __get_holding_accounts_query(self):
""" Returns all holding accounts, except Trading accounts. """
query = (
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.filter(Account.commodity == self.security)
.filter(Account.type != AccountType.trading.value)
)
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MisterY/gnucash-portfolio | gnucash_portfolio/securitiesaggregate.py | SecurityAggregate.get_income_accounts | def get_income_accounts(self) -> List[Account]:
"""
Returns all income accounts for this security.
Income accounts are accounts not under Trading, expressed in currency, and
having the same name as the mnemonic.
They should be under Assets but this requires a recursive SQL query.... | python | def get_income_accounts(self) -> List[Account]:
"""
Returns all income accounts for this security.
Income accounts are accounts not under Trading, expressed in currency, and
having the same name as the mnemonic.
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MisterY/gnucash-portfolio | gnucash_portfolio/securitiesaggregate.py | SecurityAggregate.get_income_total | def get_income_total(self) -> Decimal:
""" Sum of all income = sum of balances of all income accounts. """
accounts = self.get_income_accounts()
# log(DEBUG, "income accounts: %s", accounts)
income = Decimal(0)
for acct in accounts:
income += acct.get_balance()
... | python | def get_income_total(self) -> Decimal:
""" Sum of all income = sum of balances of all income accounts. """
accounts = self.get_income_accounts()
# log(DEBUG, "income accounts: %s", accounts)
income = Decimal(0)
for acct in accounts:
income += acct.get_balance()
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MisterY/gnucash-portfolio | gnucash_portfolio/securitiesaggregate.py | SecurityAggregate.get_income_in_period | def get_income_in_period(self, start: datetime, end: datetime) -> Decimal:
""" Returns all income in the given period """
accounts = self.get_income_accounts()
income = Decimal(0)
for acct in accounts:
acc_agg = AccountAggregate(self.book, acct)
acc_bal = acc_agg.... | python | def get_income_in_period(self, start: datetime, end: datetime) -> Decimal:
""" Returns all income in the given period """
accounts = self.get_income_accounts()
income = Decimal(0)
for acct in accounts:
acc_agg = AccountAggregate(self.book, acct)
acc_bal = acc_agg.... | [
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MisterY/gnucash-portfolio | gnucash_portfolio/securitiesaggregate.py | SecurityAggregate.get_prices | def get_prices(self) -> List[PriceModel]:
""" Returns all available prices for security """
# return self.security.prices.order_by(Price.date)
from pricedb.dal import Price
pricedb = PriceDbApplication()
repo = pricedb.get_price_repository()
query = (repo.query(Price)
... | python | def get_prices(self) -> List[PriceModel]:
""" Returns all available prices for security """
# return self.security.prices.order_by(Price.date)
from pricedb.dal import Price
pricedb = PriceDbApplication()
repo = pricedb.get_price_repository()
query = (repo.query(Price)
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MisterY/gnucash-portfolio | gnucash_portfolio/securitiesaggregate.py | SecurityAggregate.get_quantity | def get_quantity(self) -> Decimal:
"""
Returns the number of shares for the given security.
It gets the number from all the accounts in the book.
"""
from pydatum import Datum
# Use today's date but reset hour and lower.
today = Datum()
today.today()
... | python | def get_quantity(self) -> Decimal:
"""
Returns the number of shares for the given security.
It gets the number from all the accounts in the book.
"""
from pydatum import Datum
# Use today's date but reset hour and lower.
today = Datum()
today.today()
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MisterY/gnucash-portfolio | gnucash_portfolio/securitiesaggregate.py | SecurityAggregate.get_splits_query | def get_splits_query(self):
""" Returns the query for all splits for this security """
query = (
self.book.session.query(Split)
.join(Account)
.filter(Account.type != AccountType.trading.value)
.filter(Account.commodity_guid == self.security.guid)
... | python | def get_splits_query(self):
""" Returns the query for all splits for this security """
query = (
self.book.session.query(Split)
.join(Account)
.filter(Account.type != AccountType.trading.value)
.filter(Account.commodity_guid == self.security.guid)
... | [
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MisterY/gnucash-portfolio | gnucash_portfolio/securitiesaggregate.py | SecurityAggregate.get_total_paid | def get_total_paid(self) -> Decimal:
""" Returns the total amount paid, in currency, for the stocks owned """
query = (
self.get_splits_query()
)
splits = query.all()
total = Decimal(0)
for split in splits:
total += split.value
return tot... | python | def get_total_paid(self) -> Decimal:
""" Returns the total amount paid, in currency, for the stocks owned """
query = (
self.get_splits_query()
)
splits = query.all()
total = Decimal(0)
for split in splits:
total += split.value
return tot... | [
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MisterY/gnucash-portfolio | gnucash_portfolio/securitiesaggregate.py | SecurityAggregate.get_total_paid_for_remaining_stock | def get_total_paid_for_remaining_stock(self) -> Decimal:
""" Returns the amount paid only for the remaining stock """
paid = Decimal(0)
accounts = self.get_holding_accounts()
for acc in accounts:
splits = self.get_available_splits_for_account(acc)
paid += sum(spl... | python | def get_total_paid_for_remaining_stock(self) -> Decimal:
""" Returns the amount paid only for the remaining stock """
paid = Decimal(0)
accounts = self.get_holding_accounts()
for acc in accounts:
splits = self.get_available_splits_for_account(acc)
paid += sum(spl... | [
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MisterY/gnucash-portfolio | gnucash_portfolio/securitiesaggregate.py | SecurityAggregate.get_value | def get_value(self) -> Decimal:
""" Returns the current value of stocks """
quantity = self.get_quantity()
price = self.get_last_available_price()
if not price:
# raise ValueError("no price found for", self.full_symbol)
return Decimal(0)
value = quantity ... | python | def get_value(self) -> Decimal:
""" Returns the current value of stocks """
quantity = self.get_quantity()
price = self.get_last_available_price()
if not price:
# raise ValueError("no price found for", self.full_symbol)
return Decimal(0)
value = quantity ... | [
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MisterY/gnucash-portfolio | gnucash_portfolio/securitiesaggregate.py | SecurityAggregate.get_value_in_base_currency | def get_value_in_base_currency(self) -> Decimal:
""" Calculates the value of security holdings in base currency """
# check if the currency is the base currency.
amt_orig = self.get_value()
# Security currency
sec_cur = self.get_currency()
#base_cur = self.book.default_cu... | python | def get_value_in_base_currency(self) -> Decimal:
""" Calculates the value of security holdings in base currency """
# check if the currency is the base currency.
amt_orig = self.get_value()
# Security currency
sec_cur = self.get_currency()
#base_cur = self.book.default_cu... | [
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MisterY/gnucash-portfolio | gnucash_portfolio/securitiesaggregate.py | SecurityAggregate.accounts | def accounts(self) -> List[Account]:
""" Returns the asset accounts in which the security is held """
# use only Assets sub-accounts
result = (
[acct for acct in self.security.accounts if acct.fullname.startswith('Assets')]
)
return result | python | def accounts(self) -> List[Account]:
""" Returns the asset accounts in which the security is held """
# use only Assets sub-accounts
result = (
[acct for acct in self.security.accounts if acct.fullname.startswith('Assets')]
)
return result | [
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MisterY/gnucash-portfolio | gnucash_portfolio/securitiesaggregate.py | SecuritiesAggregate.find | def find(self, search_term: str) -> List[Commodity]:
""" Searches for security by part of the name """
query = (
self.query
.filter(Commodity.mnemonic.like('%' + search_term + '%') |
Commodity.fullname.like('%' + search_term + '%'))
)
return qu... | python | def find(self, search_term: str) -> List[Commodity]:
""" Searches for security by part of the name """
query = (
self.query
.filter(Commodity.mnemonic.like('%' + search_term + '%') |
Commodity.fullname.like('%' + search_term + '%'))
)
return qu... | [
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MisterY/gnucash-portfolio | gnucash_portfolio/securitiesaggregate.py | SecuritiesAggregate.get_all | def get_all(self) -> List[Commodity]:
""" Loads all non-currency commodities, assuming they are stocks. """
query = (
self.query
.order_by(Commodity.namespace, Commodity.mnemonic)
)
return query.all() | python | def get_all(self) -> List[Commodity]:
""" Loads all non-currency commodities, assuming they are stocks. """
query = (
self.query
.order_by(Commodity.namespace, Commodity.mnemonic)
)
return query.all() | [
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MisterY/gnucash-portfolio | gnucash_portfolio/securitiesaggregate.py | SecuritiesAggregate.get_by_symbol | def get_by_symbol(self, symbol: str) -> Commodity:
"""
Returns the commodity with the given symbol.
If more are found, an exception will be thrown.
"""
# handle namespace. Accept GnuCash and Yahoo-style symbols.
full_symbol = self.__parse_gc_symbol(symbol)
query ... | python | def get_by_symbol(self, symbol: str) -> Commodity:
"""
Returns the commodity with the given symbol.
If more are found, an exception will be thrown.
"""
# handle namespace. Accept GnuCash and Yahoo-style symbols.
full_symbol = self.__parse_gc_symbol(symbol)
query ... | [
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