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47fe471c7b8fc48620cb3a18e67fbdfb3e0fccd8 | Anon-Artist/autogluon | core/src/autogluon/core/utils/utils.py | [
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] | Python | shuffle_df_rows | <not_specific> | def shuffle_df_rows(X: DataFrame, seed=0, reset_index=True):
"""Returns DataFrame with rows shuffled based on seed value."""
row_count = X.shape[0]
np.random.seed(seed)
rand_shuffle = np.random.randint(0, row_count, size=row_count)
X_shuffled = X.iloc[rand_shuffle]
if reset_index:
X_shuf... | Returns DataFrame with rows shuffled based on seed value. | Returns DataFrame with rows shuffled based on seed value. | [
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row_count = X.shape[0]
np.random.seed(seed)
rand_shuffle = np.random.randint(0, row_count, size=row_count)
X_shuffled = X.iloc[rand_shuffle]
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47fe471c7b8fc48620cb3a18e67fbdfb3e0fccd8 | Anon-Artist/autogluon | core/src/autogluon/core/utils/utils.py | [
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""" Remaps the predicted probabilities to open interval (0,1) while maintaining rank order """
(pmin,pmax) = (eps, 1-eps) # predicted probs outside this range will be remapped into (0,1)
which_toobig = y_predprob > pmax
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47fe471c7b8fc48620cb3a18e67fbdfb3e0fccd8 | Anon-Artist/autogluon | core/src/autogluon/core/utils/utils.py | [
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""" Remaps the predicted probabilities to lie in (0,1) where eps controls how far from 0 smallest class-probability lies """
min_predprob = np.min(y_predprob)
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47fe471c7b8fc48620cb3a18e67fbdfb3e0fccd8 | Anon-Artist/autogluon | core/src/autogluon/core/utils/utils.py | [
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47fe471c7b8fc48620cb3a18e67fbdfb3e0fccd8 | Anon-Artist/autogluon | core/src/autogluon/core/utils/utils.py | [
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] | Python | augment_rare_classes | <not_specific> | def augment_rare_classes(X, label, threshold):
""" Use this method when using certain eval_metrics like log_loss, for which no classes may be filtered out.
This method will augment dataset with additional examples of rare classes.
"""
class_counts = X[label].value_counts()
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5b050c1f10f703d365a450036f8f68cc207f7e94 | Anon-Artist/autogluon | tabular/src/autogluon/tabular/task/tabular_prediction/predictor.py | [
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""" Use trained models to produce predicted labels (in classification) or response values (in regression).
Parameters
----------
dataset : str or :class:`TabularDataset` or `pandas.DataFrame`
The datase... | Use trained models to produce predicted labels (in classification) or response values (in regression).
Parameters
----------
dataset : str or :class:`TabularDataset` or `pandas.DataFrame`
The dataset to make predictions for. Should contain same column names as train... | Use trained models to produce predicted labels (in classification) or response values (in regression).
Parameters
dataset : str or :class:`TabularDataset` or `pandas.DataFrame`
The dataset to make predictions for. Should contain same column names as training Dataset and follow same format
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5b050c1f10f703d365a450036f8f68cc207f7e94 | Anon-Artist/autogluon | tabular/src/autogluon/tabular/task/tabular_prediction/predictor.py | [
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"""
Calculates feature importance scores for the given model via permutation importance. Refer to http... |
Calculates feature importance scores for the given model via permutation importance. Refer to https://explained.ai/rf-importance/ for an explanation of permutation importance.
A feature's importance score represents the performance drop that results when the model makes predictions on a perturbed copy ... | Calculates feature importance scores for the given model via permutation importance.
For highly accurate importance and p_value estimates, it is recommend to set `subsample_size` to at least 5,000 if possible and `num_shuffle_sets` to at least 10.
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Returns
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5b050c1f10f703d365a450036f8f68cc207f7e94 | Anon-Artist/autogluon | tabular/src/autogluon/tabular/task/tabular_prediction/predictor.py | [
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"""
Fits new weighted ensemble models to combine predictions of previously-trained models.
`cache_data` must have been set to `True` during the original training to e... |
Fits new weighted ensemble models to combine predictions of previously-trained models.
`cache_data` must have been set to `True` during the original training to enable this functionality.
Parameters
----------
base_models : list, default = None
List of model names t... | Fits new weighted ensemble models to combine predictions of previously-trained models.
`cache_data` must have been set to `True` during the original training to enable this functionality.
Parameters
base_models : list, default = None
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fit = True
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5b050c1f10f703d365a450036f8f68cc207f7e94 | Anon-Artist/autogluon | tabular/src/autogluon/tabular/task/tabular_prediction/predictor.py | [
"Apache-2.0"
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"""
Returns the positive class name in binary classification. Useful for computing metrics such as F1 which require a positive and negative class.
In binary classification, `predictor.predict_proba()` returns the estimated probability that each row belongs to the positi... |
Returns the positive class name in binary classification. Useful for computing metrics such as F1 which require a positive and negative class.
In binary classification, `predictor.predict_proba()` returns the estimated probability that each row belongs to the positive class.
Will print a warnin... | Returns the positive class name in binary classification. Useful for computing metrics such as F1 which require a positive and negative class.
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5b050c1f10f703d365a450036f8f68cc207f7e94 | Anon-Artist/autogluon | tabular/src/autogluon/tabular/task/tabular_prediction/predictor.py | [
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"""
Output the visualized stack ensemble architecture of a model trained by `fit()`.
The plot is stored to a file, `ensemble_model.png` in folder `Predictor.output_directory`
This function requires `graphviz` and `pyg... |
Output the visualized stack ensemble architecture of a model trained by `fit()`.
The plot is stored to a file, `ensemble_model.png` in folder `Predictor.output_directory`
This function requires `graphviz` and `pygraphviz` to be installed because this visualization depends on thos... | Output the visualized stack ensemble architecture of a model trained by `fit()`.
The plot is stored to a file, `ensemble_model.png` in folder `Predictor.output_directory`
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raise ImportError('Visualizing ensemble network architecture requires pygraphviz library')
G = self._trainer.model_graph.copy()
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f3799a512a5d392f1622b6e6cb73adccfddec229 | Anon-Artist/autogluon | tabular/src/autogluon/tabular/learner/default_learner.py | [
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X (DataFrame): training data
X_... | Arguments:
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X_unlabeled (DataFrame): data used for pretraining a model. This is same data format as X, without la... | X (DataFrame): training data
X_val (DataFrame): data used for hyperparameter tuning. Note: final model may be trained using this data as well as training data
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f3799a512a5d392f1622b6e6cb73adccfddec229 | Anon-Artist/autogluon | tabular/src/autogluon/tabular/learner/default_learner.py | [
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] | Python | general_data_processing | <not_specific> | def general_data_processing(self, X: DataFrame, X_val: DataFrame, X_unlabeled: DataFrame, holdout_frac: float, num_bagging_folds: int):
""" General data processing steps used for all models. """
X = copy.deepcopy(X)
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X = copy.deepcopy(X)
missinglabel_inds = [index for index, x in X[self.label].isna().iteritems() if x]
if len(missinglabel_inds) > 0:
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af4e753bb17ac160f9719beda475ecaa2fef9758 | Anon-Artist/autogluon | tabular/src/autogluon/tabular/models/knn/knn_model.py | [
"Apache-2.0"
] | Python | _fit_with_samples | <not_specific> | def _fit_with_samples(self, X_train, y_train, time_limit):
"""
Fit model with samples of the data repeatedly, gradually increasing the amount of data until time_limit is reached or all data is used.
X_train and y_train must already be preprocessed
"""
time_start = time.time()
... |
Fit model with samples of the data repeatedly, gradually increasing the amount of data until time_limit is reached or all data is used.
X_train and y_train must already be preprocessed
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ae12a0354cc62d22737aa3e78cfc66cfa30f46b2 | Anon-Artist/autogluon | tabular/src/autogluon/tabular/task/tabular_prediction/predictor_v2.py | [
"Apache-2.0"
] | Python | fit | <not_specific> | def fit(self,
train_data,
tuning_data=None,
time_limit=None,
presets=None,
hyperparameters=None,
feature_metadata=None,
**kwargs):
"""
Fit models to predict a column of data table based on the other columns.
# T... |
Fit models to predict a column of data table based on the other columns.
# TODO: Move documentation from TabularPrediction.fit to here
# TODO: Move num_cpu/num_gpu to AG_args_fit
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0e4470c4a051b899ab6ebb30c47ca81e77c3da7f | vvk17/MLEXP | run.py | [
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] | Python | Retrieve_per_day | <not_specific> | def Retrieve_per_day():
"""
Post call to retrieve data for a day for a device per user input
"""
#retrieve the json from the ajax call
jsonFile = ''
if request.method == 'POST':
jsonFile = request.json
print ("post request", file=sys.stderr)
#if jsonFile successfully posted.... |
Post call to retrieve data for a day for a device per user input
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jsonFile = ''
if request.method == 'POST':
jsonFile = request.json
print ("post request", file=sys.stderr)
if jsonFile != '':
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0e4470c4a051b899ab6ebb30c47ca81e77c3da7f | vvk17/MLEXP | run.py | [
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"""
Post call to retrieve data across days for a device per user input
"""
#retrieve the json from the ajax call
jsonFile = ''
if request.method == 'POST':
jsonFile = request.json
print ("post request")
#if jsonFile successfully posted..
if js... |
Post call to retrieve data across days for a device per user input
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jsonFile = request.json
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0e4470c4a051b899ab6ebb30c47ca81e77c3da7f | vvk17/MLEXP | run.py | [
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] | Python | Retrieve_hourly_stats_trends | <not_specific> | def Retrieve_hourly_stats_trends():
"""
Post call to retrieve data across days for a device per user input with hourly stats and trends
"""
#retrieve the json from the ajax call
jsonFile = ''
if request.method == 'POST':
jsonFile = request.json
print ("post request")
#if jso... |
Post call to retrieve data across days for a device per user input with hourly stats and trends
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jsonFile = ''
if request.method == 'POST':
jsonFile = request.json
print ("post request")
if jsonFile != '':
if not all(arg in jsonFile for arg in ["deviceId","field","startDate","endDate"]):
print("Missing arguments in post request")
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0e4470c4a051b899ab6ebb30c47ca81e77c3da7f | vvk17/MLEXP | run.py | [
"Apache-2.0"
] | Python | Retrieve_device_stats | <not_specific> | def Retrieve_device_stats():
"""
Post call to retrieve device stats for devices
"""
#retrieve the json from the ajax call
json_file = ''
if request.method == 'POST':
json_file = request.json
print ("post request")
#if json_file successfully posted..
if json_file != '':
... |
Post call to retrieve device stats for devices
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json_file = ''
if request.method == 'POST':
json_file = request.json
print ("post request")
if json_file != '':
if not all(arg in json_file for arg in ["deviceIds","field","startDate","endDate"]):
print("Missing arguments in post request")
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} |
0e4470c4a051b899ab6ebb30c47ca81e77c3da7f | vvk17/MLEXP | run.py | [
"Apache-2.0"
] | Python | Set_dataset | <not_specific> | def Set_dataset():
"""
Post call to set active dataset in dataset.json
"""
output = {}
#retrieve the json from the ajax call
jsonFile = ''
if request.method == 'POST':
jsonFile = request.json
print ("post request")
#if jsonFile successfully posted..
if jsonFile != '... |
Post call to set active dataset in dataset.json
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output = {}
jsonFile = ''
if request.method == 'POST':
jsonFile = request.json
print ("post request")
if jsonFile != '':
if not all(arg in jsonFile for arg in ["dataset"]):
print("Missing arguments in post request")
return json.dumps({"s... | [
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0e4470c4a051b899ab6ebb30c47ca81e77c3da7f | vvk17/MLEXP | run.py | [
"Apache-2.0"
] | Python | Append_dataset | <not_specific> | def Append_dataset():
"""
Post call to append dataset.json file with user inputs
"""
#retrieve the json from the ajax call
jsonFile = ''
if request.method == 'POST':
jsonFile = request.json
print ("post request")
#if jsonFile successfully posted..
if jsonFile != '':
... |
Post call to append dataset.json file with user inputs
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jsonFile = ''
if request.method == 'POST':
jsonFile = request.json
print ("post request")
if jsonFile != '':
if not all(arg in jsonFile for arg in ["deviceIds","dates","datasetName","dbName"]):
print("Missing arguments in post request")
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0e4470c4a051b899ab6ebb30c47ca81e77c3da7f | vvk17/MLEXP | run.py | [
"Apache-2.0"
] | Python | Get_datasets | <not_specific> | def Get_datasets():
"""
Get datasets name from dataset.json file
"""
#return datasets array
return json.dumps(dataset.Get_datasets()) |
Get datasets name from dataset.json file
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0e4470c4a051b899ab6ebb30c47ca81e77c3da7f | vvk17/MLEXP | run.py | [
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] | Python | Get_dataset | <not_specific> | def Get_dataset():
"""
Get dataset name from dataset.json file
"""
#return active dataset
return json.dumps(dataset.Get_dataset()) |
Get dataset name from dataset.json file
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0e4470c4a051b899ab6ebb30c47ca81e77c3da7f | vvk17/MLEXP | run.py | [
"Apache-2.0"
] | Python | Get_dates | <not_specific> | def Get_dates():
"""
Get and return the dates from dataset
"""
#return dates
return json.dumps(dataset.Get_dates()) |
Get and return the dates from dataset
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0e4470c4a051b899ab6ebb30c47ca81e77c3da7f | vvk17/MLEXP | run.py | [
"Apache-2.0"
] | Python | Get_devices | <not_specific> | def Get_devices():
"""
Get and return deviceIds from dataset
"""
#return deviceIds
return json.dumps(dataset.Get_devices()) |
Get and return deviceIds from dataset
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0e4470c4a051b899ab6ebb30c47ca81e77c3da7f | vvk17/MLEXP | run.py | [
"Apache-2.0"
] | Python | Get_db_names | <not_specific> | def Get_db_names():
"""
Get and return database name initials from the Cloudant storage for dataset initialization
"""
#return uniqueDbnames
return json.dumps(dataset.Get_db_names()) |
Get and return database name initials from the Cloudant storage for dataset initialization
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0e4470c4a051b899ab6ebb30c47ca81e77c3da7f | vvk17/MLEXP | run.py | [
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] | Python | Get_db_dates | <not_specific> | def Get_db_dates():
"""
Get and returns dates from the Cloudant storage for dataset initialization
"""
#return uniqueDates
return json.dumps(dataset.Get_db_dates()) |
Get and returns dates from the Cloudant storage for dataset initialization
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0e4470c4a051b899ab6ebb30c47ca81e77c3da7f | vvk17/MLEXP | run.py | [
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] | Python | Get_db_deviceids | <not_specific> | def Get_db_deviceids():
"""
Get and returns dates from the Cloudant storage for dataset initialization
"""
#retrun uniqueDeviceIds
return json.dumps(dataset.Get_db_deviceids()) |
Get and returns dates from the Cloudant storage for dataset initialization
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a0c525e7ee3ac371089591dd490d974b89a17925 | matkoniecz/scivision | scivision/catalog/catalog.py | [
"BSD-3-Clause"
] | Python | compatible_models | PandasQueryResult | def compatible_models(self, datasource) -> PandasQueryResult:
"""Return all models that are compatible with datasource
Parameters
----------
datasource : str or dict-like
Any dictionary-like (including CatalogDatasourceEntry) that
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----------
datasource : str or dict-like
Any dictionary-like (including CatalogDatasourceEntry) that
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a0c525e7ee3ac371089591dd490d974b89a17925 | matkoniecz/scivision | scivision/catalog/catalog.py | [
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Parameters
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Any dictionary-like (including CatalogModelEntry) that has
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Any dictionary-like (including CatalogModelEntry) that has
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56cfd11e5b3d499d3765f02b913ae83a56e854c1 | offerijns/deepmorpheus | deepmorpheus/util.py | [
"MIT"
] | Python | download_from_url | <not_specific> | def download_from_url(url, dst):
"""
@param: url to download file
@param: dst place to put the file
"""
file_size = int(requests.head(url).headers["Content-Length"])
partial_dst = dst + ".partial"
if os.path.exists(partial_dst):
first_byte = os.path.getsize(partial_dst)
else:
... |
@param: url to download file
@param: dst place to put the file
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file_size = int(requests.head(url).headers["Content-Length"])
partial_dst = dst + ".partial"
if os.path.exists(partial_dst):
first_byte = os.path.getsize(partial_dst)
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56cfd11e5b3d499d3765f02b913ae83a56e854c1 | offerijns/deepmorpheus | deepmorpheus/util.py | [
"MIT"
] | Python | tag_to_readable | <not_specific> | def tag_to_readable(tag, conversion):
"""This functions turns a 9 character tag into a human readable morphological
statement"""
parts = []
for index, char in enumerate(tag):
if char == '-': continue
parts.append(conversion[index][char] if char in conversion[index] else char)
return ... | This functions turns a 9 character tag into a human readable morphological
statement | This functions turns a 9 character tag into a human readable morphological
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] | def tag_to_readable(tag, conversion):
parts = []
for index, char in enumerate(tag):
if char == '-': continue
parts.append(conversion[index][char] if char in conversion[index] else char)
return " ".join(parts) | [
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56cfd11e5b3d499d3765f02b913ae83a56e854c1 | offerijns/deepmorpheus | deepmorpheus/util.py | [
"MIT"
] | Python | readable_conversion_file | <not_specific> | def readable_conversion_file(url):
"""Reads the provided url as a conversion file"""
conversion_dict = []
with open(url, 'r', encoding='utf-8') as f:
lines = f.readlines()
assert len(lines) == 9, "The conversion file must have exactly 9 lines detailing the 9 conversion categories"
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] | def readable_conversion_file(url):
conversion_dict = []
with open(url, 'r', encoding='utf-8') as f:
lines = f.readlines()
assert len(lines) == 9, "The conversion file must have exactly 9 lines detailing the 9 conversion categories"
for line in lines:
category_dict = {}
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72884fedb35cfa00e42c4e3b1bc012fa847de220 | offerijns/deepmorpheus | deepmorpheus/model.py | [
"MIT"
] | Python | init_word_hidden | null | def init_word_hidden(self):
"""Initialise word LSTM hidden state."""
self.word_lstm_hidden = (
torch.zeros(self.directions * self.hparams.num_lstm_layers, 1, self.hparams.word_lstm_hidden_dim).to(self.device),
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] | def init_word_hidden(self):
self.word_lstm_hidden = (
torch.zeros(self.directions * self.hparams.num_lstm_layers, 1, self.hparams.word_lstm_hidden_dim).to(self.device),
torch.zeros(self.directions * self.hparams.num_lstm_layers, 1, self.hparams.word_lstm_hidden_dim).to(self.device),
... | [
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72884fedb35cfa00e42c4e3b1bc012fa847de220 | offerijns/deepmorpheus | deepmorpheus/model.py | [
"MIT"
] | Python | init_char_hidden | null | def init_char_hidden(self):
"""Initialise char LSTM hidden state."""
self.char_lstm_hidden = (
torch.zeros(self.directions * self.hparams.num_lstm_layers, 1, self.hparams.char_lstm_hidden_dim).to(self.device),
torch.zeros(self.directions * self.hparams.num_lstm_layers, 1, self.hp... | Initialise char LSTM hidden state. | Initialise char LSTM hidden state. | [
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] | def init_char_hidden(self):
self.char_lstm_hidden = (
torch.zeros(self.directions * self.hparams.num_lstm_layers, 1, self.hparams.char_lstm_hidden_dim).to(self.device),
torch.zeros(self.directions * self.hparams.num_lstm_layers, 1, self.hparams.char_lstm_hidden_dim).to(self.device),
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72884fedb35cfa00e42c4e3b1bc012fa847de220 | offerijns/deepmorpheus | deepmorpheus/model.py | [
"MIT"
] | Python | forward | <not_specific> | def forward(self, sentence):
"""The main forward function, this does the actual heavy lifting"""
words = torch.tensor([word for word, _, _ in sentence]).to(self.device)
word_embeddings = self.word_embeddings(words)
word_embeddings_bs = word_embeddings.view(len(sentence), self.hparams.ba... | The main forward function, this does the actual heavy lifting | The main forward function, this does the actual heavy lifting | [
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words = torch.tensor([word for word, _, _ in sentence]).to(self.device)
word_embeddings = self.word_embeddings(words)
word_embeddings_bs = word_embeddings.view(len(sentence), self.hparams.batch_size, self.hparams.word_embedding_dim)
word_repr = []
if ... | [
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72884fedb35cfa00e42c4e3b1bc012fa847de220 | offerijns/deepmorpheus | deepmorpheus/model.py | [
"MIT"
] | Python | training_step | <not_specific> | def training_step(self, sentence, batch_idx):
"""Predicts the output of the provided input for the model and calculates loss over it"""
self.init_word_hidden()
outputs = self.forward(sentence)
# Shape: (sentence_len, 9, num_tag_output)
loss = self.nll_loss(sentence, outputs)
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self.init_word_hidden()
outputs = self.forward(sentence)
loss = self.nll_loss(sentence, outputs)
logs = {'train_loss': loss}
return {'loss': loss, 'log': logs} | [
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72884fedb35cfa00e42c4e3b1bc012fa847de220 | offerijns/deepmorpheus | deepmorpheus/model.py | [
"MIT"
] | Python | validation_step | <not_specific> | def validation_step(self, sentence, batch_idx):
"""Handles one single validation step, computes its loss and updates accuracy"""
self.init_word_hidden()
outputs = self.forward(sentence)
# Shape: (sentence_len, 9, num_tag_output)
loss = self.nll_loss(sentence, outputs)
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self.init_word_hidden()
outputs = self.forward(sentence)
loss = self.nll_loss(sentence, outputs)
avg_acc, acc_by_tag = self.accuracy(sentence, outputs)
return {'val_loss': loss, 'val_acc': avg_acc, 'acc_by_tag': acc_by_tag} | [
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72884fedb35cfa00e42c4e3b1bc012fa847de220 | offerijns/deepmorpheus | deepmorpheus/model.py | [
"MIT"
] | Python | validation_epoch_end | <not_specific> | def validation_epoch_end(self, outputs):
"""Called when an validation epoch ends, this prints out the average loss and accuracy"""
avg_loss = torch.stack([x['val_loss'] for x in outputs]).mean()
avg_acc = torch.stack([x['val_acc'] for x in outputs]).mean()
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avg_loss = torch.stack([x['val_loss'] for x in outputs]).mean()
avg_acc = torch.stack([x['val_acc'] for x in outputs]).mean()
print('Validation loss is %.2f, validation accuracy is %.2f%%' % (avg_loss, avg_acc * 100))
log = {'val_loss': avg_loss, ... | [
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72884fedb35cfa00e42c4e3b1bc012fa847de220 | offerijns/deepmorpheus | deepmorpheus/model.py | [
"MIT"
] | Python | nll_loss | <not_specific> | def nll_loss(self, sentence, outputs):
"""Calculates NLL loss over the combination of the predicted output and the ground truth"""
loss_all_words = 0.0
for word_idx in range(len(sentence)):
output = outputs[word_idx]
target = sentence[word_idx][2]
try:
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] | def nll_loss(self, sentence, outputs):
loss_all_words = 0.0
for word_idx in range(len(sentence)):
output = outputs[word_idx]
target = sentence[word_idx][2]
try:
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72884fedb35cfa00e42c4e3b1bc012fa847de220 | offerijns/deepmorpheus | deepmorpheus/model.py | [
"MIT"
] | Python | accuracy | <not_specific> | def accuracy(self, sentence, outputs):
"""Calculates the summed/mean accuracy for this sentence as well as the accuracy by tag"""
sum_accuracy = 0.0
sentence_len = len(sentence)
sum_acc_by_tag = [0 for i in range(self.tag_len)]
for word_idx in range(sentence_len):
out... | Calculates the summed/mean accuracy for this sentence as well as the accuracy by tag | Calculates the summed/mean accuracy for this sentence as well as the accuracy by tag | [
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] | def accuracy(self, sentence, outputs):
sum_accuracy = 0.0
sentence_len = len(sentence)
sum_acc_by_tag = [0 for i in range(self.tag_len)]
for word_idx in range(sentence_len):
output = outputs[word_idx]
target = sentence[word_idx][2]
try:
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d066b97c039f15fd1a82ea0d5e55632769fab20f | offerijns/deepmorpheus | deepmorpheus/tag.py | [
"MIT"
] | Python | attempt_vocab_load | <not_specific> | def attempt_vocab_load(vocab_path):
"""This function will try to load the vocab file from data/vocab.p.
If it fails it will abort execution since we need a vocabulary to correctly
tokenize the input data"""
if not os.path.isfile(vocab_path):
print("Vocabulary needs to be located here: %s" % voca... | This function will try to load the vocab file from data/vocab.p.
If it fails it will abort execution since we need a vocabulary to correctly
tokenize the input data | This function will try to load the vocab file from data/vocab.p.
If it fails it will abort execution since we need a vocabulary to correctly
tokenize the input data | [
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print("Vocabulary needs to be located here: %s" % vocab_path)
exit()
print("Loading vocabulary from cache: %s" % vocab_path)
with open(vocab_path, "rb") as f:
vocab = pickle.load(f)
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d066b97c039f15fd1a82ea0d5e55632769fab20f | offerijns/deepmorpheus | deepmorpheus/tag.py | [
"MIT"
] | Python | attempt_input_load | <not_specific> | def attempt_input_load(input_path):
"""Attempts to load the file at the provided path and return it as an array
of lines. If the file does not exist we will exit the program since nothing
useful can be done."""
if not os.path.isfile(input_path):
print("Input file does not exist: %s" % input_path... | Attempts to load the file at the provided path and return it as an array
of lines. If the file does not exist we will exit the program since nothing
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if not os.path.isfile(input_path):
print("Input file does not exist: %s" % input_path)
exit()
print("Loading input from file: %s" % input_path)
with open(input_path, "r", encoding='utf-8') as f:
lines = f.readlines()
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d066b97c039f15fd1a82ea0d5e55632769fab20f | offerijns/deepmorpheus | deepmorpheus/tag.py | [
"MIT"
] | Python | attempt_checkpoint_load | <not_specific> | def attempt_checkpoint_load(checkpoint_path, vocab, device, force_compatibility=False):
"""This function tries to load a pytorch checkpoint, if it fails it aborts the program"""
if not os.path.isfile(checkpoint_path):
print("Model checkpoint file does not exist: %s" % checkpoint_path)
exit()
... | This function tries to load a pytorch checkpoint, if it fails it aborts the program | This function tries to load a pytorch checkpoint, if it fails it aborts the program | [
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if not os.path.isfile(checkpoint_path):
print("Model checkpoint file does not exist: %s" % checkpoint_path)
exit()
print("Loading model from checkpoint: %s" % checkpoint_path)
checkpoint = torch.load(chec... | [
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d066b97c039f15fd1a82ea0d5e55632769fab20f | offerijns/deepmorpheus | deepmorpheus/tag.py | [
"MIT"
] | Python | tag_from_file | <not_specific> | def tag_from_file(input_path, language="ancient-greek", data_dir="data"):
"""Loads from a specified file, loads the file and then forwards to the tag_from_lines function """
# Try to load input file as list of lines, or abort
input_file = attempt_input_load(input_path)
return tag_from_lines(input_file, ... | Loads from a specified file, loads the file and then forwards to the tag_from_lines function | Loads from a specified file, loads the file and then forwards to the tag_from_lines function | [
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] | def tag_from_file(input_path, language="ancient-greek", data_dir="data"):
input_file = attempt_input_load(input_path)
return tag_from_lines(input_file, language, data_dir) | [
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4bc125da8ecfa8b8c8f82ff180a8a60bc8f4ae72 | offerijns/deepmorpheus | deepmorpheus/dataset.py | [
"MIT"
] | Python | save_vocab | null | def save_vocab(self, vocab_path):
"""This function saves the vocabulary file to the disk location provided"""
self.vocab.inverted_tags = [{v: k for k, v in tag.items()} for tag in self.vocab.tags]
with open(vocab_path, "wb") as vocab_file:
pickle.dump(self.vocab, vocab_file, protoco... | This function saves the vocabulary file to the disk location provided | This function saves the vocabulary file to the disk location provided | [
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] | def save_vocab(self, vocab_path):
self.vocab.inverted_tags = [{v: k for k, v in tag.items()} for tag in self.vocab.tags]
with open(vocab_path, "wb") as vocab_file:
pickle.dump(self.vocab, vocab_file, protocol=pickle.HIGHEST_PROTOCOL)
print("Saved vocabulary to cache: %s" % vocab_... | [
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e59a88154eea7e87f83236c9940b6230648e1097 | wellcomecollection/archivematica-infra | azure_ad_login/create_azure_client_secret.py | [
"MIT"
] | Python | login | null | def login():
"""
Logs in the current user using the Azure CLI.
"""
az("login") |
Logs in the current user using the Azure CLI.
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e59a88154eea7e87f83236c9940b6230648e1097 | wellcomecollection/archivematica-infra | azure_ad_login/create_azure_client_secret.py | [
"MIT"
] | Python | create_password | <not_specific> | def create_password():
"""
Returns a cryptographically secure new password.
"""
return secrets.token_hex(32) |
Returns a cryptographically secure new password.
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e59a88154eea7e87f83236c9940b6230648e1097 | wellcomecollection/archivematica-infra | azure_ad_login/create_azure_client_secret.py | [
"MIT"
] | Python | store_az_client_secret | null | def store_az_client_secret(*, app_id, env, password):
"""
Stores a new client secret with an Azure application.
"""
az(
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# --append = append a new credential rather than overwriting the
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e59a88154eea7e87f83236c9940b6230648e1097 | wellcomecollection/archivematica-infra | azure_ad_login/create_azure_client_secret.py | [
"MIT"
] | Python | store_secrets_manager_secret | null | def store_secrets_manager_secret(*, secret_id, secret_value, role_arn):
"""
Stores a new client secret in Secrets Manager.
"""
secrets_client = get_aws_client("secretsmanager", role_arn=role_arn)
try:
resp = secrets_client.create_secret(Name=secret_id, SecretString=secret_value,)
except... |
Stores a new client secret in Secrets Manager.
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] | def store_secrets_manager_secret(*, secret_id, secret_value, role_arn):
secrets_client = get_aws_client("secretsmanager", role_arn=role_arn)
try:
resp = secrets_client.create_secret(Name=secret_id, SecretString=secret_value,)
except ClientError as err:
if err.response["Error"]["Code"] == "Re... | [
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e59a88154eea7e87f83236c9940b6230648e1097 | wellcomecollection/archivematica-infra | azure_ad_login/create_azure_client_secret.py | [
"MIT"
] | Python | force_ecs_task_redeployment | null | def force_ecs_task_redeployment(*, cluster_name, service_name):
"""
Force an ECS task to restart, so it picks up a fresh copy of secrets in
Secrets Manager.
"""
ecs_client = get_aws_client("ecs", role_arn=WORKFLOW_DEV_ROLE_ARN)
resp = ecs_client.update_service(
cluster=cluster_name, ser... |
Force an ECS task to restart, so it picks up a fresh copy of secrets in
Secrets Manager.
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Secrets Manager. | [
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] | def force_ecs_task_redeployment(*, cluster_name, service_name):
ecs_client = get_aws_client("ecs", role_arn=WORKFLOW_DEV_ROLE_ARN)
resp = ecs_client.update_service(
cluster=cluster_name, service=service_name, forceNewDeployment=True
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adf7855e807ed056f186c4509ce318820bffbb00 | wellcomecollection/archivematica-infra | s3_start_transfer/src/archivematica.py | [
"MIT"
] | Python | am_api_post_json | <not_specific> | def am_api_post_json(api_path, data):
"""
POST json to the Archivematica API
:param api_path: URL path to request (without hostname, e.g. /api/v2/location/)
:param data: Dict of data to post
:returns: dict of json data returned by request
"""
am_url = os.environ["ARCHIVEMATICA_URL"]
am_u... |
POST json to the Archivematica API
:param api_path: URL path to request (without hostname, e.g. /api/v2/location/)
:param data: Dict of data to post
:returns: dict of json data returned by request
| POST json to the Archivematica API | [
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am_url = os.environ["ARCHIVEMATICA_URL"]
am_user = os.environ["ARCHIVEMATICA_USERNAME"]
am_api_key = os.environ["ARCHIVEMATICA_API_KEY"]
am_headers = {"Authorization": f"ApiKey {am_user}:{am_api_key}"}
url = f"{am_url}{api_path}"
print(f"URL: {url}; Data: {d... | [
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adf7855e807ed056f186c4509ce318820bffbb00 | wellcomecollection/archivematica-infra | s3_start_transfer/src/archivematica.py | [
"MIT"
] | Python | ss_api_get | <not_specific> | def ss_api_get(api_path, params=None):
"""
GET request to the Archivematica storage service API
:param api_path: URL path to request (without hostname, e.g. /api/v2/location/)
:param params: Dict of params to include in the request
:returns: dict of json data returned by request
"""
ss_url =... |
GET request to the Archivematica storage service API
:param api_path: URL path to request (without hostname, e.g. /api/v2/location/)
:param params: Dict of params to include in the request
:returns: dict of json data returned by request
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ss_url = os.environ["ARCHIVEMATICA_SS_URL"]
ss_user = os.environ["ARCHIVEMATICA_SS_USERNAME"]
ss_api_key = os.environ["ARCHIVEMATICA_SS_API_KEY"]
ss_headers = {"Authorization": f"ApiKey {ss_user}:{ss_api_key}"}
params = params or {}
url = f"{ss_url}{api_pat... | [
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adf7855e807ed056f186c4509ce318820bffbb00 | wellcomecollection/archivematica-infra | s3_start_transfer/src/archivematica.py | [
"MIT"
] | Python | find_matching_path | <not_specific> | def find_matching_path(locations, bucket, directory, key):
"""
Match the given bucket and key to a location and return a path on the
Archivematica storage service
This takes the form `<location_uuid>:<target_path>` where:
`location_uuid` is the UUID of an S3 transfer source `Location` on the
... |
Match the given bucket and key to a location and return a path on the
Archivematica storage service
This takes the form `<location_uuid>:<target_path>` where:
`location_uuid` is the UUID of an S3 transfer source `Location` on the
Archivematica storage service which is configured with the s... | Match the given bucket and key to a location and return a path on the
Archivematica storage service
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for location in locations:
relative_path = location["relative_path"].strip("/")
if relative_path == directory and location["s3_bucket"] == bucket:
target_path = "/" + key
return b"%s:%s" % (os.fsencode(location["uuid"... | [
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adf7855e807ed056f186c4509ce318820bffbb00 | wellcomecollection/archivematica-infra | s3_start_transfer/src/archivematica.py | [
"MIT"
] | Python | start_transfer | <not_specific> | def start_transfer(name, path, processing_config, accession_number=None):
"""
Start an Archivematica transfer using the automated workflow
:param name: Name of transfer
:param key: Path of transfer, of the form b'<location_uuid>:<target_path>'
:returns: transfer uuid
"""
# Archivematica pr... |
Start an Archivematica transfer using the automated workflow
:param name: Name of transfer
:param key: Path of transfer, of the form b'<location_uuid>:<target_path>'
:returns: transfer uuid
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data = {
"name": name,
"type": "zipfile",
"path": base64.b64encode(path).decode(),
"processing_config": processing_config.replace("-", "_"),
"auto_approve": True,
}
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"... |
b257601fbbb64c995f7201c6b930588649982800 | wellcomecollection/archivematica-infra | docker_run.py | [
"MIT"
] | Python | _aws_credentials_args | <not_specific> | def _aws_credentials_args():
"""
Returns the arguments to add to ``docker run`` for sharing AWS credentials
with the running container.
"""
# THE AWS_PROFILE environment allows you to run operations in a
# non-default profile. If you have multiple profiles in your ~/.aws
# config, use this ... |
Returns the arguments to add to ``docker run`` for sharing AWS credentials
with the running container.
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For details:
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try:
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We need this environment varia... | [
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214eb463134f1b027af0b580bf5ef3b703a5486a | datmellow/email-scrapper | email_scrapper/readers/base_reader.py | [
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] | Python | _get_store_email | str | def _get_store_email(self, store: Stores) -> str:
"""
Parameters
----------
store :class:Stores
Returns
-------
the email of the store that the reader will filter by
"""
if self._email_mapping:
email = self._email_mapping.get(store)
... |
Parameters
----------
store :class:Stores
Returns
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the email of the store that the reader will filter by
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store :class:Stores
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the email of the store that the reader will filter by | [
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e6ae681e8454a01332d29100f0b1bb3cb51c5e08 | vedsgit/pyEX | pyEX/stocks/corporateActions.py | [
"Apache-2.0"
] | Python | bonusIssue | <not_specific> | def bonusIssue(symbol='', refid='', token='', version='', filter=''):
'''Bonus Issue Obtain up-to-date and detailed information on all new announcements, as well as 12+ years of historical records.
Updated at 5am, 10am, 8pm UTC daily
https://iexcloud.io/docs/api/#bonus-issue
Args:
symbol (str... | Bonus Issue Obtain up-to-date and detailed information on all new announcements, as well as 12+ years of historical records.
Updated at 5am, 10am, 8pm UTC daily
https://iexcloud.io/docs/api/#bonus-issue
Args:
symbol (str): Symbol to look up
refid (str): Optional. Id that matches the refid... | Bonus Issue Obtain up-to-date and detailed information on all new announcements, as well as 12+ years of historical records.
Updated at 5am, 10am, 8pm UTC daily
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e6ae681e8454a01332d29100f0b1bb3cb51c5e08 | vedsgit/pyEX | pyEX/stocks/corporateActions.py | [
"Apache-2.0"
] | Python | distribution | <not_specific> | def distribution(symbol='', refid='', token='', version='', filter=''):
'''Distribution Obtain up-to-date and detailed information on all new announcements, as well as 12+ years of historical records.
Updated at 5am, 10am, 8pm UTC daily
https://iexcloud.io/docs/api/#distribution
Args:
symbol ... | Distribution Obtain up-to-date and detailed information on all new announcements, as well as 12+ years of historical records.
Updated at 5am, 10am, 8pm UTC daily
https://iexcloud.io/docs/api/#distribution
Args:
symbol (str): Symbol to look up
refid (str): Optional. Id that matches the ref... | Distribution Obtain up-to-date and detailed information on all new announcements, as well as 12+ years of historical records.
Updated at 5am, 10am, 8pm UTC daily
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e6ae681e8454a01332d29100f0b1bb3cb51c5e08 | vedsgit/pyEX | pyEX/stocks/corporateActions.py | [
"Apache-2.0"
] | Python | returnOfCapital | <not_specific> | def returnOfCapital(symbol='', refid='', token='', version='', filter=''):
'''Return of capital up-to-date and detailed information on all new announcements, as well as 12+ years of historical records.
Updated at 5am, 10am, 8pm UTC daily
https://iexcloud.io/docs/api/#return-of-capital
Args:
s... | Return of capital up-to-date and detailed information on all new announcements, as well as 12+ years of historical records.
Updated at 5am, 10am, 8pm UTC daily
https://iexcloud.io/docs/api/#return-of-capital
Args:
symbol (str): Symbol to look up
refid (str): Optional. Id that matches the ... | Return of capital up-to-date and detailed information on all new announcements, as well as 12+ years of historical records.
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e6ae681e8454a01332d29100f0b1bb3cb51c5e08 | vedsgit/pyEX | pyEX/stocks/corporateActions.py | [
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'''Rights issue up-to-date and detailed information on all new announcements, as well as 12+ years of historical records.
Updated at 5am, 10am, 8pm UTC daily
https://iexcloud.io/docs/api/#rights-issue
Args:
symbol (str): S... | Rights issue up-to-date and detailed information on all new announcements, as well as 12+ years of historical records.
Updated at 5am, 10am, 8pm UTC daily
https://iexcloud.io/docs/api/#rights-issue
Args:
symbol (str): Symbol to look up
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e6ae681e8454a01332d29100f0b1bb3cb51c5e08 | vedsgit/pyEX | pyEX/stocks/corporateActions.py | [
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'''Right to purchase up-to-date and detailed information on all new announcements, as well as 12+ years of historical records.
Updated at 5am, 10am, 8pm UTC daily
https://iexcloud.io/docs/api/#right-to-purchase
Args:
s... | Right to purchase up-to-date and detailed information on all new announcements, as well as 12+ years of historical records.
Updated at 5am, 10am, 8pm UTC daily
https://iexcloud.io/docs/api/#right-to-purchase
Args:
symbol (str): Symbol to look up
refid (str): Optional. Id that matches the ... | Right to purchase up-to-date and detailed information on all new announcements, as well as 12+ years of historical records.
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e6ae681e8454a01332d29100f0b1bb3cb51c5e08 | vedsgit/pyEX | pyEX/stocks/corporateActions.py | [
"Apache-2.0"
] | Python | securityReclassification | <not_specific> | def securityReclassification(symbol='', refid='', token='', version='', filter=''):
'''Security reclassification up-to-date and detailed information on all new announcements, as well as 12+ years of historical records.
Updated at 5am, 10am, 8pm UTC daily
https://iexcloud.io/docs/api/#security-reclassifica... | Security reclassification up-to-date and detailed information on all new announcements, as well as 12+ years of historical records.
Updated at 5am, 10am, 8pm UTC daily
https://iexcloud.io/docs/api/#security-reclassification
Args:
symbol (str): Symbol to look up
refid (str): Optional. Id t... | Security reclassification up-to-date and detailed information on all new announcements, as well as 12+ years of historical records.
Updated at 5am, 10am, 8pm UTC daily
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e6ae681e8454a01332d29100f0b1bb3cb51c5e08 | vedsgit/pyEX | pyEX/stocks/corporateActions.py | [
"Apache-2.0"
] | Python | securitySwap | <not_specific> | def securitySwap(symbol='', refid='', token='', version='', filter=''):
'''Security Swap up-to-date and detailed information on all new announcements, as well as 12+ years of historical records.
Updated at 5am, 10am, 8pm UTC daily
https://iexcloud.io/docs/api/#security-swap
Args:
symbol (str)... | Security Swap up-to-date and detailed information on all new announcements, as well as 12+ years of historical records.
Updated at 5am, 10am, 8pm UTC daily
https://iexcloud.io/docs/api/#security-swap
Args:
symbol (str): Symbol to look up
refid (str): Optional. Id that matches the refid fi... | Security Swap up-to-date and detailed information on all new announcements, as well as 12+ years of historical records.
Updated at 5am, 10am, 8pm UTC daily
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e6ae681e8454a01332d29100f0b1bb3cb51c5e08 | vedsgit/pyEX | pyEX/stocks/corporateActions.py | [
"Apache-2.0"
] | Python | spinoff | <not_specific> | def spinoff(symbol='', refid='', token='', version='', filter=''):
'''Security spinoff up-to-date and detailed information on all new announcements, as well as 12+ years of historical records.
Updated at 5am, 10am, 8pm UTC daily
https://iexcloud.io/docs/api/#spinoff
Args:
symbol (str): Symbol... | Security spinoff up-to-date and detailed information on all new announcements, as well as 12+ years of historical records.
Updated at 5am, 10am, 8pm UTC daily
https://iexcloud.io/docs/api/#spinoff
Args:
symbol (str): Symbol to look up
refid (str): Optional. Id that matches the refid field... | Security spinoff up-to-date and detailed information on all new announcements, as well as 12+ years of historical records.
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return _getJson('time-series/advanced_spinoff/{}/{}'.format(symbol, refid), token, version, filter)
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e6ae681e8454a01332d29100f0b1bb3cb51c5e08 | vedsgit/pyEX | pyEX/stocks/corporateActions.py | [
"Apache-2.0"
] | Python | splits | <not_specific> | def splits(symbol='', refid='', token='', version='', filter=''):
'''Security splits up-to-date and detailed information on all new announcements, as well as 12+ years of historical records.
Updated at 5am, 10am, 8pm UTC daily
https://iexcloud.io/docs/api/#splits
Args:
symbol (str): Symbol to... | Security splits up-to-date and detailed information on all new announcements, as well as 12+ years of historical records.
Updated at 5am, 10am, 8pm UTC daily
https://iexcloud.io/docs/api/#splits
Args:
symbol (str): Symbol to look up
refid (str): Optional. Id that matches the refid field r... | Security splits up-to-date and detailed information on all new announcements, as well as 12+ years of historical records.
Updated at 5am, 10am, 8pm UTC daily
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a9d82b9998621ac3f6b5dbd669e9a26405b90db2 | vedsgit/pyEX | pyEX/cryptocurrency/cryptocurrency.py | [
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a9d82b9998621ac3f6b5dbd669e9a26405b90db2 | vedsgit/pyEX | pyEX/cryptocurrency/cryptocurrency.py | [
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'''This returns the price for a specified cryptocurrency.
https://iexcloud.io/docs/api/#cryptocurrency-price
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Args:
symbol (str): cryptocurrency ticker
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a9d82b9998621ac3f6b5dbd669e9a26405b90db2 | vedsgit/pyEX | pyEX/cryptocurrency/cryptocurrency.py | [
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'''This returns the quote for a specified cryptocurrency. Quotes are available via REST and SSE Streaming.
https://iexcloud.io/docs/api/#cryptocurrency-quote
continuous
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symbol (str): cryptocurrency ticker
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https://iexcloud.io/docs/api/#cryptocurrency-quote
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29a9313871c9cc0aee0ccde05b9eb7b1c11b0c7e | vedsgit/pyEX | pyEX/points/points.py | [
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'''Data points are available per symbol and return individual plain text values.
Retrieving individual data points is useful for Excel and Google Sheet users, and applications where a single, lightweight value is needed.
We also provide u... | Data points are available per symbol and return individual plain text values.
Retrieving individual data points is useful for Excel and Google Sheet users, and applications where a single, lightweight value is needed.
We also provide update times for some endpoints which allow you to call an endpoint only once ... | Data points are available per symbol and return individual plain text values.
Retrieving individual data points is useful for Excel and Google Sheet users, and applications where a single, lightweight value is needed.
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01f37185606d3cd7bf022963bacb36df819164dc | vedsgit/pyEX | pyEX/stocks/timeseries.py | [
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'''Get inventory of available time series endpoints
Returns:
result (dict)
'''
return _getJson('time-series/', token, version) | Get inventory of available time series endpoints
Returns:
result (dict)
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01f37185606d3cd7bf022963bacb36df819164dc | vedsgit/pyEX | pyEX/stocks/timeseries.py | [
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24a5b2040dff218d21ce9d77c47061073e9ab133 | vedsgit/pyEX | pyEX/premium/stocktwits/__init__.py | [
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token='',
version='',
filter=''):
'''This endpoint provides social sentiment data from StockTwits. Data can be viewed as a daily value, or by minute for a given date... | This endpoint provides social sentiment data from StockTwits. Data can be viewed as a daily value, or by minute for a given date.
https://iexcloud.io/docs/api/#social-sentiment
Args:
symbol (str): Symbol to look up
type (Optional[str]): Can only be daily or minute. Default is daily.
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2fc244e0c43b0be787d8514182f7ae02030df077 | vedsgit/pyEX | pyEX/marketdata/cryptocurrency.py | [
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2fc244e0c43b0be787d8514182f7ae02030df077 | vedsgit/pyEX | pyEX/marketdata/cryptocurrency.py | [
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2fc244e0c43b0be787d8514182f7ae02030df077 | vedsgit/pyEX | pyEX/marketdata/cryptocurrency.py | [
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https://iexcloud.io/docs/api/#cryptocurrency-quote
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symbols (str): Tickers to request
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2fc244e0c43b0be787d8514182f7ae02030df077 | vedsgit/pyEX | pyEX/marketdata/cryptocurrency.py | [
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https://iexcloud.io/docs/api/#cryptocurrency-quote
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symbols (str): Tickers to request
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8d99b662dbf898cd26e4b3e979cdbb06af8e1977 | vedsgit/pyEX | pyEX/marketdata/fx.py | [
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https://iexcloud.io/docs/api/#forex-currencies
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... | This endpoint streams real-time foreign currency exchange rates.
https://iexcloud.io/docs/api/#forex-currencies
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8d99b662dbf898cd26e4b3e979cdbb06af8e1977 | vedsgit/pyEX | pyEX/marketdata/fx.py | [
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https://iexcloud.io/docs/api/#forex-currencies
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4963d0f82cd54e11c60cb3fc7cb4f506247184ef | vedsgit/pyEX | pyEX/rules/__init__.py | [
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4963d0f82cd54e11c60cb3fc7cb4f506247184ef | vedsgit/pyEX | pyEX/rules/__init__.py | [
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4963d0f82cd54e11c60cb3fc7cb4f506247184ef | vedsgit/pyEX | pyEX/rules/__init__.py | [
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Args:
ruleId (str): The id of an exist... | You can delete a rule by using an __HTTP DELETE__ request. This will stop rule executions and delete the rule from your dashboard. If you only want to temporarily stop a rule, use the pause/resume functionality instead.
Args:
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4963d0f82cd54e11c60cb3fc7cb4f506247184ef | vedsgit/pyEX | pyEX/rules/__init__.py | [
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Args:
ruleId (str): The id of an existing rule to puase
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4963d0f82cd54e11c60cb3fc7cb4f506247184ef | vedsgit/pyEX | pyEX/rules/__init__.py | [
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e74b06e0c2fe71ab36416f89746cdb1cf7a3fa62 | vedsgit/pyEX | pyEX/stocks/marketInfo.py | [
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https://iexcloud.io/docs/api/#market-volume-u-s
7:45am-5:15pm ET Mon-Fri
Args:
token (str): Access token
version (str): API version
filter (str): filters: https:... | This endpoint returns real time traded volume on U.S. markets.
https://iexcloud.io/docs/api/#market-volume-u-s
7:45am-5:15pm ET Mon-Fri
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token (str): Access token
version (str): API version
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e74b06e0c2fe71ab36416f89746cdb1cf7a3fa62 | vedsgit/pyEX | pyEX/stocks/marketInfo.py | [
"Apache-2.0"
] | Python | upcomingEvents | <not_specific> | def upcomingEvents(symbol='', refid='', token='', version='', filter=''):
'''This will return all upcoming estimates, dividends, splits for a given symbol or the market. If market is passed for the symbol, IPOs will also be included.
https://iexcloud.io/docs/api/#upcoming-events
Args:
symbol (str)... | This will return all upcoming estimates, dividends, splits for a given symbol or the market. If market is passed for the symbol, IPOs will also be included.
https://iexcloud.io/docs/api/#upcoming-events
Args:
symbol (str): Symbol to look up
refid (str): Optional. Id that matches the refid fiel... | This will return all upcoming estimates, dividends, splits for a given symbol or the market. If market is passed for the symbol, IPOs will also be included. | [
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_raiseIfNotStr(symbol)
if symbol:
return _getJson('stock/' + symbol + '/upcoming-events', token, version, filter)
return _getJson('stock/market/upcoming-events', token, version, filter) | [
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e74b06e0c2fe71ab36416f89746cdb1cf7a3fa62 | vedsgit/pyEX | pyEX/stocks/marketInfo.py | [
"Apache-2.0"
] | Python | upcomingEarnings | <not_specific> | def upcomingEarnings(symbol='', refid='', token='', version='', filter=''):
'''This will return all upcoming estimates, dividends, splits for a given symbol or the market. If market is passed for the symbol, IPOs will also be included.
https://iexcloud.io/docs/api/#upcoming-events
Args:
symbol (st... | This will return all upcoming estimates, dividends, splits for a given symbol or the market. If market is passed for the symbol, IPOs will also be included.
https://iexcloud.io/docs/api/#upcoming-events
Args:
symbol (str): Symbol to look up
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_raiseIfNotStr(symbol)
if symbol:
return _getJson('stock/' + symbol + '/upcoming-earnings', token, version, filter)
return _getJson('stock/market/upcoming-earnings', token, version, filter) | [
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e74b06e0c2fe71ab36416f89746cdb1cf7a3fa62 | vedsgit/pyEX | pyEX/stocks/marketInfo.py | [
"Apache-2.0"
] | Python | upcomingDividends | <not_specific> | def upcomingDividends(symbol='', refid='', token='', version='', filter=''):
'''This will return all upcoming estimates, dividends, splits for a given symbol or the market. If market is passed for the symbol, IPOs will also be included.
https://iexcloud.io/docs/api/#upcoming-events
Args:
symbol (s... | This will return all upcoming estimates, dividends, splits for a given symbol or the market. If market is passed for the symbol, IPOs will also be included.
https://iexcloud.io/docs/api/#upcoming-events
Args:
symbol (str): Symbol to look up
refid (str): Optional. Id that matches the refid fiel... | This will return all upcoming estimates, dividends, splits for a given symbol or the market. If market is passed for the symbol, IPOs will also be included. | [
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_raiseIfNotStr(symbol)
if symbol:
return _getJson('stock/' + symbol + '/upcoming-dividends', token, version, filter)
return _getJson('stock/market/upcoming-dividends', token, version, filter) | [
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e74b06e0c2fe71ab36416f89746cdb1cf7a3fa62 | vedsgit/pyEX | pyEX/stocks/marketInfo.py | [
"Apache-2.0"
] | Python | upcomingSplits | <not_specific> | def upcomingSplits(symbol='', refid='', token='', version='', filter=''):
'''This will return all upcoming estimates, dividends, splits for a given symbol or the market. If market is passed for the symbol, IPOs will also be included.
https://iexcloud.io/docs/api/#upcoming-events
Args:
symbol (str)... | This will return all upcoming estimates, dividends, splits for a given symbol or the market. If market is passed for the symbol, IPOs will also be included.
https://iexcloud.io/docs/api/#upcoming-events
Args:
symbol (str): Symbol to look up
refid (str): Optional. Id that matches the refid fiel... | This will return all upcoming estimates, dividends, splits for a given symbol or the market. If market is passed for the symbol, IPOs will also be included. | [
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_raiseIfNotStr(symbol)
if symbol:
return _getJson('stock/' + symbol + '/upcoming-splits', token, version, filter)
return _getJson('stock/market/upcoming-splits', token, version, filter) | [
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e74b06e0c2fe71ab36416f89746cdb1cf7a3fa62 | vedsgit/pyEX | pyEX/stocks/marketInfo.py | [
"Apache-2.0"
] | Python | upcomingIPOs | <not_specific> | def upcomingIPOs(symbol='', refid='', token='', version='', filter=''):
'''This will return all upcoming estimates, dividends, splits for a given symbol or the market. If market is passed for the symbol, IPOs will also be included.
https://iexcloud.io/docs/api/#upcoming-events
Args:
symbol (str): ... | This will return all upcoming estimates, dividends, splits for a given symbol or the market. If market is passed for the symbol, IPOs will also be included.
https://iexcloud.io/docs/api/#upcoming-events
Args:
symbol (str): Symbol to look up
refid (str): Optional. Id that matches the refid fiel... | This will return all upcoming estimates, dividends, splits for a given symbol or the market. If market is passed for the symbol, IPOs will also be included. | [
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_raiseIfNotStr(symbol)
if symbol:
return _getJson('stock/' + symbol + '/upcoming-ipos', token, version, filter)
return _getJson('stock/market/upcoming-ipos', token, version, filter) | [
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45effae0473a413d3c1941311800f5eb19b922fc | vedsgit/pyEX | pyEX/stocks/prices.py | [
"Apache-2.0"
] | Python | largestTrades | <not_specific> | def largestTrades(symbol, token='', version='', filter=''):
'''This returns 15 minute delayed, last sale eligible trades.
https://iexcloud.io/docs/api/#largest-trades
9:30-4pm ET M-F during regular market hours
Args:
symbol (str): Ticker to request
token (str): Access token
ver... | This returns 15 minute delayed, last sale eligible trades.
https://iexcloud.io/docs/api/#largest-trades
9:30-4pm ET M-F during regular market hours
Args:
symbol (str): Ticker to request
token (str): Access token
version (str): API version
filter (str): filters: https://iexc... | This returns 15 minute delayed, last sale eligible trades. | [
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_raiseIfNotStr(symbol)
return _getJson('stock/' + symbol + '/largest-trades', token, version, filter) | [
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dd2efe376ce981325011d5121d8e5aa7f34830f3 | vedsgit/pyEX | pyEX/refdata/symbols.py | [
"Apache-2.0"
] | Python | mutualFundSymbols | <not_specific> | def mutualFundSymbols(token='', version='', filter=''):
'''This call returns an array of mutual fund symbols that IEX Cloud supports for API calls.
https://iexcloud.io/docs/api/#mutual-fund-symbols
8am, 9am, 12pm, 1pm UTC daily
Args:
token (str): Access token
version (str): API version... | This call returns an array of mutual fund symbols that IEX Cloud supports for API calls.
https://iexcloud.io/docs/api/#mutual-fund-symbols
8am, 9am, 12pm, 1pm UTC daily
Args:
token (str): Access token
version (str): API version
filter (str): filters: https://iexcloud.io/docs/api/#f... | This call returns an array of mutual fund symbols that IEX Cloud supports for API calls. | [
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return _getJson('ref-data/mutual-funds/symbols', token, version, filter) | [
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dd2efe376ce981325011d5121d8e5aa7f34830f3 | vedsgit/pyEX | pyEX/refdata/symbols.py | [
"Apache-2.0"
] | Python | internationalSymbols | <not_specific> | def internationalSymbols(region='', exchange='', token='', version='', filter=''):
'''This call returns an array of international symbols that IEX Cloud supports for API calls.
https://iexcloud.io/docs/api/#international-symbols
8am, 9am, 12pm, 1pm UTC daily
Args:
region (str): region, 2 lette... | This call returns an array of international symbols that IEX Cloud supports for API calls.
https://iexcloud.io/docs/api/#international-symbols
8am, 9am, 12pm, 1pm UTC daily
Args:
region (str): region, 2 letter case insensitive string of country codes using ISO 3166-1 alpha-2
exchange (str)... | This call returns an array of international symbols that IEX Cloud supports for API calls. | [
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if region:
return _getJson('ref-data/region/{region}/symbols'.format(region=region), token, version, filter)
elif exchange:
return _getJson('ref-data/exchange/{exchange}/symbols'.format(exchange=exchange), token, ... | [
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dd2efe376ce981325011d5121d8e5aa7f34830f3 | vedsgit/pyEX | pyEX/refdata/symbols.py | [
"Apache-2.0"
] | Python | fxSymbols | <not_specific> | def fxSymbols(token='', version=''):
'''This call returns a list of supported currencies and currency pairs.
https://iexcloud.io/docs/api/#fx-symbols
7am, 9am, UTC daily
Args:
token (str): Access token
version (str): API version
Returns:
dict or DataFrame or list: result
... | This call returns a list of supported currencies and currency pairs.
https://iexcloud.io/docs/api/#fx-symbols
7am, 9am, UTC daily
Args:
token (str): Access token
version (str): API version
Returns:
dict or DataFrame or list: result
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f9d2e5e1b4f7917daf65986a9162b2c2d192ad44 | vedsgit/pyEX | pyEX/premium/fraudfactors/__init__.py | [
"Apache-2.0"
] | Python | nonTimelyFilings | <not_specific> | def nonTimelyFilings(symbol='', **kwargs):
'''The data set records the date in which a firm files a Non-Timely notification with the SEC.
Companies regulated by the SEC are required to file a Non-Timely notification when they are unable to file their annual or quarterly disclosures on time. In most cases, the i... | The data set records the date in which a firm files a Non-Timely notification with the SEC.
Companies regulated by the SEC are required to file a Non-Timely notification when they are unable to file their annual or quarterly disclosures on time. In most cases, the inability to file annual/quarterly disclosures on t... | The data set records the date in which a firm files a Non-Timely notification with the SEC.
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f9d2e5e1b4f7917daf65986a9162b2c2d192ad44 | vedsgit/pyEX | pyEX/premium/fraudfactors/__init__.py | [
"Apache-2.0"
] | Python | nonTimelyFilingsDF | <not_specific> | def nonTimelyFilingsDF(symbol='', **kwargs):
'''The data set records the date in which a firm files a Non-Timely notification with the SEC.
Companies regulated by the SEC are required to file a Non-Timely notification when they are unable to file their annual or quarterly disclosures on time. In most cases, the... | The data set records the date in which a firm files a Non-Timely notification with the SEC.
Companies regulated by the SEC are required to file a Non-Timely notification when they are unable to file their annual or quarterly disclosures on time. In most cases, the inability to file annual/quarterly disclosures on t... | The data set records the date in which a firm files a Non-Timely notification with the SEC.
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96f1e5ffb4b3f02630626bb303abe4be769aa598 | vedsgit/pyEX | pyEX/stocks/fundamentals.py | [
"Apache-2.0"
] | Python | cashFlow | <not_specific> | def cashFlow(symbol, period='quarter', last=1, token='', version='', filter=''):
'''Pulls cash flow data. Available quarterly (4 quarters) or annually (4 years).
https://iexcloud.io/docs/api/#cash-flow
Updates at 8am, 9am UTC daily
Args:
symbol (str): Ticker to request
period (str): P... | Pulls cash flow data. Available quarterly (4 quarters) or annually (4 years).
https://iexcloud.io/docs/api/#cash-flow
Updates at 8am, 9am UTC daily
Args:
symbol (str): Ticker to request
period (str): Period, either 'annual' or 'quarter'
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_raiseIfNotStr(symbol)
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96f1e5ffb4b3f02630626bb303abe4be769aa598 | vedsgit/pyEX | pyEX/stocks/fundamentals.py | [
"Apache-2.0"
] | Python | earnings | <not_specific> | def earnings(symbol, period='quarter', last=1, field='', token='', version='', filter=''):
'''Earnings data for a given company including the actual EPS, consensus, and fiscal period. Earnings are available quarterly (last 4 quarters) and annually (last 4 years).
https://iexcloud.io/docs/api/#earnings
Upda... | Earnings data for a given company including the actual EPS, consensus, and fiscal period. Earnings are available quarterly (last 4 quarters) and annually (last 4 years).
https://iexcloud.io/docs/api/#earnings
Updates at 9am, 11am, 12pm UTC every day
Args:
symbol (str): Ticker to request
pe... | Earnings data for a given company including the actual EPS, consensus, and fiscal period. Earnings are available quarterly (last 4 quarters) and annually (last 4 years). | [
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96f1e5ffb4b3f02630626bb303abe4be769aa598 | vedsgit/pyEX | pyEX/stocks/fundamentals.py | [
"Apache-2.0"
] | Python | financials | <not_specific> | def financials(symbol, period='quarter', token='', version='', filter=''):
'''Pulls income statement, balance sheet, and cash flow data from the four most recent reported quarters.
https://iexcloud.io/docs/api/#financials
Updates at 8am, 9am UTC daily
Args:
symbol (str): Ticker to request
... | Pulls income statement, balance sheet, and cash flow data from the four most recent reported quarters.
https://iexcloud.io/docs/api/#financials
Updates at 8am, 9am UTC daily
Args:
symbol (str): Ticker to request
period (str): Period, either 'annual' or 'quarter'
token (str): Access... | Pulls income statement, balance sheet, and cash flow data from the four most recent reported quarters. | [
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96f1e5ffb4b3f02630626bb303abe4be769aa598 | vedsgit/pyEX | pyEX/stocks/fundamentals.py | [
"Apache-2.0"
] | Python | incomeStatement | <not_specific> | def incomeStatement(symbol, period='quarter', last=1, token='', version='', filter=''):
'''Pulls income statement data. Available quarterly (4 quarters) or annually (4 years).
https://iexcloud.io/docs/api/#income-statement
Updates at 8am, 9am UTC daily
Args:
symbol (str): Ticker to request
... | Pulls income statement data. Available quarterly (4 quarters) or annually (4 years).
https://iexcloud.io/docs/api/#income-statement
Updates at 8am, 9am UTC daily
Args:
symbol (str): Ticker to request
period (str): Period, either 'annual' or 'quarter'
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_raiseIfNotStr(symbol)
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891deae5beb4137a9daa6b8bd41e3b9aa9f19ad2 | seanbreckenridge/piazza-scraper | piazza_scraper/__main__.py | [
"MIT"
] | Python | scrape | None | def scrape(courseid: str) -> None:
"Run the piazza scraper for COURSEID"
from .scraper import Scraper
s = Scraper(courseid)
s.parse()
s.write() | Run the piazza scraper for COURSEID | Run the piazza scraper for COURSEID | [
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from .scraper import Scraper
s = Scraper(courseid)
s.parse()
s.write() | [
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} |
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