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UDST/urbansim | urbansim/models/regression.py | RegressionModel.columns_used | def columns_used(self):
"""
Returns all the columns used in this model for filtering
and in the model expression.
"""
return list(tz.unique(tz.concatv(
util.columns_in_filters(self.fit_filters),
util.columns_in_filters(self.predict_filters),
u... | python | def columns_used(self):
"""
Returns all the columns used in this model for filtering
and in the model expression.
"""
return list(tz.unique(tz.concatv(
util.columns_in_filters(self.fit_filters),
util.columns_in_filters(self.predict_filters),
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UDST/urbansim | urbansim/models/regression.py | RegressionModel.fit_from_cfg | def fit_from_cfg(cls, df, cfgname, debug=False, outcfgname=None):
"""
Parameters
----------
df : DataFrame
The dataframe which contains the columns to use for the estimation.
cfgname : string
The name of the yaml config file which describes the hedonic mod... | python | def fit_from_cfg(cls, df, cfgname, debug=False, outcfgname=None):
"""
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The dataframe which contains the columns to use for the estimation.
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UDST/urbansim | urbansim/models/regression.py | RegressionModel.predict_from_cfg | def predict_from_cfg(cls, df, cfgname):
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----------
df : DataFrame
The dataframe which contains the columns to use for the estimation.
cfgname : string
The name of the yaml config file which describes the hedonic model.
Returns
... | python | def predict_from_cfg(cls, df, cfgname):
"""
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df : DataFrame
The dataframe which contains the columns to use for the estimation.
cfgname : string
The name of the yaml config file which describes the hedonic model.
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UDST/urbansim | urbansim/models/regression.py | RegressionModelGroup.add_model | def add_model(self, model):
"""
Add a `RegressionModel` instance.
Parameters
----------
model : `RegressionModel`
Should have a ``.name`` attribute matching one of
the groupby segments.
"""
logger.debug(
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"""
Add a `RegressionModel` instance.
Parameters
----------
model : `RegressionModel`
Should have a ``.name`` attribute matching one of
the groupby segments.
"""
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UDST/urbansim | urbansim/models/regression.py | RegressionModelGroup.add_model_from_params | def add_model_from_params(self, name, fit_filters, predict_filters,
model_expression, ytransform=None):
"""
Add a model by passing arguments through to `RegressionModel`.
Parameters
----------
name : any
Must match a groupby segment name... | python | def add_model_from_params(self, name, fit_filters, predict_filters,
model_expression, ytransform=None):
"""
Add a model by passing arguments through to `RegressionModel`.
Parameters
----------
name : any
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UDST/urbansim | urbansim/models/regression.py | RegressionModelGroup._iter_groups | def _iter_groups(self, data):
"""
Iterate over the groups in `data` after grouping by
`segmentation_col`. Skips any groups for which there
is no model stored.
Yields tuples of (name, df) where name is the group key
and df is the group DataFrame.
Parameters
... | python | def _iter_groups(self, data):
"""
Iterate over the groups in `data` after grouping by
`segmentation_col`. Skips any groups for which there
is no model stored.
Yields tuples of (name, df) where name is the group key
and df is the group DataFrame.
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UDST/urbansim | urbansim/models/regression.py | RegressionModelGroup.fit | def fit(self, data, debug=False):
"""
Fit each of the models in the group.
Parameters
----------
data : pandas.DataFrame
Must have a column with the same name as `segmentation_col`.
debug : bool
If set to true (default false) will pass the debug p... | python | def fit(self, data, debug=False):
"""
Fit each of the models in the group.
Parameters
----------
data : pandas.DataFrame
Must have a column with the same name as `segmentation_col`.
debug : bool
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UDST/urbansim | urbansim/models/regression.py | RegressionModelGroup.predict | def predict(self, data):
"""
Predict new data for each group in the segmentation.
Parameters
----------
data : pandas.DataFrame
Data to use for prediction. Must have a column with the
same name as `segmentation_col`.
Returns
-------
... | python | def predict(self, data):
"""
Predict new data for each group in the segmentation.
Parameters
----------
data : pandas.DataFrame
Data to use for prediction. Must have a column with the
same name as `segmentation_col`.
Returns
-------
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UDST/urbansim | urbansim/models/regression.py | SegmentedRegressionModel.from_yaml | def from_yaml(cls, yaml_str=None, str_or_buffer=None):
"""
Create a SegmentedRegressionModel instance from a saved YAML
configuration. Arguments are mutally exclusive.
Parameters
----------
yaml_str : str, optional
A YAML string from which to load model.
... | python | def from_yaml(cls, yaml_str=None, str_or_buffer=None):
"""
Create a SegmentedRegressionModel instance from a saved YAML
configuration. Arguments are mutally exclusive.
Parameters
----------
yaml_str : str, optional
A YAML string from which to load model.
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UDST/urbansim | urbansim/models/regression.py | SegmentedRegressionModel.add_segment | def add_segment(self, name, model_expression=None, ytransform='default'):
"""
Add a new segment with its own model expression and ytransform.
Parameters
----------
name :
Segment name. Must match a segment in the groupby of the data.
model_expression : str or... | python | def add_segment(self, name, model_expression=None, ytransform='default'):
"""
Add a new segment with its own model expression and ytransform.
Parameters
----------
name :
Segment name. Must match a segment in the groupby of the data.
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UDST/urbansim | urbansim/models/regression.py | SegmentedRegressionModel.fit | def fit(self, data, debug=False):
"""
Fit each segment. Segments that have not already been explicitly
added will be automatically added with default model and ytransform.
Parameters
----------
data : pandas.DataFrame
Must have a column with the same name as ... | python | def fit(self, data, debug=False):
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UDST/urbansim | urbansim/models/regression.py | SegmentedRegressionModel.predict | def predict(self, data):
"""
Predict new data for each group in the segmentation.
Parameters
----------
data : pandas.DataFrame
Data to use for prediction. Must have a column with the
same name as `segmentation_col`.
Returns
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... | python | def predict(self, data):
"""
Predict new data for each group in the segmentation.
Parameters
----------
data : pandas.DataFrame
Data to use for prediction. Must have a column with the
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UDST/urbansim | urbansim/models/regression.py | SegmentedRegressionModel._process_model_dict | def _process_model_dict(self, d):
"""
Remove redundant items from a model's configuration dict.
Parameters
----------
d : dict
Modified in place.
Returns
-------
dict
Modified `d`.
"""
del d['model_type']
... | python | def _process_model_dict(self, d):
"""
Remove redundant items from a model's configuration dict.
Parameters
----------
d : dict
Modified in place.
Returns
-------
dict
Modified `d`.
"""
del d['model_type']
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UDST/urbansim | urbansim/models/regression.py | SegmentedRegressionModel.to_dict | def to_dict(self):
"""
Returns a dict representation of this instance suitable for
conversion to YAML.
"""
return {
'model_type': 'segmented_regression',
'name': self.name,
'segmentation_col': self.segmentation_col,
'fit_filters': ... | python | def to_dict(self):
"""
Returns a dict representation of this instance suitable for
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"""
return {
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UDST/urbansim | urbansim/models/regression.py | SegmentedRegressionModel.columns_used | def columns_used(self):
"""
Returns all the columns used across all models in the group
for filtering and in the model expression.
"""
return list(tz.unique(tz.concatv(
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util.columns_in_filters(self.predict_filter... | python | def columns_used(self):
"""
Returns all the columns used across all models in the group
for filtering and in the model expression.
"""
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UDST/urbansim | urbansim/models/regression.py | SegmentedRegressionModel.fit_from_cfg | def fit_from_cfg(cls, df, cfgname, debug=False, min_segment_size=None, outcfgname=None):
"""
Parameters
----------
df : DataFrame
The dataframe which contains the columns to use for the estimation.
cfgname : string
The name of the yaml config file which de... | python | def fit_from_cfg(cls, df, cfgname, debug=False, min_segment_size=None, outcfgname=None):
"""
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The dataframe which contains the columns to use for the estimation.
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UDST/urbansim | urbansim/models/relocation.py | find_movers | def find_movers(choosers, rates, rate_column):
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choosers : pandas.DataFrame
Table of agents from which to find movers.
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UDST/urbansim | urbansim/models/supplydemand.py | _calculate_adjustment | def _calculate_adjustment(
lcm, choosers, alternatives, alt_segmenter,
clip_change_low, clip_change_high, multiplier_func=None):
"""
Calculate adjustments to prices to compensate for
supply and demand effects.
Parameters
----------
lcm : LocationChoiceModel
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lcm, choosers, alternatives, alt_segmenter,
clip_change_low, clip_change_high, multiplier_func=None):
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Calculate adjustments to prices to compensate for
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UDST/urbansim | urbansim/models/supplydemand.py | supply_and_demand | def supply_and_demand(
lcm, choosers, alternatives, alt_segmenter, price_col,
base_multiplier=None, clip_change_low=0.75, clip_change_high=1.25,
iterations=5, multiplier_func=None):
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Adjust real estate prices to compensate for supply and demand effects.
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----------
... | python | def supply_and_demand(
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iterations=5, multiplier_func=None):
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UDST/urbansim | urbansim/developer/developer.py | Developer._max_form | def _max_form(f, colname):
"""
Assumes dataframe with hierarchical columns with first index equal to the
use and second index equal to the attribute.
e.g. f.columns equal to::
mixedoffice building_cost
building_revenue
b... | python | def _max_form(f, colname):
"""
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e.g. f.columns equal to::
mixedoffice building_cost
building_revenue
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UDST/urbansim | urbansim/developer/developer.py | Developer.keep_form_with_max_profit | def keep_form_with_max_profit(self, forms=None):
"""
This converts the dataframe, which shows all profitable forms,
to the form with the greatest profit, so that more profitable
forms outcompete less profitable forms.
Parameters
----------
forms: list of strings
... | python | def keep_form_with_max_profit(self, forms=None):
"""
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forms: list of strings
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UDST/urbansim | urbansim/developer/developer.py | Developer.compute_units_to_build | def compute_units_to_build(num_agents, num_units, target_vacancy):
"""
Compute number of units to build to match target vacancy.
Parameters
----------
num_agents : int
number of agents that need units in the region
num_units : int
number of units ... | python | def compute_units_to_build(num_agents, num_units, target_vacancy):
"""
Compute number of units to build to match target vacancy.
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num_agents : int
number of agents that need units in the region
num_units : int
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UDST/urbansim | urbansim/developer/developer.py | Developer.pick | def pick(self, form, target_units, parcel_size, ave_unit_size,
current_units, max_parcel_size=200000, min_unit_size=400,
drop_after_build=True, residential=True, bldg_sqft_per_job=400.0,
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UDST/urbansim | urbansim/developer/developer.py | Developer.merge | def merge(old_df, new_df, return_index=False):
"""
Merge two dataframes of buildings. The old dataframe is
usually the buildings dataset and the new dataframe is a modified
(by the user) version of what is returned by the pick method.
Parameters
----------
old_d... | python | def merge(old_df, new_df, return_index=False):
"""
Merge two dataframes of buildings. The old dataframe is
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UDST/urbansim | urbansim/maps/dframe_explorer.py | start | def start(views,
center=[37.7792, -122.2191],
zoom=11,
shape_json='data/zones.json',
geom_name='ZONE_ID', # from JSON file
join_name='zone_id', # from data frames
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zoom=11,
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linkedin/luminol | src/luminol/__init__.py | Luminol._analyze_root_causes | def _analyze_root_causes(self):
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Conduct root cause analysis.
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"""
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linkedin/luminol | src/luminol/correlator.py | Correlator._load | def _load(self, time_series):
"""
Load time series into a TimeSeries object.
:param timeseries: a TimeSeries, a dictionary or a path to a csv file(str).
:return TimeSeries: a TimeSeries object.
"""
if isinstance(time_series, TimeSeries):
return time_series
... | python | def _load(self, time_series):
"""
Load time series into a TimeSeries object.
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linkedin/luminol | src/luminol/correlator.py | Correlator._get_algorithm_and_params | def _get_algorithm_and_params(self, algorithm_name, algorithm_params):
"""
Get the specific algorithm and merge the algorithm params.
:param str algorithm: name of the algorithm to use.
:param dict algorithm_params: additional params for the specific algorithm.
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algori... | python | def _get_algorithm_and_params(self, algorithm_name, algorithm_params):
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linkedin/luminol | src/luminol/correlator.py | Correlator._sanity_check | def _sanity_check(self):
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"""
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"""
Check if the time series have more than two data points.
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linkedin/luminol | src/luminol/correlator.py | Correlator._correlate | def _correlate(self):
"""
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Run correlation algorithm.
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linkedin/luminol | src/luminol/correlator.py | Correlator.is_correlated | def is_correlated(self, threshold=0):
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linkedin/luminol | src/luminol/algorithms/anomaly_detector_algorithms/diff_percent_threshold.py | DiffPercentThreshold._set_scores | def _set_scores(self):
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"""
anom_scores = {}
for i, (timestamp, value) in enumerate(self.time_series.items()):
baseline_value = s... | python | def _set_scores(self):
"""
Compute anomaly scores for the time series
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anom_scores = {}
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linkedin/luminol | demo/src/rca.py | RCA._load | def _load(self, metrix):
"""
Load time series.
:param timeseries: a TimeSeries, a dictionary or a path to a csv file(str).
:return TimeSeries: a TimeSeries object.
"""
if isinstance(metrix, TimeSeries):
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return TimeSeries(metrix)
retu... | python | def _load(self, metrix):
"""
Load time series.
:param timeseries: a TimeSeries, a dictionary or a path to a csv file(str).
:return TimeSeries: a TimeSeries object.
"""
if isinstance(metrix, TimeSeries):
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linkedin/luminol | demo/src/rca.py | RCA._analyze | def _analyze(self):
"""
Analyzes if a matrix has anomalies.
If any anomaly is found, determine if the matrix correlates with any other matrixes.
To be implemented.
"""
output = defaultdict(list)
output_by_name = defaultdict(list)
scores = self.anomaly_detector.get_all_scores()
if se... | python | def _analyze(self):
"""
Analyzes if a matrix has anomalies.
If any anomaly is found, determine if the matrix correlates with any other matrixes.
To be implemented.
"""
output = defaultdict(list)
output_by_name = defaultdict(list)
scores = self.anomaly_detector.get_all_scores()
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linkedin/luminol | src/luminol/algorithms/anomaly_detector_algorithms/default_detector.py | DefaultDetector._set_scores | def _set_scores(self):
"""
Set anomaly scores using a weighted sum.
"""
anom_scores_ema = self.exp_avg_detector.run()
anom_scores_deri = self.derivative_detector.run()
anom_scores = {}
for timestamp in anom_scores_ema.timestamps:
# Compute a weighted a... | python | def _set_scores(self):
"""
Set anomaly scores using a weighted sum.
"""
anom_scores_ema = self.exp_avg_detector.run()
anom_scores_deri = self.derivative_detector.run()
anom_scores = {}
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linkedin/luminol | src/luminol/algorithms/anomaly_detector_algorithms/derivative_detector.py | DerivativeDetector._compute_derivatives | def _compute_derivatives(self):
"""
Compute derivatives of the time series.
"""
derivatives = []
for i, (timestamp, value) in enumerate(self.time_series_items):
if i > 0:
pre_item = self.time_series_items[i - 1]
pre_timestamp = pre_item... | python | def _compute_derivatives(self):
"""
Compute derivatives of the time series.
"""
derivatives = []
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if i > 0:
pre_item = self.time_series_items[i - 1]
pre_timestamp = pre_item... | [
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linkedin/luminol | src/luminol/algorithms/anomaly_detector_algorithms/bitmap_detector.py | BitmapDetector._sanity_check | def _sanity_check(self):
"""
Check if there are enough data points.
"""
windows = self.lag_window_size + self.future_window_size
if (not self.lag_window_size or not self.future_window_size or self.time_series_length < windows or windows < DEFAULT_BITMAP_MINIMAL_POINTS_IN_WINDOWS)... | python | def _sanity_check(self):
"""
Check if there are enough data points.
"""
windows = self.lag_window_size + self.future_window_size
if (not self.lag_window_size or not self.future_window_size or self.time_series_length < windows or windows < DEFAULT_BITMAP_MINIMAL_POINTS_IN_WINDOWS)... | [
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linkedin/luminol | src/luminol/algorithms/anomaly_detector_algorithms/bitmap_detector.py | BitmapDetector._generate_SAX_single | def _generate_SAX_single(self, sections, value):
"""
Generate SAX representation(Symbolic Aggregate approXimation) for a single data point.
Read more about it here: Assumption-Free Anomaly Detection in Time Series(http://alumni.cs.ucr.edu/~ratana/SSDBM05.pdf).
:param dict sections: value... | python | def _generate_SAX_single(self, sections, value):
"""
Generate SAX representation(Symbolic Aggregate approXimation) for a single data point.
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linkedin/luminol | src/luminol/algorithms/anomaly_detector_algorithms/bitmap_detector.py | BitmapDetector._generate_SAX | def _generate_SAX(self):
"""
Generate SAX representation for all values of the time series.
"""
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"""
Generate SAX representation for all values of the time series.
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linkedin/luminol | src/luminol/algorithms/anomaly_detector_algorithms/bitmap_detector.py | BitmapDetector._construct_SAX_chunk_dict | def _construct_SAX_chunk_dict(self, sax):
"""
Form a chunk frequency dictionary from a SAX representation.
:param str sax: a SAX representation.
:return dict: frequency dictionary for chunks in the SAX representation.
"""
frequency = defaultdict(int)
chunk_size = ... | python | def _construct_SAX_chunk_dict(self, sax):
"""
Form a chunk frequency dictionary from a SAX representation.
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frequency = defaultdict(int)
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linkedin/luminol | src/luminol/algorithms/anomaly_detector_algorithms/bitmap_detector.py | BitmapDetector._construct_all_SAX_chunk_dict | def _construct_all_SAX_chunk_dict(self):
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linkedin/luminol | src/luminol/algorithms/anomaly_detector_algorithms/bitmap_detector.py | BitmapDetector._compute_anom_score_between_two_windows | def _compute_anom_score_between_two_windows(self, i):
"""
Compute distance difference between two windows' chunk frequencies,
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:param int i: index of the data point between two windows.
... | python | def _compute_anom_score_between_two_windows(self, i):
"""
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linkedin/luminol | src/luminol/algorithms/anomaly_detector_algorithms/bitmap_detector.py | BitmapDetector._set_scores | def _set_scores(self):
"""
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linkedin/luminol | src/luminol/algorithms/anomaly_detector_algorithms/__init__.py | AnomalyDetectorAlgorithm._denoise_scores | def _denoise_scores(self, scores):
"""
Denoise anomaly scores.
Low anomaly scores could be noisy. The following two series will have good correlation result with out denoise:
[0.08, 4.6, 4.6, 4.6, 1.0, 1.0]
[0.0010, 0.0012, 0.0012, 0.0008, 0.0008]
while the second series ... | python | def _denoise_scores(self, scores):
"""
Denoise anomaly scores.
Low anomaly scores could be noisy. The following two series will have good correlation result with out denoise:
[0.08, 4.6, 4.6, 4.6, 1.0, 1.0]
[0.0010, 0.0012, 0.0012, 0.0008, 0.0008]
while the second series ... | [
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linkedin/luminol | src/luminol/algorithms/correlator_algorithms/cross_correlator.py | CrossCorrelator._detect_correlation | def _detect_correlation(self):
"""
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then take the maximum.
"""
correlations = []
shifted_correlations = []
self.time_series_a.normalize()
self.time_series_b.normalize()
... | python | def _detect_correlation(self):
"""
Detect correlation by computing correlation coefficients for all allowed shift steps,
then take the maximum.
"""
correlations = []
shifted_correlations = []
self.time_series_a.normalize()
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linkedin/luminol | src/luminol/algorithms/anomaly_detector_algorithms/absolute_threshold.py | AbsoluteThreshold._set_scores | def _set_scores(self):
"""
Compute anomaly scores for the time series
This algorithm just takes the diff of threshold with current value as anomaly score
"""
anom_scores = {}
for timestamp, value in self.time_series.items():
anom_scores[timestamp] = 0.0
... | python | def _set_scores(self):
"""
Compute anomaly scores for the time series
This algorithm just takes the diff of threshold with current value as anomaly score
"""
anom_scores = {}
for timestamp, value in self.time_series.items():
anom_scores[timestamp] = 0.0
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linkedin/luminol | src/luminol/algorithms/anomaly_detector_algorithms/exp_avg_detector.py | ExpAvgDetector._compute_anom_score | def _compute_anom_score(self, lag_window_points, point):
"""
Compute anomaly score for a single data point.
Anomaly score for a single data point(t,v) equals: abs(v - ema(lagging window)).
:param list lag_window_points: values in the lagging window.
:param float point: data point... | python | def _compute_anom_score(self, lag_window_points, point):
"""
Compute anomaly score for a single data point.
Anomaly score for a single data point(t,v) equals: abs(v - ema(lagging window)).
:param list lag_window_points: values in the lagging window.
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linkedin/luminol | src/luminol/algorithms/anomaly_detector_algorithms/exp_avg_detector.py | ExpAvgDetector._compute_anom_data_using_window | def _compute_anom_data_using_window(self):
"""
Compute anomaly scores using a lagging window.
"""
anom_scores = {}
values = self.time_series.values
stdev = numpy.std(values)
for i, (timestamp, value) in enumerate(self.time_series_items):
if i < self.la... | python | def _compute_anom_data_using_window(self):
"""
Compute anomaly scores using a lagging window.
"""
anom_scores = {}
values = self.time_series.values
stdev = numpy.std(values)
for i, (timestamp, value) in enumerate(self.time_series_items):
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linkedin/luminol | src/luminol/algorithms/anomaly_detector_algorithms/exp_avg_detector.py | ExpAvgDetector._compute_anom_data_decay_all | def _compute_anom_data_decay_all(self):
"""
Compute anomaly scores using a lagging window covering all the data points before.
"""
anom_scores = {}
values = self.time_series.values
ema = utils.compute_ema(self.smoothing_factor, values)
stdev = numpy.std(values)
... | python | def _compute_anom_data_decay_all(self):
"""
Compute anomaly scores using a lagging window covering all the data points before.
"""
anom_scores = {}
values = self.time_series.values
ema = utils.compute_ema(self.smoothing_factor, values)
stdev = numpy.std(values)
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linkedin/luminol | src/luminol/modules/time_series.py | TimeSeries._generic_binary_op | def _generic_binary_op(self, other, op):
"""
Perform the method operation specified in the op parameter on the values
within the instance's time series values and either another time series
or a constant number value.
:param other: Time series of values or a constant number to u... | python | def _generic_binary_op(self, other, op):
"""
Perform the method operation specified in the op parameter on the values
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linkedin/luminol | src/luminol/modules/time_series.py | TimeSeries._get_value_type | def _get_value_type(self, other):
"""
Get the object type of the value within the values portion of the time series.
:return: `type` of object
"""
if self.values:
return type(self.values[0])
elif isinstance(other, TimeSeries) and other.values:
ret... | python | def _get_value_type(self, other):
"""
Get the object type of the value within the values portion of the time series.
:return: `type` of object
"""
if self.values:
return type(self.values[0])
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linkedin/luminol | src/luminol/modules/time_series.py | TimeSeries.align | def align(self, other):
"""
Align two time series so that len(self) == len(other) and self.timstamps == other.timestamps.
:return: :tuple:(`TimeSeries` object(the aligned self), `TimeSeries` object(the aligned other))
"""
if isinstance(other, TimeSeries):
aligned, ot... | python | def align(self, other):
"""
Align two time series so that len(self) == len(other) and self.timstamps == other.timestamps.
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linkedin/luminol | src/luminol/modules/time_series.py | TimeSeries.smooth | def smooth(self, smoothing_factor):
"""
return a new time series which is a exponential smoothed version of the original data series.
soomth forward once, backward once, and then take the average.
:param float smoothing_factor: smoothing factor
:return: :class:`TimeSeries` objec... | python | def smooth(self, smoothing_factor):
"""
return a new time series which is a exponential smoothed version of the original data series.
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linkedin/luminol | src/luminol/modules/time_series.py | TimeSeries.add_offset | def add_offset(self, offset):
"""
Return a new time series with all timestamps incremented by some offset.
:param int offset: The number of seconds to offset the time series.
:return: `None`
"""
self.timestamps = [ts + offset for ts in self.timestamps] | python | def add_offset(self, offset):
"""
Return a new time series with all timestamps incremented by some offset.
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:return: `None`
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linkedin/luminol | src/luminol/modules/time_series.py | TimeSeries.normalize | def normalize(self):
"""
Return a new time series with all values normalized to 0 to 1.
:return: `None`
"""
maximum = self.max()
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self.values = [value / maximum for value in self.values] | python | def normalize(self):
"""
Return a new time series with all values normalized to 0 to 1.
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linkedin/luminol | src/luminol/modules/time_series.py | TimeSeries.crop | def crop(self, start_timestamp, end_timestamp):
"""
Return a new TimeSeries object contains all the timstamps and values within
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:param int start_timestamp: the start timestamp value
:param int end_timestamp: the end timestamp value
:return: :class:`T... | python | def crop(self, start_timestamp, end_timestamp):
"""
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linkedin/luminol | src/luminol/modules/time_series.py | TimeSeries.average | def average(self, default=None):
"""
Calculate the average value over the time series.
:param default: Value to return as a default should the calculation not be possible.
:return: Float representing the average value or `None`.
"""
return numpy.asscalar(numpy.average(se... | python | def average(self, default=None):
"""
Calculate the average value over the time series.
:param default: Value to return as a default should the calculation not be possible.
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"""
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linkedin/luminol | src/luminol/modules/time_series.py | TimeSeries.median | def median(self, default=None):
"""
Calculate the median value over the time series.
:param default: Value to return as a default should the calculation not be possible.
:return: Float representing the median value or `None`.
"""
return numpy.asscalar(numpy.median(self.v... | python | def median(self, default=None):
"""
Calculate the median value over the time series.
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linkedin/luminol | src/luminol/modules/time_series.py | TimeSeries.max | def max(self, default=None):
"""
Calculate the maximum value over the time series.
:param default: Value to return as a default should the calculation not be possible.
:return: Float representing the maximum value or `None`.
"""
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"""
Calculate the maximum value over the time series.
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linkedin/luminol | src/luminol/modules/time_series.py | TimeSeries.min | def min(self, default=None):
"""
Calculate the minimum value over the time series.
:param default: Value to return as a default should the calculation not be possible.
:return: Float representing the maximum value or `None`.
"""
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"""
Calculate the minimum value over the time series.
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linkedin/luminol | src/luminol/modules/time_series.py | TimeSeries.percentile | def percentile(self, n, default=None):
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:param default: Value to return as a default should the calculation not be possible.
:return: Float representing the Nt... | python | def percentile(self, n, default=None):
"""
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linkedin/luminol | src/luminol/modules/time_series.py | TimeSeries.stdev | def stdev(self, default=None):
"""
Calculate the standard deviation of the time series.
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:return: Float representing the standard deviation value or `None`.
"""
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"""
Calculate the standard deviation of the time series.
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linkedin/luminol | src/luminol/modules/time_series.py | TimeSeries.sum | def sum(self, default=None):
"""
Calculate the sum of all the values in the times series.
:param default: Value to return as a default should the calculation not be possible.
:return: Float representing the sum or `None`.
"""
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"""
Calculate the sum of all the values in the times series.
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linkedin/luminol | src/luminol/anomaly_detector.py | AnomalyDetector._get_algorithm | def _get_algorithm(self, algorithm_name):
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Get the specific algorithm.
:param str algorithm_name: name of the algorithm to use(file name).
:return: algorithm object.
"""
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algorithm = anomaly_detector_algorithms[algorithm_name]
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Get the specific algorithm.
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linkedin/luminol | src/luminol/anomaly_detector.py | AnomalyDetector._detect | def _detect(self, score_only):
"""
Detect anomaly periods.
:param bool score_only: if true, only anomaly scores are computed.
"""
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algorithm = self.algorithm(**self.algorithm_params)
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"""
Detect anomaly periods.
:param bool score_only: if true, only anomaly scores are computed.
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algorithm = self.algorithm(**self.algorithm_params)
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linkedin/luminol | src/luminol/anomaly_detector.py | AnomalyDetector._detect_anomalies | def _detect_anomalies(self):
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Detect anomalies using a threshold on anomaly scores.
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anom_scores = self.anom_scores
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linkedin/luminol | src/luminol/utils.py | compute_ema | def compute_ema(smoothing_factor, points):
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Compute exponential moving average of a list of points.
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:param list points: the data points.
:return list: all ema in a list.
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linkedin/luminol | src/luminol/utils.py | to_epoch | def to_epoch(t_str):
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jpoullet2000/atlasclient | atlasclient/models.py | Classification.create | def create(self, **kwargs):
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jpoullet2000/atlasclient | atlasclient/models.py | Classification.update | def update(self, **kwargs):
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jpoullet2000/atlasclient | atlasclient/models.py | EntityGuidClassificationCollection.update | def update(self, **kwargs):
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jpoullet2000/atlasclient | atlasclient/models.py | EntityBulkCollection.create | def create(self, data, **kwargs):
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jpoullet2000/atlasclient | atlasclient/models.py | EntityBulkCollection.delete | def delete(self, guid):
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jpoullet2000/atlasclient | atlasclient/base.py | QueryableModelCollection.url | def url(self):
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pieces = [self.client.base_url, 'api', 'atlas', 'v2']
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jpoullet2000/atlasclient | atlasclient/base.py | QueryableModelCollection.inflate | def inflate(self):
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jpoullet2000/atlasclient | atlasclient/base.py | QueryableModelCollection.create | def create(self, *args, **kwargs):
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jpoullet2000/atlasclient | atlasclient/base.py | QueryableModelCollection.delete | def delete(self, **kwargs):
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jpoullet2000/atlasclient | atlasclient/base.py | QueryableModel.load | def load(self, response):
"""The load method parses the raw JSON response from the server.
Most models are not returned in the main response body, but in a key
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Also, related objects are often returned and can be used to... | python | def load(self, response):
"""The load method parses the raw JSON response from the server.
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jpoullet2000/atlasclient | atlasclient/base.py | QueryableModel.create | def create(self, **kwargs):
"""Create a new instance of this resource type.
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"""Create a new instance of this resource type.
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jpoullet2000/atlasclient | atlasclient/base.py | QueryableModel.delete | def delete(self, **kwargs):
"""Delete a resource by issuing a DELETE http request against it."""
self.method = 'delete'
if len(kwargs) > 0:
self.load(self.client.delete(self.url, params=kwargs))
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self.load(self.client.delete(self.url))
self.parent.rem... | python | def delete(self, **kwargs):
"""Delete a resource by issuing a DELETE http request against it."""
self.method = 'delete'
if len(kwargs) > 0:
self.load(self.client.delete(self.url, params=kwargs))
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self.load(self.client.delete(self.url))
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jpoullet2000/atlasclient | atlasclient/events.py | publish | def publish(obj, event, event_state, **kwargs):
"""Publish an event from an object.
This is a really basic pub-sub event system to allow for tracking progress
on methods externally. It fires the events for the first match it finds in
the object hierarchy, going most specific to least. If no match is ... | python | def publish(obj, event, event_state, **kwargs):
"""Publish an event from an object.
This is a really basic pub-sub event system to allow for tracking progress
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jpoullet2000/atlasclient | atlasclient/events.py | subscribe | def subscribe(obj, event, callback, event_state=None):
"""Subscribe an event from an class.
Subclasses of the class/object will also fire events for this class,
unless a more specific event exists.
"""
if inspect.isclass(obj):
cls = obj.__name__
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cls = obj.__class__.__name_... | python | def subscribe(obj, event, callback, event_state=None):
"""Subscribe an event from an class.
Subclasses of the class/object will also fire events for this class,
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"""
if inspect.isclass(obj):
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psolin/cleanco | cleanco.py | cleanco.clean_name | def clean_name(self, suffix=True, prefix=False, middle=False, multi=False):
"return cleared version of the business name"
name = self.business_name
# Run it through the string_stripper once more
name = self.string_stripper(name)
loname = name.lower()
# return name without suffixed/prefixed/middle type ... | python | def clean_name(self, suffix=True, prefix=False, middle=False, multi=False):
"return cleared version of the business name"
name = self.business_name
# Run it through the string_stripper once more
name = self.string_stripper(name)
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mosdef-hub/foyer | foyer/smarts_graph.py | _find_chordless_cycles | def _find_chordless_cycles(bond_graph, max_cycle_size):
"""Find all chordless cycles (i.e. rings) in the bond graph
Traverses the bond graph to determine all cycles (i.e. rings) each
atom is contained within. Algorithm has been adapted from:
https://stackoverflow.com/questions/4022662/find-all-chordles... | python | def _find_chordless_cycles(bond_graph, max_cycle_size):
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mosdef-hub/foyer | foyer/smarts_graph.py | _prepare_atoms | def _prepare_atoms(topology, compute_cycles=False):
"""Compute cycles and add white-/blacklists to atoms."""
atom1 = next(topology.atoms())
has_whitelists = hasattr(atom1, 'whitelist')
has_cycles = hasattr(atom1, 'cycles')
compute_cycles = compute_cycles and not has_cycles
if compute_cycles or ... | python | def _prepare_atoms(topology, compute_cycles=False):
"""Compute cycles and add white-/blacklists to atoms."""
atom1 = next(topology.atoms())
has_whitelists = hasattr(atom1, 'whitelist')
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mosdef-hub/foyer | foyer/smarts_graph.py | SMARTSGraph._add_nodes | def _add_nodes(self):
"""Add all atoms in the SMARTS string as nodes in the graph."""
for n, atom in enumerate(self.ast.select('atom')):
self.add_node(n, atom=atom)
self._atom_indices[id(atom)] = n | python | def _add_nodes(self):
"""Add all atoms in the SMARTS string as nodes in the graph."""
for n, atom in enumerate(self.ast.select('atom')):
self.add_node(n, atom=atom)
self._atom_indices[id(atom)] = n | [
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