autom8qc.qaqc.limit
GlobalMinimumTest
Class
- class autom8qc.qaqc.limit.GlobalMinimumTest(min_val, min_lim=None)
Bases:
autom8qc.qaqc.base.QAQCTest
This class implements the Global Minimum test. Each instance of the class needs the parameter min_val that defines the lower limit of valid data points. Optionally you can also use the parameter min_lim for doubtful values. The probabilities for data points between min_val and min_lim will be linear interpolated (0, 1).
- Parameters
NAME (str) – Name of the test
DESCRIPTION (str) – Description of the test
CATEGORY (str) – Category of the test
SUPPORTED_STRUCTURES (tuple or BaseStructure) – Supported data structures (e.g., Series)
parameters (ParameterList) – Supported parameters (default: None)
- Supported parameters:
min_val (float): Lower limit for valid points
min_lim (float): Lower limit for doubtful points (optional)
- SUPPORTED_STRUCTURES
alias of
autom8qc.core.structures.Series
- perform(series)
Performs the test and returns the probabilities. If a data point has nan as value, the probability for the point will also nan.
- Raises
InvalidType – If structure is not supported
- Parameters
series (pd.Series) – Series
- Returns
Probabilities
- Return type
pd.Series
- static supported_parameters()
Returns the supported parameters.
- Returns
Supported parameters
- Return type
ParameterList
Example
# Generate sample data
import numpy as np
import pandas as pd
np.random.seed(42)
mu, sigma = 50, 4
values = np.random.normal(mu, sigma, 300)
index = pd.date_range(start="1/1/2021", periods=300, freq="min")
series = pd.Series(values, index=index)
# Perform test
from autom8qc.qaqc.limit import GlobalMinimumTest
test = GlobalMinimumTest(min_val=43, min_lim=42)
test.plot(series=series, series_name="Example")
Visualization
GlobalMaximumTest
Class
- class autom8qc.qaqc.limit.GlobalMaximumTest(max_val, max_lim=None)
Bases:
autom8qc.qaqc.base.QAQCTest
This class implements the Global maximum test. Each instance of the class needs the parameter max_val that defines the upper limit for valid data points. Optionally you can also use the parameter max_lim that defines the upper limit for doubtful limits. The probabilities for data points between max_val and max_lim will be linear interpolated (0, 1).
- Parameters
NAME (str) – Name of the test
DESCRIPTION (str) – Description of the test
CATEGORY (str) – Category of the test
SUPPORTED_STRUCTURES (tuple or BaseStructure) – Supported data structures (e.g., Series)
parameters (ParameterList) – Supported parameters (default: None)
- Supported parameters:
max_val (float): Upper limit for valid points
max_lim (float): Upper limit for doubtful points (optional)
- SUPPORTED_STRUCTURES
alias of
autom8qc.core.structures.Series
- perform(series)
Performs the test and returns the probabilities. If a nan value is passed, nan also will return for it.
- Raises
InvalidType – If structure is not supported
- Parameters
series (pd.Series) – Series
- Returns
Probabilities (1=Valid, 0=Invalid)
- Return type
pd.Series
- static supported_parameters()
Returns the supported parameters.
- Returns
Supported parameters
- Return type
ParameterList
Example
# Generate sample data
import numpy as np
import pandas as pd
np.random.seed(42)
mu, sigma = 50, 5
values = np.random.normal(mu, sigma, 300)
index = pd.date_range(start="1/1/2021", periods=300, freq="min")
series = pd.Series(values, index=index)
# Perform test
from autom8qc.qaqc.limit import GlobalMaximumTest
test = GlobalMaximumTest(max_val=59, max_lim=60)
test.plot(series=series, series_name="Example")
Visualization
GlobalRangeTest
Class
- class autom8qc.qaqc.limit.GlobalRangeTest(min_val, max_val, min_lim=None, max_lim=None)
Bases:
autom8qc.qaqc.base.QAQCTest
This class implements the Global range test. Each instance of the class needs the parameters max_val that defines the upper limit for valid data points and the parameter min_val that defines the lower limit for valid data points. A data point is valid if min_val < data point < max_val. Optionally you can also use the parameters max_lim that defines the upper limit for doubtful limits and min_lim that defines the lower limit for doubtful values. The probabilities for data points between min_val and min_lim (same for max_val and max_lim) will be linear interpolated (0, 1).
- Parameters
NAME (str) – Name of the test
DESCRIPTION (str) – Description of the test
CATEGORY (str) – Category of the test
SUPPORTED_STRUCTURES (tuple or BaseStructure) – Supported data structures (e.g., Series)
parameters (ParameterList) – Supported parameters (default: None)
- Supported parameters:
min_val (float): Lower limit for valid points
min_lim (float): Lower limit for doubtful points (optional)
max_val (float): Upper limit for valid points
max_lim (float): Upper limit for doubtful points (optional)
- SUPPORTED_STRUCTURES
alias of
autom8qc.core.structures.Series
- perform(series)
Performs the test and returns the probabilities. If a nan value is passed, nan also will return for it.
- Raises
InvalidType – If structure is not supported
- Parameters
series (pd.Series) – Series
- Returns
Probabilities (1=Valid, 0=Invalid)
- Return type
pd.Series
- static supported_parameters()
Returns the supported parameters.
- Returns
Supported parameters
- Return type
ParameterList
Example
# Generate sample data
import numpy as np
import pandas as pd
np.random.seed(42)
mu, sigma = 50, 2
values = np.random.normal(mu, sigma, 300)
index = pd.date_range(start="1/1/2021", periods=300, freq="min")
series = pd.Series(values, index=index)
# Perform test
from autom8qc.qaqc.limit import GlobalRangeTest
test = GlobalRangeTest(min_lim=46, min_val=47, max_val=54, max_lim=55)
test.plot(series=series, series_name="Example")