umetrics.simca | index (built-in)

`Contains core functionality for handling SIMCA projects.`

Classes
`` |  | Boost.Python.enum(builtins.int) DatasetType blockscaletype compressionmethod data_explorer_mode derivativeorder detrendmode ewma_type filter_scaletype filtertype hierarchical_types id_type merge_match merge_type modeltype no_match_option operator phase_iteration_treatment polynomialorder powerexponent prediction_source scaletype transformtype trimming_type waveletfunctiontype ytreatment Boost.Python.instance(builtins.object) BatchLevelCreator DataCollection Dataset DatasetInfo ExcludeStruct Filter ModelInfo ModelOptions PhaseCropper Predictionset Project ProjectData ProjectDataBuilder ProjectHandler ProjectOptions TrimOptions UmMat UmVec ValueIDs Workset data_explorer_settings
class BatchLevelCreator(Boost.Python.instance)
`` | `Handles specification and creation of batch level datasets. To create batch level datasets, specify which types of datasets to create by setting the dataset_types property. Optionally add or remove variables/scores etc and call apply(). After apply has been called, the object should not be used anymore.`
| Method resolution order: BatchLevelCreator Boost.Python.instance builtins.object Static methods defined here: __init__(...) `__init__( (object)self, (Project)project, (int)model_number) -> None : Constructs a BatchLevelCreator that can create batch level datasets in the specified project using the specified model or model group. If the model is a mother model (batch evolution model, BEM) all models in the group will be used. If the model is a model in a BEM, all other models in the same BEM will also be used.` __reduce__ = (...) apply(...) `apply( (BatchLevelCreator)arg1) -> list : Creates the specified datasets. After calling apply, the object should not be used anymore. Returns a list containing the numbers of the created datasets.` create_BL_datasets_from_unused_batches(...) `create_BL_datasets_from_unused_batches( (int)model_number, (Project)project) -> list : Creates additional batch level datasets from all batches that has not already been used in batch level datasets. The new datasets are created with the same settings as the existing ones. model_number --- The number of a batch evolution model that is used to create the batch level datasets. If the model is a mother model (batch evolution model, BEM) all models in the group will be used. If the model is a model in a BEM, all other models in the same BEM will also be used. Returns a list containing the numbers of the created datasets.` exists(...) `exists( (BatchLevelCreator)self, (DatasetType)dataset_type) -> bool : Returns true if a dataset of the specified type has already been created. Only one dataset of each type can be created per batch evolution model.` get_available_variables(...) `get_available_variables( (BatchLevelCreator)self, (int)phase_index) -> list : Returns the names of all variables that are available to use for summary statistics and raw data batch level datasets for the given phase. phase_index --- The phase for which variables are to be returned.` get_included_variables(...) `get_included_variables( (BatchLevelCreator)self, (int)phase_index) -> list : Returns the names of the currently included variables. These are the variables that will be used to create summary statistics and raw data batch level datasets. The default is to include all variables that has been included in the corresponding phase model. phase_index --- The index of the phase for which the variable names are returned. The first phase has index 0.` get_used_models(...) `get_used_models( (BatchLevelCreator)self) -> list : Returns a list containing a ModelInfo for each of the models that are used to create the batch level datasets. Note that if the CreateBatchProject was created to use a model group or one model in a model group, the list contains all models in the group` set_components(...) `set_components( (BatchLevelCreator)self, (int)phase_index, (object)components) -> None : Sets the components to use when creating batch level datasets of type DatasetType.Scores. The number of components available for each phase can be obtained from the ModelInfo returned by get_used_models. The default is to use all components. phase_index --- The index of the phase. components --- A list of the components to use.` set_variables(...) `set_variables( (BatchLevelCreator)self, (int)phase_index, (object)variable_names) -> None : Sets the variables to use when creating summary statistics and raw data batch level datasets. The default is to use all available variables. phase_index --- The index of the phase for which the variables are to be used. variable_names --- The names of the variables to use.` Data descriptors defined here: dataset_types `The types of the dataset(s) that are to be created. This is one or more of the DatasetType enums ORed together. Default is DatasetType.RawData | DatasetType.DurationEndPoint` statistics_summary_types `The types of statistics which are to be used when creating batch level datasets of type DatasetType.Statistics. One or more of the StatisticsSummaryTypes enums ORed together. Default is StatisticsSummaryTypes.Mean | StatisticsSummaryTypes.StdDev` Data and other attributes defined here: DatasetType = <class 'umetrics.simca.DatasetType'> `The different types of batch level datasets. Only one dataset of each type can be created per batch evolution model. DurationEndPoint --- Contains duration and end points for each phase. Scores --- Contains scores from the batch evolution models (not available for OPLS batch models). RawData --- Contains raw data. Statistics --- Contains summary statistics of raw data (mean, standard deviation etc)` StatisticsSummaryTypes = <class 'umetrics.simca.StatisticsSummaryTypes'> `The different types of statistics that can be used with batch level datasets of type DatasetType.Statistics Min --- The minimum value. Max --- The maximum value. Mean --- The mean. Median --- The median. StdDev --- The standard deviation. RobustStdDev --- The robust standard deviation (interquartile range divided by 1.075). Interquartile --- Interquartile range (the first interquartile minus the third) Slope --- A variables slope.` __instance_size__ = 208 Static methods inherited from Boost.Python.instance: __new__(*args, **kwargs) from Boost.Python.class `Create and return a new object. See help(type) for accurate signature.` Data descriptors inherited from Boost.Python.instance: __dict__ __weakref__
class DataCollection(Boost.Python.instance)
`` | `Defines the complete data used by Datasets, prediction sets and work sets.`
| Method resolution order: DataCollection Boost.Python.instance builtins.object Static methods defined here: __copy__(...) `__copy__( (DataCollection)arg1) -> DataCollection` __deepcopy__(...) `__deepcopy__( (DataCollection)arg1, (object)arg2) -> DataCollection` __init__(...) `Raises an exception This class cannot be instantiated from Python` __reduce__ = (...) __str__(...) `__str__( (DataCollection)self) -> str` compare_with(...) `compare_with( (DataCollection)arg1, (DataCollection)arg2, (float)arg3) -> bool : Compares two DataCollections and raises an umetrics.UmetricsException if they differ. This is primarily intended for internal use at Umetrics. precision is the precision of the comparison of floating point numbers. precision = 0 => The numbers must be exactly equal. precision = 1E-3 => The numbers can not differ in the third digit precision = 1 => The numbers can not differ in the first digit` get_data(...) `get_data( (DataCollection)self) -> UmMat : Returns a matrix with all values in the data collection.` get_datasets(...) `get_datasets( (DataCollection)self) -> list : Returns a list of all datasets that is included in this data collection.` get_obs_aliases(...) `get_obs_aliases( (DataCollection)self) -> list : Returns a list of the names of the observation aliases used in this data collection.` get_obs_names(...) `get_obs_names( (DataCollection)self [, (int)alias=0]) -> list : Returns the observation names in the datasets used by this model. alias --- the index of the desired observation alias (default 0 for primary ID)` get_string_values(...) `get_string_values( (DataCollection)self, (object)variable) -> list : Returns the values of a variable as strings, used for qualitative and time variables. variable --- the index or name of the desired variable (default 0 for the first)` get_var_aliases(...) `get_var_aliases( (DataCollection)self) -> list : Returns a list of the names of the variable aliases used in this data collection.` get_var_names(...) `get_var_names( (DataCollection)self [, (int)alias=0]) -> list : Returns the variable names in the datasets used by this model. alias --- the index of the desired variable alias (default 0 for primary ID)` is_qualitative(...) `is_qualitative( (DataCollection)self, (object)variable) -> bool : Returns true if the variable is a time variable. variable --- the index or name of the desired variable (default 0 for the first)` is_time(...) `is_time( (DataCollection)self, (object)variable) -> bool : Returns true if the variable is a time variable. variable --- the index or name of the desired variable (default 0 for the first)` size_observations(...) `size_observations( (DataCollection)self) -> int : Returns number of observations in the data collection.` size_variables(...) `size_variables( (DataCollection)self) -> int : Returns number of variables in the data collection.` Static methods inherited from Boost.Python.instance: __new__(*args, **kwargs) from Boost.Python.class `Create and return a new object. See help(type) for accurate signature.` Data descriptors inherited from Boost.Python.instance: __dict__ __weakref__
class Dataset(DataCollection)
`` | `Represents a SIMCA dataset.`
| Method resolution order: Dataset DataCollection Boost.Python.instance builtins.object Static methods defined here: __init__(...) `Raises an exception This class cannot be instantiated from Python` __reduce__ = (...) __repr__(...) `__repr__( (Dataset)self) -> str` __str__(...) `__str__( (Dataset)self) -> str` add_variable(...) `add_variable( (Dataset)self, (object)name, (object)values [, (object)identifiers=None]) -> str : Adds a variable to the dataset. name --- The name of the new variable. It can also be a list of strings where the first will be used as the primary variable ID and subsequent strings as secondary variable IDs. values --- Can be a list of numbers (which will create a quantitative variable) or a list of strings (which will create a qualitative variable). It can also be a dictionary that maps observation identifiers (observation index or names) to values (which can be numbers or strings). If it is a list and no identifiers are supplied it must be the same length as the number of observations in the dataset. identifiers --- A list of identifiers (indexes or observation names) that is used to identify what observation each value in 'values' belongs to. This can be omitted if 'values' is a dictionary or the length of 'values' is the same as the number of observations in the dataset. Returns the primary identifier (name) of the new variable. Note that this will not be the same as the 'name' argument if the project already contains a variable with that name.` merge(...) `merge( (Dataset)self, (object)right_dataset, (object)left_on, (object)right_on [, (bool)side_by_side=True [, (int)how=2 [, (int)match_type=1 [, (bool)sort_on_last=False [, (int)no_match_type=1 [, (bool)last_good_crossing_phases=False [, (bool)keep_right_on_data=False]]]]]]]) -> None : Merge two datasets and the result will be in the destination dataset. The source dataset will be deleted and all models depending on the source dataset will be removed. right_dataset --- The right or bottom dataset in the merge, this dataset will be deleted. Can be dataset number or name left_on --- A column/row or a list of columns/rows to match in the top/left dataset. Use '$Index' to match by row or column numbers, use the name of the variable or observation ID for example 'Primary ID' of $BatchID Use name or index of the variables or observations for example ['Time', 7] right_on --- see left_on. side_by_side --- if True the datasets will be merged side by side, if not; top_bottom. how --- the type of join, see 'merge_type'; 'full' or left'. match_type --- how to match the last column (columns other than the last always match exact), see 'merge_match'; 'exact', 'nearest', 'forward' or 'interpolate'. sort_on_last --- Determines where rows that don't match are inserted for full outer join, if True the last row/column in left_on is assumed to be sorted and SIMCA tries to insert the new values sorted also. The order in the top/left dataset is not changed so if it is not supported to begin with it will not be sorted after. if False the rows without a match are inserted last. no_match_type --- what to do with rows/columns without a match, see no_match_option last_good_crossing_phases ---If we insert last good value, this can be set to True to allow last good values to be copied from previous batch keep_right_on_data --- Set to True to keep matching values in a separate column/row` remove_observations(...) `remove_observations( (Dataset)self, (object)observations) -> None : Removes observations from the dataset. observations --- The name of the observation or the index (0 based). It can also be a list.` remove_variables(...) `remove_variables( (Dataset)self, (object)variables) -> None : Removes variables from the dataset. variables --- The name of the variable or the index (0 based). It can also be a list.` set_value(...) `set_value( (Dataset)self, (object)observation, (object)variable, (float)value) -> None : Sets the value of a variable and observation in the dataset. observation --- The name of the observation or the index (0 based). variable --- The name of the variable or the index (0 based). value --- The floating point value to set.` trim_variable(...) `trim_variable( (Dataset)self, (object)variables, (ExcludeStruct)options, (bool)is_high_limit) -> None : Trim variables variables --- The parameter can either be an index, a variable name, a list of indices and variable names or an empty list for all variables. options --- The limits and replace value, see trim_options class. is_high_limit --- Set this to True if the options are for the high limit and to False if it is for the low limit.` Static methods inherited from DataCollection: __copy__(...) `__copy__( (DataCollection)arg1) -> DataCollection` __deepcopy__(...) `__deepcopy__( (DataCollection)arg1, (object)arg2) -> DataCollection` compare_with(...) `compare_with( (DataCollection)arg1, (DataCollection)arg2, (float)arg3) -> bool : Compares two DataCollections and raises an umetrics.UmetricsException if they differ. This is primarily intended for internal use at Umetrics. precision is the precision of the comparison of floating point numbers. precision = 0 => The numbers must be exactly equal. precision = 1E-3 => The numbers can not differ in the third digit precision = 1 => The numbers can not differ in the first digit` get_data(...) `get_data( (DataCollection)self) -> UmMat : Returns a matrix with all values in the data collection.` get_datasets(...) `get_datasets( (DataCollection)self) -> list : Returns a list of all datasets that is included in this data collection.` get_obs_aliases(...) `get_obs_aliases( (DataCollection)self) -> list : Returns a list of the names of the observation aliases used in this data collection.` get_obs_names(...) `get_obs_names( (DataCollection)self [, (int)alias=0]) -> list : Returns the observation names in the datasets used by this model. alias --- the index of the desired observation alias (default 0 for primary ID)` get_string_values(...) `get_string_values( (DataCollection)self, (object)variable) -> list : Returns the values of a variable as strings, used for qualitative and time variables. variable --- the index or name of the desired variable (default 0 for the first)` get_var_aliases(...) `get_var_aliases( (DataCollection)self) -> list : Returns a list of the names of the variable aliases used in this data collection.` get_var_names(...) `get_var_names( (DataCollection)self [, (int)alias=0]) -> list : Returns the variable names in the datasets used by this model. alias --- the index of the desired variable alias (default 0 for primary ID)` is_qualitative(...) `is_qualitative( (DataCollection)self, (object)variable) -> bool : Returns true if the variable is a time variable. variable --- the index or name of the desired variable (default 0 for the first)` is_time(...) `is_time( (DataCollection)self, (object)variable) -> bool : Returns true if the variable is a time variable. variable --- the index or name of the desired variable (default 0 for the first)` size_observations(...) `size_observations( (DataCollection)self) -> int : Returns number of observations in the data collection.` size_variables(...) `size_variables( (DataCollection)self) -> int : Returns number of variables in the data collection.` Static methods inherited from Boost.Python.instance: __new__(*args, **kwargs) from Boost.Python.class `Create and return a new object. See help(type) for accurate signature.` Data descriptors inherited from Boost.Python.instance: __dict__ __weakref__
class DatasetInfo(Boost.Python.instance)
`` | `Contains information about a dataset.`
| Method resolution order: DatasetInfo Boost.Python.instance builtins.object Static methods defined here: __init__(...) `Raises an exception This class cannot be instantiated from Python` __reduce__ = (...) __repr__(...) `__repr__( (DatasetInfo)self) -> str` __str__(...) `__str__( (DatasetInfo)self) -> str` Data descriptors defined here: ID `A number that uniquely identifies the dataset` Yvariables `The number of Y variables in the dataset` name `The name of the dataset` observations `The number of observations in the dataset.` type `The type of the dataset. This is a a combination of the DatasetType enums` variables `The number of variables` Data and other attributes defined here: DatasetType = <class 'umetrics.simca.DatasetType'> `Different dataset types A dataset type can be a combination of these ORed together. regular --- Not a batch level, preprocessed or hierarchical dataset preprocessing --- Contains preprocessed data, can be combined with batchevolution and batchlevel hierarchical --- Contains hierarchical data, can be combined with batchevolution and batchlevel plsda --- Contains Y variables used with PLS - DA models, can be combined with batchevolution and batchlevel batchlevel --- Batch level dataset. batchevolution --- Batch evolution level dataset. batchcondition --- A batch level dataset that only contain imported data. lagdistance --- Contains distance variables used for dynamic lags.` Static methods inherited from Boost.Python.instance: __new__(*args, **kwargs) from Boost.Python.class `Create and return a new object. See help(type) for accurate signature.` Data descriptors inherited from Boost.Python.instance: __dict__ __weakref__
class DatasetType(Boost.Python.enum)
`` | `Different dataset types A dataset type can be a combination of these ORed together. regular --- Not a batch level, preprocessed or hierarchical dataset preprocessing --- Contains preprocessed data, can be combined with batchevolution and batchlevel hierarchical --- Contains hierarchical data, can be combined with batchevolution and batchlevel plsda --- Contains Y variables used with PLS - DA models, can be combined with batchevolution and batchlevel batchlevel --- Batch level dataset. batchevolution --- Batch evolution level dataset. batchcondition --- A batch level dataset that only contain imported data. lagdistance --- Contains distance variables used for dynamic lags.`
| Method resolution order: DatasetType Boost.Python.enum builtins.int builtins.object Data and other attributes defined here: batchcondition = umetrics.simca.DatasetType.batchcondition batchevolution = umetrics.simca.DatasetType.batchevolution batchlevel = umetrics.simca.DatasetType.batchlevel hierarchical = umetrics.simca.DatasetType.hierarchical lagdistance = umetrics.simca.DatasetType.lagdistance names = {'batchcondition': umetrics.simca.DatasetType.batchcondition, 'batchevolution': umetrics.simca.DatasetType.batchevolution, 'batchlevel': umetrics.simca.DatasetType.batchlevel, 'hierarchical': umetrics.simca.DatasetType.hierarchical, 'lagdistance': umetrics.simca.DatasetType.lagdistance, 'plsda': umetrics.simca.DatasetType.plsda, 'regular': umetrics.simca.DatasetType.regular, 'spectral': umetrics.simca.DatasetType.spectral} plsda = umetrics.simca.DatasetType.plsda regular = umetrics.simca.DatasetType.regular spectral = umetrics.simca.DatasetType.spectral values = {1: umetrics.simca.DatasetType.regular, 2: umetrics.simca.DatasetType.spectral, 4: umetrics.simca.DatasetType.hierarchical, 8: umetrics.simca.DatasetType.plsda, 16: umetrics.simca.DatasetType.batchlevel, 32: umetrics.simca.DatasetType.batchevolution, 64: umetrics.simca.DatasetType.batchcondition, 256: umetrics.simca.DatasetType.lagdistance} Methods inherited from Boost.Python.enum: __repr__(self, /) `Return repr(self).` __str__(self, /) `Return str(self).` Data descriptors inherited from Boost.Python.enum: name Methods inherited from builtins.int: __abs__(self, /) `abs(self)` __add__(self, value, /) `Return self+value.` __and__(self, value, /) `Return self&value.` __bool__(self, /) `self != 0` __ceil__(...) `Ceiling of an Integral returns itself.` __divmod__(self, value, /) `Return divmod(self, value).` __eq__(self, value, /) `Return self==value.` __float__(self, /) `float(self)` __floor__(...) `Flooring an Integral returns itself.` __floordiv__(self, value, /) `Return self//value.` __format__(self, format_spec, /) `Default object formatter.` __ge__(self, value, /) `Return self>=value.` __getattribute__(self, name, /) `Return getattr(self, name).` __getnewargs__(self, /) __gt__(self, value, /) `Return self>value.` __hash__(self, /) `Return hash(self).` __index__(self, /) `Return self converted to an integer, if self is suitable for use as an index into a list.` __int__(self, /) `int(self)` __invert__(self, /) `~self` __le__(self, value, /) `Return self<=value.` __lshift__(self, value, /) `Return self<<value.` __lt__(self, value, /) `Return self<value.` __mod__(self, value, /) `Return self%value.` __mul__(self, value, /) `Return self*value.` __ne__(self, value, /) `Return self!=value.` __neg__(self, /) `-self` __or__(self, value, /) `Return self|value.` __pos__(self, /) `+self` __pow__(self, value, mod=None, /) `Return pow(self, value, mod).` __radd__(self, value, /) `Return value+self.` __rand__(self, value, /) `Return value&self.` __rdivmod__(self, value, /) `Return divmod(value, self).` __rfloordiv__(self, value, /) `Return value//self.` __rlshift__(self, value, /) `Return value<<self.` __rmod__(self, value, /) `Return value%self.` __rmul__(self, value, /) `Return value*self.` __ror__(self, value, /) `Return value|self.` __round__(...) `Rounding an Integral returns itself. Rounding with an ndigits argument also returns an integer.` __rpow__(self, value, mod=None, /) `Return pow(value, self, mod).` __rrshift__(self, value, /) `Return value>>self.` __rshift__(self, value, /) `Return self>>value.` __rsub__(self, value, /) `Return value-self.` __rtruediv__(self, value, /) `Return value/self.` __rxor__(self, value, /) `Return value^self.` __sizeof__(self, /) `Returns size in memory, in bytes.` __sub__(self, value, /) `Return self-value.` __truediv__(self, value, /) `Return self/value.` __trunc__(...) `Truncating an Integral returns itself.` __xor__(self, value, /) `Return self^value.` bit_length(self, /) `Number of bits necessary to represent self in binary. >>> bin(37) '0b100101' >>> (37).bit_length() 6` conjugate(...) `Returns self, the complex conjugate of any int.` to_bytes(self, /, length, byteorder, *, signed=False) `Return an array of bytes representing an integer. length Length of bytes object to use. An OverflowError is raised if the integer is not representable with the given number of bytes. byteorder The byte order used to represent the integer. If byteorder is 'big', the most significant byte is at the beginning of the byte array. If byteorder is 'little', the most significant byte is at the end of the byte array. To request the native byte order of the host system, use `sys.byteorder' as the byte order value. signed Determines whether two's complement is used to represent the integer. If signed is False and a negative integer is given, an OverflowError is raised.` Class methods inherited from builtins.int: from_bytes(bytes, byteorder, *, signed=False) from builtins.type `Return the integer represented by the given array of bytes. bytes Holds the array of bytes to convert. The argument must either support the buffer protocol or be an iterable object producing bytes. Bytes and bytearray are examples of built-in objects that support the buffer protocol. byteorder The byte order used to represent the integer. If byteorder is 'big', the most significant byte is at the beginning of the byte array. If byteorder is 'little', the most significant byte is at the end of the byte array. To request the native byte order of the host system, use `sys.byteorder' as the byte order value. signed Indicates whether two's complement is used to represent the integer.` Static methods inherited from builtins.int: __new__(*args, **kwargs) from builtins.type `Create and return a new object. See help(type) for accurate signature.` Data descriptors inherited from builtins.int: denominator `the denominator of a rational number in lowest terms` imag `the imaginary part of a complex number` numerator `the numerator of a rational number in lowest terms` real `the real part of a complex number`
class ExcludeStruct(Boost.Python.instance)
`` |  | Method resolution order: ExcludeStruct Boost.Python.instance builtins.object Static methods defined here: __init__(...) `__init__( (object)arg1) -> None` __reduce__ = (...) Data and other attributes defined here: __instance_size__ = 216 Static methods inherited from Boost.Python.instance: __new__(*args, **kwargs) from Boost.Python.class `Create and return a new object. See help(type) for accurate signature.` Data descriptors inherited from Boost.Python.instance: __dict__ __weakref__
class Filter(Boost.Python.instance)
`` | `Used to add different filtering options. After finished filtering, a new filtered dataset is created. All original variables is unchanged in the original dataset. Apply several filters in combination by using add_filter_type method. The filters can be applied in any order although each filter can be applied only once. To finish filtering and create a dataset, call apply_filter.`
| Method resolution order: Filter Boost.Python.instance builtins.object Static methods defined here: __init__(...) `Raises an exception This class cannot be instantiated from Python` __reduce__ = (...) add_filter_type(...) `add_filter_type( (Filter)self, (filtertype)filtertype) -> None : Add a specific filter. filtertype --- The filter type to use and change settings for. See filtertype enum for different options.` apply_filter(...) `apply_filter( (Filter)self, (str)datasetname) -> None : After a completed filtering, a new filtered dataset is created. datasetname --- The name of the new dataset that will be created or the observation names.` exclude_observations(...) `exclude_observations( (Filter)self, (object)observations) -> None : excludes observations from use in the filter. observations --- Contains the IDs or index of the observations that should be excluded. The parameter can either be a list of indices or the observation names.` exclude_variables(...) `exclude_variables( (Filter)self, (object)variables) -> None : excludes variables from use in the filter. variables --- Contains the IDs or index of the variables that should be excluded. The parameter can either be a list of indices or the variable names.` get_available_wavelet_coefficients(...) `get_available_wavelet_coefficients( (Filter)self) -> int : Returns the total number of available wavelet coefficients when energy is retained by variance, see set_energy_retained_by_variance which must have been called first` get_detail_level_energy(...) `get_detail_level_energy( (Filter)self, (int)detail_level_index) -> float : Returns the retained energy for a specific detail index when energy is retained by details, see set_energy_retained_by_detail which must have been called first detail_level_index --- the detail index to get information from, between 0 and (get_num_detail_levels - 1)` get_detail_level_size(...) `get_detail_level_size( (Filter)self, (int)detail_level_index) -> int : Returns the number of coefficients for a specific detail index when energy is retained by details, see set_energy_retained_by_detail which must have been called first detail_level_index --- the detail index to get information from, between 0 and (get_num_detail_levels - 1)` get_num_detail_levels(...) `get_num_detail_levels( (Filter)self) -> int : Returns the total available number of detail for wavelet filters when energy is retained by details, see set_energy_retained_by_detail which must have been called first` get_osc_angle_ss(...) `get_osc_angle_ss( (Filter)self) -> float : returns the angle after the last component in an an OSC filter,` get_osc_eigenvalue(...) `get_osc_eigenvalue( (Filter)self) -> float : returns the eigenvalue after the last component in an an OSC filter,` get_osc_remaining_ss(...) `get_osc_remaining_ss( (Filter)self) -> float : returns the remaining sum of square after the last component in an an OSC filter,` get_retained_energy(...) `get_retained_energy( (Filter)self) -> float : Returns the % energy retained with the current settings for wavelet filters set_energy_retained_by_detail or set_energy_retained_by_variance must have been called first` set_derivative_order(...) `set_derivative_order( (Filter)self, (derivativeorder)derivativeorder) -> None : Sets the derivative order. Default set to firstderivative. derivativeorder --- See derivativeorder enum for different options.` set_detrend(...) `set_detrend( (Filter)self, (detrendmode)detrendmode) -> None : Set the detrend mode for wavelet filter. detrendmode --- See detrendmode enum for different options.` set_distance_between_points(...) `set_distance_between_points( (Filter)self, (float)distancepoints) -> None : Sets the distance between each point. . distancepoints --- The distance between each point. Default set to 1.` set_energy_retained_by_detail(...) `set_energy_retained_by_detail( (Filter)self) -> None : Sets the energy retained by detail for wavelet filter.` set_energy_retained_by_variance(...) `set_energy_retained_by_variance( (Filter)self, (compressionmethod)compressionmethod) -> None : Set the energy retained by variance for wavelet filter. compressionmethod --- See compressionmethod enum for different options.` set_ewma_type(...) `set_ewma_type( (Filter)self, (ewma_type)ewma_type) -> None : Sets the type of EWMA (filter or predictive) ewma_type --- the EWMA type, filter or predictive. Default is filter.` set_lambda(...) `set_lambda( (Filter)self, (float)lambda) -> None : Sets the distance between each point. The lambda value must be between 0 and 1. Default set to missing. lambda --- The lambda value.` set_observations(...) `set_observations( (Filter)self, (object)observations) -> None : Set the observations to include in the new dataset. observations --- Contains the IDs or index of the observations that should be included. The parameter can either be a list of indices or the observation names.` set_osc_components(...) `set_osc_components( (Filter)arg1, (int)self, num_components) -> None : sets the number of components to use in an OSC filter, num_components --- the desired number of components <=1` set_plugin(...) `set_plugin( (Filter)self, (str)pluginname) -> None : set the plugin name to use pluginname --- the name of the plugin` set_plugin_paths(...) `set_plugin_paths( (object)paths) -> str : Load plugin preprocessing / spectral filters from the specified folders. returns a status text for the plugins loaded. paths --- List of folders containing plugin filters` set_polynomial_order(...) `set_polynomial_order( (Filter)self, (polynomialorder)polynomialorder) -> None : Sets the polynomial order. Default set to quadratic. polynomialorder --- See polynomialorder enum for different options.` set_scaling(...) `set_scaling( (Filter)self, (object)variables, (filter_scaletype)filter_scaletype) -> None : Set the variables to include in the new dataset. Should be called after set_variables and set_y_variables. variables --- Contains the IDs or index of the variables that should be scaled. The parameter can either be a list of indices or the variable names.filter_scaletype --- See filter_scaletype enum for different options.` set_submodel_points(...) `set_submodel_points( (Filter)self, (int)submodelpoints) -> None : Sets the number of points in each sub-model. This number is default 15 and has to be odd and >= 5.submodelpoints --- The number of points in each sub-model.` set_target_variable(...) `set_target_variable( (Filter)self, (object)variable) -> None : set the target variable used by time series filters variable --- The name or index of the target variable` set_transformation(...) `set_transformation( (Filter)self, (object)variables, (transformtype)transformtype, (powerexponent)powerexponent, (float)constant1, (float)constant2) -> None : Set the variables to transform. Should be called after set_variables and set_y_variables. variables --- Contains the IDs or index of the variables that should be transformed. The parameter can either be a list of indices or the variable names.` set_variables(...) `set_variables( (Filter)self, (object)variables) -> None : Set the variables to include in the new dataset. variables --- Contains the IDs or index of the variables that should be included. The parameter can either be a list of indices or the variable names.` set_wavelet_coefficients(...) `set_wavelet_coefficients( (Filter)arg1, (int)self, num_coefficients) -> None : sets the number of available wavelet coefficients to use when energy is retained by variance, see set_energy_retained_by_variance which must have been called first num_coefficients --- the desired number of wavelet coefficients <=2` set_wavelet_details(...) `set_wavelet_details( (Filter)arg1, (list)self, detail_inices) -> None : set which detail indices to use in a wavelet filter when energy is retained by details, see set_energy_retained_by_detail which must have been called first detail_inices --- a list containing desired detail indexes between 1 and [get_num_detail_levels]` set_waveletfunction(...) `set_waveletfunction( (Filter)self, (waveletfunctiontype)waveletfunctiontype [, (int)order=1]) -> None : Sets the wavelet function type and order for wavelet filter. waveletfunctiontype --- See waveletfunctiontype enum for different options. order --- See detrendmode enum for valid orders for different waveletfunctiontype.` set_wdts_decimation(...) `set_wdts_decimation( (Filter)self, (int)decimation) -> None : Set decimation to use for WDTS, wavelet denoising time series filter, the resulting dataset will include only every n't observation where n is the decimation specified. should be called after set_observations decimation --- the decimation to use have to be a power of 2, ex. 1, 2, 4, 8, or 16. with maximum value the number of observation / 2 rounded down to a power of 2.` set_y_variables(...) `set_y_variables( (Filter)self, (object)variables) -> None : Set the Y variables to use in the filter. variables --- Contains the IDs or index of the variables that should be included. The parameter can either be a list of indices or the variable names.` Static methods inherited from Boost.Python.instance: __new__(*args, **kwargs) from Boost.Python.class `Create and return a new object. See help(type) for accurate signature.` Data descriptors inherited from Boost.Python.instance: __dict__ __weakref__
class ModelInfo(Boost.Python.instance)
`` | `Contains information about a model.`
| Method resolution order: ModelInfo Boost.Python.instance builtins.object Static methods defined here: __init__(...) `Raises an exception This class cannot be instantiated from Python` __reduce__ = (...) __repr__(...) `__repr__( (ModelInfo)self) -> str` __str__(...) `__str__( (ModelInfo)self) -> str` set_description(...) `set_description( (ModelInfo)self, (str)title) -> None : Sets the models title.` Data descriptors defined here: XblockPCA `The number of X-block orthogonal components that are PCA components. Always zero for non OPLS/O2PLS models.` Xvariables `The number of X variables.` YblockPCA `The number of Y-block orthogonal components that are PCA components. Always zero for non OPLS/O2PLS models.` Yvariables `The number of Y variables.` children `The number of the child models. An empty list if the model doesn't have children.` classnames `Class names in the model. Also used for block names in MOCA models.` components `The number of components. For OPLS and O2PLS models this is the number of predictive components.` created `A datetime object representing when the model was created in UTC.` description `The models description.` expanded `The number of expanded terms.` isfitted `True if the model is fitted.` jointcomponents `The number of joint components for MOCA models.` lagged `The number of lagged terms.` mother `The number of the mother model. Zero if there is no mother model.` name `The model name.` number `The model number. If this number is negative the model is a mother to other models (a model group).` observations `The number of observations.` orthogonalinX `The number of X-block orthogonal components. Always zero for non OPLS/O2PLS models.` orthogonalinY `The number of Y-block orthogonal components. Always zero for non OPLS/O2PLS models.` type `The type of the model.` uniquecomponents `The number of unique components for MOCA models.` Static methods inherited from Boost.Python.instance: __new__(*args, **kwargs) from Boost.Python.class `Create and return a new object. See help(type) for accurate signature.` Data descriptors inherited from Boost.Python.instance: __dict__ __weakref__
class ModelOptions(Boost.Python.instance)
`` | `Various options that effects coefficient types, DmodX etc.`
| Method resolution order: ModelOptions Boost.Python.instance builtins.object Static methods defined here: __init__(...) `Raises an exception This class cannot be instantiated from Python` __reduce__ = (...) __str__(...) `__str__( (ModelOptions)self) -> str` apply(...) `apply( (ModelOptions)arg1) -> None : Sets the options to the model.` Data descriptors defined here: adjusted_R2 `If True, R2 is adjusted by the degrees of freedom.` coefficient_type `The type of coefficients that will be used by default. One of the coefficient_types enum.` limits `A dictionary containing alarm limits for this model. limits property is a dictionary which you can set and get alarm limits. Available types are : var, t, DModX, DModX+, PModX, PModX+ and T2Range. For each type of vector there are target, lolo, lo, hi and hihi limits. Each alarm type can have triggers to raise an alarm. Example 1 : Setting a score vector alarm with limits. Extra argument is to set which component the alarm is for. limits = [{'type' : 't', 'comp' : 1, 'limits' : { 'lolo' : -4, 'lo' : -3, 'hi' : 3, 'hihi' : 4 }] Example 2 : Setting a variable vector alarm with limits. Extra argument is 'name' to specify the variable name to set the alarm for. limits = [{'type' : 'var', 'name' : 'Agitation speed', 'limits' : { 'lolo' : -100, 'target' : 100, 'hihi' : 200 }] Example 3 : Setting a DModX vector alarm. limits = [{'type' : 'DModX', 'limits' : { 'hihi' : 20 }] Example 4 : Setting a T2Range vector alarm. Extra argument 'comp' is used for setting the component range. The 'comp' argument is not used for OPLS models. limits = [{'type' : 'T2Range', 'comp' : { 'from' : 1, 'to' : 4 }, 'limits' : { 'hihi' : 20 }] Adding triggers is done by adding a 'triggerLogic' member to the alarm. limits[0]['triggerLogic'] = { 'numObservations' : 3, 'missing' : True, 'numObsInWindow' : 8, 'sizeWindow' : 100 } Available members are 'numObservations' which is how many consecutive observations that can be outside the limits before triggering alarm. 'missing' is used when a missing observation should trigger alarms. 'numObsInWindow' and 'sizeWindow' fields are used for how many observations that can be outside limits withing a window before triggering an alarm. This is a moving window across all observations. The settings 'numObsInWindow' : 8 and 'sizeWindow' : 100 means that an alarm will be triggered if there are more than 8 observations in a window of 100 observations.` normalize_DModX `If True, DModX is normalized in units of standard deviation.` parameter_confidence_level `The confidence level for model parameters (coefficients, VIP etc). Must be less than 1 and greater than 0.` resolve_hierarchical_coefficients `If True, top level hierarchical coefficients is resolved into the original variables.` scale_predictions `If True, predictions are in the scaled metric. Scaled predictions also implies transformed predictions (see above).` significance_for_DModX_T2 `The significance level used for DModX and Hotellings T2. Must be less than 1 and larger than 0.` standardized_residuals `If True, residuals are standardized in units of standard deviations.` transform_predictions `If True, predictions is in the transformed metric. Untransformed predictions also implies unscaled predictions (see below).` trim_predictions `If true, predictions will be trimmed as the workset.` use_o2pls_pca_components `True to calculate O2PLS PCA components on residuals in autofit.` weighted_DModX `If True, DModX is weighted by the modeling power of each variable.` zero_T2_range_mean `This value should only be set to True if the model is to be used to create lead-centered onion designs in MODDE. If True, 0 will be used as the T center value when calculating T2Range, otherwise, the arithmetic mean will be used.` Data and other attributes defined here: coefficient_types = <class 'umetrics.simca.coefficient_types'> `One of: scaled_and_centered --- Coefficients for scaled and centered X and scaled Y MLR --- Coefficients for scaled and centered X and unscaled and uncentered Y unscaled --- Coefficients for both X and Y in the original metric (unscaled, uncentered). rotated --- Coefficients rotated to correspond as much as possible to pure profiles. See the statistical appendix in SIMCAs help.` Static methods inherited from Boost.Python.instance: __new__(*args, **kwargs) from Boost.Python.class `Create and return a new object. See help(type) for accurate signature.` Data descriptors inherited from Boost.Python.instance: __dict__ __weakref__
class PhaseCropper(Boost.Python.instance)
`` | `Defines trimming for phases in a workset.`
| Method resolution order: PhaseCropper Boost.Python.instance builtins.object Static methods defined here: __init__(...) `Raises an exception This class cannot be instantiated from Python` __reduce__ = (...) crop_beginning(...) `crop_beginning( (PhaseCropper)self, (int)size) -> None : Set how many observations at the beginning of all batches that should be removed. size --- Number of observations to remove.` crop_end(...) `crop_end( (PhaseCropper)self, (int)size) -> None : Set how many observations at the end of all batches that should be removed. size --- Number of observations to remove.` crop_variable(...) `crop_variable( (PhaseCropper)self, (object)variable, (operator)operation, (float)firstlimit [, (float)secondlimit=0]) -> None : Crop batches according to . variable --- The name or the index of the variable to crop. operation --- The name or the index of the variable to crop. See operator enum for different values. firstlimit --- The first limit to compare with. secondlimit --- The second limit to compare with. Only valid for` down_size(...) `down_size( (PhaseCropper)self, (int)interval) -> None : Remove every Nth observation. interval --- How often an observation should be removed.` Static methods inherited from Boost.Python.instance: __new__(*args, **kwargs) from Boost.Python.class `Create and return a new object. See help(type) for accurate signature.` Data descriptors inherited from Boost.Python.instance: __dict__ __weakref__
class Predictionset(Boost.Python.instance)
`` | `Defines observations that can be saved as a predictionset.`
| Method resolution order: Predictionset Boost.Python.instance builtins.object Static methods defined here: __init__(...) `Raises an exception This class cannot be instantiated from Python` __reduce__ = (...) __repr__(...) __str__(...) add_source(...) `Adds observations to the predictionset from the given source for example a dataset or a class. source --- The source to use. See prediction_source enum for more information. name --- The name or the index to use for different sources: Workset: "name" is ignored, set to 0 Complement: "name" is ignored, set to 0 Class: "name" is a class number or name of the class Dataset: "name" is a dataset number or name of the dataset Predictionset:"name" is the name of the predictionset observations --- A list of names or indexes of the observations that should be included or an empty list to include all observations. indexes are 0 based indexes in the dataset, predictionset or the entire data container of the model, never an index in a specific class or workset` as_class(...) `Creates a prediction set with the observations from a class in the workset class --- The name or number of the class to use` as_complement(...) `Creates a prediction set with the complement observations as in the workset or if it's a batch evolution model, complement batches` as_current_predictionset(...) `Creates a prediction set with the same observations as the current active prediction set in SIMCA` as_dataset(...) `Creates a prediction set with the observations from a dataset dataset --- The name or number of the dataset to use` as_workset(...) `Creates a prediction set with the same observations as in the workset` data_explorer(...) `Adds What-If, score space or solver observations to the predictionset settings --- The data_explorer_settings object that contains all the settings` predict_x_from_y(...) `Calculate which x to use to get a specific Y. the model have to be an OPLS modely_settings --- a dictionary matching y to values or a list of y values the same length as the number of y's in the model. x_constraints --- a dictionary matching x to values. allow_extrapolation --- allow model extrapolation outside T-Crit in the score space Returns a list of x values` save(...) `Save the prediction set to be able to use it for predictions name --- The name of the prediction set. If the name exist already, then that prediction set will be replaced` Static methods inherited from Boost.Python.instance: __new__(*args, **kwargs) from Boost.Python.class `Create and return a new object. See help(type) for accurate signature.` Data descriptors inherited from Boost.Python.instance: __dict__ __weakref__
class Project(Boost.Python.instance)
`` | `The interface to SIMCA projects. ---- Project life time ---- Projects differ from ordinary Python objects because it is not the Python runtime that 'owns' them, the C++ code in the embedding application does. Projects can be closed by the application which leaves corresponding Python Project objects 'empty'. Also, the embedding application might not close projects even if a Python program tries to. To handle this, the scope of Python Projects should be restricted as much as possible and be disposed of when they are no longer needed.The following design is recommended: >>> with ProjectHandler.create_project(file_path) as project: ... if project: ... # Do stuff with project. >>> # The Python object will always be deleted. The underlying C++ object might not. If projects are cached, check 'is_open' to see if the project is still open.`
| Method resolution order: Project Boost.Python.instance builtins.object Static methods defined here: __bool__(...) `__bool__( (Project)self) -> bool` __enter__(...) `__enter__( (Project)self) -> Project` __eq__(...) `__eq__( (Project)arg1, (Project)arg2) -> object` __exit__(...) `__exit__( (Project)self, (object)type, (object)value, (object)traceback) -> None : Closes the project if it isn't the active project` __init__(...) `Raises an exception This class cannot be instantiated from Python` __reduce__ = (...) __str__(...) `__str__( (Project)self) -> str` add_local_centering(...) `add_local_centering( (Project)self, (str)variable, (str)center_id_name, (str)center, (float)value [, (str)phase='']) -> None : Add local centering informatio. variable --- Name of the variable center_id_name --- Name of the secondary ID to use for centering, it can be empty if phase is supplied center --- The center id for the observations value --- The offset value phase --- Optional, the name of the phase for batch projects` create_dataset(...) `create_dataset( (Project)arg1, (ImportData)self, (str)datasetname [, (bool)throw_on_illegals=True]) -> int : Create a new dataset from the specified data. Returns the number of the new dataset. data --- The data to create the dataset from datasetname --- The unique name of the dataset throw_on_illegals --- if False: text in the data is converted to nan, if True the function throws an exception if there are illegal values` create_filter(...) `create_filter( (Project)self [, (object)dataset=None]) -> Filter : Returns a new filter/preprocessing object. dataset --- The dataset to use for filtering. This can be a dataset number or name.` create_predictionset(...) `create_predictionset( (Project)self, (int)model) -> Predictionset : Creates a new predictionset from the given model. model --- Number of the model` create_workset(...) `create_workset( (Project)self [, (object)datasets=[] [, (object)phase_iterations=[]]]) -> Workset : Returns a new default Workset. datasets --- Contains the IDs or names of the dataset(s) that the new workset will use. If this argument is an empty list, the default datasets will be used. If no default has been specified, the first dataset will be used phase_iterations, see phase_iteration_treatment a list of treatments for the phase iterations for batch level models.` data_builder(...) `data_builder( (Project)self) -> ProjectDataBuilder : Create a new project data builder object.` delete_dataset(...) `delete_dataset( (Project)self, (object)dataset) -> None : Delete a dataset and all models depending on the dataset will be removed. Note that dataset 1 can not be deleted. dataset --- The dataset to delete This can be dataset number or name` delete_model(...) `delete_model( (Project)self, (int)model) -> None : Deletes the indicated model. If the model is a mother model (with a negative number), all models in the model group will be deleted. model --- Number of the model` delete_predictionset(...) `delete_predictionset( (Project)self, (str)name) -> None : Delete a predictionset. name --- Name of the predictionset to delete` edit_workset(...) `edit_workset( (Project)self, (int)model) -> Workset : Returns the workset of the selected model. model --- the number of the model to edit (negative number for mother models)` enable_embedded_scripts(...) `enable_embedded_scripts( (Project)arg1) -> None : Enable embedded python scripts for preprocessing filters Scripts might contain security hazards and are default disabled in the project, you should only enable it if you trust the content.` fit_model(...) `fit_model( (Project)self, (int)model [, (int)numcomp=-1 [, (int)mincomp=-1 [, (int)maxcomp=-1 [, (int)numorthX=0 [, (int)numorthY=0 [, (int)numpcaX=0 [, (int)numpcaY=0]]]]]]]) -> tuple : Fit the model. If no parameters are specified, then it will generate best number of components. Returns number of predictive, X orthogonal and Y orthogonal components. model --- The number of the model to fit.` generate_variables(...) `generate_variables( (Project)self, (object)dataset, (str)formula [, (object)names=None]) -> None : Generates new variables in the supplied dataset dataset --- Contains the number or name of the dataset to generate variables in. formula --- The recipe for the new variable(s) names --- Optional list of name(s) of the new variables, the length must match the number of generated variables. Do not use this argument if you want the default names` get_active_model(...) `get_active_model( (Project)arg1) -> int : Returns the number of the active model. A negative number indicates that the model is a mother model and a whole model group is active. A return value of zero indicates that there are no active models.` get_dataset(...) `get_dataset( (Project)self, (object)dataset) -> Dataset : Returns the dataset dataset --- The number or name of the dataset` get_dataset_info(...) `get_dataset_info( (Project)self, (object)dataset) -> DatasetInfo : Returns a DatasetInfo` get_dataset_infos(...) `get_dataset_infos( (Project)self) -> list : Returns a list containing a DatasetInfo for each dataset in the project.` get_group_model(...) `get_group_model( (Project)self, (int)model) -> str : Returns the name of the group model for a batch or class model or empty string if the model doesn't belong to any group. model --- The number of the model to get group model from.` get_hierarchical_base_type(...) `get_hierarchical_base_type( (Project)self, (int)model) -> list : Returns a list with the hierarchical types exported for the model. see hierarchical_types for the types returned model --- Number of the model` get_model_info(...) `get_model_info( (Project)self, (object)model) -> ModelInfo : Returns a ModelInfo` get_model_infos(...) `get_model_infos( (Project)self) -> list : Returns a list containing a ModelInfo for each model in the project.` get_model_options(...) `get_model_options( (Project)self [, (int)model_number=-1]) -> ModelOptions : Returns the options for the specified model.` get_name(...) `get_name( (Project)self) -> str : Get project name.` get_predictionset_data(...) `get_predictionset_data( (Project)self [, (str)name='' [, (int)number=0]]) -> DataCollection : Get a data collection object of what is included in the predictionset. name --- Name of the predictionset to get data for` get_predictionsets(...) `get_predictionsets( (Project)self) -> list : Returns a list containing the names of all predictionsets.` get_project_options(...) `get_project_options( (Project)self) -> ProjectOptions : Returns the projects options.` get_project_storage(...) `get_project_storage( (Project)self) -> object : Allows for saving data to the project file. The returned object is a simple key-object store which behaves like a dictionary with some restrictions. Keys must be strings and the objects must either be 'bytes' objects or be possible to pickle. Although objects are saved to the project, the project itself is not automatically saved. To save the project, use umetrics.simca.project.save() Note that most objects in the umetrics packages does not support pickling. Note also that instances of this class can only be obtained from umetrics.simca.project.get_project_storage(). Example: >>> storage = project.get_project_storage() >>> storage['first'] = [1,2,3] >>> print(storage['first']) [1, 2, 3] >>> storage['second']=3.6 >>> print(list(storage.keys())) ['first', 'second'] >>> del storage['first'] >>> print(list(storage.keys())) ['second'] >>> # We need to save the project to save the project storage to the project file. >>> project.save()` is_model_fitted(...) `is_model_fitted( (Project)self, (int)model) -> bool : Returns true if a model is fitted model --- The number of the model to test.` merge_datasets(...) `merge_datasets( (Project)self, (object)destination, (object)source) -> None : Merge two datasets and the result will be in the destination dataset. The source dataset will be deleted and all models depending on the source dataset will be removed. see Dataset merge for more options. destination --- The dataset where that will contain the merged result. This can be dataset number or name source --- The source dataset to merge with the destination dataset. This dataset will be removed after merging This can be dataset number or name` new_as_workset(...) `new_as_workset( (Project)self, (int)model) -> Workset : Returns a copy of the workset of the selected model but with a new model number. model --- the number of the model to copy (negative number for mother models)` save(...) `save( (Project)self [, (str)path=None]) -> bool : Saves the project. Returns True if successful. path --- Optional path mainly for in-memory projects. Behaves like save-as if other path than in the open project.` set_active_model(...) `set_active_model( (Project)self, (int)model [, (bool)modelgroup=False]) -> None : Sets the active model. If modelgroup is true, the active model will be set to the mother of the indicated model if it has one, i.e. the whole model group that that the model belongs to will be active. If the model number is negative (i.e. a mother model), the whole group will also be selected.` set_hierarchical_base_type(...) `set_hierarchical_base_type( (Project)self, (int)model, (object)hierarchical_types) -> None : Export vectors from the model to use in new models. If vectors are already exported, the old ones and all dependent models will be deleted. types not not available for the selected model type will be ignored (ex y_orthogonal_res for a PCA model) model --- The number of the model hierarchical_types --- A list of types to export from the model see hierarchical_types` set_predictionset(...) `set_predictionset( (Project)self, (str)name) -> None : Set the predictionset that should be used. name --- Name of the predictionset to use` transpose_dataset(...) `transpose_dataset( (Project)self, (object)dataset) -> None : Transpose a dataset and all models depending on the dataset will be removed. dataset --- The dataset to transpose This can be dataset number or name` Data descriptors defined here: is_batch_project `True if the project is a batch project, otherwise False Closed projects should not be used.` is_open `True if the project is open (still valid), otherwise False Closed projects should not be used.` num_datasets `The number of datasets` Static methods inherited from Boost.Python.instance: __new__(*args, **kwargs) from Boost.Python.class `Create and return a new object. See help(type) for accurate signature.` Data descriptors inherited from Boost.Python.instance: __dict__ __weakref__
class ProjectData(Boost.Python.instance)
`` | `Matrix of data from a project. Each ProjectData has a matrix containing series of values, series names and IDs that identifies the values in each series. Series names are just arbitrary strings that describes each series but value IDs are used to match values from different series with each other and can be variable or observation names or arbitrary (but unique) strings.`
| Method resolution order: ProjectData Boost.Python.instance builtins.object Static methods defined here: __init__(...) `__init__( (object)self, (object)mat, (ValueIDs)value_ids, (object)series_names, (Project)project) -> None : Constructs a ProjectData from a matrix, column ids and series names. mat --- a row major matrix containing the data. value_ids --- identifies the values (columns) in the data. series_names --- the name of each series (row) in the data. project --- The project associated with the data.` __reduce__ = (...) __str__(...) `__str__( (ProjectData)self) -> str` get_id_names(...) `get_id_names( (ProjectData)self [, (bool)desc=True]) -> list : Return the name of the identifiers. desc --- Descriptive name (True) or short name (False). Default is True.` get_value_ids(...) `get_value_ids( (ProjectData)self) -> ValueIDs : Returns the ValueIDs that identifies the values in each series.` matrix(...) `matrix( (ProjectData)self) -> UmMat : Returns a matrix with all values in the project data.` series_names(...) `series_names( (ProjectData)self [, (int)alias=1]) -> list : Returns the series names for the matrix of this project data. alias --- the index of the desired alias (default 1 for primary name)` size_series_aliases(...) `size_series_aliases( (ProjectData)self) -> int : Returns number of row alias names.` Static methods inherited from Boost.Python.instance: __new__(*args, **kwargs) from Boost.Python.class `Create and return a new object. See help(type) for accurate signature.` Data descriptors inherited from Boost.Python.instance: __dict__ __weakref__
class ProjectDataBuilder(Boost.Python.instance)
`` | `Creates project data from model or predictions.`
| Method resolution order: ProjectDataBuilder Boost.Python.instance builtins.object Static methods defined here: __init__(...) `Raises an exception This class cannot be instantiated from Python` __reduce__ = (...) contribution(...) `contribution( (ProjectDataBuilder)self, (ContributionType)type, (int)model, (object)reference=None, (object)group, (object)weight [, (object)comp=None [, (bool)predicted=False [, (str)variable='']]]) -> ProjectData : Creates a contribution project data object. type --- Type of contribution to calculate model --- Model number reference --- Reference group of observations. Or reference list/tuple of ([string]batch, [double]maturity, [string]phase iteration(if the project has phase iterations) for batch projects. Empty batch means average observation at that maturity. group --- Comparison group of observations. Or reference list/tuple of ([string]batch, [double]maturity, [string]phase iteration(if the project has phase iterations) for batch projects. Need at least one observation or (batch, maturity, phase iteration) pair. weight --- A list of weight contributions. At least one weight is necessary. comp --- A list of components matching weights list when loadings are used. predicted --- If the calculations are done on predicted observations. variable --- A Y variable` create(...) `create( (ProjectDataBuilder)self, (str)name [, (int)model=0 [, (int)dataset=0 [, (object)block=None [, (object)comp=None [, (int)selcv=0 [, (str)variable='' [, (str)variable2='' [, (str)observation='' [, (str)batch='' [, (str)phase='' [, (str)identifier='' [, (bool)transformed=False [, (bool)scaled=False [, (bool)aligned=False [, (bool)percomponent=False]]]]]]]]]]]]]]]) -> ProjectData : Creates a project data object. name --- Name of the vector or matrix data to calculate model --- Model number dataset --- Dataset number. Only dataset vectors will be searched if this argument is set block --- Block name. Currently index. comp --- Component argument for score or loading selcv --- Cross validation round in scores variable --- Variable to display observation --- Observation to display batch --- Batch to display phase --- Phase to display identifier --- Secondary observation or variable identifier to display transformed --- If variable should be transformed or back transformed scaled --- If variable should be scaled or unscaled aligned --- If vector should be aligned or unaligned percomponent --- If the vector should be separated as components Available vectors grouped by these types Variables and scores - Vectors with one element per observation. Observations and loadings - Vectors with one element per variable. F(Component) - Function of component, vectors that apply per component. F(Lags) - Function of Lags, vectors specific to a lagged model, such as pLag. Aligned vectors - Aligned vectors available in batch control charts for batch evolution models. Batch vectors - Out Of Control(OOC) variables for the aligned vectors for batch evolution models. Variables and Scores -------------------- CVGroups: The cross-validation group that each observation is assigned to. Arguments: model=<int> Date/Time: The default date or time variable Arguments: model=<int> Date/Time-PS: The default date or time variable Arguments: model=<int> DModX: Distance to the model in X space (row residual SD), after all components (the selected model dimension), for the observations used to fit the model. (A) = Absolute distance. (N) = Normalized distance. (M) = Mpow weighted residuals. Arguments: model=<int>, comp=<predictive comp> DModX: Distance to the model in X space (row residual SD), after all components. (A) = Absolute distance. (N) = Normalized distance. (M) = Mpow weighted residuals. Arguments: model=<int> DModXPS: Distance to the model in the X space (row residual SD), after A components (the selected dimension), for new observations in the predictionset. Displaying component 0, it is the standard deviation of the observations with scaling as specified in the workset times w (correction factor for possible workset observations, see statistical appendix), i.e., it is the distance to the origin of the scaled coordinate system. Arguments: model=<int>, comp=<predictive comp> Arguments: model=<int> DModXPS+: Combination of DModXPS and Hotelling's T2 when the latter is outside the critical limit for observations in the predictionset. Arguments: model=<int>, comp=<predictive comp> DModXPS+ (OPLS): Combination of DModXPS and Hotelling's T2 when the latter is outside the critical limit for observations in the predictionset. Arguments: model=<int> DModY: Distance to the model in the Y space (row residual SD) after all components, for the observations used to fit the model. Arguments: model=<int> DModY: Distance to the model in the Y space (row residual SD) after A components (the selected model dimension) for the observations used to fit the model. If you select component 0, it is the standard deviation of the observations with scaling and centering as specified in the workset. Arguments: model=<int>, comp=<predictive comp> DModYPS: Distance to the model in the Y space (row residual SD) after all components, for observations in the predictionset. Arguments: model=<int> DModYPS: Distance to the model in the Y space (row residual SD) after A components (the selected model dimension) for observations in the predictionset. If you select component 0, it is the standard deviation of the observations with scaling and centering as specified in the workset. Arguments: model=<int>, comp=<predictive comp> LocalCentering: Arguments: model=<int> LocalCenteringPS: Arguments: model=<int> ObsID: Numerical observation identifiers, primary, secondary, batch, or phase. Arguments: model=<int>, identifier=<secondary id name> ObsID: Arguments: dataset=<int>, identifier=<secondary id name> Arguments: identifier=<secondary id name> OLevX: The leverage is a measure of the influence of a point (observation) on the PC model or the PLS model in the X space. The observations leverages are computed as the diagonal elements of the matrix H0 after A dimensions. H0 = T[T'T]-1T'. Arguments: model=<int>, comp=<predictive comp> OLevY: The leverage is a measure of the influence of a point (observation) on the PLS model in the Y space. The observations leverages are computed as the diagonal elements of the matrix Hy after A dimensions. Hy= U[U'U]-1U'. Arguments: model=<int>, comp=<predictive comp> PModX: Probability of belonging to the model in the X space, for observations used to fit the model. Component 0 corresponds to a point model, i.e., the center of the coordinate system. Observations with probability of belonging of less than 5% are considered to be non-members, i.e., they are different from the normal observations used to build the model. Arguments: model=<int>, comp=<predictive comp> PModX: Probability of belonging to the model in the X space, for observations used to fit the model. Observations with probability of belonging of less than 5% are considered to be non-members, i.e., they are different from the normal observations used to build the model. Arguments: model=<int> PModXPS: Probability of belonging to the model in the Y space, for new observations in the predictionset. Observations with probability of belonging of less than 5% are considered to be non-members, i.e., they are different from the normal observations used to build the model. Arguments: model=<int> PModXPS: Probability of belonging to the model in the Y space, for new observations in the predictionset. Component 0 corresponds to a point model, i.e., the center of the coordinate system. Observations with probability of belonging of less than 5% are considered to be non-members, i.e., they are different from the normal observations used to build the model. Arguments: model=<int>, comp=<predictive comp> PModXPS+: Combination of PModXPS and Hotelling's T2 when the latter is outside the critical limit for observations in the predictionset. Arguments: model=<int> PModXPS+: Combination of PModXPS and Hotelling's T2 when the latter is outside the critical limit for observations in the predictionset. Arguments: model=<int>, comp=<predictive comp> PModY: Probability of belonging to the model in the Y space, for observations used to fit the model. Component 0 corresponds to a point model, i.e., the center of the coordinate system. Observations with probability of belonging of less than 5% are considered to be non-members, i.e., they are different from the normal observations used to build the model. Arguments: model=<int> Arguments: model=<int>, comp=<predictive comp> PModYPS: Probability of belonging to the model in the Y space, for new observations in the predictionset. Component 0 corresponds to a point model, i.e., the center of the coordinate system. Observations with probability of belonging of less than 5% are considered to be non-members, i.e., they are different from the normal observations used to build the model. Arguments: model=<int> Arguments: model=<int>, comp=<predictive comp> SerrL: Lower limit of the standard error of the predicted response Y for an observation in the workset. Arguments: model=<int>, comp=<predictive comp>, variable=<y variable name> Arguments: model=<int>, variable=<y variable name> SerrLPS: Lower limit of the standard error of the predicted response Y for a new observation in the predictionset. Arguments: model=<int>, variable=<y variable name> Arguments: model=<int>, comp=<predictive comp>, variable=<y variable name> SerrU: Upper limit of the standard error of the predicted response Y for an observation in the workset. Arguments: model=<int>, variable=<y variable name> Arguments: model=<int>, comp=<predictive comp>, variable=<y variable name> SerrUPS: Upper limit of the standard error of the predicted response Y for a new observation in the predictionset. Arguments: model=<int>, variable=<y variable name> Arguments: model=<int>, comp=<predictive comp>, variable=<y variable name> t: Scores t, one vector for each model dimension, are new variables computed as linear combinations of X. They provide a summary of X that best approximates the variation of X only (PC model), and both approximate X and predict Y (PLS model). Arguments: model=<int>, comp=<predictive comp> T2Range: Hotelling's T2 for the selected range of components. It is a distance measure of how far away an observation is from the center of a PCA or PLS/OPLS model hyperplane. Arguments: model=<int>, comp=(<comp1>, <comp2>) Arguments: model=<int> T2RangePS: Predicted Hotelling's T2 for the selected range of components. Arguments: model=<int>, comp=(<comp1>, <comp2>) Arguments: model=<int> tcv: X score t for the selected model dimension, computed from the selected cross validation round. Arguments: model=<int>, comp=<predictive comp>, selcv=<int> Arguments: model=<int>, comp=<predictive comp> tcvSE: Jack knife standard error of the X score t computed from all rounds of cross validation. Arguments: model=<int>, comp=<predictive comp> tj: Joint scores between groups. Arguments: model=<int>, block=<block>, comp=<joint comp> tj(avg): Joint scores tj averaged over all blocks. Arguments: model=<int>, comp=<joint comp> to: Orthogonal X score to of the X-part of the OPLS model, for the selected component. It summarises the unique X variation, i.e., the X variation orthogonal to Y. Arguments: model=<int>, comp=<xorth comp> tocv: Orthogonal X score to from the X-part of the OPLS model, for a selected model dimension, computed from the selected cross-validation round. Arguments: model=<int>, comp=<xorth comp> tocvSE: Jack knife standard error of the orthogonal X score to from the X-part of the OPLS model, computed from all rounds of cross validation. Arguments: model=<int>, comp=<xorth comp> toPS: Predicted orthogonal X score to of the X-part of the OPLS model, for the observations in the predictionset. Arguments: model=<int>, comp=<xorth comp> tPS: Predicted X score t, for the selected model dimension, for the observations in the predictionset. Arguments: model=<int>, comp=<predictive comp> tPScv: Predicted X score t for the selected model dimension, computed from the selected cross validation round. Arguments: model=<int>, comp=<predictive comp>, selcv=<int> tPScvSE: Jack knife standard error of the X score t, for the observations in the predictionset, computed from all rounds of cross validation. Arguments: model=<int>, comp=<predictive comp> tu: Unique scores tu for one block, one vector for each unique component. They provide a summary that best approximates the variation of the block. Arguments: model=<int>, block=<block>, comp=<Unique comp> u: Scores u, one vector for each model dimension, are new variables summarizing Y so as to maximize the correlation with the X scores t. Arguments: model=<int>, comp=<predictive comp> ucv: Y score u for the selected model dimension, computed from the selected cross validation round. Arguments: model=<int>, comp=<predictive comp> uo: Orthogonal Y score uo of the Y-part of the OPLS model, for the selected component. It summarises the unique Y variation, i.e., the Y variation orthogonal to X. Arguments: model=<int>, comp=<yorth comp> uocv: Orthogonal Y score uo of the Y-part of the OPLS model, computed from the selected cross validation round. Arguments: model=<int>, comp=<yorth comp> VarDS: Variable from the selected dataset, in original units. Available after selecting a DS in the Data box. Arguments: dataset=<int>, variable=<variable name> VarDS: Variable from the selected model, in original units. Arguments: model=<int>, variable=<variable name> VarPS: Variable (X or Y), from the current predictionset, in original units. Available after selecting PS in the Data box. Arguments: variable=<variable name> XVar: X-variable from the workset. Can be displayed in transformed or scaled units. Arguments: model=<int>, variable=<variable name>, transformed=<bool>, scaled=<bool> XVarPred: A reconstructed variable from the workset. For PLS and PCA models, an X-variable from the workset is reconstructed as X=TP'. For OPLS models XVarPred represents the X-values predicted from the given Y- values. Arguments: model=<int>, comp=<predictive comp>, variable=<variable name>, transformed=<bool>, scaled=<bool> Arguments: model=<int>, variable=<variable name>, transformed=<bool>, scaled=<bool> XVarPredPS: A reconstructed variable from the predictionset. For PLS and PCA models, an X-variable from the predictionset is reconstructed as X=TPS * P'. For OPLS models XVarPredPS represents the X-values predicted from the given Y-values. Arguments: model=<int>, comp=<predictive comp>, variable=<variable name>, transformed=<bool>, scaled=<bool> Arguments: model=<int>, variable=<variable name>, transformed=<bool>, scaled=<bool> XVarPS: X-variable from the predictionset. Can be displayed in transformed or scaled units. Arguments: model=<int>, variable=<variable name>, transformed=<bool>, scaled=<bool> XVarPS(coded): Variables from the predictionset. Unlike XVarPS, qualitative variables are listed with there original settings instead of expanded into one column per setting Arguments: model=<int> XVarRes: X-variable residuals for observations in the workset, in original units. Can be displayed in transformed or scaled units. Arguments: model=<int>, comp=<predictive comp>, variable=<variable name>, transformed=<bool>, scaled=<bool> Arguments: model=<int>, comp=(<predictive comp>, <xorth comp>, <yorth comp>), variable=<variable name>, transformed=<bool>, scaled=<bool> XVarResPS: X-variable residuals for observations in the predictionset, in original units. Can be displayed in transformed or scaled units. Arguments: model=<int>, comp=<predictive comp>, variable=<variable name>, transformed=<bool>, scaled=<bool> Arguments: model=<int>, variable=<variable name>, transformed=<bool>, scaled=<bool> XVaResPSST: X-variable residuals for observations in the predictionset, in standardized units (divided by the residual standard deviation). Can be displayed in transformed or scaled units. Arguments: model=<int>, variable=<variable name>, transformed=<bool>, scaled=<bool> XVarResPSSt: X-variable residuals for observations in the predictionset, in standardized units (divided by the residual standard deviation). Can be displayed in transformed or scaled units. Arguments: model=<int>, comp=<predictive comp>, variable=<variable name>, transformed=<bool>, scaled=<bool> XVaResST: X-variable residuals for observations in the workset, in standardized units (divided by the residual standard deviation). Can be displayed in transformed or scaled units. Arguments: model=<int>, variable=<variable name>, transformed=<bool>, scaled=<bool> XVarResSt: X-variable residuals for observations in the workset, in standardized units (divided by the residual standard deviation). Can be displayed in transformed or scaled units. Arguments: model=<int>, comp=<predictive comp>, variable=<variable name>, transformed=<bool>, scaled=<bool> XVarResYRelated: X-variable residuals where the systematic variation orthogonal to Y has been removed. Arguments: model=<int>, variable=<variable name>, transformed=<bool>, scaled=<bool> YPred: Predicted values of Y-variables for observations in the workset, in original units, i.e., back-transformed when transformations are present. Can be displayed in transformed or scaled units. Arguments: model=<int>, comp=<predictive comp>, variable=<y variable name>, transformed=<bool>, scaled=<bool> Arguments: model=<int>, variable=<y variable name>, transformed=<bool>, scaled=<bool> YPredcv: Predicted values of the fitted Ys for observations in the workset, computed from the cross validation procedure. Arguments: model=<int>, variable=<y variable name> Arguments: model=<int>, comp=<predictive comp>, variable=<y variable name> YPredErrcv: Prediction error of the fitted Ys for observations in the workset, computed from the cross validation procedure. Arguments: model=<int>, comp=<predictive comp>, variable=<y variable name> Arguments: model=<int>, variable=<y variable name> YPredPS: Predicted values for Y-variables for observations in the predictionset, in original units, i.e. back transformed when transformations are present. Can be displayed in transformed or scaled units. Arguments: model=<int>, comp=<predictive comp>, variable=<y variable name>, transformed=<bool>, scaled=<bool> Arguments: model=<int>, variable=<y variable name>, transformed=<bool>, scaled=<bool> YPredPSConfInt+: Upper limit for the confidence interval of predicted Ys from the predictionset. The limit is calculated from the cross validation and the confidence level specified in model options. Arguments: model=<int>, comp=<predictive comp>, variable=<y variable name>, transformed=<bool>, scaled=<bool> Arguments: model=<int>, variable=<y variable name>, transformed=<bool>, scaled=<bool> YPredPSConfInt-: Lower limit for the confidence interval of predicted Ys from the predictionset. The limit is calculated from the cross validation and the confidence level specified in model options. Arguments: model=<int>, comp=<predictive comp>, variable=<y variable name>, transformed=<bool>, scaled=<bool> Arguments: model=<int>, variable=<y variable name>, transformed=<bool>, scaled=<bool> YPredPScv: Predicted values of the modelled Ys for observations in the predictionset, computed from the cross validation procedure. Arguments: model=<int>, comp=<predictive comp>, selcv=<int>, variable=<y variable name>, transformed=<bool>, scaled=<bool> Arguments: model=<int>, selcv=<int>, variable=<y variable name>, transformed=<bool>, scaled=<bool> YPredPScvSE: Jack knife standard error of the prediction of Y for observations in the predictionset, computed from all rounds of cross validation. Arguments: model=<int>, variable=<y variable name> Arguments: model=<int>, comp=<predictive comp>, variable=<y variable name> YVar: Y-variable from the workset. Can be displayed in transformed or scaled units. Arguments: model=<int>, variable=<y variable name>, transformed=<bool>, scaled=<bool> YVarPS: Y-variable from the predictionset. Can be displayed in transformed or scaled units. Arguments: model=<int>, variable=<y variable name>, transformed=<bool>, scaled=<bool> YVarRes: Y-variable residuals for observations in the workset, in original units. Can be displayed in transformed or scaled units. Arguments: model=<int>, comp=<predictive comp>, variable=<y variable name>, transformed=<bool>, scaled=<bool> Arguments: model=<int>, variable=<y variable name>, transformed=<bool>, scaled=<bool> YVarResPS: Y-variable residuals for observations in the predictionset, in original units. Can be displayed in transformed or scaled units. Arguments: model=<int>, variable=<y variable name>, transformed=<bool>, scaled=<bool> Arguments: model=<int>, comp=<predictive comp>, variable=<y variable name>, transformed=<bool>, scaled=<bool> YVarResPSSt: Y-variable residuals for observations in the predictionset, in standardized units (divided by the residual standard deviation). Can be displayed in transformed or scaled units. Arguments: model=<int>, comp=<predictive comp>, variable=<y variable name>, transformed=<bool>, scaled=<bool> YVaResPSST: Y-variable residuals for observations in the predictionset, in standardized units (divided by the residual standard deviation). Can be displayed in transformed or scaled units. Arguments: model=<int>, variable=<y variable name>, transformed=<bool>, scaled=<bool> YVarResSt: Y-variable residuals for observations in the workset, in standardized units (divided by the residual standard deviation). Can be displayed in transformed or scaled units. Arguments: model=<int>, comp=<predictive comp>, variable=<y variable name>, transformed=<bool>, scaled=<bool> Observations and Loadings ------------------------- BatchVIP: The Batch Variable Importance plot (Batch VIP) is available for batch level projects. It displays the overall importance of the variable on the final quality of the batch. With phases, the plot displays the importance of a variable by phase. With a PLS model, the Batch VIP displays one plot for each y-variable with a column per selected phase. Note: The Batch VIP is only available when the scores are selected as evolution variables for the primary dataset of the batch level project. Arguments: model=<int>, comp=<predictive comp>, variable=<y variable name> Arguments: model=<int>, comp=<predictive comp>, phase=<phase name> c: For every dimension in the PLS model there is a c vector. It contains the Y loading weights used to linearly combine the Y's to form the Y score vector u. This means the c vector actually expresses the correlation between the Y's and the X score vector t. Arguments: model=<int>, comp=<predictive comp> c(corr): Y loading weight c scaled as a correlation coefficient between Y and u. Arguments: model=<int>, comp=<predictive comp> ccv: Y loading weight c for a selected model dimension, computed from the selected cross-validation round. Arguments: model=<int>, comp=<predictive comp>, selcv=<int> ccvSE: Jack-knife standard error of the Y loading weight c computed from the rounds of cross-validation. Arguments: model=<int>, comp=<predictive comp> co: Orthogonal Y loading weights co combine the Y variables (first dimension) or the Y residuals (subsequent dimensions) to form the scores Uo. These orthogonal Y loading weights are selected so as to minimize the correlation between Uo and T, thereby indirectly between Uo and X. Arguments: model=<int>, comp=<yorth comp> cocv: Orthogonal Y loading weights co from the Y-part of the model, for a selected model dimension, computed from the selected cross-validation round. Arguments: model=<int>, comp=<yorth comp>, selcv=<int> cocvSE: Jack-knife standard error of the orthogonal Y loading weights co, computed from the cross-validation procedure. Arguments: model=<int>, comp=<yorth comp> Coeff: PLS regression coefficients corresponding to the unscaled and uncentered X and Y. This vector is cumulative over all components up to the selected one. Arguments: model=<int>, comp=<predictive comp>, variable=<y variable name> Coeff: OPLS regression coefficients corresponding to the unscaled and uncentered X and Y.This vector is cumulative over all components. Arguments: model=<int>, variable=<y variable name> CoeffC: PLS regression coefficients corresponding to the unscaled but centered X and unscaled Y. This vector is cumulative over all components up to the selected one. Arguments: model=<int>, comp=<predictive comp>, variable=<y variable name> CoeffC: OPLS regression coefficients corresponding to the unscaled but centered X and unscaled Y. This vector is cumulative over all components. Arguments: model=<int>, variable=<y variable name> CoeffCS: PLS regression coefficients corresponding to centered and scaled X, and scaled (but uncentered) Y. This vector is cumulative over all components up to the selected one. Arguments: model=<int>, comp=<predictive comp>, variable=<y variable name> CoeffCS: OPLS regression coefficients corresponding to centered and scaled X, and scaled (but uncentered) Y. This vector is cumulative over all components. Arguments: model=<int>, variable=<y variable name> CoeffCScv: PLS regression coefficients corresponding to the centered and scaled X and the scaled (but uncentered) Y computed from selected the cross- validation round. Arguments: model=<int>, comp=<predictive comp>, selcv=<int>, variable=<y variable name> CoeffCScv: OPLS regression coefficients corresponding to the centered and scaled X and the scaled (but uncentered) Y computed from selected the cross- validation round. Arguments: model=<int>, selcv=<int>, variable=<y variable name> CoeffCScvSE(PLS): Jack-knife standard error of the coefficients CoeffCS computed from all rounds of cross-validation. Arguments: model=<int>, comp=<predictive comp>, variable=<y variable name> CoeffCScvSE(OPLS): Jack-knife standard error of the coefficients CoeffCS computed from all rounds of cross-validation. Arguments: model=<int>, variable=<y variable name> CoeffMLR: PLS regression coefficients corresponding to the scaled and centered X but unscaled and uncentered Y. This vector is cumulative over all components up to the selected one. Arguments: model=<int>, comp=<predictive comp>, variable=<y variable name> CoeffMLR: OPLS regression coefficients corresponding to the scaled and centered X but unscaled and uncentered Y. This vector is cumulative over all components. Arguments: model=<int>, variable=<y variable name> CoeffRot: Rotated PLS regression coefficients corresponding to the unscaled and uncentered X and Y. This vector is cumulative over all components up to the selected one. Arguments: model=<int>, comp=<predictive comp>, variable=<y variable name> CoeffRot: Rotated OPLS regression coefficients corresponding to the unscaled and uncentered X and Y. This vector is cumulative over all components. Arguments: model=<int>, variable=<y variable name> MPowX: The modeling power of variable X is the fraction of its standard deviation explained by the model after the specified component. Arguments: model=<int>, comp=<predictive comp> ObsDS: Observation in the primary or secondary dataset (selected in the Data box) in original units. Arguments: dataset=<int>, observation=<observation name> ObsPS: Observation in the current predictionset, in original units. There is only one defined predictionset at a time. Arguments: observation=<observation name> p: Loadings of the X-part of the model. With a PCA model, the loadings are the coefficients with which the X-variables are combined to form the X scores, t. The loading, p, for a selected PCA dimension, represent the importance of the X-variables in that dimension. With a PLS model, p expresses the importance of the variables in approximating X in the selected component. Arguments: model=<int>, comp=<predictive comp> p(corr): X loading p scaled as a correlation coefficient between X and t. Arguments: model=<int>, comp=<predictive comp> pc(corr): X loading p and Y loading weight c scaled as correlation coefficients between X and t (p) and Y and u (c), and combined to one vector. Arguments: model=<int>, comp=<predictive comp> pccvSE: Jack knife standard error of the combined X loading p and Y loading weight c computed from all rounds of cross-validation. Arguments: model=<int>, comp=<predictive comp> pcv: X loading p for a selected model dimension, computed from the selected cross validation round. Arguments: model=<int>, comp=<predictive comp>, selcv=<int> pcvSE: Jack knife standard error of the X loading p computed from all rounds of cross validation. Arguments: model=<int>, comp=<predictive comp> pj: Loading weight for joint components. Arguments: model=<int>, block=<block>, comp=<joint comp> pj(all): Joint loadings from all blocks combined. Arguments: model=<int>, comp=<joint comp> pj(all)(corr): joint loading pj scaled as a correlation coefficient between the variables and tj from all blocks combined. Arguments: model=<int>, comp=<joint comp> pj(corr): loading pj scaled as a correlation coefficient between the variables and tj. Arguments: model=<int>, block=<block>, comp=<joint comp> po: Orthogonal loading po of the X-part of the OPLS model. po expresses the unique variability in X not found in Y, i.e, X variation orthogonal to Y, in the selected component. Arguments: model=<int>, comp=<xorth comp> po(corr): Orthogonal loading po of the X-part of the OPLS model, scaled as the correlation coefficient between X and to, in the selected component. Arguments: model=<int>, comp=<xorth comp> pocv: Orthogonal loading po of the X-part of the OPLS model, for a selected model dimension, computed from the selected cross validation round. Arguments: model=<int>, comp=<xorth comp>, selcv=<int> pocvSE: Jack knife standard error of the orthogonal loading po of the X-part of the OPLS model, computed from all rounds of cross validation. Arguments: model=<int>, comp=<xorth comp> poso(corr): Loadings of the Y-part of the model (po) and the projection of To on Y (so) concatenated to one vector, scaled as the correlation coefficient between X and to, in the selected component. Arguments: model=<int>, comp=<xorth comp> pq(corr): X loading p and Y loading q scaled as correlation coefficients between X and t (p) and Y and u (q), and combined to one vector. Arguments: model=<int>, comp=<predictive comp> pu: Loadings for the unique components of the block. The loadings are the coefficients with which the variables are combined to form the scores, tu. The loading, pu, for a selected PCA dimension, represent the importance of the X-variables in that dimension. Arguments: model=<int>, block=<block>, comp=<Unique comp> q: Loadings of the Y-part of the PLS/OPLS model. q expresses the importance of the variables in approximating Y variation correlated to X, in the selected component. Y-variables with large q (positive or negative) are highly correlated with t (and X). Arguments: model=<int>, comp=<predictive comp> Q2VX: Predicted fraction, according to cross-validation, of the variation of the X-variables, for the selected component of a PCA model. Arguments: model=<int>, comp=<predictive comp> Q2VXcum: Cumulative predicted fraction, according to cross- validation, of the variation of the X-variables. Arguments: model=<int>, comp=<predictive comp> Q2VY: Predicted fraction, according to cross-validation, of the variation of the Y-variables, for the selected component of a PLS/OPLS model. Arguments: model=<int>, comp=<predictive comp> Q2VYcum: Cumulative predicted fraction, according to cross- validation, of the variation of the Y-variables. Arguments: model=<int>, comp=<predictive comp> qcv: Y loading q for a selected model dimension, computed from the selected cross validation round. Arguments: model=<int>, comp=<predictive comp>, selcv=<int> qcvSE: Jack knife standard error of the Y loading q computed from all rounds of cross validation. Arguments: model=<int>, comp=<predictive comp> qo: Orthogonal loading qo of the Y-part of the OPLS model. qo expresses the unique variability in Y not found in X, i.e, Y variation orthogonal to X, in the selected component. Arguments: model=<int>, comp=<yorth comp> qocv: Orthogonal loading qo of the Y-part of the OPLS model, for a selected model dimension, computed from the selected cross validation round. Arguments: model=<int>, comp=<yorth comp>, selcv=<int> qocvSE: Jack knife standard error of the orthogonal loading qo of the Y-part of the OPLS model, computed from all rounds of cross-validation. Arguments: model=<int>, comp=<yorth comp> r: R is the projection of uo onto X. R contains non-zero entries when the score matrix Uo is not completely orthogonal to X. The norm of this matrix is usually very small but is used to enhance the predictions of X. Arguments: model=<int>, comp=<yorth comp> R2VX: Explained fraction of the variation of the X-variables, for the selected component. Arguments: model=<int>, comp=<predictive comp> R2VXAdj: Explained fraction of the variation of the X-variables, adjusted for degrees of freedom, for the selected component. Arguments: model=<int>, comp=<predictive comp> R2VXAdjcum: Cumulative explained fraction of the variation of the X-variables, adjusted for degrees of freedom. Arguments: model=<int>, comp=<predictive comp> R2VXcum: Cumulative explained fraction of the variation of the X-variables. Arguments: model=<int>, comp=<predictive comp> R2VXj: Explained fraction of the variation of the variables for the joint component and block. Arguments: model=<int>, block=<block>, comp=<joint comp> R2VXjCum: Cumulative explained fraction of the variation of the variables for the joint component and block. Arguments: model=<int>, block=<block>, comp=<joint comp> R2VXu: Explained fraction of the variation of the variables for the unique component and block. Arguments: model=<int>, block=<block>, comp=<Unique comp> R2VXuCum: Cumulative explained fraction of the variation of the variables for the unique component and block. Arguments: model=<int>, block=<block>, comp=<Unique comp> R2VY: Explained fraction of the variation of the Y-variables, for the selected component. Arguments: model=<int>, comp=<predictive comp> R2VYAdj: Explained fraction of the variation of the Y-variables, adjusted for degrees of freedom, for the selected component. Arguments: model=<int>, comp=<predictive comp> R2VYAdjcum: Cumulative explained fraction of the variation of the Y variables, adjusted for degrees of freedom. Arguments: model=<int>, comp=<predictive comp> R2VYcum: Cumulative explained fraction of the variation of the Y variables. Arguments: model=<int>, comp=<predictive comp> RMSEcv: Arguments: model=<int> RMSEcv: Root Mean Square Error, computed from the selected cross validation round. Arguments: model=<int>, comp=<predictive comp> RMSEE: Root Mean Square Error of the Estimation (the fit) for observations in the workset. Arguments: model=<int> Arguments: model=<int>, comp=<predictive comp> RMSEP: Root Mean Square Error of the Prediction for observations in the predictionset. Arguments: model=<int> Arguments: model=<int>, comp=<predictive comp> S2VX: Residual variance of the X-variables, after the selected component, scaled as specified in the workset. Arguments: model=<int>, comp=<predictive comp> S2VY: Residual variance of the Y-variables, after the selected component, scaled as specified in the workset. Arguments: model=<int>, comp=<predictive comp> so: The projection of To on Y. So contains non-zero entries when the score matrix To is not completely orthogonal to Y. The norm of this matrix is usually very small but is used to enhance the predictions of Y. Arguments: model=<int>, comp=<xorth comp> VarID: Arguments: model=<int>, identifier=<secondary id name> VarID: Variable Identifier. Arguments: dataset=<int>, identifier=<secondary id name> VarID: Arguments: identifier=<secondary id name> VIP: (Variable Importance for the Projection) summarizes the importance of the variables both to explain X and to correlate to Y. Terms with VIP>1 have an above average influence on the model. Arguments: model=<int> Arguments: model=<int>, comp=<predictive comp> VIPcv: VIP computed from the selected cross validation round. Arguments: model=<int>, selcv=<int> Arguments: model=<int>, comp=<predictive comp>, selcv=<int> VIPcvSE: Jack knife standard error of the VIP computed from all rounds of cross validation. Arguments: model=<int> Arguments: model=<int>, comp=<predictive comp> VIPorth: (Orthogonal Variable Importance for the Projection) summarizes the importance of the variables explaining the part of X orthogonal to Y. Terms with VIP>1 have an above average influence on the model. Arguments: model=<int> VIPpred: (Predictive Variable Importance for the Projection) summarizes the importance of the variables explaining the part of X related to Y. Terms with VIP>1 have an above average influence on the model. Arguments: model=<int> w: X loading weight that combine the X-variables (first dimension) or the X residuals (subsequent dimensions) to form the scores t. This loading weight is selected so as to maximize the correlation between t and u, thereby indirectly between t and Y. X-variables with large w's (positive or negative) are highly correlated with u (and Y). Arguments: model=<int>, comp=<predictive comp> w*: X loading weight that combines the original X variables (not their residuals in contrast to w) to form the scores t. In the first dimension w* is equal to w. w* is related to the correlation between the X variables and the Y scores u. W* = W(P'W)-1 X-variables with large w* (positive or negative) are highly correlated with u (and Y). Arguments: model=<int>, comp=<predictive comp> w*cv: X loading weight w*, for a selected model dimension, computed from the selected cross validation round. Arguments: model=<int>, comp=<predictive comp>, selcv=<int> w*cvSE: Jack knife standard error of the X loading weight w* computed from all rounds of cross validation. Arguments: model=<int>, comp=<predictive comp> wcv: X loading weight w, for a selected model dimension, computed from the selected cross validation round. Arguments: model=<int>, comp=<predictive comp>, selcv=<int> wcvSE: Jack knife standard error of the X loading weight w computed from all rounds of cross validation. Arguments: model=<int>, comp=<predictive comp> wj: Loading weight for joint components. Arguments: model=<int>, block=<block>, comp=<joint comp> wo: Orthogonal loading weight wo of the X-part of the OPLS model. It combines the X residuals (subsequent dimensions) to form the orthogonal X score to. This loading weight is selected so as to minimize the correlation between to and u, thereby indirectly between to and Y. Arguments: model=<int>, comp=<xorth comp> wocv: Orthogonal loading weight wo of the X-part of the OPLS model, for a selected model dimension, computed from the selected cross validation round. Arguments: model=<int>, comp=<xorth comp>, selcv=<int> wocvSE: Jack knife standard error of the orthogonal loading weight wo of the X-part of the OPLS model, computed from all rounds of cross validation. Arguments: model=<int>, comp=<xorth comp> wu: Loading weight for unique components of one block. Arguments: model=<int>, block=<block>, comp=<Unique comp> Xavg: Averages of X-variables, in original units. If the variable is transformed, the average is in the transformed metric. Arguments: model=<int> XObs: X-variables for the selected observation in the workset in original units. Can be displayed in transformed or scaled units. Arguments: model=<int>, observation=<observation name>, transformed=<bool>, scaled=<bool> XObsPred: Reconstructed observations as X=TP' from the workset. Can be displayed in transformed or scaled units. Arguments: model=<int>, observation=<observation name>, transformed=<bool>, scaled=<bool> Arguments: model=<int>, comp=<predictive comp>, observation=<observation name>, transformed=<bool>, scaled=<bool> XObsPredPS: Reconstructed observations as X=TP' from the predictionset. Can be displayed in transformed or scaled units. Arguments: model=<int>, observation=<observation name>, transformed=<bool>, scaled=<bool> Arguments: model=<int>, comp=<predictive comp>, observation=<observation name>, transformed=<bool>, scaled=<bool> XObsPS: Observations from the predictionset. Can be displayed in transformed or scaled units. Arguments: model=<int>, observation=<observation name>, transformed=<bool>, scaled=<bool> XObsRes: Residuals of observations (X space) in the workset, in original units. Can be displayed in transformed or scaled units. Arguments: model=<int>, observation=<observation name>, transformed=<bool>, scaled=<bool> Arguments: model=<int>, comp=<predictive comp>, observation=<observation name>, transformed=<bool>, scaled=<bool> XObsResPS: Residuals of observations (X space) in the predictionset, in original units. Can be displayed in transformed or scaled units. Arguments: model=<int>, observation=<observation name>, transformed=<bool>, scaled=<bool> Arguments: model=<int>, comp=<predictive comp>, observation=<observation name>, transformed=<bool>, scaled=<bool> Xws: Scaling weights of the X-variables. Arguments: model=<int> Yavg: Averages of Y-variables, in original units. If the variable is transformed, the average is in the transformed metric. Arguments: model=<int> YObs: Y-variables for the selected observation in the workset in original units. Can be displayed in transformed or scaled units. Arguments: model=<int>, observation=<observation name>, transformed=<bool>, scaled=<bool> YObsPS: Observed y variables in the predictionset, can be displayed in transformed or scaled units. Arguments: model=<int>, observation=<observation name>, transformed=<bool>, scaled=<bool> YObsRes: Residuals of observations (Y space) in the workset, in original units. Can be displayed in transformed or scaled units. Arguments: model=<int>, comp=<predictive comp>, observation=<observation name>, transformed=<bool>, scaled=<bool> YObsResPS: Residuals of observations (Y space) in the predictionset, in original units. Can be displayed in transformed or scaled units. Arguments: model=<int>, comp=<predictive comp>, observation=<observation name>, transformed=<bool>, scaled=<bool> Arguments: model=<int>, comp=<predictive comp>, observation=<observation name>, transformed=<bool>, scaled=<bool> YPredPS: Predicted y variables, can be displayed in transformed or scaled units. Arguments: model=<int>, observation=<observation name>, transformed=<bool>, scaled=<bool> Arguments: model=<int>, comp=<predictive comp>, observation=<observation name>, transformed=<bool>, scaled=<bool> YPredPSConfInt+: Upper limit for the confidence interval of predicted Ys from the predictionset. The limit is calculated from the cross validation and the confidence level specified in model options. Arguments: model=<int>, observation=<observation name>, transformed=<bool>, scaled=<bool> Arguments: model=<int>, comp=<predictive comp>, observation=<observation name>, transformed=<bool>, scaled=<bool> YPredPSConfInt-: Lower limit for the confidence interval of predicted Ys from the predictionset. The limit is calculated from the cross validation and the confidence level specified in model options. Arguments: model=<int>, comp=<predictive comp>, observation=<observation name>, transformed=<bool>, scaled=<bool> Arguments: model=<int>, observation=<observation name>, transformed=<bool>, scaled=<bool> YRelatedProfile: Displays the estimated pure profiles of the underlying constituents in X under the assumption of additive Y-variables. Estimation includes a linear transformation of the Coefficient matrix, Bp(BpTBp)-1, where Bp is the Coefficient matrix using only the predictive components to compute the Coefficient matrix (i.e., the components orthogonal to Y are not included in the computation of Bp). Arguments: model=<int>, variable=<y variable name> Yws: Scaling weights of the Y-variables. Arguments: model=<int> F(Component) ------------ Eig: Eigenvalues of the X matrix. Arguments: model=<int> Iter: Number of iterations of the algorithm till convergence. Arguments: model=<int> Q2: Fraction of the total variation of the X block (PCA) or the Y block (PLS) that can be predicted by each component. Arguments: model=<int> Q2(cum)progression: Cumulative Q2 for the extracted components, showing the progression of cumulative values for each added orthogonal component in the OPLS model, e.g. 1+0, 1+1, 1+2. Arguments: model=<int> Q2cum: Cumulative Q2 for the extracted components. Arguments: model=<int> Q2VX: Predicted fraction, according to cross-validation, of the variation of the X-variables, for the selected component of a PCA model. Arguments: model=<int>, variable=<variable name> Q2VXcum: Cumulative predicted fraction, according to cross- validation, of the variation of the X-variables. Arguments: model=<int>, variable=<variable name> Q2VY: Predicted fraction, according to cross-validation, of the variation of the Y-variables, for the selected component of a PLS/OPLS model. Arguments: model=<int>, variable=<y variable name>, percomponent=False To distinguish this vector from the Observation/Loading vector with the same name the 'percomponent' argument must be set to True, otherwise the Observation/Loading vector will be returned. Q2VYcum: Cumulative predicted fraction, according to cross- validation, of the variation of the Y-variables. Arguments: model=<int>, variable=<y variable name>, percomponent=False To distinguish this vector from the Observation/Loading vector with the same name the 'percomponent' argument must be set to True, otherwise the Observation/Loading vector will be returned. R2(cum)progression: Cumulative fraction of Y variation, showing the progression of cumulative values for each added orthogonal component in the OPLS model, e.g. 1+0, 1+1, 1+2. Arguments: model=<int> R2VXcum: Explained fraction of the variation of the X-variables. Arguments: model=<int>, variable=<variable name>, percomponent=False To distinguish this vector from the Observation/Loading vector with the same name the 'percomponent' argument must be set to True, otherwise the Observation/Loading vector will be returned. R2VXAdjcum: Cumulative explained fraction of the variation of the X-variables, adjusted for degrees of freedom. Arguments: model=<int>, variable=<variable name>, percomponent=False To distinguish this vector from the Observation/Loading vector with the same name the 'percomponent' argument must be set to True, otherwise the Observation/Loading vector will be returned. R2VXcum: Cumulative explained fraction of the variation of the X-variables. Arguments: model=<int>, variable=<variable name>, percomponent=False To distinguish this vector from the Observation/Loading vector with the same name the 'percomponent' argument must be set to True, otherwise the Observation/Loading vector will be returned. R2VY: Explained fraction of the variation of the Y-variables, for the selected component. Arguments: model=<int>, variable=<y variable name>, percomponent=False To distinguish this vector from the Observation/Loading vector with the same name the 'percomponent' argument must be set to True, otherwise the Observation/Loading vector will be returned. R2VYAdjcum: Cumulative explained fraction of the variation of the Y variables, adjusted for degrees of freedom. Arguments: model=<int>, comp=<predictive comp>, variable=<y variable name>, percomponent=False To distinguish this vector from the Observation/Loading vector with the same name the 'percomponent' argument must be set to True, otherwise the Observation/Loading vector will be returned. R2VYcum: Cumulative explained fraction of the variation of the Y variables. Arguments: model=<int>, variable=<y variable name>, percomponent=False To distinguish this vector from the Observation/Loading vector with the same name the 'percomponent' argument must be set to True, otherwise the Observation/Loading vector will be returned. R2X: Fraction of the total variation of the X block that can be explained by each component. Arguments: model=<int> R2XAdj: Explained fraction of the variation of the X block, adjusted for degrees of freedom, for the selected component. Arguments: model=<int> R2XAdjcum: Cumulative explained fraction of the variation of the X block, adjusted for degrees of freedom. Arguments: model=<int> R2Xcum: Cumulative explained fraction of the variation of the X block. Arguments: model=<int> R2Xj: Fraction of the total variation of the block that can be explained by each joint component. Arguments: model=<int>, block=<block> R2Xu: Fraction of the total variation of the block that can be explained by each unique component. Arguments: model=<int>, block=<block> R2Y: Fraction of the total variation of the Y block that can be explained by each component. Arguments: model=<int> R2YAdj: Explained fraction of the variation of the Y block, adjusted for degrees of freedom, for the selected component. Arguments: model=<int> R2YAdjcum: Cumulative explained fraction of the variation of the Y block, adjusted for degrees of freedom. Arguments: model=<int> R2Ycum: Cumulative explained fraction of the variation of the Y block. Arguments: model=<int> RMSEcv: Root Mean Square Error, computed from the selected cross validation round. Arguments: model=<int>, variable=<y variable name> RMSEcv-progression: Root Mean Square Error showing the progression of RMSEcv values for each added orthogonal component in the OPLS model, e.g. 1+0, 1+1, 1+2. Arguments: model=<int>, variable=<y variable name> RMSEP: Root Mean Square Error of the Prediction for observations in the predictionset. Arguments: model=<int>, variable=<y variable name> S2X: Variance of the X block. For component number A, it is the residual variance of X after component A. Arguments: model=<int> S2Y: Variance of the Y block. For component number A, it is the residual variance of Y after component A. Arguments: model=<int> SDt: Standard deviation of the X scores, T. Arguments: model=<int> SDu: Standard deviation of the Y scores, U. Arguments: model=<int> SSX: Sum of squares of the X block. For component number A, it is the X residual Sum of Squares after component A. Arguments: model=<int> SSY: Sum of squares of the Y block. For component number A, it is the Y residual Sum of Squares after component A. Arguments: model=<int> YPredErrcvSE: Jack knife standard error of the prediction error of the fitted Ys for observations in the workset, computed from the cross validation rounds. Arguments: model=<int>, variable=<y variable name> F(Lag) ------ CoeffCScvSELag: Jack-knife standard error on the coefficients as a function of lag, computed from all cross-validation rounds. Arguments: model=<int>, comp=<predictive comp>, variable=<variable name>, variable2=<y variable name> CoeffCSLag: Coefficients (for scaled and centered data) of a lagged variable x, for a selected Y as a function of lags. Arguments: model=<int>, comp=<predictive comp>, variable=<variable name>, variable2=<y variable name> LagID: The lagged steps of a lagged variable X. Arguments: model=<int>, variable=<variable name> pLag: X loading p of a lagged variable X, as a function of lags. Arguments: model=<int>, comp=<predictive comp>, variable=<variable name> VIPLag: VIP of a lagged variable X as a function of lags. Arguments: model=<int>, comp=<predictive comp>, variable=<variable name> VIPLag: Arguments: model=<int>, variable=<variable name> VIPorthLag: Arguments: model=<int>, variable=<variable name> VIPpredLag: Arguments: model=<int>, variable=<variable name> w*Lag: X loading weight w* of a lagged variable as a function of lags Arguments: model=<int>, comp=<predictive comp>, variable=<variable name> wLag: X loading weight w of a lagged variable as a function of lags Arguments: model=<int>, comp=<predictive comp>, variable=<variable name> Aligned Vectors --------------- DModX Aligned: Distance to the model in X space (row residual SD), after A components (the selected model dimension), for the observations used to fit the model. If you select component 0, it is the standard deviation of the observations with scaling and centering as specified in the workset , i.e., it is the distance to the origin of the scaled coordinate system. (A) = Absolute distance. (N) = Normalized distance. (M) = Mpow weighted residuals. Arguments: model=<int>, comp=<predictive comp>, batch=<batch name>, aligned=True DModX Aligned: Distance to the model in X space (row residual SD), after all components, for the observations used to fit the model. (A) = Absolute distance. (N) = Normalized distance. (M) = Mpow weighted residuals. Arguments: model=<int>, batch=<batch name>, aligned=True DModXPS Aligned: Distance to the model in the X space (row residual SD), after A components (the selected dimension), for new observations in the predictionset. Displaying component 0, it is the standard deviation of the observations with scaling as specified in the workset times w (correction factor for possible workset observations, see statistical appendix), i.e., it is the distance to the origin of the scaled coordinate system. Arguments: model=<int>, comp=<predictive comp>, batch=<batch name>, aligned=True DModXPS Aligned: Distance to the model in the X space (row residual SD), after all components , for new observations in the predictionset. Arguments: model=<int>, batch=<batch name>, aligned=True t: Scores t, one vector for each model dimension, are new variables computed as linear combinations of X. They provide a summary of X that best approximates the variation of X only (PC model), and both approximate X and predict Y (PLS model). Arguments: model=<int>, comp=<predictive comp>, batch=<batch name>, aligned=True T2Range: Hotelling's T2 for the selected range of components. It is a distance measure of how far away an observation is from the center of a PCA or PLS/OPLS model hyperplane. Arguments: model=<int>, batch=<batch name>, aligned=True Arguments: model=<int>, comp=(<comp1>, <comp2>), batch=<batch name>, aligned=True T2RangePS: Predicted Hotelling's T2 for the selected range of components. Arguments: model=<int>, comp=(<comp1>, <comp2>), batch=<batch name>, aligned=True Arguments: model=<int>, batch=<batch name>, aligned=True Time/Maturity: Time or Maturity variable determining the end point of a Batch/Phase and used as Y in the observation level models. This variable is used to align Batch/Phase to the median length. Arguments: model=<int>, aligned=True Time/MaturityPS: Time or Maturity variable determining the end point of a Batch/Phase and used as Y in the observation level models. This variable is used to align Batch/Phase to the median length. Arguments: model=<int>, aligned=True to: Orthogonal X score to of the X-part of the OPLS model, for the selected component. It summarises the unique X variation, i.e., the X variation orthogonal to Y. Arguments: model=<int>, comp=<xorth comp>, batch=<batch name>, aligned=True toPS: Predicted orthogonal X score to of the X-part of the OPLS model, for the observations in the predictionset. Arguments: model=<int>, comp=<xorth comp>, batch=<batch name>, aligned=True tPS: Predicted X score t, for the selected model dimension, for the observations in the predictionset. Arguments: model=<int>, comp=<predictive comp>, batch=<batch name>, aligned=True XVar: X-variable from the workset. Can be displayed in transformed or scaled units. Arguments: model=<int>, variable=<variable name>, batch=<batch name>, transformed=<bool>, scaled=<bool>, aligned=True XVarPS: X-variable from the predictionset. Can be displayed in transformed or scaled units. Arguments: model=<int>, variable=<variable name>, batch=<batch name>, transformed=<bool>, scaled=<bool>, aligned=True YPred: Predicted values of Y-variables for observations in the workset, in original units, i.e., back-transformed when transformations are present. Can be displayed in transformed or scaled units. Arguments: model=<int>, comp=<predictive comp>, variable=<y variable name>, batch=<batch name>, transformed=<bool>, scaled=<bool>, aligned=True Arguments: model=<int>, variable=<y variable name>, batch=<batch name>, transformed=<bool>, scaled=<bool>, aligned=True YPredPS: Predicted values for Y-variables for observations in the predictionset, in original units, i.e. back transformed when transformations are present. Can be displayed in transformed or scaled units. Arguments: model=<int>, comp=<predictive comp>, variable=<y variable name>, batch=<batch name>, transformed=<bool>, scaled=<bool>, aligned=True Arguments: model=<int>, variable=<y variable name>, batch=<batch name>, transformed=<bool>, scaled=<bool>, aligned=True YVar: Y-variable from the workset. Can be displayed in transformed or scaled units. Arguments: model=<int>, variable=<y variable name>, batch=<batch name>, transformed=<bool>, scaled=<bool>, aligned=True YVarPS: Y-variable from the predictionset. Can be displayed in transformed or scaled units. Arguments: model=<int>, variable=<y variable name>, batch=<batch name>, transformed=<bool>, scaled=<bool>, aligned=True Batch Vectors ------------- DModXAlignedOOCSum: For a plot of DModX vs. Maturity for a batch in the workset the out of control (OOC) sum is the area outside the control limits expressed as a percentage of the total area. The summation is made relative to a DModX vector aligned to median length. Arguments: model=<int>, comp=<predictive comp> DModXPSAlignedOOCSum: For a plot of DModX vs. Maturity for a batch in the predictionset the out of control (OOC) sum is the area outside the control limits expressed as a percentage of the total area. Arguments: model=<int>, comp=<predictive comp> T2RangeAlignedOOCSum: For a plot of T2Range vs. Maturity for a batch in the workset the out of control (OOC) sum is the area outside the control limits expressed as a percentage of the total area. The summation is made relative to a T2Range vector aligned to median length. Arguments: model=<int>, comp=(<comp1>, <comp2>) T2RangePSAlignedOOCSum: For a plot of T2Range vs. Maturity for a batch in the predictionset the out of control (OOC) sum is the area outside the control limits expressed as a percentage of the total area. The summation is made relative to a T2RangePS vector aligned to median length. Arguments: model=<int>, comp=(<comp1>, <comp2>) TAlignedOOCSum: For a plot of scores (t) vs. Maturity for a batch in the workset the out of control (OOC) sum is the area outside the control limits expressed as a percentage of the total area. The summation is made relative to a t vector aligned to median length. Arguments: model=<int>, comp=<predictive comp> TOAlignedOOCSum: For a plot of orthogonal scores (to) vs. Maturity for a batch in the workset the out of control (OOC) sum is the area outside the control limits expressed as a percentage of the total area. The summation is made relative to a to vector aligned to median length. Arguments: model=<int>, comp=<xorth comp> TOPSAlignedOOCSum: For a plot of orthogonal scores (to) vs. Maturity for a batch in the predictionset the out of control (OOC) sum is the area outside the control limits expressed as a percentage of the total area. The summation is made relative to a toPS vector aligned to median length. Arguments: model=<int>, comp=<xorth comp> TPSAlignedOOCSum: For a plot of scores (t) vs. Maturity for a batch in the predictionset the out of control (OOC) sum is the area outside the control limits expressed as a percentage of the total area. The summation is made relative to a tPS vector aligned to median length. Arguments: model=<int>, comp=<predictive comp> XVarAlignedOOCSum: For a plot of an X-variable vs. Maturity for a batch in the workset the out of control (OOC) sum is the area outside the control limits expressed as a percentage of the total area. The summation is made relative to an XVar vector aligned to median length. Arguments: model=<int>, variable=<variable name>, transformed=<bool>, scaled=<bool> XVarPSAlignedOOCSum: For a plot of an X-variable vs. Maturity for a batch in the predictionset the out of control (OOC) sum s the area outside the control limits expressed as a percentage of the total area. The summation is made relative to an XVarPS vector aligned to median length. Arguments: model=<int>, variable=<variable name>, transformed=<bool>, scaled=<bool> YPredAlignedOOCSum: For a plot of YPred vs. Maturity for a batch in the workset the out of control (OOC) sum is the area outside the control limits expressed as a percentage of the total area. The summation is made relative to an YPred vector aligned to median length. Arguments: model=<int>, comp=<predictive comp>, variable=<y variable name>, transformed=<bool>, scaled=<bool> YPredPSAlignedOOCSum: For a plot of YPred vs. Maturity for a batch in the predictionset the out of control (OOC) sum is the area outside the control limits expressed as a percentage of the total area. The summation is made relative to an YPredPS vector aligned to median length. Arguments: model=<int>, comp=<predictive comp>, variable=<y variable name>, transformed=<bool>, scaled=<bool>` limits(...) `limits( (ProjectDataBuilder)self, (str)name, (int)model [, (float)probability=0.95 [, (object)comp=None]]) -> ProjectData : Returns critical limits indicating god process conditions. Any value exceding these limits is a potential outlier. name --- Name of the critical limit (see below). model --- Model number. probability --- The probability level of the limit. comp --- The components for the requested limit(s). This can be None (returns limits for all available components), a sinle component or a sequence of components (list, set etc). Available limits are: DModXCrit : The critical upper limit for absolute DModX at the specified significanse level. T2Crit : The critical upper limit of T2Range for the specified significance level and component range. TCrit : The critical limit of T(scores) at the specified significance level and component(s). The confidence interval is + / -the returned value. ToCrit : The critical limit of To(scores orthogonal to Y) at the specified significance level and orthogonal component(s). The confidence interval is + / -the returned value. This is only available for OPLS/O2PLS models with at least one orthogonal component.` moca_score_correlation(...) `moca_score_correlation( (ProjectDataBuilder)self, (int)model) -> ProjectData : Create a MOCA Score correlation matrix model --- Model number` moca_score_variable_correlation(...) `moca_score_variable_correlation( (ProjectDataBuilder)self, (int)model) -> ProjectData : Create a MOCA Score and variable correlation matrix model --- Model number` Data and other attributes defined here: ContributionType = <class 'umetrics.simca.ContributionType'> `Different contribution types scores --- Contributions for scores dmodx --- Contributions for distance to model X dmody --- Contributions for distance to model Y` WeightType = <class 'umetrics.simca.WeightType'> `Different contribution weight types normalized --- Normalized contributions p --- Weighted with P po --- Weighted with Po. wstar --- Weighted with W* rx --- Weighted with RX ry --- Weighted with RY coeffcs --- Weighted with CoeffCS coeffcsraw --- Weighted with CoeffCSRaw raw --- Weighted with Raw vip --- Weighted with VIP` Static methods inherited from Boost.Python.instance: __new__(*args, **kwargs) from Boost.Python.class `Create and return a new object. See help(type) for accurate signature.` Data descriptors inherited from Boost.Python.instance: __dict__ __weakref__
class ProjectHandler(Boost.Python.instance)
`` | `Handles creating new projects, opening projects, closing projects. If python runs embedded, open projects might not be used by the application until it is activated. See documentation for the application object.`
| Method resolution order: ProjectHandler Boost.Python.instance builtins.object Static methods defined here: __init__(...) `Raises an exception This class cannot be instantiated from Python` __reduce__ = (...) close_all_projects(...) `close_all_projects() -> bool : Closes all currently open projects. Returns False if all project could not be closed, otherwise True. If the method succeeds, all Project instances will be closed and should not be used.` close_project(...) `close_project( (Project)arg1 [, (bool)confirm=True]) -> bool : Closes the project. Returns False if the project could not be closed, otherwise True. If the method succeeds, the supplied project will be closed and should not be used. If confirm is True, the application (if running embedded) will get confirmation from the user before closing the project if it has unsaved data. If it is False any unsaved data will be lost.` create_project(...) `create_project([ (str)projectfilepath=None]) -> Project : Create a new empty project. The new project will use 'projectfilepath' as the new project file. The project file must not exist. If 'projectfilepath' is ommitted, an in-memory project is created. Note that it is not possible to set in-memory prjects as active in SIMCA. projectfilepath --- The project file path and name including '.usp' extension` open_project(...) `open_project( (str)projectpath) -> Project : Opens a project or returns an existing project if it is already open.` save_project_as(...) `save_project_as( (Project)project, (str)newprojectpath) -> bool : Saves the project to a new file. If the new file already exists, it will be overwritten. Returns True if successful and False if it fails in which case the project is still open using the old file).` Static methods inherited from Boost.Python.instance: __new__(*args, **kwargs) from Boost.Python.class `Create and return a new object. See help(type) for accurate signature.` Data descriptors inherited from Boost.Python.instance: __dict__ __weakref__
class ProjectOptions(Boost.Python.instance)
`` | `Options specific to a project.`
| Method resolution order: ProjectOptions Boost.Python.instance builtins.object Static methods defined here: __init__(...) `Raises an exception This class cannot be instantiated from Python` __reduce__ = (...) __str__(...) `__str__( (ProjectOptions)self) -> str` apply(...) `apply( (ProjectOptions)arg1) -> None : Applies the current settings.` Data descriptors defined here: bring_secondary_ids_to_batch_level `If True, secondary variable IDs will be kept in batch level datasets created from raw data and statistics.` cut_long_extrapolate_short_batches `If True, long batches will be cut to median length and short batches extrapolated to median length when batch level datasets are created.If False, all data for long batches will be kept and short batches will be padded with missing values.` keep_whole_batches_in_the_same_cv_group `If True, all observations belonging to the same batch will be grouped in the same cross validation group when batch evolution models are fitted. If False, default CV groups are used (every nth observation in the same group).` use_average_batch_for_missing_phases `If True, average batch will be used for missing phases when batch level datasets are created. If False, missing values will be used.` Static methods inherited from Boost.Python.instance: __new__(*args, **kwargs) from Boost.Python.class `Create and return a new object. See help(type) for accurate signature.` Data descriptors inherited from Boost.Python.instance: __dict__ __weakref__
class TrimOptions(Boost.Python.instance)
`` | `Trimming options.`
| Method resolution order: TrimOptions Boost.Python.instance builtins.object Static methods defined here: __init__(...) `Raises an exception This class cannot be instantiated from Python` __reduce__ = (...) exclude_observation(...) `exclude_observation( (float)limit [, (int)limit_type=1 [, (object)phase=None]]) -> ExcludeStruct : Exclude observations outside limits. limit_type - optional (default is 'value'), defines how the limit is interpreted, see trimming_type limit - the low limit phase - The name of a phase or list of phase names. If this parameter is set, only observations belonging to that phase will be trimmed.` last_good_value(...) `last_good_value( (float)limit [, (int)limit_type=1 [, (object)phase=None]]) -> ExcludeStruct : Replace values with the last good value. limit_type - optional (default is 'value'), defines how the limit is interpreted, see trimming_type limit - the low limit phase - The name of a phase or list of phase names. If this parameter is set, only observations belonging to that phase will be trimmed.` missing_value(...) `missing_value( (float)limit [, (int)limit_type=1 [, (object)phase=None]]) -> ExcludeStruct : Replace values with missing values NaN. limit_type - optional (default is 'value'), defines how the limit is interpreted, see trimming_type limit - the low limit phase - The name of a phase or list of phase names. If this parameter is set, only observations belonging to that phase will be trimmed.` new_value(...) `new_value( (float)limit, (float)new_value [, (int)limit_type=1 [, (int)value_type=1 [, (object)phase=None]]]) -> ExcludeStruct : Replace values with another value. Tip: If you want to set a different value_type you can use 'keyword arguments', for example: new_value(10, 50, value_type=trimming_type.percent), this way you can omit setting limit_type instead of having to set it to its default value. limit - the low limit new_value - what we replace with limit_type - optional (default is 'value'), defines how the limit is interpreted, see trimming_type value_type - value, percent of stdDev, other types are not allowed, optional (default is 'value'), defines how new_value is interpreted, see trimming_type phase - The name of a phase or list of phase names. If this parameter is set, only observations belonging to that phase will be trimmed.` Static methods inherited from Boost.Python.instance: __new__(*args, **kwargs) from Boost.Python.class `Create and return a new object. See help(type) for accurate signature.` Data descriptors inherited from Boost.Python.instance: __dict__ __weakref__
class UmMat(Boost.Python.instance)
`` | `A row major matrix of floating point numbers. Can in most cases be treated as a list of UmVec. The matrix can be made 'jagged' (all rows are not the same length) but matrices returned by SIMCA will always have rows that are the same length.`
| Method resolution order: UmMat Boost.Python.instance builtins.object Static methods defined here: __contains__(...) __copy__(...) __deepcopy__(...) __delitem__(...) __eq__(...) __getitem__(...) __init__(...) __iter__(...) __len__(...) __reduce__ = (...) __repr__(...) __setitem__(...) __str__(...) append(...) extend(...) Data and other attributes defined here: __instance_size__ = 48 Static methods inherited from Boost.Python.instance: __new__(*args, **kwargs) from Boost.Python.class `Create and return a new object. See help(type) for accurate signature.` Data descriptors inherited from Boost.Python.instance: __dict__ __weakref__
class UmVec(Boost.Python.instance)
`` | `Can in most cases be threated as a list of floating point numbers. The class uses less memory than a list of numbers because the numbers are single precision and they are not Python objects.`
| Method resolution order: UmVec Boost.Python.instance builtins.object Static methods defined here: __contains__(...) __copy__(...) __deepcopy__(...) __delitem__(...) __eq__(...) __getitem__(...) __init__(...) __iter__(...) __len__(...) __reduce__ = (...) __repr__(...) __setitem__(...) __str__(...) append(...) extend(...) Data and other attributes defined here: __instance_size__ = 88 Static methods inherited from Boost.Python.instance: __new__(*args, **kwargs) from Boost.Python.class `Create and return a new object. See help(type) for accurate signature.` Data descriptors inherited from Boost.Python.instance: __dict__ __weakref__
class ValueIDs(Boost.Python.instance)
`` | `Identifies values in a ProjectData. The IDs can be variable names, observation names or arbitrary strings. If the names are arbitrary strings, the strings must be unique`
| Method resolution order: ValueIDs Boost.Python.instance builtins.object Static methods defined here: __init__(...) `__init__( (object)self, (object)ids, (int)id_type, (Project)project) -> None : ids --- A sequence of strings that are used to identify values in a ProjectData id_type --- Specifies what 'ids' refer to (see id_type enum). project --- The project that the ValueIDs instance is to be used with.` __len__(...) `__len__( (ValueIDs)arg1) -> int` __reduce__ = (...) __str__(...) `__str__( (ValueIDs)self) -> str` get_id_names(...) `get_id_names( (ValueIDs)self [, (bool)desc=True]) -> list : Return the name of the identifiers. desc --- Descriptive name (True) or short name (False). Default is True.` get_names(...) `get_names( (ValueIDs)self [, (int)alias=1]) -> list : Returns the value names. alias --- the index of the desired alias (default 1 for primary name)` get_type(...) `get_type( (ValueIDs)self) -> id_type : Returns the id_type.` size_aliases(...) `size_aliases( (ValueIDs)self) -> int : Returns the number of aliases` Data and other attributes defined here: id_type = <class 'umetrics.simca.id_type'> `Used in ValueIDs constructor to specify what the id's refer to. observation --- Strings are observation names. variable --- Strings are variable names. other --- Strings are arbitrary strings.` Static methods inherited from Boost.Python.instance: __new__(*args, **kwargs) from Boost.Python.class `Create and return a new object. See help(type) for accurate signature.` Data descriptors inherited from Boost.Python.instance: __dict__ __weakref__
class Workset(Boost.Python.instance)
`` | `Defines the data used to create a model. The class defines which data to include and scaling, transformations etc. To create a new unfitted model from the data, call create_model`
| Method resolution order: Workset Boost.Python.instance builtins.object Static methods defined here: __init__(...) `Raises an exception This class cannot be instantiated from Python` __reduce__ = (...) __str__(...) `__str__( (Workset)self) -> str` add_dataset(...) `add_dataset( (Workset)self, (object)dataset) -> None : Add a new dataset to the workset. dataset --- Contains the dataset number or name of the dataset to add to the workset.` auto_transform(...) `auto_transform( (Workset)self, (object)variables, (bool)all_if_any) -> None : Set the transform for the selected variables . variables --- The IDs of the variable(s). It can an either be an index, a variable name, a list of indices or names or an empty list to set all. all_if_any --- True to log transform all variables if at least one needs it, False to only transform those who really need it.` clear_trim_variables(...) `clear_trim_variables( (Workset)self) -> None : Removes all trim options added through trim_variable.` create_model(...) `create_model( (Workset)self [, (bool)check_missing_values=True [, (bool)remove_bad_data=True]]) -> list : Creates a new model from the current specification. check_missing_values --- if true, observations/variables that has more missing values then the limit will be excluded See set_missing_obs_percent and set_missing_var_percent remove_bad_data --- if true rows with no variance and/or a lot of missing values are excluded if false no rows are excluded (check_missing_values parameter is ignored) Returns a list with the numbers of the new models.` create_phase_cropper(...) `create_phase_cropper( (Workset)self) -> PhaseCropper : Create an object to specify the cropping of batches.` crop_phases(...) `crop_phases( (Workset)self, (object)phases, (PhaseCropper)cropper) -> None : Crop phases in a batch model. phases --- Contains the phase names. The parameter is the name of the phase as a string or a list of names or empty list to get all. cropper --- A PhaseCropper object to define cropping of the phases.` exclude_batches(...) `exclude_batches( (Workset)self, (object)batch) -> None : Exclude batches from a batch model. batches --- Contains the batch name or index. The parameter is the name of the batch as a string or a list of names or the indices or empty list to get all.` exclude_obs(...) `exclude_obs( (Workset)self, (object)observations) -> None : observations --- Contains the IDs or index of the observations that should be excluded. The parameter can either be a list of indices or the observation names.` exclude_phases(...) `exclude_phases( (Workset)self, (object)phases) -> None : Exclude phases from a batch model. phases --- Contains the phase names. The parameter is the name of the phase as a string or a list of names or empty list to get all.` exclude_variables(...) `exclude_variables( (Workset)self, (object)variables [, (object)expanded2=-1 [, (object)expanded3=-1 [, (object)Lag=0 [, (object)lag_time_variable=[] [, (object)lag_speed_variable=[]]]]]]) -> None : Excludes a variable variables --- Contains the IDs of the variables that should be excluded from the workset. The parameter can either be a list of indices or the variable names or empty list to get all.expanded2 --- The second variable in an expanded term only one at a time -1 or empty string if it is not used expanded3 --- The third variable in an expanded term only one at a time -1 or empty string if it is not used Lags --- The number of steps or the distance the variable is lagged or a list of steps or distances if expanded terms are supplied. --- default 0, if lag_distance_variable is supplied Lags is the dist else it is the number of stepslag_time_variable --- The name of the time variable used to calculate dynamic lags. The parameter can either be a list of indices or the variable names empty for lag by steps otherwise the same length as Lag lag_speed_variable --- The name of the speed variable used to calculate dynamic lags. The parameter can either be a list of indices or the variable names empty for lag by steps otherwise the same length as Lag` get_class_phase_names(...) `get_class_phase_names( (Workset)self) -> list : return a list with the names of the used classes or phases in this workset` get_data_collection(...) `get_data_collection( (Workset)self) -> DataCollection : Returns the data collection used as source for this model.` get_included_obs(...) `get_included_obs( (Workset)self) -> list : return a list with indexes of included observations` get_missing_obs_percent(...) `get_missing_obs_percent( (Workset)self) -> float : Get how many percent missing that is allowed for observations. Returns a value between 0 and 100` get_missing_var_percent(...) `get_missing_var_percent( (Workset)self) -> float : Get how many percent missing that is allowed for variables. Returns a value between 0 and 100` get_obs_class_phase_numbers(...) `get_obs_class_phase_numbers( (Workset)self) -> list : return a list with the class or phase number for each observation excluded observations are also returned by this function` get_observations(...) `get_observations( (Workset)self [, (int)alias=1]) -> list : Returns a list of the observations included in the workset. alias --- which observation alias to use starting with 1. Default=1.` get_transform_constants(...) `get_transform_constants( (Workset)self, (object)variable) -> list : returns a list with the transform constants a, b and c for a variable. See transformtype enum for more information variable --- Contains the index or name of a variable.` get_type(...) `get_type( (Workset)self) -> modeltype : Get the type of the new model. Returns the type of the model to be created. See "modeltype" for different types.` get_variable_class(...) `get_variable_class( (Workset)self, (object)variable) -> list : Gets the class a variable belongs to variable --- Contains the ID of the variable that should be get class names for. The parameter can either be an index or the variable name or empty list to get all. Returns a list of class names for the variable` get_variable_phase(...) `get_variable_phase( (Workset)self, (object)variables) -> list : Gets the phase a variable belongs to variable --- Contains the ID of the variable that should be get class names for. The parameter can either be an index or the variable name or empty list to get all. Returns a list of names of phases that a variable belongs to.` get_variable_scale_block(...) `get_variable_scale_block( (Workset)self, (object)variable) -> int : returns the scaling block number for a variable. variable --- Contains the index or name of a variable.` get_variable_scale_block_weight(...) `get_variable_scale_block_weight( (Workset)self, (object)variable) -> blockscaletype : returns the block scaling weight for a variable. See blockscaletype enum for return value types variable --- Contains the index or name of a variable.` get_variable_scale_modifier(...) `get_variable_scale_modifier( (Workset)self, (object)variable) -> float : returns the scale modifier for a variable. variable --- Contains the index or name of a variable.` get_variable_scale_type(...) `get_variable_scale_type( (Workset)self, (object)variable) -> scaletype : returns the base scale type for a variable. See scaletype enum for return value types variable --- Contains the index or name of a variable.` get_variable_transform(...) `get_variable_transform( (Workset)self, (object)variable) -> transformtype : returns the transform for a variable. See transformtype enum for return value types variable --- Contains the index or name of a variable.` get_x_variables(...) `get_x_variables( (Workset)self [, (int)alias=1]) -> list : Returns a list the of X variables used by the workset. This does not include expanded and/or laged variables. alias --- which variable alias to use starting with 1. Default=1` get_y_treatment(...) `get_y_treatment( (Workset)self, (object)variables) -> list : For a batch evolution model, get info of how the Y variable is treated variables --- Contains the IDs of the variables that should be tested. The parameter can either be a list of indices or variable names. Returns a list of Y treatments for the variables See ytreatment enum for different options.` get_y_variables(...) `get_y_variables( (Workset)self [, (int)alias=1]) -> list : Returns a list of Y variables used by the workset. This does not include laged variables. alias --- which variable alias to use starting with 1. Default=1.` include_batches(...) `include_batches( (Workset)self, (object)batches) -> None : Include batches from a batch model. batches --- Contains the batch name or index. The parameter is the name of the batch as a string or a list of names or the indices or empty list to get all.` include_obs(...) `include_obs( (Workset)self, (object)observations) -> None : observations --- Contains the IDs or index of the observations that should be included. The parameter can either be a list of indices or the observation names.` include_phases(...) `include_phases( (Workset)self, (object)phases) -> None : Include phases that has been excluded or cropped in a batch model. phases --- Contains the phase names. The parameter is the name of the phase as a string or a list of names or empty list to get all.` is_batch_excluded(...) `is_batch_excluded( (Workset)self, (object)batches) -> list : Returns if a batch is excluded or not. batches --- Contains the batch name or index. The parameter is the name of the batch as a string or a list of names or indices. or empty list to get all. Returns a list with True for excluded batches, otherwise False` is_phase_cropped(...) `is_phase_cropped( (Workset)self, (object)phases) -> list : Returns if a phase is cropped. phases --- Contains the phase names. The parameter is the name of the phase as a string or a list of names or empty list to get all. Returns a list with True for cropped phases, otherwise False` is_phase_excluded(...) `is_phase_excluded( (Workset)self, (object)phases) -> list : Returns if a phase is excluded or not. phases --- Contains the phase names. The parameter is the name of the phase as a string or a list of names or empty list to get all. Returns a list with True for excluded phases, otherwise False` is_variable_excluded(...) `is_variable_excluded( (Workset)self, (object)variables) -> list : Test if a variable is excluded variables --- Contains the IDs of the variables to test if excluded from the workset. The parameter can either be a list of indices or the variable names or empty list to get all. Returns a list with True if excluded otherwise False` is_x(...) `is_x( (Workset)self, (object)variables [, (object)expanded2=-1 [, (object)expanded3=-1 [, (object)Lag=0 [, (object)lag_time_variable=[] [, (object)lag_speed_variable=[]]]]]]) -> list : Returns if a variable is X or Y variables --- Contains the IDs of the variables that should be checked if X variables. The parameter can either be a list of indices or the variable names or empty list to get all. expanded2 --- The second variable in an expanded term only one at a time -1 or empty string if it is not used expanded3 --- The third variable in an expanded term only one at a time -1 or empty string if it is not used Lags --- The number of steps or the distance the variable is lagged or a list of steps or distances if expanded terms are supplied. --- default 0, if lag_distance_variable is supplied Lags is the dist else it is the number of stepslag_time_variable --- The name of the time variable used to calculate dynamic lags. The parameter can either be a list of indices or the variable names empty for lag by steps otherwise the same length as Lag lag_speed_variable --- The name of the speed variable used to calculate dynamic lags. The parameter can either be a list of indices or the variable names empty for lag by steps otherwise the same length as Lag Returns a list with true if X or false if Y or excluded` recreate_batch_level(...) `recreate_batch_level( (Workset)self [, (bool)fullrecreate=True [, (bool)check_missing_values=True [, (bool)remove_bad_data=True]]]) -> tuple : Tries to recreate dependent batch level datasets and models. This method can only be used if the workset is a batch-evolution workset and was created by calling umetrics.simca.Project.new_as_workset() or umetrics.simca.Project.edit_workset() The batch evolution model(s) and any recreated models will be auto-fitted. fullrecreate --- if true, all batch level datasets and models will be recreated and fitted the same aligned maturity vector is reused, this is not recommended if batches that differs a lot in length are added if false, the batch level datasets are recreated and a unfitted default batch level model is created check_missing_values --- if true, observations/variables that has more missing values then the limit will be excluded See set_missing_obs_percent and set_missing_var_percent remove_bad_data --- if true rows with no variance and/or a lot of missing values are excluded if false no rows are excluded (check_missing_values parameter is ignored) Returns a list with the numbers of the new models. The method returns a tuple containing: - A string containing information of problems encountered when the datasets/models was recreated. If no problems was encountered the string is empty. - A list containing the numbers of the recreated models. - A list containing the numbers of the recreated datasets.` set_class_name(...) `set_class_name( (Workset)self, (int)classnum, (str)classname) -> None : Set a new name for a class classnum the number of the class, should be > 0 classname the new name of the class.` set_frozen(...) `set_frozen( (Workset)self, (float)weight, (float)offset, (object)variables [, (object)phase=None]) -> None : Sets the scaling weight and offset for the specified variable(s) The weight or offset can be set to NaN (float('NaN')) to indicate that they should be calculated from the data. If weight is set to NaN, the unit variance weight will be used. If offset is set to NaN, the mean will be used. The method will set the scale type to frozen. offset --- The offset that will be subtracted from the variable(s) when the workset is scaled. weight --- The weight that the variable(s) will be multiplied with when the workset is scaled. variables --- The IDs of the variable(s). It can an either be an index, a variable name, a list of indices or names or an empty list to set all. phase --- The name of a phase. If this parameter is set, only observations belonging to that phase will be scaled with the weight and offset.` set_min_non_median_values(...) `set_min_non_median_values( (Workset)self, (int)values) -> None : Set the minimum number of non median values option. A value >=0` set_missing_obs_percent(...) `set_missing_obs_percent( (Workset)self, (float)percent) -> None : Set how many percent missing that is allowed for observations. A value between 0 and 100` set_missing_var_percent(...) `set_missing_var_percent( (Workset)self, (float)percent) -> None : Set how many percent missing that is allowed for variables. A value between 0 and 100` set_obs_as_model(...) `set_obs_as_model( (Workset)self, (int)model [, (bool)asmother=False]) -> None : Copy the observation settings from another model model --- the number of the model to copy from asmother if true, the mother workset will be copied ie all classes/phases if false, only the current class model will be copied ignored for non class/phase models` set_obs_class(...) `set_obs_class( (Workset)self, (object)observations, (int)classnum [, (str)classname='']) -> None : observations --- Contains the IDs or index of the observations that should be set. The parameter can either be a list of indices or the observation names. classnum the number of the class, should be > 0 classname the name of the class. If the workset already contains a class with the same number, it will be renamed. If classname is omitted classnum will be used as name` set_type(...) `set_type( (Workset)self, (modeltype)modeltype) -> None : Set the type for the new model. modeltype --- The type of the model to set, e.g. PLS PCA, OPLS-Class etc. See umetrics.simca.modeltype enum for available model types.` set_var_as_model(...) `set_var_as_model( (Workset)self, (int)model) -> None : Copy the variable settings from another model model --- The model number to copy settings from.` set_variable_class(...) `set_variable_class( (Workset)self, (object)variables, (object)classes) -> None : Set the class a variable should belong to variables --- Contains the IDs of the variables that should be set to a class The parameter can either be a list of indices or the variable names or empty list to get all. classes --- Contains the class numbers. The parameter can either be a positive integer or a list of integers` set_variable_phase(...) `set_variable_phase( (Workset)self, (object)variables, (object)phases) -> None : Set the phase a variable should belong to variables --- Contains the IDs of the variables that should be set to a class The parameter can either be a list of indices or the variable names or empty list to get all. phases --- Contains the phase names. The parameter is the name of the phase as a string or a list of names or empty list to get all.` set_variable_scale_block_and_weight(...) `set_variable_scale_block_and_weight( (Workset)self, (object)variables, (int)block, (blockscaletype)blockscaletype) -> None : Block-wise variable scaling. variables --- The IDs of the variable(s). It can an either be an index, a variable name, a list of indices or names or an empty list to set all. block --- The block number. If the block number is zero, the blockscaletype is ignored.` set_variable_scale_modifier(...) `set_variable_scale_modifier( (Workset)self, (object)variables, (float)modifier) -> None : Scaling variables up or down relative to their base weight. variables --- Contains the IDs of the variables that should be changed. The parameter can either be a list of indices or the variable names or empty list to get all. modifier --- The value to modify the variable scaling relative to its base weight.` set_variable_scale_type(...) `set_variable_scale_type( (Workset)self, (object)variables, (scaletype)scaletype) -> None : Base type variable scaling . variables --- Contains the IDs of the variables that should be changed. The parameter can either be a list of indices or the variable names or empty list to get all. scaletype --- Describes how the variable should be scaled. See scaletype enum for different options.` set_variable_transform(...) `set_variable_transform( (Workset)self, (object)variables, (transformtype)transformtype, (float)a, (float)b, (float)c) -> None : Set the transform for the selected variables . variables --- The IDs of the variable(s). It can an either be an index, a variable name, a list of indices or names or an empty list to set all.` set_x(...) `set_x( (Workset)self, (object)variables [, (object)expanded2=-1 [, (object)expanded3=-1 [, (object)Lag=0 [, (object)lag_time_variable=[] [, (object)lag_speed_variable=[]]]]]]) -> None : Sets a variable to X variables --- Contains the IDs of the variables that should be set as X variables. The parameter can either be a list of indices or the variable names or empty list to get all. expanded2 --- The second variable in an expanded term only one at a time -1 or empty string if it is not used expanded3 --- The third variable in an expanded term only one at a time -1 or empty string if it is not used Lags --- The number of steps or the distance the variable is lagged or a list of steps or distances if expanded terms are supplied. --- default 0, if lag_distance_variable is supplied Lags is the dist else it is the number of steps lag_time_variable --- The name of the time variable used to calculate dynamic lags. The parameter can either be a list of indices or the variable names empty for lag by steps otherwise the same length as Lag lag_speed_variable --- The name of the speed variable used to calculate dynamic lags. The parameter can either be a list of indices or the variable names empty for lag by steps otherwise the same length as Lag` set_y(...) `set_y( (Workset)self, (object)variables [, (object)Lag=0 [, (object)lag_time_variable=[] [, (object)lag_speed_variable=[]]]]) -> None : Sets a variable to Y variables --- Contains the IDs of the variables that should be set as Y variables. The parameter can either be a list of indices or the variable names or empty list to get all. Lags --- The number of steps or the distance the variable is lagged (only one variable). --- default 0, if lag_distance_variable is supplied Lags is the dist else it is the number of steps lag_time_variable --- The name of the time variable used to calculate dynamic lags. The parameter can either be a list of indices or the variable names empty for lag by steps otherwise the same length as Lag lag_speed_variable --- The name of the speed variable used to calculate dynamic lags. The parameter can either be a list of indices or the variable names empty for lag by steps otherwise the same length as Lag` set_y_treatment(...) `set_y_treatment( (Workset)self, (object)variables, (ytreatment)treatment) -> None : For a batch evolution model, change how Y variable is treated variables --- Contains the IDs of the Y variables that should be changed. The parameter can either be a list of indices or the variable names or empty list to get all. ytreatment --- Describes how the Y variable should be treated. See ytreatment enum for different options.` trim_variable(...) `trim_variable( (Workset)self, (object)variables, (ExcludeStruct)options, (bool)is_high_limit) -> None : Trim variables variables --- The parameter can either be an index, a variable name, a list of indices and variable names or an empty list for all variables. options --- The limits and replace value, see trim_options class. is_high_limit --- Set this to True if the options are for the high limit and to False if it is for the low limit.` Static methods inherited from Boost.Python.instance: __new__(*args, **kwargs) from Boost.Python.class `Create and return a new object. See help(type) for accurate signature.` Data descriptors inherited from Boost.Python.instance: __dict__ __weakref__
class blockscaletype(Boost.Python.enum)
`` | `Block scaling types none --- The variable is not block scaled. squareroot --- Scale by the inverse of the square root of the number of variables in the block. fourthroot --- Scale by the inverse of the fourth root of the number of variables in the block.`
| Method resolution order: blockscaletype Boost.Python.enum builtins.int builtins.object Data and other attributes defined here: fourthroot = umetrics.simca.blockscaletype.fourthroot names = {'fourthroot': umetrics.simca.blockscaletype.fourthroot, 'none': umetrics.simca.blockscaletype.none, 'squareroot': umetrics.simca.blockscaletype.squareroot} none = umetrics.simca.blockscaletype.none squareroot = umetrics.simca.blockscaletype.squareroot values = {0: umetrics.simca.blockscaletype.none, 1: umetrics.simca.blockscaletype.squareroot, 2: umetrics.simca.blockscaletype.fourthroot} Methods inherited from Boost.Python.enum: __repr__(self, /) `Return repr(self).` __str__(self, /) `Return str(self).` Data descriptors inherited from Boost.Python.enum: name Methods inherited from builtins.int: __abs__(self, /) `abs(self)` __add__(self, value, /) `Return self+value.` __and__(self, value, /) `Return self&value.` __bool__(self, /) `self != 0` __ceil__(...) `Ceiling of an Integral returns itself.` __divmod__(self, value, /) `Return divmod(self, value).` __eq__(self, value, /) `Return self==value.` __float__(self, /) `float(self)` __floor__(...) `Flooring an Integral returns itself.` __floordiv__(self, value, /) `Return self//value.` __format__(self, format_spec, /) `Default object formatter.` __ge__(self, value, /) `Return self>=value.` __getattribute__(self, name, /) `Return getattr(self, name).` __getnewargs__(self, /) __gt__(self, value, /) `Return self>value.` __hash__(self, /) `Return hash(self).` __index__(self, /) `Return self converted to an integer, if self is suitable for use as an index into a list.` __int__(self, /) `int(self)` __invert__(self, /) `~self` __le__(self, value, /) `Return self<=value.` __lshift__(self, value, /) `Return self<<value.` __lt__(self, value, /) `Return self<value.` __mod__(self, value, /) `Return self%value.` __mul__(self, value, /) `Return self*value.` __ne__(self, value, /) `Return self!=value.` __neg__(self, /) `-self` __or__(self, value, /) `Return self|value.` __pos__(self, /) `+self` __pow__(self, value, mod=None, /) `Return pow(self, value, mod).` __radd__(self, value, /) `Return value+self.` __rand__(self, value, /) `Return value&self.` __rdivmod__(self, value, /) `Return divmod(value, self).` __rfloordiv__(self, value, /) `Return value//self.` __rlshift__(self, value, /) `Return value<<self.` __rmod__(self, value, /) `Return value%self.` __rmul__(self, value, /) `Return value*self.` __ror__(self, value, /) `Return value|self.` __round__(...) `Rounding an Integral returns itself. Rounding with an ndigits argument also returns an integer.` __rpow__(self, value, mod=None, /) `Return pow(value, self, mod).` __rrshift__(self, value, /) `Return value>>self.` __rshift__(self, value, /) `Return self>>value.` __rsub__(self, value, /) `Return value-self.` __rtruediv__(self, value, /) `Return value/self.` __rxor__(self, value, /) `Return value^self.` __sizeof__(self, /) `Returns size in memory, in bytes.` __sub__(self, value, /) `Return self-value.` __truediv__(self, value, /) `Return self/value.` __trunc__(...) `Truncating an Integral returns itself.` __xor__(self, value, /) `Return self^value.` bit_length(self, /) `Number of bits necessary to represent self in binary. >>> bin(37) '0b100101' >>> (37).bit_length() 6` conjugate(...) `Returns self, the complex conjugate of any int.` to_bytes(self, /, length, byteorder, *, signed=False) `Return an array of bytes representing an integer. length Length of bytes object to use. An OverflowError is raised if the integer is not representable with the given number of bytes. byteorder The byte order used to represent the integer. If byteorder is 'big', the most significant byte is at the beginning of the byte array. If byteorder is 'little', the most significant byte is at the end of the byte array. To request the native byte order of the host system, use `sys.byteorder' as the byte order value. signed Determines whether two's complement is used to represent the integer. If signed is False and a negative integer is given, an OverflowError is raised.` Class methods inherited from builtins.int: from_bytes(bytes, byteorder, *, signed=False) from builtins.type `Return the integer represented by the given array of bytes. bytes Holds the array of bytes to convert. The argument must either support the buffer protocol or be an iterable object producing bytes. Bytes and bytearray are examples of built-in objects that support the buffer protocol. byteorder The byte order used to represent the integer. If byteorder is 'big', the most significant byte is at the beginning of the byte array. If byteorder is 'little', the most significant byte is at the end of the byte array. To request the native byte order of the host system, use `sys.byteorder' as the byte order value. signed Indicates whether two's complement is used to represent the integer.` Static methods inherited from builtins.int: __new__(*args, **kwargs) from builtins.type `Create and return a new object. See help(type) for accurate signature.` Data descriptors inherited from builtins.int: denominator `the denominator of a rational number in lowest terms` imag `the imaginary part of a complex number` numerator `the numerator of a rational number in lowest terms` real `the real part of a complex number`
class compressionmethod(Boost.Python.enum)
`` | `Wavelet filter - desired decomposition and Compression method. dwt --- Discrete wavelet transform. Recommended for low frequency signals. bestbasis --- Recommended for high frequency signals.`
| Method resolution order: compressionmethod Boost.Python.enum builtins.int builtins.object Data and other attributes defined here: bestbasis = umetrics.simca.compressionmethod.bestbasis dwt = umetrics.simca.compressionmethod.dwt names = {'bestbasis': umetrics.simca.compressionmethod.bestbasis, 'dwt': umetrics.simca.compressionmethod.dwt} values = {0: umetrics.simca.compressionmethod.dwt, 1: umetrics.simca.compressionmethod.bestbasis} Methods inherited from Boost.Python.enum: __repr__(self, /) `Return repr(self).` __str__(self, /) `Return str(self).` Data descriptors inherited from Boost.Python.enum: name Methods inherited from builtins.int: __abs__(self, /) `abs(self)` __add__(self, value, /) `Return self+value.` __and__(self, value, /) `Return self&value.` __bool__(self, /) `self != 0` __ceil__(...) `Ceiling of an Integral returns itself.` __divmod__(self, value, /) `Return divmod(self, value).` __eq__(self, value, /) `Return self==value.` __float__(self, /) `float(self)` __floor__(...) `Flooring an Integral returns itself.` __floordiv__(self, value, /) `Return self//value.` __format__(self, format_spec, /) `Default object formatter.` __ge__(self, value, /) `Return self>=value.` __getattribute__(self, name, /) `Return getattr(self, name).` __getnewargs__(self, /) __gt__(self, value, /) `Return self>value.` __hash__(self, /) `Return hash(self).` __index__(self, /) `Return self converted to an integer, if self is suitable for use as an index into a list.` __int__(self, /) `int(self)` __invert__(self, /) `~self` __le__(self, value, /) `Return self<=value.` __lshift__(self, value, /) `Return self<<value.` __lt__(self, value, /) `Return self<value.` __mod__(self, value, /) `Return self%value.` __mul__(self, value, /) `Return self*value.` __ne__(self, value, /) `Return self!=value.` __neg__(self, /) `-self` __or__(self, value, /) `Return self|value.` __pos__(self, /) `+self` __pow__(self, value, mod=None, /) `Return pow(self, value, mod).` __radd__(self, value, /) `Return value+self.` __rand__(self, value, /) `Return value&self.` __rdivmod__(self, value, /) `Return divmod(value, self).` __rfloordiv__(self, value, /) `Return value//self.` __rlshift__(self, value, /) `Return value<<self.` __rmod__(self, value, /) `Return value%self.` __rmul__(self, value, /) `Return value*self.` __ror__(self, value, /) `Return value|self.` __round__(...) `Rounding an Integral returns itself. Rounding with an ndigits argument also returns an integer.` __rpow__(self, value, mod=None, /) `Return pow(value, self, mod).` __rrshift__(self, value, /) `Return value>>self.` __rshift__(self, value, /) `Return self>>value.` __rsub__(self, value, /) `Return value-self.` __rtruediv__(self, value, /) `Return value/self.` __rxor__(self, value, /) `Return value^self.` __sizeof__(self, /) `Returns size in memory, in bytes.` __sub__(self, value, /) `Return self-value.` __truediv__(self, value, /) `Return self/value.` __trunc__(...) `Truncating an Integral returns itself.` __xor__(self, value, /) `Return self^value.` bit_length(self, /) `Number of bits necessary to represent self in binary. >>> bin(37) '0b100101' >>> (37).bit_length() 6` conjugate(...) `Returns self, the complex conjugate of any int.` to_bytes(self, /, length, byteorder, *, signed=False) `Return an array of bytes representing an integer. length Length of bytes object to use. An OverflowError is raised if the integer is not representable with the given number of bytes. byteorder The byte order used to represent the integer. If byteorder is 'big', the most significant byte is at the beginning of the byte array. If byteorder is 'little', the most significant byte is at the end of the byte array. To request the native byte order of the host system, use `sys.byteorder' as the byte order value. signed Determines whether two's complement is used to represent the integer. If signed is False and a negative integer is given, an OverflowError is raised.` Class methods inherited from builtins.int: from_bytes(bytes, byteorder, *, signed=False) from builtins.type `Return the integer represented by the given array of bytes. bytes Holds the array of bytes to convert. The argument must either support the buffer protocol or be an iterable object producing bytes. Bytes and bytearray are examples of built-in objects that support the buffer protocol. byteorder The byte order used to represent the integer. If byteorder is 'big', the most significant byte is at the beginning of the byte array. If byteorder is 'little', the most significant byte is at the end of the byte array. To request the native byte order of the host system, use `sys.byteorder' as the byte order value. signed Indicates whether two's complement is used to represent the integer.` Static methods inherited from builtins.int: __new__(*args, **kwargs) from builtins.type `Create and return a new object. See help(type) for accurate signature.` Data descriptors inherited from builtins.int: denominator `the denominator of a rational number in lowest terms` imag `the imaginary part of a complex number` numerator `the numerator of a rational number in lowest terms` real `the real part of a complex number`
class data_explorer_mode(Boost.Python.enum)
`` | `The What-If/ solver/ score space setting determines how the values are used offset --- Add the specified value to all What-If prediction rows. rangepercent --- Add the specified value, transformed from % of variable range, to all What-If prediction rows. constant --- Change to the specified value for all What-If prediction rows. valuepercent --- Add the specified value, transformed from % of value, to all What-If prediction rows. score --- Specify a potential score point to see its variable settings. solver --- Specify a y values and x constraints to see corresponding variable settings.`
| Method resolution order: data_explorer_mode Boost.Python.enum builtins.int builtins.object Data and other attributes defined here: constant = umetrics.simca.data_explorer_mode.constant names = {'constant': umetrics.simca.data_explorer_mode.constant, 'offset': umetrics.simca.data_explorer_mode.offset, 'rangepercent': umetrics.simca.data_explorer_mode.rangepercent, 'score': umetrics.simca.data_explorer_mode.score, 'solver': umetrics.simca.data_explorer_mode.solver, 'valuepercent': umetrics.simca.data_explorer_mode.valuepercent} offset = umetrics.simca.data_explorer_mode.offset rangepercent = umetrics.simca.data_explorer_mode.rangepercent score = umetrics.simca.data_explorer_mode.score solver = umetrics.simca.data_explorer_mode.solver valuepercent = umetrics.simca.data_explorer_mode.valuepercent values = {0: umetrics.simca.data_explorer_mode.offset, 1: umetrics.simca.data_explorer_mode.rangepercent, 2: umetrics.simca.data_explorer_mode.constant, 3: umetrics.simca.data_explorer_mode.valuepercent, 4: umetrics.simca.data_explorer_mode.score, 5: umetrics.simca.data_explorer_mode.solver} Methods inherited from Boost.Python.enum: __repr__(self, /) `Return repr(self).` __str__(self, /) `Return str(self).` Data descriptors inherited from Boost.Python.enum: name Methods inherited from builtins.int: __abs__(self, /) `abs(self)` __add__(self, value, /) `Return self+value.` __and__(self, value, /) `Return self&value.` __bool__(self, /) `self != 0` __ceil__(...) `Ceiling of an Integral returns itself.` __divmod__(self, value, /) `Return divmod(self, value).` __eq__(self, value, /) `Return self==value.` __float__(self, /) `float(self)` __floor__(...) `Flooring an Integral returns itself.` __floordiv__(self, value, /) `Return self//value.` __format__(self, format_spec, /) `Default object formatter.` __ge__(self, value, /) `Return self>=value.` __getattribute__(self, name, /) `Return getattr(self, name).` __getnewargs__(self, /) __gt__(self, value, /) `Return self>value.` __hash__(self, /) `Return hash(self).` __index__(self, /) `Return self converted to an integer, if self is suitable for use as an index into a list.` __int__(self, /) `int(self)` __invert__(self, /) `~self` __le__(self, value, /) `Return self<=value.` __lshift__(self, value, /) `Return self<<value.` __lt__(self, value, /) `Return self<value.` __mod__(self, value, /) `Return self%value.` __mul__(self, value, /) `Return self*value.` __ne__(self, value, /) `Return self!=value.` __neg__(self, /) `-self` __or__(self, value, /) `Return self|value.` __pos__(self, /) `+self` __pow__(self, value, mod=None, /) `Return pow(self, value, mod).` __radd__(self, value, /) `Return value+self.` __rand__(self, value, /) `Return value&self.` __rdivmod__(self, value, /) `Return divmod(value, self).` __rfloordiv__(self, value, /) `Return value//self.` __rlshift__(self, value, /) `Return value<<self.` __rmod__(self, value, /) `Return value%self.` __rmul__(self, value, /) `Return value*self.` __ror__(self, value, /) `Return value|self.` __round__(...) `Rounding an Integral returns itself. Rounding with an ndigits argument also returns an integer.` __rpow__(self, value, mod=None, /) `Return pow(value, self, mod).` __rrshift__(self, value, /) `Return value>>self.` __rshift__(self, value, /) `Return self>>value.` __rsub__(self, value, /) `Return value-self.` __rtruediv__(self, value, /) `Return value/self.` __rxor__(self, value, /) `Return value^self.` __sizeof__(self, /) `Returns size in memory, in bytes.` __sub__(self, value, /) `Return self-value.` __truediv__(self, value, /) `Return self/value.` __trunc__(...) `Truncating an Integral returns itself.` __xor__(self, value, /) `Return self^value.` bit_length(self, /) `Number of bits necessary to represent self in binary. >>> bin(37) '0b100101' >>> (37).bit_length() 6` conjugate(...) `Returns self, the complex conjugate of any int.` to_bytes(self, /, length, byteorder, *, signed=False) `Return an array of bytes representing an integer. length Length of bytes object to use. An OverflowError is raised if the integer is not representable with the given number of bytes. byteorder The byte order used to represent the integer. If byteorder is 'big', the most significant byte is at the beginning of the byte array. If byteorder is 'little', the most significant byte is at the end of the byte array. To request the native byte order of the host system, use `sys.byteorder' as the byte order value. signed Determines whether two's complement is used to represent the integer. If signed is False and a negative integer is given, an OverflowError is raised.` Class methods inherited from builtins.int: from_bytes(bytes, byteorder, *, signed=False) from builtins.type `Return the integer represented by the given array of bytes. bytes Holds the array of bytes to convert. The argument must either support the buffer protocol or be an iterable object producing bytes. Bytes and bytearray are examples of built-in objects that support the buffer protocol. byteorder The byte order used to represent the integer. If byteorder is 'big', the most significant byte is at the beginning of the byte array. If byteorder is 'little', the most significant byte is at the end of the byte array. To request the native byte order of the host system, use `sys.byteorder' as the byte order value. signed Indicates whether two's complement is used to represent the integer.` Static methods inherited from builtins.int: __new__(*args, **kwargs) from builtins.type `Create and return a new object. See help(type) for accurate signature.` Data descriptors inherited from builtins.int: denominator `the denominator of a rational number in lowest terms` imag `the imaginary part of a complex number` numerator `the numerator of a rational number in lowest terms` real `the real part of a complex number`
class data_explorer_settings(Boost.Python.instance)
`` | `Expects two arguments. mode - The whatif_mode indicating the type. Returns a new What-If settings object.`
| Method resolution order: data_explorer_settings Boost.Python.instance builtins.object Static methods defined here: __eq__(...) __init__(...) __ne__(...) __reduce__ = (...) add_setting(...) `name - The variable name or score vector name. values - A floating point value or a list of values. A list of values is only used in batch. maturities - A list of maturities the settings are for` allow_model_extrapolation(...) `True to allow model extrapolation outside T-Crit in the scores for solver` remove(...) `Remove the setting for a specific variable name - The variable name or score vector name` remove_all(...) `Removes all created What-If settings.` remove_at(...) `Removed the settings at the specified index. position - Position to remove, starts at 0.` Data and other attributes defined here: __instance_size__ = 56 Static methods inherited from Boost.Python.instance: __new__(*args, **kwargs) from Boost.Python.class `Create and return a new object. See help(type) for accurate signature.` Data descriptors inherited from Boost.Python.instance: __dict__ __weakref__
class derivativeorder(Boost.Python.enum)
`` | `Different derivative orders. Only valid for derivative filters. firstderivative --- First derivasetctive order. secondderivative --- Second, or third derivative order. thirdderivative --- Third derivative order.`
| Method resolution order: derivativeorder Boost.Python.enum builtins.int builtins.object Data and other attributes defined here: firstderivative = umetrics.simca.derivativeorder.firstderivative names = {'firstderivative': umetrics.simca.derivativeorder.firstderivative, 'secondderivative': umetrics.simca.derivativeorder.secondderivative, 'thirdderivative': umetrics.simca.derivativeorder.thirdderivative} secondderivative = umetrics.simca.derivativeorder.secondderivative thirdderivative = umetrics.simca.derivativeorder.thirdderivative values = {1: umetrics.simca.derivativeorder.firstderivative, 2: umetrics.simca.derivativeorder.secondderivative, 3: umetrics.simca.derivativeorder.thirdderivative} Methods inherited from Boost.Python.enum: __repr__(self, /) `Return repr(self).` __str__(self, /) `Return str(self).` Data descriptors inherited from Boost.Python.enum: name Methods inherited from builtins.int: __abs__(self, /) `abs(self)` __add__(self, value, /) `Return self+value.` __and__(self, value, /) `Return self&value.` __bool__(self, /) `self != 0` __ceil__(...) `Ceiling of an Integral returns itself.` __divmod__(self, value, /) `Return divmod(self, value).` __eq__(self, value, /) `Return self==value.` __float__(self, /) `float(self)` __floor__(...) `Flooring an Integral returns itself.` __floordiv__(self, value, /) `Return self//value.` __format__(self, format_spec, /) `Default object formatter.` __ge__(self, value, /) `Return self>=value.` __getattribute__(self, name, /) `Return getattr(self, name).` __getnewargs__(self, /) __gt__(self, value, /) `Return self>value.` __hash__(self, /) `Return hash(self).` __index__(self, /) `Return self converted to an integer, if self is suitable for use as an index into a list.` __int__(self, /) `int(self)` __invert__(self, /) `~self` __le__(self, value, /) `Return self<=value.` __lshift__(self, value, /) `Return self<<value.` __lt__(self, value, /) `Return self<value.` __mod__(self, value, /) `Return self%value.` __mul__(self, value, /) `Return self*value.` __ne__(self, value, /) `Return self!=value.` __neg__(self, /) `-self` __or__(self, value, /) `Return self|value.` __pos__(self, /) `+self` __pow__(self, value, mod=None, /) `Return pow(self, value, mod).` __radd__(self, value, /) `Return value+self.` __rand__(self, value, /) `Return value&self.` __rdivmod__(self, value, /) `Return divmod(value, self).` __rfloordiv__(self, value, /) `Return value//self.` __rlshift__(self, value, /) `Return value<<self.` __rmod__(self, value, /) `Return value%self.` __rmul__(self, value, /) `Return value*self.` __ror__(self, value, /) `Return value|self.` __round__(...) `Rounding an Integral returns itself. Rounding with an ndigits argument also returns an integer.` __rpow__(self, value, mod=None, /) `Return pow(value, self, mod).` __rrshift__(self, value, /) `Return value>>self.` __rshift__(self, value, /) `Return self>>value.` __rsub__(self, value, /) `Return value-self.` __rtruediv__(self, value, /) `Return value/self.` __rxor__(self, value, /) `Return value^self.` __sizeof__(self, /) `Returns size in memory, in bytes.` __sub__(self, value, /) `Return self-value.` __truediv__(self, value, /) `Return self/value.` __trunc__(...) `Truncating an Integral returns itself.` __xor__(self, value, /) `Return self^value.` bit_length(self, /) `Number of bits necessary to represent self in binary. >>> bin(37) '0b100101' >>> (37).bit_length() 6` conjugate(...) `Returns self, the complex conjugate of any int.` to_bytes(self, /, length, byteorder, *, signed=False) `Return an array of bytes representing an integer. length Length of bytes object to use. An OverflowError is raised if the integer is not representable with the given number of bytes. byteorder The byte order used to represent the integer. If byteorder is 'big', the most significant byte is at the beginning of the byte array. If byteorder is 'little', the most significant byte is at the end of the byte array. To request the native byte order of the host system, use `sys.byteorder' as the byte order value. signed Determines whether two's complement is used to represent the integer. If signed is False and a negative integer is given, an OverflowError is raised.` Class methods inherited from builtins.int: from_bytes(bytes, byteorder, *, signed=False) from builtins.type `Return the integer represented by the given array of bytes. bytes Holds the array of bytes to convert. The argument must either support the buffer protocol or be an iterable object producing bytes. Bytes and bytearray are examples of built-in objects that support the buffer protocol. byteorder The byte order used to represent the integer. If byteorder is 'big', the most significant byte is at the beginning of the byte array. If byteorder is 'little', the most significant byte is at the end of the byte array. To request the native byte order of the host system, use `sys.byteorder' as the byte order value. signed Indicates whether two's complement is used to represent the integer.` Static methods inherited from builtins.int: __new__(*args, **kwargs) from builtins.type `Create and return a new object. See help(type) for accurate signature.` Data descriptors inherited from builtins.int: denominator `the denominator of a rational number in lowest terms` imag `the imaginary part of a complex number` numerator `the numerator of a rational number in lowest terms` real `the real part of a complex number`
class detrendmode(Boost.Python.enum)
`` | `Wavelet filter detrend mode. none --- No detrend. mean --- Removes the mean. linear --- Removes the best linear fit.`
| Method resolution order: detrendmode Boost.Python.enum builtins.int builtins.object Data and other attributes defined here: linear = umetrics.simca.detrendmode.linear mean = umetrics.simca.detrendmode.mean names = {'linear': umetrics.simca.detrendmode.linear, 'mean': umetrics.simca.detrendmode.mean, 'none': umetrics.simca.detrendmode.none} none = umetrics.simca.detrendmode.none values = {0: umetrics.simca.detrendmode.none, 1: umetrics.simca.detrendmode.mean, 2: umetrics.simca.detrendmode.linear} Methods inherited from Boost.Python.enum: __repr__(self, /) `Return repr(self).` __str__(self, /) `Return str(self).` Data descriptors inherited from Boost.Python.enum: name Methods inherited from builtins.int: __abs__(self, /) `abs(self)` __add__(self, value, /) `Return self+value.` __and__(self, value, /) `Return self&value.` __bool__(self, /) `self != 0` __ceil__(...) `Ceiling of an Integral returns itself.` __divmod__(self, value, /) `Return divmod(self, value).` __eq__(self, value, /) `Return self==value.` __float__(self, /) `float(self)` __floor__(...) `Flooring an Integral returns itself.` __floordiv__(self, value, /) `Return self//value.` __format__(self, format_spec, /) `Default object formatter.` __ge__(self, value, /) `Return self>=value.` __getattribute__(self, name, /) `Return getattr(self, name).` __getnewargs__(self, /) __gt__(self, value, /) `Return self>value.` __hash__(self, /) `Return hash(self).` __index__(self, /) `Return self converted to an integer, if self is suitable for use as an index into a list.` __int__(self, /) `int(self)` __invert__(self, /) `~self` __le__(self, value, /) `Return self<=value.` __lshift__(self, value, /) `Return self<<value.` __lt__(self, value, /) `Return self<value.` __mod__(self, value, /) `Return self%value.` __mul__(self, value, /) `Return self*value.` __ne__(self, value, /) `Return self!=value.` __neg__(self, /) `-self` __or__(self, value, /) `Return self|value.` __pos__(self, /) `+self` __pow__(self, value, mod=None, /) `Return pow(self, value, mod).` __radd__(self, value, /) `Return value+self.` __rand__(self, value, /) `Return value&self.` __rdivmod__(self, value, /) `Return divmod(value, self).` __rfloordiv__(self, value, /) `Return value//self.` __rlshift__(self, value, /) `Return value<<self.` __rmod__(self, value, /) `Return value%self.` __rmul__(self, value, /) `Return value*self.` __ror__(self, value, /) `Return value|self.` __round__(...) `Rounding an Integral returns itself. Rounding with an ndigits argument also returns an integer.` __rpow__(self, value, mod=None, /) `Return pow(value, self, mod).` __rrshift__(self, value, /) `Return value>>self.` __rshift__(self, value, /) `Return self>>value.` __rsub__(self, value, /) `Return value-self.` __rtruediv__(self, value, /) `Return value/self.` __rxor__(self, value, /) `Return value^self.` __sizeof__(self, /) `Returns size in memory, in bytes.` __sub__(self, value, /) `Return self-value.` __truediv__(self, value, /) `Return self/value.` __trunc__(...) `Truncating an Integral returns itself.` __xor__(self, value, /) `Return self^value.` bit_length(self, /) `Number of bits necessary to represent self in binary. >>> bin(37) '0b100101' >>> (37).bit_length() 6` conjugate(...) `Returns self, the complex conjugate of any int.` to_bytes(self, /, length, byteorder, *, signed=False) `Return an array of bytes representing an integer. length Length of bytes object to use. An OverflowError is raised if the integer is not representable with the given number of bytes. byteorder The byte order used to represent the integer. If byteorder is 'big', the most significant byte is at the beginning of the byte array. If byteorder is 'little', the most significant byte is at the end of the byte array. To request the native byte order of the host system, use `sys.byteorder' as the byte order value. signed Determines whether two's complement is used to represent the integer. If signed is False and a negative integer is given, an OverflowError is raised.` Class methods inherited from builtins.int: from_bytes(bytes, byteorder, *, signed=False) from builtins.type `Return the integer represented by the given array of bytes. bytes Holds the array of bytes to convert. The argument must either support the buffer protocol or be an iterable object producing bytes. Bytes and bytearray are examples of built-in objects that support the buffer protocol. byteorder The byte order used to represent the integer. If byteorder is 'big', the most significant byte is at the beginning of the byte array. If byteorder is 'little', the most significant byte is at the end of the byte array. To request the native byte order of the host system, use `sys.byteorder' as the byte order value. signed Indicates whether two's complement is used to represent the integer.` Static methods inherited from builtins.int: __new__(*args, **kwargs) from builtins.type `Create and return a new object. See help(type) for accurate signature.` Data descriptors inherited from builtins.int: denominator `the denominator of a rational number in lowest terms` imag `the imaginary part of a complex number` numerator `the numerator of a rational number in lowest terms` real `the real part of a complex number`
class ewma_type(Boost.Python.enum)
`` | `Different EWMA types. filter --- Filter EWMA. predictive --- Predictive EWMA.`
| Method resolution order: ewma_type Boost.Python.enum builtins.int builtins.object Data and other attributes defined here: filter = umetrics.simca.ewma_type.filter names = {'filter': umetrics.simca.ewma_type.filter, 'predictive': umetrics.simca.ewma_type.predictive} predictive = umetrics.simca.ewma_type.predictive values = {0: umetrics.simca.ewma_type.filter, 1: umetrics.simca.ewma_type.predictive} Methods inherited from Boost.Python.enum: __repr__(self, /) `Return repr(self).` __str__(self, /) `Return str(self).` Data descriptors inherited from Boost.Python.enum: name Methods inherited from builtins.int: __abs__(self, /) `abs(self)` __add__(self, value, /) `Return self+value.` __and__(self, value, /) `Return self&value.` __bool__(self, /) `self != 0` __ceil__(...) `Ceiling of an Integral returns itself.` __divmod__(self, value, /) `Return divmod(self, value).` __eq__(self, value, /) `Return self==value.` __float__(self, /) `float(self)` __floor__(...) `Flooring an Integral returns itself.` __floordiv__(self, value, /) `Return self//value.` __format__(self, format_spec, /) `Default object formatter.` __ge__(self, value, /) `Return self>=value.` __getattribute__(self, name, /) `Return getattr(self, name).` __getnewargs__(self, /) __gt__(self, value, /) `Return self>value.` __hash__(self, /) `Return hash(self).` __index__(self, /) `Return self converted to an integer, if self is suitable for use as an index into a list.` __int__(self, /) `int(self)` __invert__(self, /) `~self` __le__(self, value, /) `Return self<=value.` __lshift__(self, value, /) `Return self<<value.` __lt__(self, value, /) `Return self<value.` __mod__(self, value, /) `Return self%value.` __mul__(self, value, /) `Return self*value.` __ne__(self, value, /) `Return self!=value.` __neg__(self, /) `-self` __or__(self, value, /) `Return self|value.` __pos__(self, /) `+self` __pow__(self, value, mod=None, /) `Return pow(self, value, mod).` __radd__(self, value, /) `Return value+self.` __rand__(self, value, /) `Return value&self.` __rdivmod__(self, value, /) `Return divmod(value, self).` __rfloordiv__(self, value, /) `Return value//self.` __rlshift__(self, value, /) `Return value<<self.` __rmod__(self, value, /) `Return value%self.` __rmul__(self, value, /) `Return value*self.` __ror__(self, value, /) `Return value|self.` __round__(...) `Rounding an Integral returns itself. Rounding with an ndigits argument also returns an integer.` __rpow__(self, value, mod=None, /) `Return pow(value, self, mod).` __rrshift__(self, value, /) `Return value>>self.` __rshift__(self, value, /) `Return self>>value.` __rsub__(self, value, /) `Return value-self.` __rtruediv__(self, value, /) `Return value/self.` __rxor__(self, value, /) `Return value^self.` __sizeof__(self, /) `Returns size in memory, in bytes.` __sub__(self, value, /) `Return self-value.` __truediv__(self, value, /) `Return self/value.` __trunc__(...) `Truncating an Integral returns itself.` __xor__(self, value, /) `Return self^value.` bit_length(self, /) `Number of bits necessary to represent self in binary. >>> bin(37) '0b100101' >>> (37).bit_length() 6` conjugate(...) `Returns self, the complex conjugate of any int.` to_bytes(self, /, length, byteorder, *, signed=False) `Return an array of bytes representing an integer. length Length of bytes object to use. An OverflowError is raised if the integer is not representable with the given number of bytes. byteorder The byte order used to represent the integer. If byteorder is 'big', the most significant byte is at the beginning of the byte array. If byteorder is 'little', the most significant byte is at the end of the byte array. To request the native byte order of the host system, use `sys.byteorder' as the byte order value. signed Determines whether two's complement is used to represent the integer. If signed is False and a negative integer is given, an OverflowError is raised.` Class methods inherited from builtins.int: from_bytes(bytes, byteorder, *, signed=False) from builtins.type `Return the integer represented by the given array of bytes. bytes Holds the array of bytes to convert. The argument must either support the buffer protocol or be an iterable object producing bytes. Bytes and bytearray are examples of built-in objects that support the buffer protocol. byteorder The byte order used to represent the integer. If byteorder is 'big', the most significant byte is at the beginning of the byte array. If byteorder is 'little', the most significant byte is at the end of the byte array. To request the native byte order of the host system, use `sys.byteorder' as the byte order value. signed Indicates whether two's complement is used to represent the integer.` Static methods inherited from builtins.int: __new__(*args, **kwargs) from builtins.type `Create and return a new object. See help(type) for accurate signature.` Data descriptors inherited from builtins.int: denominator `the denominator of a rational number in lowest terms` imag `the imaginary part of a complex number` numerator `the numerator of a rational number in lowest terms` real `the real part of a complex number`
class filter_scaletype(Boost.Python.enum)
`` | `Filter scaling types. none --- No scaling. center --- Scaled by the center. pareto --- Scaled by the inverse of the square root of the standard deviation. unitvariance --- Scaled by the inverse of the standard deviation.`
| Method resolution order: filter_scaletype Boost.Python.enum builtins.int builtins.object Data and other attributes defined here: center = umetrics.simca.filter_scaletype.center names = {'center': umetrics.simca.filter_scaletype.center, 'none': umetrics.simca.filter_scaletype.none, 'pareto': umetrics.simca.filter_scaletype.pareto, 'unitvariance': umetrics.simca.filter_scaletype.unitvariance} none = umetrics.simca.filter_scaletype.none pareto = umetrics.simca.filter_scaletype.pareto unitvariance = umetrics.simca.filter_scaletype.unitvariance values = {1: umetrics.simca.filter_scaletype.none, 2: umetrics.simca.filter_scaletype.center, 3: umetrics.simca.filter_scaletype.pareto, 4: umetrics.simca.filter_scaletype.unitvariance} Methods inherited from Boost.Python.enum: __repr__(self, /) `Return repr(self).` __str__(self, /) `Return str(self).` Data descriptors inherited from Boost.Python.enum: name Methods inherited from builtins.int: __abs__(self, /) `abs(self)` __add__(self, value, /) `Return self+value.` __and__(self, value, /) `Return self&value.` __bool__(self, /) `self != 0` __ceil__(...) `Ceiling of an Integral returns itself.` __divmod__(self, value, /) `Return divmod(self, value).` __eq__(self, value, /) `Return self==value.` __float__(self, /) `float(self)` __floor__(...) `Flooring an Integral returns itself.` __floordiv__(self, value, /) `Return self//value.` __format__(self, format_spec, /) `Default object formatter.` __ge__(self, value, /) `Return self>=value.` __getattribute__(self, name, /) `Return getattr(self, name).` __getnewargs__(self, /) __gt__(self, value, /) `Return self>value.` __hash__(self, /) `Return hash(self).` __index__(self, /) `Return self converted to an integer, if self is suitable for use as an index into a list.` __int__(self, /) `int(self)` __invert__(self, /) `~self` __le__(self, value, /) `Return self<=value.` __lshift__(self, value, /) `Return self<<value.` __lt__(self, value, /) `Return self<value.` __mod__(self, value, /) `Return self%value.` __mul__(self, value, /) `Return self*value.` __ne__(self, value, /) `Return self!=value.` __neg__(self, /) `-self` __or__(self, value, /) `Return self|value.` __pos__(self, /) `+self` __pow__(self, value, mod=None, /) `Return pow(self, value, mod).` __radd__(self, value, /) `Return value+self.` __rand__(self, value, /) `Return value&self.` __rdivmod__(self, value, /) `Return divmod(value, self).` __rfloordiv__(self, value, /) `Return value//self.` __rlshift__(self, value, /) `Return value<<self.` __rmod__(self, value, /) `Return value%self.` __rmul__(self, value, /) `Return value*self.` __ror__(self, value, /) `Return value|self.` __round__(...) `Rounding an Integral returns itself. Rounding with an ndigits argument also returns an integer.` __rpow__(self, value, mod=None, /) `Return pow(value, self, mod).` __rrshift__(self, value, /) `Return value>>self.` __rshift__(self, value, /) `Return self>>value.` __rsub__(self, value, /) `Return value-self.` __rtruediv__(self, value, /) `Return value/self.` __rxor__(self, value, /) `Return value^self.` __sizeof__(self, /) `Returns size in memory, in bytes.` __sub__(self, value, /) `Return self-value.` __truediv__(self, value, /) `Return self/value.` __trunc__(...) `Truncating an Integral returns itself.` __xor__(self, value, /) `Return self^value.` bit_length(self, /) `Number of bits necessary to represent self in binary. >>> bin(37) '0b100101' >>> (37).bit_length() 6` conjugate(...) `Returns self, the complex conjugate of any int.` to_bytes(self, /, length, byteorder, *, signed=False) `Return an array of bytes representing an integer. length Length of bytes object to use. An OverflowError is raised if the integer is not representable with the given number of bytes. byteorder The byte order used to represent the integer. If byteorder is 'big', the most significant byte is at the beginning of the byte array. If byteorder is 'little', the most significant byte is at the end of the byte array. To request the native byte order of the host system, use `sys.byteorder' as the byte order value. signed Determines whether two's complement is used to represent the integer. If signed is False and a negative integer is given, an OverflowError is raised.` Class methods inherited from builtins.int: from_bytes(bytes, byteorder, *, signed=False) from builtins.type `Return the integer represented by the given array of bytes. bytes Holds the array of bytes to convert. The argument must either support the buffer protocol or be an iterable object producing bytes. Bytes and bytearray are examples of built-in objects that support the buffer protocol. byteorder The byte order used to represent the integer. If byteorder is 'big', the most significant byte is at the beginning of the byte array. If byteorder is 'little', the most significant byte is at the end of the byte array. To request the native byte order of the host system, use `sys.byteorder' as the byte order value. signed Indicates whether two's complement is used to represent the integer.` Static methods inherited from builtins.int: __new__(*args, **kwargs) from builtins.type `Create and return a new object. See help(type) for accurate signature.` Data descriptors inherited from builtins.int: denominator `the denominator of a rational number in lowest terms` imag `the imaginary part of a complex number` numerator `the numerator of a rational number in lowest terms` real `the real part of a complex number`
class filtertype(Boost.Python.enum)
`` | `Different preprocessing types derivatives --- Applying derivatives transforms the dataset from the original domain to the first, second, or third derivate. msc --- Multiplicative signal correction. snv --- Standard normal variate. rowcenter --- Applying the rowcenter filter subtracts the row mean from each row value. savitzky --- Savitzky-Golay. A filter that removes noise by applying a moving polynomial to the data. EWMA --- Exponentially weighted moving average. wcs --- Wavelet compression spectral. wds --- Wavelet denoising spectral. osc --- Orthogonal signal correction. wcts --- Wavelet compress time series. wdts --- Wavelet denoising/decimation time series. plugin --- Plugin.`
| Method resolution order: filtertype Boost.Python.enum builtins.int builtins.object Data and other attributes defined here: EWMA = umetrics.simca.filtertype.EWMA derivatives = umetrics.simca.filtertype.derivatives msc = umetrics.simca.filtertype.msc names = {'EWMA': umetrics.simca.filtertype.EWMA, 'derivatives': umetrics.simca.filtertype.derivatives, 'msc': umetrics.simca.filtertype.msc, 'osc': umetrics.simca.filtertype.osc, 'plugin': umetrics.simca.filtertype.plugin, 'rowcenter': umetrics.simca.filtertype.rowcenter, 'savitzky': umetrics.simca.filtertype.savitzky, 'snv': umetrics.simca.filtertype.snv, 'wcs': umetrics.simca.filtertype.wcs, 'wcts': umetrics.simca.filtertype.wcts, ...} osc = umetrics.simca.filtertype.osc plugin = umetrics.simca.filtertype.plugin rowcenter = umetrics.simca.filtertype.rowcenter savitzky = umetrics.simca.filtertype.savitzky snv = umetrics.simca.filtertype.snv values = {0: umetrics.simca.filtertype.snv, 1: umetrics.simca.filtertype.msc, 2: umetrics.simca.filtertype.derivatives, 3: umetrics.simca.filtertype.savitzky, 4: umetrics.simca.filtertype.rowcenter, 5: umetrics.simca.filtertype.EWMA, 6: umetrics.simca.filtertype.osc, 7: umetrics.simca.filtertype.wcs, 8: umetrics.simca.filtertype.wds, 9: umetrics.simca.filtertype.wcts, ...} wcs = umetrics.simca.filtertype.wcs wcts = umetrics.simca.filtertype.wcts wds = umetrics.simca.filtertype.wds wdts = umetrics.simca.filtertype.wdts Methods inherited from Boost.Python.enum: __repr__(self, /) `Return repr(self).` __str__(self, /) `Return str(self).` Data descriptors inherited from Boost.Python.enum: name Methods inherited from builtins.int: __abs__(self, /) `abs(self)` __add__(self, value, /) `Return self+value.` __and__(self, value, /) `Return self&value.` __bool__(self, /) `self != 0` __ceil__(...) `Ceiling of an Integral returns itself.` __divmod__(self, value, /) `Return divmod(self, value).` __eq__(self, value, /) `Return self==value.` __float__(self, /) `float(self)` __floor__(...) `Flooring an Integral returns itself.` __floordiv__(self, value, /) `Return self//value.` __format__(self, format_spec, /) `Default object formatter.` __ge__(self, value, /) `Return self>=value.` __getattribute__(self, name, /) `Return getattr(self, name).` __getnewargs__(self, /) __gt__(self, value, /) `Return self>value.` __hash__(self, /) `Return hash(self).` __index__(self, /) `Return self converted to an integer, if self is suitable for use as an index into a list.` __int__(self, /) `int(self)` __invert__(self, /) `~self` __le__(self, value, /) `Return self<=value.` __lshift__(self, value, /) `Return self<<value.` __lt__(self, value, /) `Return self<value.` __mod__(self, value, /) `Return self%value.` __mul__(self, value, /) `Return self*value.` __ne__(self, value, /) `Return self!=value.` __neg__(self, /) `-self` __or__(self, value, /) `Return self|value.` __pos__(self, /) `+self` __pow__(self, value, mod=None, /) `Return pow(self, value, mod).` __radd__(self, value, /) `Return value+self.` __rand__(self, value, /) `Return value&self.` __rdivmod__(self, value, /) `Return divmod(value, self).` __rfloordiv__(self, value, /) `Return value//self.` __rlshift__(self, value, /) `Return value<<self.` __rmod__(self, value, /) `Return value%self.` __rmul__(self, value, /) `Return value*self.` __ror__(self, value, /) `Return value|self.` __round__(...) `Rounding an Integral returns itself. Rounding with an ndigits argument also returns an integer.` __rpow__(self, value, mod=None, /) `Return pow(value, self, mod).` __rrshift__(self, value, /) `Return value>>self.` __rshift__(self, value, /) `Return self>>value.` __rsub__(self, value, /) `Return value-self.` __rtruediv__(self, value, /) `Return value/self.` __rxor__(self, value, /) `Return value^self.` __sizeof__(self, /) `Returns size in memory, in bytes.` __sub__(self, value, /) `Return self-value.` __truediv__(self, value, /) `Return self/value.` __trunc__(...) `Truncating an Integral returns itself.` __xor__(self, value, /) `Return self^value.` bit_length(self, /) `Number of bits necessary to represent self in binary. >>> bin(37) '0b100101' >>> (37).bit_length() 6` conjugate(...) `Returns self, the complex conjugate of any int.` to_bytes(self, /, length, byteorder, *, signed=False) `Return an array of bytes representing an integer. length Length of bytes object to use. An OverflowError is raised if the integer is not representable with the given number of bytes. byteorder The byte order used to represent the integer. If byteorder is 'big', the most significant byte is at the beginning of the byte array. If byteorder is 'little', the most significant byte is at the end of the byte array. To request the native byte order of the host system, use `sys.byteorder' as the byte order value. signed Determines whether two's complement is used to represent the integer. If signed is False and a negative integer is given, an OverflowError is raised.` Class methods inherited from builtins.int: from_bytes(bytes, byteorder, *, signed=False) from builtins.type `Return the integer represented by the given array of bytes. bytes Holds the array of bytes to convert. The argument must either support the buffer protocol or be an iterable object producing bytes. Bytes and bytearray are examples of built-in objects that support the buffer protocol. byteorder The byte order used to represent the integer. If byteorder is 'big', the most significant byte is at the beginning of the byte array. If byteorder is 'little', the most significant byte is at the end of the byte array. To request the native byte order of the host system, use `sys.byteorder' as the byte order value. signed Indicates whether two's complement is used to represent the integer.` Static methods inherited from builtins.int: __new__(*args, **kwargs) from builtins.type `Create and return a new object. See help(type) for accurate signature.` Data descriptors inherited from builtins.int: denominator `the denominator of a rational number in lowest terms` imag `the imaginary part of a complex number` numerator `the numerator of a rational number in lowest terms` real `the real part of a complex number`
class hierarchical_types(Boost.Python.enum)
`` | `Different hierarchical model types none --- scores --- Scores. residuals --- Residuals. y_orthogonal_res --- X residuals orthogonal to Y for O2PLS models. y_related_res --- X residuals related to Y for O2PLS models. y_orthogonal_scores --- y_pred --- predicted y variables`
| Method resolution order: hierarchical_types Boost.Python.enum builtins.int builtins.object Data and other attributes defined here: names = {'none': umetrics.simca.hierarchical_types.none, 'residuals': umetrics.simca.hierarchical_types.residuals, 'scores': umetrics.simca.hierarchical_types.scores, 'y_orthogonal_res': umetrics.simca.hierarchical_types.y_orthogonal_res, 'y_orthogonal_scores': umetrics.simca.hierarchical_types.y_orthogonal_scores, 'y_pred': umetrics.simca.hierarchical_types.y_pred, 'y_related_res': umetrics.simca.hierarchical_types.y_related_res} none = umetrics.simca.hierarchical_types.none residuals = umetrics.simca.hierarchical_types.residuals scores = umetrics.simca.hierarchical_types.scores values = {0: umetrics.simca.hierarchical_types.none, 1: umetrics.simca.hierarchical_types.scores, 2: umetrics.simca.hierarchical_types.residuals, 4: umetrics.simca.hierarchical_types.y_orthogonal_res, 8: umetrics.simca.hierarchical_types.y_related_res, 16: umetrics.simca.hierarchical_types.y_orthogonal_scores, 32: umetrics.simca.hierarchical_types.y_pred} y_orthogonal_res = umetrics.simca.hierarchical_types.y_orthogonal_res y_orthogonal_scores = umetrics.simca.hierarchical_types.y_orthogonal_scores y_pred = umetrics.simca.hierarchical_types.y_pred y_related_res = umetrics.simca.hierarchical_types.y_related_res Methods inherited from Boost.Python.enum: __repr__(self, /) `Return repr(self).` __str__(self, /) `Return str(self).` Data descriptors inherited from Boost.Python.enum: name Methods inherited from builtins.int: __abs__(self, /) `abs(self)` __add__(self, value, /) `Return self+value.` __and__(self, value, /) `Return self&value.` __bool__(self, /) `self != 0` __ceil__(...) `Ceiling of an Integral returns itself.` __divmod__(self, value, /) `Return divmod(self, value).` __eq__(self, value, /) `Return self==value.` __float__(self, /) `float(self)` __floor__(...) `Flooring an Integral returns itself.` __floordiv__(self, value, /) `Return self//value.` __format__(self, format_spec, /) `Default object formatter.` __ge__(self, value, /) `Return self>=value.` __getattribute__(self, name, /) `Return getattr(self, name).` __getnewargs__(self, /) __gt__(self, value, /) `Return self>value.` __hash__(self, /) `Return hash(self).` __index__(self, /) `Return self converted to an integer, if self is suitable for use as an index into a list.` __int__(self, /) `int(self)` __invert__(self, /) `~self` __le__(self, value, /) `Return self<=value.` __lshift__(self, value, /) `Return self<<value.` __lt__(self, value, /) `Return self<value.` __mod__(self, value, /) `Return self%value.` __mul__(self, value, /) `Return self*value.` __ne__(self, value, /) `Return self!=value.` __neg__(self, /) `-self` __or__(self, value, /) `Return self|value.` __pos__(self, /) `+self` __pow__(self, value, mod=None, /) `Return pow(self, value, mod).` __radd__(self, value, /) `Return value+self.` __rand__(self, value, /) `Return value&self.` __rdivmod__(self, value, /) `Return divmod(value, self).` __rfloordiv__(self, value, /) `Return value//self.` __rlshift__(self, value, /) `Return value<<self.` __rmod__(self, value, /) `Return value%self.` __rmul__(self, value, /) `Return value*self.` __ror__(self, value, /) `Return value|self.` __round__(...) `Rounding an Integral returns itself. Rounding with an ndigits argument also returns an integer.` __rpow__(self, value, mod=None, /) `Return pow(value, self, mod).` __rrshift__(self, value, /) `Return value>>self.` __rshift__(self, value, /) `Return self>>value.` __rsub__(self, value, /) `Return value-self.` __rtruediv__(self, value, /) `Return value/self.` __rxor__(self, value, /) `Return value^self.` __sizeof__(self, /) `Returns size in memory, in bytes.` __sub__(self, value, /) `Return self-value.` __truediv__(self, value, /) `Return self/value.` __trunc__(...) `Truncating an Integral returns itself.` __xor__(self, value, /) `Return self^value.` bit_length(self, /) `Number of bits necessary to represent self in binary. >>> bin(37) '0b100101' >>> (37).bit_length() 6` conjugate(...) `Returns self, the complex conjugate of any int.` to_bytes(self, /, length, byteorder, *, signed=False) `Return an array of bytes representing an integer. length Length of bytes object to use. An OverflowError is raised if the integer is not representable with the given number of bytes. byteorder The byte order used to represent the integer. If byteorder is 'big', the most significant byte is at the beginning of the byte array. If byteorder is 'little', the most significant byte is at the end of the byte array. To request the native byte order of the host system, use `sys.byteorder' as the byte order value. signed Determines whether two's complement is used to represent the integer. If signed is False and a negative integer is given, an OverflowError is raised.` Class methods inherited from builtins.int: from_bytes(bytes, byteorder, *, signed=False) from builtins.type `Return the integer represented by the given array of bytes. bytes Holds the array of bytes to convert. The argument must either support the buffer protocol or be an iterable object producing bytes. Bytes and bytearray are examples of built-in objects that support the buffer protocol. byteorder The byte order used to represent the integer. If byteorder is 'big', the most significant byte is at the beginning of the byte array. If byteorder is 'little', the most significant byte is at the end of the byte array. To request the native byte order of the host system, use `sys.byteorder' as the byte order value. signed Indicates whether two's complement is used to represent the integer.` Static methods inherited from builtins.int: __new__(*args, **kwargs) from builtins.type `Create and return a new object. See help(type) for accurate signature.` Data descriptors inherited from builtins.int: denominator `the denominator of a rational number in lowest terms` imag `the imaginary part of a complex number` numerator `the numerator of a rational number in lowest terms` real `the real part of a complex number`
class id_type(Boost.Python.enum)
`` | `Used in ValueIDs constructor to specify what the id's refer to. observation --- Strings are observation names. variable --- Strings are variable names. other --- Strings are arbitrary strings.`
| Method resolution order: id_type Boost.Python.enum builtins.int builtins.object Data and other attributes defined here: names = {'observation': umetrics.simca.id_type.observation, 'other': umetrics.simca.id_type.other, 'variable': umetrics.simca.id_type.variable} observation = umetrics.simca.id_type.observation other = umetrics.simca.id_type.other values = {0: umetrics.simca.id_type.variable, 1: umetrics.simca.id_type.observation, 2: umetrics.simca.id_type.other} variable = umetrics.simca.id_type.variable Methods inherited from Boost.Python.enum: __repr__(self, /) `Return repr(self).` __str__(self, /) `Return str(self).` Data descriptors inherited from Boost.Python.enum: name Methods inherited from builtins.int: __abs__(self, /) `abs(self)` __add__(self, value, /) `Return self+value.` __and__(self, value, /) `Return self&value.` __bool__(self, /) `self != 0` __ceil__(...) `Ceiling of an Integral returns itself.` __divmod__(self, value, /) `Return divmod(self, value).` __eq__(self, value, /) `Return self==value.` __float__(self, /) `float(self)` __floor__(...) `Flooring an Integral returns itself.` __floordiv__(self, value, /) `Return self//value.` __format__(self, format_spec, /) `Default object formatter.` __ge__(self, value, /) `Return self>=value.` __getattribute__(self, name, /) `Return getattr(self, name).` __getnewargs__(self, /) __gt__(self, value, /) `Return self>value.` __hash__(self, /) `Return hash(self).` __index__(self, /) `Return self converted to an integer, if self is suitable for use as an index into a list.` __int__(self, /) `int(self)` __invert__(self, /) `~self` __le__(self, value, /) `Return self<=value.` __lshift__(self, value, /) `Return self<<value.` __lt__(self, value, /) `Return self<value.` __mod__(self, value, /) `Return self%value.` __mul__(self, value, /) `Return self*value.` __ne__(self, value, /) `Return self!=value.` __neg__(self, /) `-self` __or__(self, value, /) `Return self|value.` __pos__(self, /) `+self` __pow__(self, value, mod=None, /) `Return pow(self, value, mod).` __radd__(self, value, /) `Return value+self.` __rand__(self, value, /) `Return value&self.` __rdivmod__(self, value, /) `Return divmod(value, self).` __rfloordiv__(self, value, /) `Return value//self.` __rlshift__(self, value, /) `Return value<<self.` __rmod__(self, value, /) `Return value%self.` __rmul__(self, value, /) `Return value*self.` __ror__(self, value, /) `Return value|self.` __round__(...) `Rounding an Integral returns itself. Rounding with an ndigits argument also returns an integer.` __rpow__(self, value, mod=None, /) `Return pow(value, self, mod).` __rrshift__(self, value, /) `Return value>>self.` __rshift__(self, value, /) `Return self>>value.` __rsub__(self, value, /) `Return value-self.` __rtruediv__(self, value, /) `Return value/self.` __rxor__(self, value, /) `Return value^self.` __sizeof__(self, /) `Returns size in memory, in bytes.` __sub__(self, value, /) `Return self-value.` __truediv__(self, value, /) `Return self/value.` __trunc__(...) `Truncating an Integral returns itself.` __xor__(self, value, /) `Return self^value.` bit_length(self, /) `Number of bits necessary to represent self in binary. >>> bin(37) '0b100101' >>> (37).bit_length() 6` conjugate(...) `Returns self, the complex conjugate of any int.` to_bytes(self, /, length, byteorder, *, signed=False) `Return an array of bytes representing an integer. length Length of bytes object to use. An OverflowError is raised if the integer is not representable with the given number of bytes. byteorder The byte order used to represent the integer. If byteorder is 'big', the most significant byte is at the beginning of the byte array. If byteorder is 'little', the most significant byte is at the end of the byte array. To request the native byte order of the host system, use `sys.byteorder' as the byte order value. signed Determines whether two's complement is used to represent the integer. If signed is False and a negative integer is given, an OverflowError is raised.` Class methods inherited from builtins.int: from_bytes(bytes, byteorder, *, signed=False) from builtins.type `Return the integer represented by the given array of bytes. bytes Holds the array of bytes to convert. The argument must either support the buffer protocol or be an iterable object producing bytes. Bytes and bytearray are examples of built-in objects that support the buffer protocol. byteorder The byte order used to represent the integer. If byteorder is 'big', the most significant byte is at the beginning of the byte array. If byteorder is 'little', the most significant byte is at the end of the byte array. To request the native byte order of the host system, use `sys.byteorder' as the byte order value. signed Indicates whether two's complement is used to represent the integer.` Static methods inherited from builtins.int: __new__(*args, **kwargs) from builtins.type `Create and return a new object. See help(type) for accurate signature.` Data descriptors inherited from builtins.int: denominator `the denominator of a rational number in lowest terms` imag `the imaginary part of a complex number` numerator `the numerator of a rational number in lowest terms` real `the real part of a complex number`
class merge_match(Boost.Python.enum)
`` | `The different ways to match for merge. exact --- Matches the row in the right/bottom dataset exactly in all keys with the left/top dataset's key. nearest --- Matches the row in the right/bottom dataset with the row closest in absolute distance in the left/top dataset. forward --- Matches the row in the left/top dataset with the last row in the right/bottom dataset that is less than or equal to the key in the left dataset. interpolate --- Matches the row in the left/top dataset with the interpolated values from the row closest after and closest before in the right/bottom dataset`
| Method resolution order: merge_match Boost.Python.enum builtins.int builtins.object Data and other attributes defined here: exact = umetrics.simca.merge_match.exact forward = umetrics.simca.merge_match.forward interpolate = umetrics.simca.merge_match.interpolate names = {'exact': umetrics.simca.merge_match.exact, 'forward': umetrics.simca.merge_match.forward, 'interpolate': umetrics.simca.merge_match.interpolate, 'nearest': umetrics.simca.merge_match.nearest} nearest = umetrics.simca.merge_match.nearest values = {1: umetrics.simca.merge_match.exact, 2: umetrics.simca.merge_match.nearest, 3: umetrics.simca.merge_match.forward, 4: umetrics.simca.merge_match.interpolate} Methods inherited from Boost.Python.enum: __repr__(self, /) `Return repr(self).` __str__(self, /) `Return str(self).` Data descriptors inherited from Boost.Python.enum: name Methods inherited from builtins.int: __abs__(self, /) `abs(self)` __add__(self, value, /) `Return self+value.` __and__(self, value, /) `Return self&value.` __bool__(self, /) `self != 0` __ceil__(...) `Ceiling of an Integral returns itself.` __divmod__(self, value, /) `Return divmod(self, value).` __eq__(self, value, /) `Return self==value.` __float__(self, /) `float(self)` __floor__(...) `Flooring an Integral returns itself.` __floordiv__(self, value, /) `Return self//value.` __format__(self, format_spec, /) `Default object formatter.` __ge__(self, value, /) `Return self>=value.` __getattribute__(self, name, /) `Return getattr(self, name).` __getnewargs__(self, /) __gt__(self, value, /) `Return self>value.` __hash__(self, /) `Return hash(self).` __index__(self, /) `Return self converted to an integer, if self is suitable for use as an index into a list.` __int__(self, /) `int(self)` __invert__(self, /) `~self` __le__(self, value, /) `Return self<=value.` __lshift__(self, value, /) `Return self<<value.` __lt__(self, value, /) `Return self<value.` __mod__(self, value, /) `Return self%value.` __mul__(self, value, /) `Return self*value.` __ne__(self, value, /) `Return self!=value.` __neg__(self, /) `-self` __or__(self, value, /) `Return self|value.` __pos__(self, /) `+self` __pow__(self, value, mod=None, /) `Return pow(self, value, mod).` __radd__(self, value, /) `Return value+self.` __rand__(self, value, /) `Return value&self.` __rdivmod__(self, value, /) `Return divmod(value, self).` __rfloordiv__(self, value, /) `Return value//self.` __rlshift__(self, value, /) `Return value<<self.` __rmod__(self, value, /) `Return value%self.` __rmul__(self, value, /) `Return value*self.` __ror__(self, value, /) `Return value|self.` __round__(...) `Rounding an Integral returns itself. Rounding with an ndigits argument also returns an integer.` __rpow__(self, value, mod=None, /) `Return pow(value, self, mod).` __rrshift__(self, value, /) `Return value>>self.` __rshift__(self, value, /) `Return self>>value.` __rsub__(self, value, /) `Return value-self.` __rtruediv__(self, value, /) `Return value/self.` __rxor__(self, value, /) `Return value^self.` __sizeof__(self, /) `Returns size in memory, in bytes.` __sub__(self, value, /) `Return self-value.` __truediv__(self, value, /) `Return self/value.` __trunc__(...) `Truncating an Integral returns itself.` __xor__(self, value, /) `Return self^value.` bit_length(self, /) `Number of bits necessary to represent self in binary. >>> bin(37) '0b100101' >>> (37).bit_length() 6` conjugate(...) `Returns self, the complex conjugate of any int.` to_bytes(self, /, length, byteorder, *, signed=False) `Return an array of bytes representing an integer. length Length of bytes object to use. An OverflowError is raised if the integer is not representable with the given number of bytes. byteorder The byte order used to represent the integer. If byteorder is 'big', the most significant byte is at the beginning of the byte array. If byteorder is 'little', the most significant byte is at the end of the byte array. To request the native byte order of the host system, use `sys.byteorder' as the byte order value. signed Determines whether two's complement is used to represent the integer. If signed is False and a negative integer is given, an OverflowError is raised.` Class methods inherited from builtins.int: from_bytes(bytes, byteorder, *, signed=False) from builtins.type `Return the integer represented by the given array of bytes. bytes Holds the array of bytes to convert. The argument must either support the buffer protocol or be an iterable object producing bytes. Bytes and bytearray are examples of built-in objects that support the buffer protocol. byteorder The byte order used to represent the integer. If byteorder is 'big', the most significant byte is at the beginning of the byte array. If byteorder is 'little', the most significant byte is at the end of the byte array. To request the native byte order of the host system, use `sys.byteorder' as the byte order value. signed Indicates whether two's complement is used to represent the integer.` Static methods inherited from builtins.int: __new__(*args, **kwargs) from builtins.type `Create and return a new object. See help(type) for accurate signature.` Data descriptors inherited from builtins.int: denominator `the denominator of a rational number in lowest terms` imag `the imaginary part of a complex number` numerator `the numerator of a rational number in lowest terms` real `the real part of a complex number`
class merge_type(Boost.Python.enum)
`` |  | Method resolution order: merge_type Boost.Python.enum builtins.int builtins.object Data and other attributes defined here: full = umetrics.simca.merge_type.full intersection = umetrics.simca.merge_type.intersection left = umetrics.simca.merge_type.left names = {'full': umetrics.simca.merge_type.full, 'intersection': umetrics.simca.merge_type.intersection, 'left': umetrics.simca.merge_type.left} values = {1: umetrics.simca.merge_type.intersection, 2: umetrics.simca.merge_type.full, 3: umetrics.simca.merge_type.left} Methods inherited from Boost.Python.enum: __repr__(self, /) `Return repr(self).` __str__(self, /) `Return str(self).` Data descriptors inherited from Boost.Python.enum: name Methods inherited from builtins.int: __abs__(self, /) `abs(self)` __add__(self, value, /) `Return self+value.` __and__(self, value, /) `Return self&value.` __bool__(self, /) `self != 0` __ceil__(...) `Ceiling of an Integral returns itself.` __divmod__(self, value, /) `Return divmod(self, value).` __eq__(self, value, /) `Return self==value.` __float__(self, /) `float(self)` __floor__(...) `Flooring an Integral returns itself.` __floordiv__(self, value, /) `Return self//value.` __format__(self, format_spec, /) `Default object formatter.` __ge__(self, value, /) `Return self>=value.` __getattribute__(self, name, /) `Return getattr(self, name).` __getnewargs__(self, /) __gt__(self, value, /) `Return self>value.` __hash__(self, /) `Return hash(self).` __index__(self, /) `Return self converted to an integer, if self is suitable for use as an index into a list.` __int__(self, /) `int(self)` __invert__(self, /) `~self` __le__(self, value, /) `Return self<=value.` __lshift__(self, value, /) `Return self<<value.` __lt__(self, value, /) `Return self<value.` __mod__(self, value, /) `Return self%value.` __mul__(self, value, /) `Return self*value.` __ne__(self, value, /) `Return self!=value.` __neg__(self, /) `-self` __or__(self, value, /) `Return self|value.` __pos__(self, /) `+self` __pow__(self, value, mod=None, /) `Return pow(self, value, mod).` __radd__(self, value, /) `Return value+self.` __rand__(self, value, /) `Return value&self.` __rdivmod__(self, value, /) `Return divmod(value, self).` __rfloordiv__(self, value, /) `Return value//self.` __rlshift__(self, value, /) `Return value<<self.` __rmod__(self, value, /) `Return value%self.` __rmul__(self, value, /) `Return value*self.` __ror__(self, value, /) `Return value|self.` __round__(...) `Rounding an Integral returns itself. Rounding with an ndigits argument also returns an integer.` __rpow__(self, value, mod=None, /) `Return pow(value, self, mod).` __rrshift__(self, value, /) `Return value>>self.` __rshift__(self, value, /) `Return self>>value.` __rsub__(self, value, /) `Return value-self.` __rtruediv__(self, value, /) `Return value/self.` __rxor__(self, value, /) `Return value^self.` __sizeof__(self, /) `Returns size in memory, in bytes.` __sub__(self, value, /) `Return self-value.` __truediv__(self, value, /) `Return self/value.` __trunc__(...) `Truncating an Integral returns itself.` __xor__(self, value, /) `Return self^value.` bit_length(self, /) `Number of bits necessary to represent self in binary. >>> bin(37) '0b100101' >>> (37).bit_length() 6` conjugate(...) `Returns self, the complex conjugate of any int.` to_bytes(self, /, length, byteorder, *, signed=False) `Return an array of bytes representing an integer. length Length of bytes object to use. An OverflowError is raised if the integer is not representable with the given number of bytes. byteorder The byte order used to represent the integer. If byteorder is 'big', the most significant byte is at the beginning of the byte array. If byteorder is 'little', the most significant byte is at the end of the byte array. To request the native byte order of the host system, use `sys.byteorder' as the byte order value. signed Determines whether two's complement is used to represent the integer. If signed is False and a negative integer is given, an OverflowError is raised.` Class methods inherited from builtins.int: from_bytes(bytes, byteorder, *, signed=False) from builtins.type `Return the integer represented by the given array of bytes. bytes Holds the array of bytes to convert. The argument must either support the buffer protocol or be an iterable object producing bytes. Bytes and bytearray are examples of built-in objects that support the buffer protocol. byteorder The byte order used to represent the integer. If byteorder is 'big', the most significant byte is at the beginning of the byte array. If byteorder is 'little', the most significant byte is at the end of the byte array. To request the native byte order of the host system, use `sys.byteorder' as the byte order value. signed Indicates whether two's complement is used to represent the integer.` Static methods inherited from builtins.int: __new__(*args, **kwargs) from builtins.type `Create and return a new object. See help(type) for accurate signature.` Data descriptors inherited from builtins.int: denominator `the denominator of a rational number in lowest terms` imag `the imaginary part of a complex number` numerator `the numerator of a rational number in lowest terms` real `the real part of a complex number`
class modeltype(Boost.Python.enum)
`` | `Different model types A model can be one of these different types. pcaX --- PCA model on X variables pcaY --- PCA model on Y variables pcaXY --- PCA model on both X and Y variables pcaClass --- PLS model with classes in the X block pls --- Model with PLS relationship between X and Y variables plsDa --- Model as PLS discriminant analysis plsClass --- PLS model with classes in the X and Y block opls --- Model with OPLS relationship between X and Y variables to separate systematic variation into predictive and orthogonal Y oplsDa --- Model as OPLS discriminant analysis oplsClass --- OPLS model with classes in the X and Y block o2pls --- Model with O2PLS relationship between X and Y variables to separate systematic variation into predictive and orthogonal Y o2plsDa --- Model as O2PLS discriminant analysis o2plsClass --- O2PLS model with classes in the X and Y block moca --- MOCA model with variable classes as blocks default --- Use the default type depending on workset data`
| Method resolution order: modeltype Boost.Python.enum builtins.int builtins.object Data and other attributes defined here: default = umetrics.simca.modeltype.default moca = umetrics.simca.modeltype.moca names = {'default': umetrics.simca.modeltype.default, 'moca': umetrics.simca.modeltype.moca, 'o2pls': umetrics.simca.modeltype.o2pls, 'o2plsClass': umetrics.simca.modeltype.o2plsClass, 'o2plsDa': umetrics.simca.modeltype.o2plsDa, 'opls': umetrics.simca.modeltype.opls, 'oplsClass': umetrics.simca.modeltype.oplsClass, 'oplsDa': umetrics.simca.modeltype.oplsDa, 'pcaClass': umetrics.simca.modeltype.pcaClass, 'pcaX': umetrics.simca.modeltype.pcaX, ...} o2pls = umetrics.simca.modeltype.o2pls o2plsClass = umetrics.simca.modeltype.o2plsClass o2plsDa = umetrics.simca.modeltype.o2plsDa opls = umetrics.simca.modeltype.opls oplsClass = umetrics.simca.modeltype.oplsClass oplsDa = umetrics.simca.modeltype.oplsDa pcaClass = umetrics.simca.modeltype.pcaClass pcaX = umetrics.simca.modeltype.pcaX pcaXY = umetrics.simca.modeltype.pcaXY pcaY = umetrics.simca.modeltype.pcaY pls = umetrics.simca.modeltype.pls plsClass = umetrics.simca.modeltype.plsClass plsDa = umetrics.simca.modeltype.plsDa values = {0: umetrics.simca.modeltype.pcaX, 1: umetrics.simca.modeltype.pcaY, 2: umetrics.simca.modeltype.pcaXY, 3: umetrics.simca.modeltype.pcaClass, 4: umetrics.simca.modeltype.pls, 5: umetrics.simca.modeltype.plsDa, 6: umetrics.simca.modeltype.plsClass, 7: umetrics.simca.modeltype.opls, 8: umetrics.simca.modeltype.oplsDa, 9: umetrics.simca.modeltype.oplsClass, ...} Methods inherited from Boost.Python.enum: __repr__(self, /) `Return repr(self).` __str__(self, /) `Return str(self).` Data descriptors inherited from Boost.Python.enum: name Methods inherited from builtins.int: __abs__(self, /) `abs(self)` __add__(self, value, /) `Return self+value.` __and__(self, value, /) `Return self&value.` __bool__(self, /) `self != 0` __ceil__(...) `Ceiling of an Integral returns itself.` __divmod__(self, value, /) `Return divmod(self, value).` __eq__(self, value, /) `Return self==value.` __float__(self, /) `float(self)` __floor__(...) `Flooring an Integral returns itself.` __floordiv__(self, value, /) `Return self//value.` __format__(self, format_spec, /) `Default object formatter.` __ge__(self, value, /) `Return self>=value.` __getattribute__(self, name, /) `Return getattr(self, name).` __getnewargs__(self, /) __gt__(self, value, /) `Return self>value.` __hash__(self, /) `Return hash(self).` __index__(self, /) `Return self converted to an integer, if self is suitable for use as an index into a list.` __int__(self, /) `int(self)` __invert__(self, /) `~self` __le__(self, value, /) `Return self<=value.` __lshift__(self, value, /) `Return self<<value.` __lt__(self, value, /) `Return self<value.` __mod__(self, value, /) `Return self%value.` __mul__(self, value, /) `Return self*value.` __ne__(self, value, /) `Return self!=value.` __neg__(self, /) `-self` __or__(self, value, /) `Return self|value.` __pos__(self, /) `+self` __pow__(self, value, mod=None, /) `Return pow(self, value, mod).` __radd__(self, value, /) `Return value+self.` __rand__(self, value, /) `Return value&self.` __rdivmod__(self, value, /) `Return divmod(value, self).` __rfloordiv__(self, value, /) `Return value//self.` __rlshift__(self, value, /) `Return value<<self.` __rmod__(self, value, /) `Return value%self.` __rmul__(self, value, /) `Return value*self.` __ror__(self, value, /) `Return value|self.` __round__(...) `Rounding an Integral returns itself. Rounding with an ndigits argument also returns an integer.` __rpow__(self, value, mod=None, /) `Return pow(value, self, mod).` __rrshift__(self, value, /) `Return value>>self.` __rshift__(self, value, /) `Return self>>value.` __rsub__(self, value, /) `Return value-self.` __rtruediv__(self, value, /) `Return value/self.` __rxor__(self, value, /) `Return value^self.` __sizeof__(self, /) `Returns size in memory, in bytes.` __sub__(self, value, /) `Return self-value.` __truediv__(self, value, /) `Return self/value.` __trunc__(...) `Truncating an Integral returns itself.` __xor__(self, value, /) `Return self^value.` bit_length(self, /) `Number of bits necessary to represent self in binary. >>> bin(37) '0b100101' >>> (37).bit_length() 6` conjugate(...) `Returns self, the complex conjugate of any int.` to_bytes(self, /, length, byteorder, *, signed=False) `Return an array of bytes representing an integer. length Length of bytes object to use. An OverflowError is raised if the integer is not representable with the given number of bytes. byteorder The byte order used to represent the integer. If byteorder is 'big', the most significant byte is at the beginning of the byte array. If byteorder is 'little', the most significant byte is at the end of the byte array. To request the native byte order of the host system, use `sys.byteorder' as the byte order value. signed Determines whether two's complement is used to represent the integer. If signed is False and a negative integer is given, an OverflowError is raised.` Class methods inherited from builtins.int: from_bytes(bytes, byteorder, *, signed=False) from builtins.type `Return the integer represented by the given array of bytes. bytes Holds the array of bytes to convert. The argument must either support the buffer protocol or be an iterable object producing bytes. Bytes and bytearray are examples of built-in objects that support the buffer protocol. byteorder The byte order used to represent the integer. If byteorder is 'big', the most significant byte is at the beginning of the byte array. If byteorder is 'little', the most significant byte is at the end of the byte array. To request the native byte order of the host system, use `sys.byteorder' as the byte order value. signed Indicates whether two's complement is used to represent the integer.` Static methods inherited from builtins.int: __new__(*args, **kwargs) from builtins.type `Create and return a new object. See help(type) for accurate signature.` Data descriptors inherited from builtins.int: denominator `the denominator of a rational number in lowest terms` imag `the imaginary part of a complex number` numerator `the numerator of a rational number in lowest terms` real `the real part of a complex number`
class no_match_option(Boost.Python.enum)
`` |  | Method resolution order: no_match_option Boost.Python.enum builtins.int builtins.object Data and other attributes defined here: insert_last_match = umetrics.simca.no_match_option.insert_last_match keep_non_matching = umetrics.simca.no_match_option.keep_non_matching names = {'insert_last_match': umetrics.simca.no_match_option.insert_last_match, 'keep_non_matching': umetrics.simca.no_match_option.keep_non_matching} values = {1: umetrics.simca.no_match_option.keep_non_matching, 2: umetrics.simca.no_match_option.insert_last_match} Methods inherited from Boost.Python.enum: __repr__(self, /) `Return repr(self).` __str__(self, /) `Return str(self).` Data descriptors inherited from Boost.Python.enum: name Methods inherited from builtins.int: __abs__(self, /) `abs(self)` __add__(self, value, /) `Return self+value.` __and__(self, value, /) `Return self&value.` __bool__(self, /) `self != 0` __ceil__(...) `Ceiling of an Integral returns itself.` __divmod__(self, value, /) `Return divmod(self, value).` __eq__(self, value, /) `Return self==value.` __float__(self, /) `float(self)` __floor__(...) `Flooring an Integral returns itself.` __floordiv__(self, value, /) `Return self//value.` __format__(self, format_spec, /) `Default object formatter.` __ge__(self, value, /) `Return self>=value.` __getattribute__(self, name, /) `Return getattr(self, name).` __getnewargs__(self, /) __gt__(self, value, /) `Return self>value.` __hash__(self, /) `Return hash(self).` __index__(self, /) `Return self converted to an integer, if self is suitable for use as an index into a list.` __int__(self, /) `int(self)` __invert__(self, /) `~self` __le__(self, value, /) `Return self<=value.` __lshift__(self, value, /) `Return self<<value.` __lt__(self, value, /) `Return self<value.` __mod__(self, value, /) `Return self%value.` __mul__(self, value, /) `Return self*value.` __ne__(self, value, /) `Return self!=value.` __neg__(self, /) `-self` __or__(self, value, /) `Return self|value.` __pos__(self, /) `+self` __pow__(self, value, mod=None, /) `Return pow(self, value, mod).` __radd__(self, value, /) `Return value+self.` __rand__(self, value, /) `Return value&self.` __rdivmod__(self, value, /) `Return divmod(value, self).` __rfloordiv__(self, value, /) `Return value//self.` __rlshift__(self, value, /) `Return value<<self.` __rmod__(self, value, /) `Return value%self.` __rmul__(self, value, /) `Return value*self.` __ror__(self, value, /) `Return value|self.` __round__(...) `Rounding an Integral returns itself. Rounding with an ndigits argument also returns an integer.` __rpow__(self, value, mod=None, /) `Return pow(value, self, mod).` __rrshift__(self, value, /) `Return value>>self.` __rshift__(self, value, /) `Return self>>value.` __rsub__(self, value, /) `Return value-self.` __rtruediv__(self, value, /) `Return value/self.` __rxor__(self, value, /) `Return value^self.` __sizeof__(self, /) `Returns size in memory, in bytes.` __sub__(self, value, /) `Return self-value.` __truediv__(self, value, /) `Return self/value.` __trunc__(...) `Truncating an Integral returns itself.` __xor__(self, value, /) `Return self^value.` bit_length(self, /) `Number of bits necessary to represent self in binary. >>> bin(37) '0b100101' >>> (37).bit_length() 6` conjugate(...) `Returns self, the complex conjugate of any int.` to_bytes(self, /, length, byteorder, *, signed=False) `Return an array of bytes representing an integer. length Length of bytes object to use. An OverflowError is raised if the integer is not representable with the given number of bytes. byteorder The byte order used to represent the integer. If byteorder is 'big', the most significant byte is at the beginning of the byte array. If byteorder is 'little', the most significant byte is at the end of the byte array. To request the native byte order of the host system, use `sys.byteorder' as the byte order value. signed Determines whether two's complement is used to represent the integer. If signed is False and a negative integer is given, an OverflowError is raised.` Class methods inherited from builtins.int: from_bytes(bytes, byteorder, *, signed=False) from builtins.type `Return the integer represented by the given array of bytes. bytes Holds the array of bytes to convert. The argument must either support the buffer protocol or be an iterable object producing bytes. Bytes and bytearray are examples of built-in objects that support the buffer protocol. byteorder The byte order used to represent the integer. If byteorder is 'big', the most significant byte is at the beginning of the byte array. If byteorder is 'little', the most significant byte is at the end of the byte array. To request the native byte order of the host system, use `sys.byteorder' as the byte order value. signed Indicates whether two's complement is used to represent the integer.` Static methods inherited from builtins.int: __new__(*args, **kwargs) from builtins.type `Create and return a new object. See help(type) for accurate signature.` Data descriptors inherited from builtins.int: denominator `the denominator of a rational number in lowest terms` imag `the imaginary part of a complex number` numerator `the numerator of a rational number in lowest terms` real `the real part of a complex number`
class operator(Boost.Python.enum)
`` | `Operator how to compare a variable to a value smaller --- The variable is smaller than given value. larger --- The variable is larger than given value. smallerOrEqual --- The variable is smaller or equal than given value. largerOrEqual --- The variable is larger or equal than given value outside --- The variable is outside min and max values`
| Method resolution order: operator Boost.Python.enum builtins.int builtins.object Data and other attributes defined here: larger = umetrics.simca.operator.larger largerOrEqual = umetrics.simca.operator.largerOrEqual names = {'larger': umetrics.simca.operator.larger, 'largerOrEqual': umetrics.simca.operator.largerOrEqual, 'outside': umetrics.simca.operator.outside, 'smaller': umetrics.simca.operator.smaller, 'smallerOrEqual': umetrics.simca.operator.smallerOrEqual} outside = umetrics.simca.operator.outside smaller = umetrics.simca.operator.smaller smallerOrEqual = umetrics.simca.operator.smallerOrEqual values = {0: umetrics.simca.operator.smaller, 1: umetrics.simca.operator.larger, 2: umetrics.simca.operator.smallerOrEqual, 3: umetrics.simca.operator.largerOrEqual, 4: umetrics.simca.operator.outside} Methods inherited from Boost.Python.enum: __repr__(self, /) `Return repr(self).` __str__(self, /) `Return str(self).` Data descriptors inherited from Boost.Python.enum: name Methods inherited from builtins.int: __abs__(self, /) `abs(self)` __add__(self, value, /) `Return self+value.` __and__(self, value, /) `Return self&value.` __bool__(self, /) `self != 0` __ceil__(...) `Ceiling of an Integral returns itself.` __divmod__(self, value, /) `Return divmod(self, value).` __eq__(self, value, /) `Return self==value.` __float__(self, /) `float(self)` __floor__(...) `Flooring an Integral returns itself.` __floordiv__(self, value, /) `Return self//value.` __format__(self, format_spec, /) `Default object formatter.` __ge__(self, value, /) `Return self>=value.` __getattribute__(self, name, /) `Return getattr(self, name).` __getnewargs__(self, /) __gt__(self, value, /) `Return self>value.` __hash__(self, /) `Return hash(self).` __index__(self, /) `Return self converted to an integer, if self is suitable for use as an index into a list.` __int__(self, /) `int(self)` __invert__(self, /) `~self` __le__(self, value, /) `Return self<=value.` __lshift__(self, value, /) `Return self<<value.` __lt__(self, value, /) `Return self<value.` __mod__(self, value, /) `Return self%value.` __mul__(self, value, /) `Return self*value.` __ne__(self, value, /) `Return self!=value.` __neg__(self, /) `-self` __or__(self, value, /) `Return self|value.` __pos__(self, /) `+self` __pow__(self, value, mod=None, /) `Return pow(self, value, mod).` __radd__(self, value, /) `Return value+self.` __rand__(self, value, /) `Return value&self.` __rdivmod__(self, value, /) `Return divmod(value, self).` __rfloordiv__(self, value, /) `Return value//self.` __rlshift__(self, value, /) `Return value<<self.` __rmod__(self, value, /) `Return value%self.` __rmul__(self, value, /) `Return value*self.` __ror__(self, value, /) `Return value|self.` __round__(...) `Rounding an Integral returns itself. Rounding with an ndigits argument also returns an integer.` __rpow__(self, value, mod=None, /) `Return pow(value, self, mod).` __rrshift__(self, value, /) `Return value>>self.` __rshift__(self, value, /) `Return self>>value.` __rsub__(self, value, /) `Return value-self.` __rtruediv__(self, value, /) `Return value/self.` __rxor__(self, value, /) `Return value^self.` __sizeof__(self, /) `Returns size in memory, in bytes.` __sub__(self, value, /) `Return self-value.` __truediv__(self, value, /) `Return self/value.` __trunc__(...) `Truncating an Integral returns itself.` __xor__(self, value, /) `Return self^value.` bit_length(self, /) `Number of bits necessary to represent self in binary. >>> bin(37) '0b100101' >>> (37).bit_length() 6` conjugate(...) `Returns self, the complex conjugate of any int.` to_bytes(self, /, length, byteorder, *, signed=False) `Return an array of bytes representing an integer. length Length of bytes object to use. An OverflowError is raised if the integer is not representable with the given number of bytes. byteorder The byte order used to represent the integer. If byteorder is 'big', the most significant byte is at the beginning of the byte array. If byteorder is 'little', the most significant byte is at the end of the byte array. To request the native byte order of the host system, use `sys.byteorder' as the byte order value. signed Determines whether two's complement is used to represent the integer. If signed is False and a negative integer is given, an OverflowError is raised.` Class methods inherited from builtins.int: from_bytes(bytes, byteorder, *, signed=False) from builtins.type `Return the integer represented by the given array of bytes. bytes Holds the array of bytes to convert. The argument must either support the buffer protocol or be an iterable object producing bytes. Bytes and bytearray are examples of built-in objects that support the buffer protocol. byteorder The byte order used to represent the integer. If byteorder is 'big', the most significant byte is at the beginning of the byte array. If byteorder is 'little', the most significant byte is at the end of the byte array. To request the native byte order of the host system, use `sys.byteorder' as the byte order value. signed Indicates whether two's complement is used to represent the integer.` Static methods inherited from builtins.int: __new__(*args, **kwargs) from builtins.type `Create and return a new object. See help(type) for accurate signature.` Data descriptors inherited from builtins.int: denominator `the denominator of a rational number in lowest terms` imag `the imaginary part of a complex number` numerator `the numerator of a rational number in lowest terms` real `the real part of a complex number`
class phase_iteration_treatment(Boost.Python.enum)
`` | `Enum for which phase iterations to use when creating batch level use_all_rows --- Compare phase iterations within a phase, creates one row per phase iteration and batch. average_iteration --- Use an average of all phase iterations. last_iteration --- Use the last phase iteration. all_iterations --- Use all phase iterations. first_iterations --- Use the first phase iterations.`
| Method resolution order: phase_iteration_treatment Boost.Python.enum builtins.int builtins.object Data and other attributes defined here: all_iterations = umetrics.simca.phase_iteration_treatment.all_iterations average_iteration = umetrics.simca.phase_iteration_treatment.average_iteration first_iterations = umetrics.simca.phase_iteration_treatment.first_iterations last_iteration = umetrics.simca.phase_iteration_treatment.last_iteration names = {'all_iterations': umetrics.simca.phase_iteration_treatment.all_iterations, 'average_iteration': umetrics.simca.phase_iteration_treatment.average_iteration, 'first_iterations': umetrics.simca.phase_iteration_treatment.first_iterations, 'last_iteration': umetrics.simca.phase_iteration_treatment.last_iteration, 'use_all_rows': umetrics.simca.phase_iteration_treatment.use_all_rows} use_all_rows = umetrics.simca.phase_iteration_treatment.use_all_rows values = {-5: umetrics.simca.phase_iteration_treatment.all_iterations, -3: umetrics.simca.phase_iteration_treatment.last_iteration, -2: umetrics.simca.phase_iteration_treatment.average_iteration, -1: umetrics.simca.phase_iteration_treatment.use_all_rows, 0: umetrics.simca.phase_iteration_treatment.first_iterations} Methods inherited from Boost.Python.enum: __repr__(self, /) `Return repr(self).` __str__(self, /) `Return str(self).` Data descriptors inherited from Boost.Python.enum: name Methods inherited from builtins.int: __abs__(self, /) `abs(self)` __add__(self, value, /) `Return self+value.` __and__(self, value, /) `Return self&value.` __bool__(self, /) `self != 0` __ceil__(...) `Ceiling of an Integral returns itself.` __divmod__(self, value, /) `Return divmod(self, value).` __eq__(self, value, /) `Return self==value.` __float__(self, /) `float(self)` __floor__(...) `Flooring an Integral returns itself.` __floordiv__(self, value, /) `Return self//value.` __format__(self, format_spec, /) `Default object formatter.` __ge__(self, value, /) `Return self>=value.` __getattribute__(self, name, /) `Return getattr(self, name).` __getnewargs__(self, /) __gt__(self, value, /) `Return self>value.` __hash__(self, /) `Return hash(self).` __index__(self, /) `Return self converted to an integer, if self is suitable for use as an index into a list.` __int__(self, /) `int(self)` __invert__(self, /) `~self` __le__(self, value, /) `Return self<=value.` __lshift__(self, value, /) `Return self<<value.` __lt__(self, value, /) `Return self<value.` __mod__(self, value, /) `Return self%value.` __mul__(self, value, /) `Return self*value.` __ne__(self, value, /) `Return self!=value.` __neg__(self, /) `-self` __or__(self, value, /) `Return self|value.` __pos__(self, /) `+self` __pow__(self, value, mod=None, /) `Return pow(self, value, mod).` __radd__(self, value, /) `Return value+self.` __rand__(self, value, /) `Return value&self.` __rdivmod__(self, value, /) `Return divmod(value, self).` __rfloordiv__(self, value, /) `Return value//self.` __rlshift__(self, value, /) `Return value<<self.` __rmod__(self, value, /) `Return value%self.` __rmul__(self, value, /) `Return value*self.` __ror__(self, value, /) `Return value|self.` __round__(...) `Rounding an Integral returns itself. Rounding with an ndigits argument also returns an integer.` __rpow__(self, value, mod=None, /) `Return pow(value, self, mod).` __rrshift__(self, value, /) `Return value>>self.` __rshift__(self, value, /) `Return self>>value.` __rsub__(self, value, /) `Return value-self.` __rtruediv__(self, value, /) `Return value/self.` __rxor__(self, value, /) `Return value^self.` __sizeof__(self, /) `Returns size in memory, in bytes.` __sub__(self, value, /) `Return self-value.` __truediv__(self, value, /) `Return self/value.` __trunc__(...) `Truncating an Integral returns itself.` __xor__(self, value, /) `Return self^value.` bit_length(self, /) `Number of bits necessary to represent self in binary. >>> bin(37) '0b100101' >>> (37).bit_length() 6` conjugate(...) `Returns self, the complex conjugate of any int.` to_bytes(self, /, length, byteorder, *, signed=False) `Return an array of bytes representing an integer. length Length of bytes object to use. An OverflowError is raised if the integer is not representable with the given number of bytes. byteorder The byte order used to represent the integer. If byteorder is 'big', the most significant byte is at the beginning of the byte array. If byteorder is 'little', the most significant byte is at the end of the byte array. To request the native byte order of the host system, use `sys.byteorder' as the byte order value. signed Determines whether two's complement is used to represent the integer. If signed is False and a negative integer is given, an OverflowError is raised.` Class methods inherited from builtins.int: from_bytes(bytes, byteorder, *, signed=False) from builtins.type `Return the integer represented by the given array of bytes. bytes Holds the array of bytes to convert. The argument must either support the buffer protocol or be an iterable object producing bytes. Bytes and bytearray are examples of built-in objects that support the buffer protocol. byteorder The byte order used to represent the integer. If byteorder is 'big', the most significant byte is at the beginning of the byte array. If byteorder is 'little', the most significant byte is at the end of the byte array. To request the native byte order of the host system, use `sys.byteorder' as the byte order value. signed Indicates whether two's complement is used to represent the integer.` Static methods inherited from builtins.int: __new__(*args, **kwargs) from builtins.type `Create and return a new object. See help(type) for accurate signature.` Data descriptors inherited from builtins.int: denominator `the denominator of a rational number in lowest terms` imag `the imaginary part of a complex number` numerator `the numerator of a rational number in lowest terms` real `the real part of a complex number`
class polynomialorder(Boost.Python.enum)
`` | `Different polynomial orders. Only valid for derivative filters. quadratic --- Quadratic polynomial order. cubic --- Cubic polynomial order.`
| Method resolution order: polynomialorder Boost.Python.enum builtins.int builtins.object Data and other attributes defined here: cubic = umetrics.simca.polynomialorder.cubic names = {'cubic': umetrics.simca.polynomialorder.cubic, 'quadratic': umetrics.simca.polynomialorder.quadratic} quadratic = umetrics.simca.polynomialorder.quadratic values = {2: umetrics.simca.polynomialorder.quadratic, 3: umetrics.simca.polynomialorder.cubic} Methods inherited from Boost.Python.enum: __repr__(self, /) `Return repr(self).` __str__(self, /) `Return str(self).` Data descriptors inherited from Boost.Python.enum: name Methods inherited from builtins.int: __abs__(self, /) `abs(self)` __add__(self, value, /) `Return self+value.` __and__(self, value, /) `Return self&value.` __bool__(self, /) `self != 0` __ceil__(...) `Ceiling of an Integral returns itself.` __divmod__(self, value, /) `Return divmod(self, value).` __eq__(self, value, /) `Return self==value.` __float__(self, /) `float(self)` __floor__(...) `Flooring an Integral returns itself.` __floordiv__(self, value, /) `Return self//value.` __format__(self, format_spec, /) `Default object formatter.` __ge__(self, value, /) `Return self>=value.` __getattribute__(self, name, /) `Return getattr(self, name).` __getnewargs__(self, /) __gt__(self, value, /) `Return self>value.` __hash__(self, /) `Return hash(self).` __index__(self, /) `Return self converted to an integer, if self is suitable for use as an index into a list.` __int__(self, /) `int(self)` __invert__(self, /) `~self` __le__(self, value, /) `Return self<=value.` __lshift__(self, value, /) `Return self<<value.` __lt__(self, value, /) `Return self<value.` __mod__(self, value, /) `Return self%value.` __mul__(self, value, /) `Return self*value.` __ne__(self, value, /) `Return self!=value.` __neg__(self, /) `-self` __or__(self, value, /) `Return self|value.` __pos__(self, /) `+self` __pow__(self, value, mod=None, /) `Return pow(self, value, mod).` __radd__(self, value, /) `Return value+self.` __rand__(self, value, /) `Return value&self.` __rdivmod__(self, value, /) `Return divmod(value, self).` __rfloordiv__(self, value, /) `Return value//self.` __rlshift__(self, value, /) `Return value<<self.` __rmod__(self, value, /) `Return value%self.` __rmul__(self, value, /) `Return value*self.` __ror__(self, value, /) `Return value|self.` __round__(...) `Rounding an Integral returns itself. Rounding with an ndigits argument also returns an integer.` __rpow__(self, value, mod=None, /) `Return pow(value, self, mod).` __rrshift__(self, value, /) `Return value>>self.` __rshift__(self, value, /) `Return self>>value.` __rsub__(self, value, /) `Return value-self.` __rtruediv__(self, value, /) `Return value/self.` __rxor__(self, value, /) `Return value^self.` __sizeof__(self, /) `Returns size in memory, in bytes.` __sub__(self, value, /) `Return self-value.` __truediv__(self, value, /) `Return self/value.` __trunc__(...) `Truncating an Integral returns itself.` __xor__(self, value, /) `Return self^value.` bit_length(self, /) `Number of bits necessary to represent self in binary. >>> bin(37) '0b100101' >>> (37).bit_length() 6` conjugate(...) `Returns self, the complex conjugate of any int.` to_bytes(self, /, length, byteorder, *, signed=False) `Return an array of bytes representing an integer. length Length of bytes object to use. An OverflowError is raised if the integer is not representable with the given number of bytes. byteorder The byte order used to represent the integer. If byteorder is 'big', the most significant byte is at the beginning of the byte array. If byteorder is 'little', the most significant byte is at the end of the byte array. To request the native byte order of the host system, use `sys.byteorder' as the byte order value. signed Determines whether two's complement is used to represent the integer. If signed is False and a negative integer is given, an OverflowError is raised.` Class methods inherited from builtins.int: from_bytes(bytes, byteorder, *, signed=False) from builtins.type `Return the integer represented by the given array of bytes. bytes Holds the array of bytes to convert. The argument must either support the buffer protocol or be an iterable object producing bytes. Bytes and bytearray are examples of built-in objects that support the buffer protocol. byteorder The byte order used to represent the integer. If byteorder is 'big', the most significant byte is at the beginning of the byte array. If byteorder is 'little', the most significant byte is at the end of the byte array. To request the native byte order of the host system, use `sys.byteorder' as the byte order value. signed Indicates whether two's complement is used to represent the integer.` Static methods inherited from builtins.int: __new__(*args, **kwargs) from builtins.type `Create and return a new object. See help(type) for accurate signature.` Data descriptors inherited from builtins.int: denominator `the denominator of a rational number in lowest terms` imag `the imaginary part of a complex number` numerator `the numerator of a rational number in lowest terms` real `the real part of a complex number`
class powerexponent(Boost.Python.enum)
`` | `The powerexponent for power transform filter type. invsquare --- -2 inverse --- -1 invsquareroot --- -0.5 invfourthroot --- -0.25 fourthroot --- 0.25 squareroot --- 0.5 identity --- 1 square --- 2`
| Method resolution order: powerexponent Boost.Python.enum builtins.int builtins.object Data and other attributes defined here: fourthroot = umetrics.simca.powerexponent.fourthroot identity = umetrics.simca.powerexponent.identity inverse = umetrics.simca.powerexponent.inverse invfourthroot = umetrics.simca.powerexponent.invfourthroot invsquare = umetrics.simca.powerexponent.invsquare invsquareroot = umetrics.simca.powerexponent.invsquareroot names = {'fourthroot': umetrics.simca.powerexponent.fourthroot, 'identity': umetrics.simca.powerexponent.identity, 'inverse': umetrics.simca.powerexponent.inverse, 'invfourthroot': umetrics.simca.powerexponent.invfourthroot, 'invsquare': umetrics.simca.powerexponent.invsquare, 'invsquareroot': umetrics.simca.powerexponent.invsquareroot, 'square': umetrics.simca.powerexponent.square, 'squareroot': umetrics.simca.powerexponent.squareroot} square = umetrics.simca.powerexponent.square squareroot = umetrics.simca.powerexponent.squareroot values = {0: umetrics.simca.powerexponent.invsquare, 1: umetrics.simca.powerexponent.inverse, 2: umetrics.simca.powerexponent.invsquareroot, 3: umetrics.simca.powerexponent.invfourthroot, 4: umetrics.simca.powerexponent.fourthroot, 5: umetrics.simca.powerexponent.squareroot, 6: umetrics.simca.powerexponent.identity, 7: umetrics.simca.powerexponent.square} Methods inherited from Boost.Python.enum: __repr__(self, /) `Return repr(self).` __str__(self, /) `Return str(self).` Data descriptors inherited from Boost.Python.enum: name Methods inherited from builtins.int: __abs__(self, /) `abs(self)` __add__(self, value, /) `Return self+value.` __and__(self, value, /) `Return self&value.` __bool__(self, /) `self != 0` __ceil__(...) `Ceiling of an Integral returns itself.` __divmod__(self, value, /) `Return divmod(self, value).` __eq__(self, value, /) `Return self==value.` __float__(self, /) `float(self)` __floor__(...) `Flooring an Integral returns itself.` __floordiv__(self, value, /) `Return self//value.` __format__(self, format_spec, /) `Default object formatter.` __ge__(self, value, /) `Return self>=value.` __getattribute__(self, name, /) `Return getattr(self, name).` __getnewargs__(self, /) __gt__(self, value, /) `Return self>value.` __hash__(self, /) `Return hash(self).` __index__(self, /) `Return self converted to an integer, if self is suitable for use as an index into a list.` __int__(self, /) `int(self)` __invert__(self, /) `~self` __le__(self, value, /) `Return self<=value.` __lshift__(self, value, /) `Return self<<value.` __lt__(self, value, /) `Return self<value.` __mod__(self, value, /) `Return self%value.` __mul__(self, value, /) `Return self*value.` __ne__(self, value, /) `Return self!=value.` __neg__(self, /) `-self` __or__(self, value, /) `Return self|value.` __pos__(self, /) `+self` __pow__(self, value, mod=None, /) `Return pow(self, value, mod).` __radd__(self, value, /) `Return value+self.` __rand__(self, value, /) `Return value&self.` __rdivmod__(self, value, /) `Return divmod(value, self).` __rfloordiv__(self, value, /) `Return value//self.` __rlshift__(self, value, /) `Return value<<self.` __rmod__(self, value, /) `Return value%self.` __rmul__(self, value, /) `Return value*self.` __ror__(self, value, /) `Return value|self.` __round__(...) `Rounding an Integral returns itself. Rounding with an ndigits argument also returns an integer.` __rpow__(self, value, mod=None, /) `Return pow(value, self, mod).` __rrshift__(self, value, /) `Return value>>self.` __rshift__(self, value, /) `Return self>>value.` __rsub__(self, value, /) `Return value-self.` __rtruediv__(self, value, /) `Return value/self.` __rxor__(self, value, /) `Return value^self.` __sizeof__(self, /) `Returns size in memory, in bytes.` __sub__(self, value, /) `Return self-value.` __truediv__(self, value, /) `Return self/value.` __trunc__(...) `Truncating an Integral returns itself.` __xor__(self, value, /) `Return self^value.` bit_length(self, /) `Number of bits necessary to represent self in binary. >>> bin(37) '0b100101' >>> (37).bit_length() 6` conjugate(...) `Returns self, the complex conjugate of any int.` to_bytes(self, /, length, byteorder, *, signed=False) `Return an array of bytes representing an integer. length Length of bytes object to use. An OverflowError is raised if the integer is not representable with the given number of bytes. byteorder The byte order used to represent the integer. If byteorder is 'big', the most significant byte is at the beginning of the byte array. If byteorder is 'little', the most significant byte is at the end of the byte array. To request the native byte order of the host system, use `sys.byteorder' as the byte order value. signed Determines whether two's complement is used to represent the integer. If signed is False and a negative integer is given, an OverflowError is raised.` Class methods inherited from builtins.int: from_bytes(bytes, byteorder, *, signed=False) from builtins.type `Return the integer represented by the given array of bytes. bytes Holds the array of bytes to convert. The argument must either support the buffer protocol or be an iterable object producing bytes. Bytes and bytearray are examples of built-in objects that support the buffer protocol. byteorder The byte order used to represent the integer. If byteorder is 'big', the most significant byte is at the beginning of the byte array. If byteorder is 'little', the most significant byte is at the end of the byte array. To request the native byte order of the host system, use `sys.byteorder' as the byte order value. signed Indicates whether two's complement is used to represent the integer.` Static methods inherited from builtins.int: __new__(*args, **kwargs) from builtins.type `Create and return a new object. See help(type) for accurate signature.` Data descriptors inherited from builtins.int: denominator `the denominator of a rational number in lowest terms` imag `the imaginary part of a complex number` numerator `the numerator of a rational number in lowest terms` real `the real part of a complex number`
class prediction_source(Boost.Python.enum)
`` | `Different sources that can be used for creating a predictionset workset --- Take observations that is included in the workset. complement --- Take observation or batches that isn't included in the workset. classnumber --- Take observations that is included in a certain class in the workset. dataset --- Take observations from a dataset. predictionset --- Take observations from another predictionset.`
| Method resolution order: prediction_source Boost.Python.enum builtins.int builtins.object Data and other attributes defined here: classnumber = umetrics.simca.prediction_source.classnumber complement = umetrics.simca.prediction_source.complement dataset = umetrics.simca.prediction_source.dataset names = {'classnumber': umetrics.simca.prediction_source.classnumber, 'complement': umetrics.simca.prediction_source.complement, 'dataset': umetrics.simca.prediction_source.dataset, 'predictionset': umetrics.simca.prediction_source.predictionset, 'workset': umetrics.simca.prediction_source.workset} predictionset = umetrics.simca.prediction_source.predictionset values = {0: umetrics.simca.prediction_source.workset, 1: umetrics.simca.prediction_source.complement, 2: umetrics.simca.prediction_source.dataset, 3: umetrics.simca.prediction_source.classnumber, 4: umetrics.simca.prediction_source.predictionset} workset = umetrics.simca.prediction_source.workset Methods inherited from Boost.Python.enum: __repr__(self, /) `Return repr(self).` __str__(self, /) `Return str(self).` Data descriptors inherited from Boost.Python.enum: name Methods inherited from builtins.int: __abs__(self, /) `abs(self)` __add__(self, value, /) `Return self+value.` __and__(self, value, /) `Return self&value.` __bool__(self, /) `self != 0` __ceil__(...) `Ceiling of an Integral returns itself.` __divmod__(self, value, /) `Return divmod(self, value).` __eq__(self, value, /) `Return self==value.` __float__(self, /) `float(self)` __floor__(...) `Flooring an Integral returns itself.` __floordiv__(self, value, /) `Return self//value.` __format__(self, format_spec, /) `Default object formatter.` __ge__(self, value, /) `Return self>=value.` __getattribute__(self, name, /) `Return getattr(self, name).` __getnewargs__(self, /) __gt__(self, value, /) `Return self>value.` __hash__(self, /) `Return hash(self).` __index__(self, /) `Return self converted to an integer, if self is suitable for use as an index into a list.` __int__(self, /) `int(self)` __invert__(self, /) `~self` __le__(self, value, /) `Return self<=value.` __lshift__(self, value, /) `Return self<<value.` __lt__(self, value, /) `Return self<value.` __mod__(self, value, /) `Return self%value.` __mul__(self, value, /) `Return self*value.` __ne__(self, value, /) `Return self!=value.` __neg__(self, /) `-self` __or__(self, value, /) `Return self|value.` __pos__(self, /) `+self` __pow__(self, value, mod=None, /) `Return pow(self, value, mod).` __radd__(self, value, /) `Return value+self.` __rand__(self, value, /) `Return value&self.` __rdivmod__(self, value, /) `Return divmod(value, self).` __rfloordiv__(self, value, /) `Return value//self.` __rlshift__(self, value, /) `Return value<<self.` __rmod__(self, value, /) `Return value%self.` __rmul__(self, value, /) `Return value*self.` __ror__(self, value, /) `Return value|self.` __round__(...) `Rounding an Integral returns itself. Rounding with an ndigits argument also returns an integer.` __rpow__(self, value, mod=None, /) `Return pow(value, self, mod).` __rrshift__(self, value, /) `Return value>>self.` __rshift__(self, value, /) `Return self>>value.` __rsub__(self, value, /) `Return value-self.` __rtruediv__(self, value, /) `Return value/self.` __rxor__(self, value, /) `Return value^self.` __sizeof__(self, /) `Returns size in memory, in bytes.` __sub__(self, value, /) `Return self-value.` __truediv__(self, value, /) `Return self/value.` __trunc__(...) `Truncating an Integral returns itself.` __xor__(self, value, /) `Return self^value.` bit_length(self, /) `Number of bits necessary to represent self in binary. >>> bin(37) '0b100101' >>> (37).bit_length() 6` conjugate(...) `Returns self, the complex conjugate of any int.` to_bytes(self, /, length, byteorder, *, signed=False) `Return an array of bytes representing an integer. length Length of bytes object to use. An OverflowError is raised if the integer is not representable with the given number of bytes. byteorder The byte order used to represent the integer. If byteorder is 'big', the most significant byte is at the beginning of the byte array. If byteorder is 'little', the most significant byte is at the end of the byte array. To request the native byte order of the host system, use `sys.byteorder' as the byte order value. signed Determines whether two's complement is used to represent the integer. If signed is False and a negative integer is given, an OverflowError is raised.` Class methods inherited from builtins.int: from_bytes(bytes, byteorder, *, signed=False) from builtins.type `Return the integer represented by the given array of bytes. bytes Holds the array of bytes to convert. The argument must either support the buffer protocol or be an iterable object producing bytes. Bytes and bytearray are examples of built-in objects that support the buffer protocol. byteorder The byte order used to represent the integer. If byteorder is 'big', the most significant byte is at the beginning of the byte array. If byteorder is 'little', the most significant byte is at the end of the byte array. To request the native byte order of the host system, use `sys.byteorder' as the byte order value. signed Indicates whether two's complement is used to represent the integer.` Static methods inherited from builtins.int: __new__(*args, **kwargs) from builtins.type `Create and return a new object. See help(type) for accurate signature.` Data descriptors inherited from builtins.int: denominator `the denominator of a rational number in lowest terms` imag `the imaginary part of a complex number` numerator `the numerator of a rational number in lowest terms` real `the real part of a complex number`
class scaletype(Boost.Python.enum)
`` | `Different scale types for variables none --- No centering or scaling. uv --- Centered and scaled to unit variance. uvn --- Scaled to unit variance (not centered). par --- Centered and scaled to pareto variance. parn --- Scaled to pareto variance (not centered). ctr --- Centered but not scaled. freeze --- The scaling weight of the variable is frozen and will not be re-computed when observations in the workset change or the variable metric is modified after the freezing. lag --- Scaling reserved for lagged variables (same as it's mother's scaling) cannot be set percentofmean --- scale with a weight calculated from % of mean for the variable, cannot be set by scripts`
| Method resolution order: scaletype Boost.Python.enum builtins.int builtins.object Data and other attributes defined here: ctr = umetrics.simca.scaletype.ctr freeze = umetrics.simca.scaletype.freeze lag = umetrics.simca.scaletype.lag names = {'ctr': umetrics.simca.scaletype.ctr, 'freeze': umetrics.simca.scaletype.freeze, 'lag': umetrics.simca.scaletype.lag, 'none': umetrics.simca.scaletype.none, 'par': umetrics.simca.scaletype.par, 'parn': umetrics.simca.scaletype.parn, 'percentofmean': umetrics.simca.scaletype.percentofmean, 'uv': umetrics.simca.scaletype.uv, 'uvn': umetrics.simca.scaletype.uvn} none = umetrics.simca.scaletype.none par = umetrics.simca.scaletype.par parn = umetrics.simca.scaletype.parn percentofmean = umetrics.simca.scaletype.percentofmean uv = umetrics.simca.scaletype.uv uvn = umetrics.simca.scaletype.uvn values = {0: umetrics.simca.scaletype.none, 1: umetrics.simca.scaletype.uv, 2: umetrics.simca.scaletype.uvn, 3: umetrics.simca.scaletype.par, 4: umetrics.simca.scaletype.parn, 5: umetrics.simca.scaletype.ctr, 6: umetrics.simca.scaletype.freeze, 7: umetrics.simca.scaletype.lag, 8: umetrics.simca.scaletype.percentofmean} Methods inherited from Boost.Python.enum: __repr__(self, /) `Return repr(self).` __str__(self, /) `Return str(self).` Data descriptors inherited from Boost.Python.enum: name Methods inherited from builtins.int: __abs__(self, /) `abs(self)` __add__(self, value, /) `Return self+value.` __and__(self, value, /) `Return self&value.` __bool__(self, /) `self != 0` __ceil__(...) `Ceiling of an Integral returns itself.` __divmod__(self, value, /) `Return divmod(self, value).` __eq__(self, value, /) `Return self==value.` __float__(self, /) `float(self)` __floor__(...) `Flooring an Integral returns itself.` __floordiv__(self, value, /) `Return self//value.` __format__(self, format_spec, /) `Default object formatter.` __ge__(self, value, /) `Return self>=value.` __getattribute__(self, name, /) `Return getattr(self, name).` __getnewargs__(self, /) __gt__(self, value, /) `Return self>value.` __hash__(self, /) `Return hash(self).` __index__(self, /) `Return self converted to an integer, if self is suitable for use as an index into a list.` __int__(self, /) `int(self)` __invert__(self, /) `~self` __le__(self, value, /) `Return self<=value.` __lshift__(self, value, /) `Return self<<value.` __lt__(self, value, /) `Return self<value.` __mod__(self, value, /) `Return self%value.` __mul__(self, value, /) `Return self*value.` __ne__(self, value, /) `Return self!=value.` __neg__(self, /) `-self` __or__(self, value, /) `Return self|value.` __pos__(self, /) `+self` __pow__(self, value, mod=None, /) `Return pow(self, value, mod).` __radd__(self, value, /) `Return value+self.` __rand__(self, value, /) `Return value&self.` __rdivmod__(self, value, /) `Return divmod(value, self).` __rfloordiv__(self, value, /) `Return value//self.` __rlshift__(self, value, /) `Return value<<self.` __rmod__(self, value, /) `Return value%self.` __rmul__(self, value, /) `Return value*self.` __ror__(self, value, /) `Return value|self.` __round__(...) `Rounding an Integral returns itself. Rounding with an ndigits argument also returns an integer.` __rpow__(self, value, mod=None, /) `Return pow(value, self, mod).` __rrshift__(self, value, /) `Return value>>self.` __rshift__(self, value, /) `Return self>>value.` __rsub__(self, value, /) `Return value-self.` __rtruediv__(self, value, /) `Return value/self.` __rxor__(self, value, /) `Return value^self.` __sizeof__(self, /) `Returns size in memory, in bytes.` __sub__(self, value, /) `Return self-value.` __truediv__(self, value, /) `Return self/value.` __trunc__(...) `Truncating an Integral returns itself.` __xor__(self, value, /) `Return self^value.` bit_length(self, /) `Number of bits necessary to represent self in binary. >>> bin(37) '0b100101' >>> (37).bit_length() 6` conjugate(...) `Returns self, the complex conjugate of any int.` to_bytes(self, /, length, byteorder, *, signed=False) `Return an array of bytes representing an integer. length Length of bytes object to use. An OverflowError is raised if the integer is not representable with the given number of bytes. byteorder The byte order used to represent the integer. If byteorder is 'big', the most significant byte is at the beginning of the byte array. If byteorder is 'little', the most significant byte is at the end of the byte array. To request the native byte order of the host system, use `sys.byteorder' as the byte order value. signed Determines whether two's complement is used to represent the integer. If signed is False and a negative integer is given, an OverflowError is raised.` Class methods inherited from builtins.int: from_bytes(bytes, byteorder, *, signed=False) from builtins.type `Return the integer represented by the given array of bytes. bytes Holds the array of bytes to convert. The argument must either support the buffer protocol or be an iterable object producing bytes. Bytes and bytearray are examples of built-in objects that support the buffer protocol. byteorder The byte order used to represent the integer. If byteorder is 'big', the most significant byte is at the beginning of the byte array. If byteorder is 'little', the most significant byte is at the end of the byte array. To request the native byte order of the host system, use `sys.byteorder' as the byte order value. signed Indicates whether two's complement is used to represent the integer.` Static methods inherited from builtins.int: __new__(*args, **kwargs) from builtins.type `Create and return a new object. See help(type) for accurate signature.` Data descriptors inherited from builtins.int: denominator `the denominator of a rational number in lowest terms` imag `the imaginary part of a complex number` numerator `the numerator of a rational number in lowest terms` real `the real part of a complex number`
class transformtype(Boost.Python.enum)
`` | `Filter transform types. none --- The variable is not transformed. linear --- Linear transform. a*X+b log --- Log10 transform. log(a*X+b) neglog --- Negative log10 transform. -log(a*X+b) logit --- log10((X-a)/(b-X)) exp --- Natural exponent. e^(a*X+b) power --- (a*X+b)^C`
| Method resolution order: transformtype Boost.Python.enum builtins.int builtins.object Data and other attributes defined here: exp = umetrics.simca.transformtype.exp linear = umetrics.simca.transformtype.linear log = umetrics.simca.transformtype.log logit = umetrics.simca.transformtype.logit names = {'exp': umetrics.simca.transformtype.exp, 'linear': umetrics.simca.transformtype.linear, 'log': umetrics.simca.transformtype.log, 'logit': umetrics.simca.transformtype.logit, 'neglog': umetrics.simca.transformtype.neglog, 'none': umetrics.simca.transformtype.none, 'power': umetrics.simca.transformtype.power} neglog = umetrics.simca.transformtype.neglog none = umetrics.simca.transformtype.none power = umetrics.simca.transformtype.power values = {1: umetrics.simca.transformtype.none, 2: umetrics.simca.transformtype.linear, 3: umetrics.simca.transformtype.log, 4: umetrics.simca.transformtype.neglog, 5: umetrics.simca.transformtype.logit, 6: umetrics.simca.transformtype.exp, 7: umetrics.simca.transformtype.power} Methods inherited from Boost.Python.enum: __repr__(self, /) `Return repr(self).` __str__(self, /) `Return str(self).` Data descriptors inherited from Boost.Python.enum: name Methods inherited from builtins.int: __abs__(self, /) `abs(self)` __add__(self, value, /) `Return self+value.` __and__(self, value, /) `Return self&value.` __bool__(self, /) `self != 0` __ceil__(...) `Ceiling of an Integral returns itself.` __divmod__(self, value, /) `Return divmod(self, value).` __eq__(self, value, /) `Return self==value.` __float__(self, /) `float(self)` __floor__(...) `Flooring an Integral returns itself.` __floordiv__(self, value, /) `Return self//value.` __format__(self, format_spec, /) `Default object formatter.` __ge__(self, value, /) `Return self>=value.` __getattribute__(self, name, /) `Return getattr(self, name).` __getnewargs__(self, /) __gt__(self, value, /) `Return self>value.` __hash__(self, /) `Return hash(self).` __index__(self, /) `Return self converted to an integer, if self is suitable for use as an index into a list.` __int__(self, /) `int(self)` __invert__(self, /) `~self` __le__(self, value, /) `Return self<=value.` __lshift__(self, value, /) `Return self<<value.` __lt__(self, value, /) `Return self<value.` __mod__(self, value, /) `Return self%value.` __mul__(self, value, /) `Return self*value.` __ne__(self, value, /) `Return self!=value.` __neg__(self, /) `-self` __or__(self, value, /) `Return self|value.` __pos__(self, /) `+self` __pow__(self, value, mod=None, /) `Return pow(self, value, mod).` __radd__(self, value, /) `Return value+self.` __rand__(self, value, /) `Return value&self.` __rdivmod__(self, value, /) `Return divmod(value, self).` __rfloordiv__(self, value, /) `Return value//self.` __rlshift__(self, value, /) `Return value<<self.` __rmod__(self, value, /) `Return value%self.` __rmul__(self, value, /) `Return value*self.` __ror__(self, value, /) `Return value|self.` __round__(...) `Rounding an Integral returns itself. Rounding with an ndigits argument also returns an integer.` __rpow__(self, value, mod=None, /) `Return pow(value, self, mod).` __rrshift__(self, value, /) `Return value>>self.` __rshift__(self, value, /) `Return self>>value.` __rsub__(self, value, /) `Return value-self.` __rtruediv__(self, value, /) `Return value/self.` __rxor__(self, value, /) `Return value^self.` __sizeof__(self, /) `Returns size in memory, in bytes.` __sub__(self, value, /) `Return self-value.` __truediv__(self, value, /) `Return self/value.` __trunc__(...) `Truncating an Integral returns itself.` __xor__(self, value, /) `Return self^value.` bit_length(self, /) `Number of bits necessary to represent self in binary. >>> bin(37) '0b100101' >>> (37).bit_length() 6` conjugate(...) `Returns self, the complex conjugate of any int.` to_bytes(self, /, length, byteorder, *, signed=False) `Return an array of bytes representing an integer. length Length of bytes object to use. An OverflowError is raised if the integer is not representable with the given number of bytes. byteorder The byte order used to represent the integer. If byteorder is 'big', the most significant byte is at the beginning of the byte array. If byteorder is 'little', the most significant byte is at the end of the byte array. To request the native byte order of the host system, use `sys.byteorder' as the byte order value. signed Determines whether two's complement is used to represent the integer. If signed is False and a negative integer is given, an OverflowError is raised.` Class methods inherited from builtins.int: from_bytes(bytes, byteorder, *, signed=False) from builtins.type `Return the integer represented by the given array of bytes. bytes Holds the array of bytes to convert. The argument must either support the buffer protocol or be an iterable object producing bytes. Bytes and bytearray are examples of built-in objects that support the buffer protocol. byteorder The byte order used to represent the integer. If byteorder is 'big', the most significant byte is at the beginning of the byte array. If byteorder is 'little', the most significant byte is at the end of the byte array. To request the native byte order of the host system, use `sys.byteorder' as the byte order value. signed Indicates whether two's complement is used to represent the integer.` Static methods inherited from builtins.int: __new__(*args, **kwargs) from builtins.type `Create and return a new object. See help(type) for accurate signature.` Data descriptors inherited from builtins.int: denominator `the denominator of a rational number in lowest terms` imag `the imaginary part of a complex number` numerator `the numerator of a rational number in lowest terms` real `the real part of a complex number`
class trimming_type(Boost.Python.enum)
`` | `Defines how to interpret the limit or value provided for trimming. value --- A low or high limit, the value is not computed further. percent --- In percent unit, a low or high limit. stdDev --- in standard deviation unit, a low or high limit. equal_value --- value for exact match with find and replace. missing_value --- for replacing missing values in the data.`
| Method resolution order: trimming_type Boost.Python.enum builtins.int builtins.object Data and other attributes defined here: equal_value = umetrics.simca.trimming_type.equal_value missing_value = umetrics.simca.trimming_type.missing_value names = {'equal_value': umetrics.simca.trimming_type.equal_value, 'missing_value': umetrics.simca.trimming_type.missing_value, 'percent': umetrics.simca.trimming_type.percent, 'stddev': umetrics.simca.trimming_type.stddev, 'value': umetrics.simca.trimming_type.value} percent = umetrics.simca.trimming_type.percent stddev = umetrics.simca.trimming_type.stddev value = umetrics.simca.trimming_type.value values = {0: umetrics.simca.trimming_type.stddev, 1: umetrics.simca.trimming_type.value, 2: umetrics.simca.trimming_type.percent, 7: umetrics.simca.trimming_type.missing_value, 8: umetrics.simca.trimming_type.equal_value} Methods inherited from Boost.Python.enum: __repr__(self, /) `Return repr(self).` __str__(self, /) `Return str(self).` Data descriptors inherited from Boost.Python.enum: name Methods inherited from builtins.int: __abs__(self, /) `abs(self)` __add__(self, value, /) `Return self+value.` __and__(self, value, /) `Return self&value.` __bool__(self, /) `self != 0` __ceil__(...) `Ceiling of an Integral returns itself.` __divmod__(self, value, /) `Return divmod(self, value).` __eq__(self, value, /) `Return self==value.` __float__(self, /) `float(self)` __floor__(...) `Flooring an Integral returns itself.` __floordiv__(self, value, /) `Return self//value.` __format__(self, format_spec, /) `Default object formatter.` __ge__(self, value, /) `Return self>=value.` __getattribute__(self, name, /) `Return getattr(self, name).` __getnewargs__(self, /) __gt__(self, value, /) `Return self>value.` __hash__(self, /) `Return hash(self).` __index__(self, /) `Return self converted to an integer, if self is suitable for use as an index into a list.` __int__(self, /) `int(self)` __invert__(self, /) `~self` __le__(self, value, /) `Return self<=value.` __lshift__(self, value, /) `Return self<<value.` __lt__(self, value, /) `Return self<value.` __mod__(self, value, /) `Return self%value.` __mul__(self, value, /) `Return self*value.` __ne__(self, value, /) `Return self!=value.` __neg__(self, /) `-self` __or__(self, value, /) `Return self|value.` __pos__(self, /) `+self` __pow__(self, value, mod=None, /) `Return pow(self, value, mod).` __radd__(self, value, /) `Return value+self.` __rand__(self, value, /) `Return value&self.` __rdivmod__(self, value, /) `Return divmod(value, self).` __rfloordiv__(self, value, /) `Return value//self.` __rlshift__(self, value, /) `Return value<<self.` __rmod__(self, value, /) `Return value%self.` __rmul__(self, value, /) `Return value*self.` __ror__(self, value, /) `Return value|self.` __round__(...) `Rounding an Integral returns itself. Rounding with an ndigits argument also returns an integer.` __rpow__(self, value, mod=None, /) `Return pow(value, self, mod).` __rrshift__(self, value, /) `Return value>>self.` __rshift__(self, value, /) `Return self>>value.` __rsub__(self, value, /) `Return value-self.` __rtruediv__(self, value, /) `Return value/self.` __rxor__(self, value, /) `Return value^self.` __sizeof__(self, /) `Returns size in memory, in bytes.` __sub__(self, value, /) `Return self-value.` __truediv__(self, value, /) `Return self/value.` __trunc__(...) `Truncating an Integral returns itself.` __xor__(self, value, /) `Return self^value.` bit_length(self, /) `Number of bits necessary to represent self in binary. >>> bin(37) '0b100101' >>> (37).bit_length() 6` conjugate(...) `Returns self, the complex conjugate of any int.` to_bytes(self, /, length, byteorder, *, signed=False) `Return an array of bytes representing an integer. length Length of bytes object to use. An OverflowError is raised if the integer is not representable with the given number of bytes. byteorder The byte order used to represent the integer. If byteorder is 'big', the most significant byte is at the beginning of the byte array. If byteorder is 'little', the most significant byte is at the end of the byte array. To request the native byte order of the host system, use `sys.byteorder' as the byte order value. signed Determines whether two's complement is used to represent the integer. If signed is False and a negative integer is given, an OverflowError is raised.` Class methods inherited from builtins.int: from_bytes(bytes, byteorder, *, signed=False) from builtins.type `Return the integer represented by the given array of bytes. bytes Holds the array of bytes to convert. The argument must either support the buffer protocol or be an iterable object producing bytes. Bytes and bytearray are examples of built-in objects that support the buffer protocol. byteorder The byte order used to represent the integer. If byteorder is 'big', the most significant byte is at the beginning of the byte array. If byteorder is 'little', the most significant byte is at the end of the byte array. To request the native byte order of the host system, use `sys.byteorder' as the byte order value. signed Indicates whether two's complement is used to represent the integer.` Static methods inherited from builtins.int: __new__(*args, **kwargs) from builtins.type `Create and return a new object. See help(type) for accurate signature.` Data descriptors inherited from builtins.int: denominator `the denominator of a rational number in lowest terms` imag `the imaginary part of a complex number` numerator `the numerator of a rational number in lowest terms` real `the real part of a complex number`
class waveletfunctiontype(Boost.Python.enum)
`` | `Wavelet filter function type. beylkin --- Wavelet order: N/A. coiflet --- Wavelet order: 2, 3, 4, 5. daubechies --- Wavelet order: 4, 6, 8, 10, 12, 20, 50. symmlet --- Wavelet order: 4, 6, 8, 10. biorthogonal1 --- Wavelet order: 1, 3, 5. biorthogonal2 --- Wavelet order: 2, 4, 6, 8. biorthogonal3 --- Wavelet order: 1, 3, 5, 7, 9. biorthogonal4 --- Wavelet order: 4. biorthogonal5 --- Wavelet order: 5. biorthogonal6 --- Wavelet order: 8.`
| Method resolution order: waveletfunctiontype Boost.Python.enum builtins.int builtins.object Data and other attributes defined here: beylkin = umetrics.simca.waveletfunctiontype.beylkin biorthogonal1 = umetrics.simca.waveletfunctiontype.biorthogonal1 biorthogonal2 = umetrics.simca.waveletfunctiontype.biorthogonal2 biorthogonal3 = umetrics.simca.waveletfunctiontype.biorthogonal3 biorthogonal4 = umetrics.simca.waveletfunctiontype.biorthogonal4 biorthogonal5 = umetrics.simca.waveletfunctiontype.biorthogonal5 biorthogonal6 = umetrics.simca.waveletfunctiontype.biorthogonal6 coiflet = umetrics.simca.waveletfunctiontype.coiflet daubechies = umetrics.simca.waveletfunctiontype.daubechies names = {'beylkin': umetrics.simca.waveletfunctiontype.beylkin, 'biorthogonal1': umetrics.simca.waveletfunctiontype.biorthogonal1, 'biorthogonal2': umetrics.simca.waveletfunctiontype.biorthogonal2, 'biorthogonal3': umetrics.simca.waveletfunctiontype.biorthogonal3, 'biorthogonal4': umetrics.simca.waveletfunctiontype.biorthogonal4, 'biorthogonal5': umetrics.simca.waveletfunctiontype.biorthogonal5, 'biorthogonal6': umetrics.simca.waveletfunctiontype.biorthogonal6, 'coiflet': umetrics.simca.waveletfunctiontype.coiflet, 'daubechies': umetrics.simca.waveletfunctiontype.daubechies, 'symmlet': umetrics.simca.waveletfunctiontype.symmlet} symmlet = umetrics.simca.waveletfunctiontype.symmlet values = {0: umetrics.simca.waveletfunctiontype.beylkin, 1: umetrics.simca.waveletfunctiontype.coiflet, 2: umetrics.simca.waveletfunctiontype.daubechies, 3: umetrics.simca.waveletfunctiontype.symmlet, 4: umetrics.simca.waveletfunctiontype.biorthogonal1, 5: umetrics.simca.waveletfunctiontype.biorthogonal2, 6: umetrics.simca.waveletfunctiontype.biorthogonal3, 7: umetrics.simca.waveletfunctiontype.biorthogonal4, 8: umetrics.simca.waveletfunctiontype.biorthogonal5, 9: umetrics.simca.waveletfunctiontype.biorthogonal6} Methods inherited from Boost.Python.enum: __repr__(self, /) `Return repr(self).` __str__(self, /) `Return str(self).` Data descriptors inherited from Boost.Python.enum: name Methods inherited from builtins.int: __abs__(self, /) `abs(self)` __add__(self, value, /) `Return self+value.` __and__(self, value, /) `Return self&value.` __bool__(self, /) `self != 0` __ceil__(...) `Ceiling of an Integral returns itself.` __divmod__(self, value, /) `Return divmod(self, value).` __eq__(self, value, /) `Return self==value.` __float__(self, /) `float(self)` __floor__(...) `Flooring an Integral returns itself.` __floordiv__(self, value, /) `Return self//value.` __format__(self, format_spec, /) `Default object formatter.` __ge__(self, value, /) `Return self>=value.` __getattribute__(self, name, /) `Return getattr(self, name).` __getnewargs__(self, /) __gt__(self, value, /) `Return self>value.` __hash__(self, /) `Return hash(self).` __index__(self, /) `Return self converted to an integer, if self is suitable for use as an index into a list.` __int__(self, /) `int(self)` __invert__(self, /) `~self` __le__(self, value, /) `Return self<=value.` __lshift__(self, value, /) `Return self<<value.` __lt__(self, value, /) `Return self<value.` __mod__(self, value, /) `Return self%value.` __mul__(self, value, /) `Return self*value.` __ne__(self, value, /) `Return self!=value.` __neg__(self, /) `-self` __or__(self, value, /) `Return self|value.` __pos__(self, /) `+self` __pow__(self, value, mod=None, /) `Return pow(self, value, mod).` __radd__(self, value, /) `Return value+self.` __rand__(self, value, /) `Return value&self.` __rdivmod__(self, value, /) `Return divmod(value, self).` __rfloordiv__(self, value, /) `Return value//self.` __rlshift__(self, value, /) `Return value<<self.` __rmod__(self, value, /) `Return value%self.` __rmul__(self, value, /) `Return value*self.` __ror__(self, value, /) `Return value|self.` __round__(...) `Rounding an Integral returns itself. Rounding with an ndigits argument also returns an integer.` __rpow__(self, value, mod=None, /) `Return pow(value, self, mod).` __rrshift__(self, value, /) `Return value>>self.` __rshift__(self, value, /) `Return self>>value.` __rsub__(self, value, /) `Return value-self.` __rtruediv__(self, value, /) `Return value/self.` __rxor__(self, value, /) `Return value^self.` __sizeof__(self, /) `Returns size in memory, in bytes.` __sub__(self, value, /) `Return self-value.` __truediv__(self, value, /) `Return self/value.` __trunc__(...) `Truncating an Integral returns itself.` __xor__(self, value, /) `Return self^value.` bit_length(self, /) `Number of bits necessary to represent self in binary. >>> bin(37) '0b100101' >>> (37).bit_length() 6` conjugate(...) `Returns self, the complex conjugate of any int.` to_bytes(self, /, length, byteorder, *, signed=False) `Return an array of bytes representing an integer. length Length of bytes object to use. An OverflowError is raised if the integer is not representable with the given number of bytes. byteorder The byte order used to represent the integer. If byteorder is 'big', the most significant byte is at the beginning of the byte array. If byteorder is 'little', the most significant byte is at the end of the byte array. To request the native byte order of the host system, use `sys.byteorder' as the byte order value. signed Determines whether two's complement is used to represent the integer. If signed is False and a negative integer is given, an OverflowError is raised.` Class methods inherited from builtins.int: from_bytes(bytes, byteorder, *, signed=False) from builtins.type `Return the integer represented by the given array of bytes. bytes Holds the array of bytes to convert. The argument must either support the buffer protocol or be an iterable object producing bytes. Bytes and bytearray are examples of built-in objects that support the buffer protocol. byteorder The byte order used to represent the integer. If byteorder is 'big', the most significant byte is at the beginning of the byte array. If byteorder is 'little', the most significant byte is at the end of the byte array. To request the native byte order of the host system, use `sys.byteorder' as the byte order value. signed Indicates whether two's complement is used to represent the integer.` Static methods inherited from builtins.int: __new__(*args, **kwargs) from builtins.type `Create and return a new object. See help(type) for accurate signature.` Data descriptors inherited from builtins.int: denominator `the denominator of a rational number in lowest terms` imag `the imaginary part of a complex number` numerator `the numerator of a rational number in lowest terms` real `the real part of a complex number`
class ytreatment(Boost.Python.enum)
`` | `Different Y variable treatments for batch project noprocessing --- No preprocessing, use original values. smooth --- Smooth the original values. smoothShift --- Smooth and shift values so all phases and batches starts at 0. smoothShiftNormalize --- Smooth, normalize and shift values so all phases and batches starts at 0. smoothNormalize --- Smooth and normalize the original values. shift --- Shift values so all phases and batches starts at 0. shiftNormalize --- Normalize and shift values so all phases and batches starts at 0. normalize --- Normalize values. nobatchY --- Not a batch Y, create non batch models if this is the maturity. default --- The treatment is decided from the variable.`
| Method resolution order: ytreatment Boost.Python.enum builtins.int builtins.object Data and other attributes defined here: default = umetrics.simca.ytreatment.default names = {'default': umetrics.simca.ytreatment.default, 'nobatchY': umetrics.simca.ytreatment.nobatchY, 'noprocessing': umetrics.simca.ytreatment.noprocessing, 'normalize': umetrics.simca.ytreatment.normalize, 'shift': umetrics.simca.ytreatment.shift, 'shiftNormalize': umetrics.simca.ytreatment.shiftNormalize, 'smooth': umetrics.simca.ytreatment.smooth, 'smoothNormalize': umetrics.simca.ytreatment.smoothNormalize, 'smoothShift': umetrics.simca.ytreatment.smoothShift, 'smoothShiftNormalize': umetrics.simca.ytreatment.smoothShiftNormalize} nobatchY = umetrics.simca.ytreatment.nobatchY noprocessing = umetrics.simca.ytreatment.noprocessing normalize = umetrics.simca.ytreatment.normalize shift = umetrics.simca.ytreatment.shift shiftNormalize = umetrics.simca.ytreatment.shiftNormalize smooth = umetrics.simca.ytreatment.smooth smoothNormalize = umetrics.simca.ytreatment.smoothNormalize smoothShift = umetrics.simca.ytreatment.smoothShift smoothShiftNormalize = umetrics.simca.ytreatment.smoothShiftNormalize values = {0: umetrics.simca.ytreatment.noprocessing, 1: umetrics.simca.ytreatment.smooth, 2: umetrics.simca.ytreatment.smoothShift, 3: umetrics.simca.ytreatment.smoothShiftNormalize, 4: umetrics.simca.ytreatment.smoothNormalize, 5: umetrics.simca.ytreatment.shift, 6: umetrics.simca.ytreatment.shiftNormalize, 7: umetrics.simca.ytreatment.normalize, 8: umetrics.simca.ytreatment.nobatchY, 9: umetrics.simca.ytreatment.default} Methods inherited from Boost.Python.enum: __repr__(self, /) `Return repr(self).` __str__(self, /) `Return str(self).` Data descriptors inherited from Boost.Python.enum: name Methods inherited from builtins.int: __abs__(self, /) `abs(self)` __add__(self, value, /) `Return self+value.` __and__(self, value, /) `Return self&value.` __bool__(self, /) `self != 0` __ceil__(...) `Ceiling of an Integral returns itself.` __divmod__(self, value, /) `Return divmod(self, value).` __eq__(self, value, /) `Return self==value.` __float__(self, /) `float(self)` __floor__(...) `Flooring an Integral returns itself.` __floordiv__(self, value, /) `Return self//value.` __format__(self, format_spec, /) `Default object formatter.` __ge__(self, value, /) `Return self>=value.` __getattribute__(self, name, /) `Return getattr(self, name).` __getnewargs__(self, /) __gt__(self, value, /) `Return self>value.` __hash__(self, /) `Return hash(self).` __index__(self, /) `Return self converted to an integer, if self is suitable for use as an index into a list.` __int__(self, /) `int(self)` __invert__(self, /) `~self` __le__(self, value, /) `Return self<=value.` __lshift__(self, value, /) `Return self<<value.` __lt__(self, value, /) `Return self<value.` __mod__(self, value, /) `Return self%value.` __mul__(self, value, /) `Return self*value.` __ne__(self, value, /) `Return self!=value.` __neg__(self, /) `-self` __or__(self, value, /) `Return self|value.` __pos__(self, /) `+self` __pow__(self, value, mod=None, /) `Return pow(self, value, mod).` __radd__(self, value, /) `Return value+self.` __rand__(self, value, /) `Return value&self.` __rdivmod__(self, value, /) `Return divmod(value, self).` __rfloordiv__(self, value, /) `Return value//self.` __rlshift__(self, value, /) `Return value<<self.` __rmod__(self, value, /) `Return value%self.` __rmul__(self, value, /) `Return value*self.` __ror__(self, value, /) `Return value|self.` __round__(...) `Rounding an Integral returns itself. Rounding with an ndigits argument also returns an integer.` __rpow__(self, value, mod=None, /) `Return pow(value, self, mod).` __rrshift__(self, value, /) `Return value>>self.` __rshift__(self, value, /) `Return self>>value.` __rsub__(self, value, /) `Return value-self.` __rtruediv__(self, value, /) `Return value/self.` __rxor__(self, value, /) `Return value^self.` __sizeof__(self, /) `Returns size in memory, in bytes.` __sub__(self, value, /) `Return self-value.` __truediv__(self, value, /) `Return self/value.` __trunc__(...) `Truncating an Integral returns itself.` __xor__(self, value, /) `Return self^value.` bit_length(self, /) `Number of bits necessary to represent self in binary. >>> bin(37) '0b100101' >>> (37).bit_length() 6` conjugate(...) `Returns self, the complex conjugate of any int.` to_bytes(self, /, length, byteorder, *, signed=False) `Return an array of bytes representing an integer. length Length of bytes object to use. An OverflowError is raised if the integer is not representable with the given number of bytes. byteorder The byte order used to represent the integer. If byteorder is 'big', the most significant byte is at the beginning of the byte array. If byteorder is 'little', the most significant byte is at the end of the byte array. To request the native byte order of the host system, use `sys.byteorder' as the byte order value. signed Determines whether two's complement is used to represent the integer. If signed is False and a negative integer is given, an OverflowError is raised.` Class methods inherited from builtins.int: from_bytes(bytes, byteorder, *, signed=False) from builtins.type `Return the integer represented by the given array of bytes. bytes Holds the array of bytes to convert. The argument must either support the buffer protocol or be an iterable object producing bytes. Bytes and bytearray are examples of built-in objects that support the buffer protocol. byteorder The byte order used to represent the integer. If byteorder is 'big', the most significant byte is at the beginning of the byte array. If byteorder is 'little', the most significant byte is at the end of the byte array. To request the native byte order of the host system, use `sys.byteorder' as the byte order value. signed Indicates whether two's complement is used to represent the integer.` Static methods inherited from builtins.int: __new__(*args, **kwargs) from builtins.type `Create and return a new object. See help(type) for accurate signature.` Data descriptors inherited from builtins.int: denominator `the denominator of a rational number in lowest terms` imag `the imaginary part of a complex number` numerator `the numerator of a rational number in lowest terms` real `the real part of a complex number`