bio_rtd.pdf

GaussianFixedDispersion

class bio_rtd.pdf.GaussianFixedDispersion(t, dispersion_index, cutoff=0.0001, pdf_id='GaussianFixedDispersion')

Bases: bio_rtd.core.PDF

Gaussian PDF with fixed dispersion

Parameters

• dispersion_index (float) – Dispersion index, defined as sigma * sigma / rt_mean. Where rt_mean is a mean residence time and sigma is a standard deviation.

• cutoff (float) – Cutoff limit for trimming front and end tailing. Cutoff limit is relative to the peak max value.

See also

The

Examples

>>> t = _np.linspace(0, 100, 1001)
>>> dt = t[1]
>>> pdf = GaussianFixedDispersion(t, dispersion_index=0.2)
>>> pdf.update_pdf(rt_mean=40)
>>> p = pdf.get_p()
>>> print(round(p.sum() * dt, 8))
1.0
>>> t[p.argmax()]
40.0

assert_and_get_provided_kv_pairs(**kwargs)

Parameters

kwargs – Inputs to calc_pdf(**kwargs) function

Returns

Filtered kwargs so the keys contain first possible key group in _possible_key_groups and any number of optional keys from _optional_keys.

Return type

dict

Raises

ValueError – If **kwargs do not contain keys from any of the groups in _possible_key_groups.

get_p()

Get probability distribution.

Returns

p – Evaluated probability distribution function. sum(p * self._dt) == 1 Corresponding time axis starts with 0 and has a fixed step of self._dt.

Return type

np.ndarray

property log

Logger.

If logger is not set, then a DefaultLogger is instantiated. Setter also plants a data tree into passed logger.

Return type

RtdLogger

set_logger_from_parent(parent_id, logger)

Inherit logger from parent.

Parameters

• parent_id (str) –

• logger (RtdLogger) –

update_pdf(**kwargs)

Re-calculate PDF based on specified parameters.

Parameters

kwargs – Should contain keys from one of the group in self._possible_key_groups. It may contain additional keys from self._optional_keys.

GaussianFixedRelativeWidth

class bio_rtd.pdf.GaussianFixedRelativeWidth(t, relative_sigma, cutoff=0.0001, pdf_id='')

Bases: bio_rtd.core.PDF

Gaussian PDF with fixed relative peak width

relative_sigma = sigma / rt_mean

assert_and_get_provided_kv_pairs(**kwargs)

Parameters

kwargs – Inputs to calc_pdf(**kwargs) function

Returns

Filtered kwargs so the keys contain first possible key group in _possible_key_groups and any number of optional keys from _optional_keys.

Return type

dict

Raises

ValueError – If **kwargs do not contain keys from any of the groups in _possible_key_groups.

get_p()

Get probability distribution.

Returns

p – Evaluated probability distribution function. sum(p * self._dt) == 1 Corresponding time axis starts with 0 and has a fixed step of self._dt.

Return type

np.ndarray

property log

Logger.

If logger is not set, then a DefaultLogger is instantiated. Setter also plants a data tree into passed logger.

Return type

RtdLogger

set_logger_from_parent(parent_id, logger)

Inherit logger from parent.

Parameters

• parent_id (str) –

• logger (RtdLogger) –

update_pdf(**kwargs)

Re-calculate PDF based on specified parameters.

Parameters

kwargs – Should contain keys from one of the group in self._possible_key_groups. It may contain additional keys from self._optional_keys.

ExpModGaussianFixedDispersion

class bio_rtd.pdf.ExpModGaussianFixedDispersion(t, dispersion_index, skew, pdf_id='')

Bases: bio_rtd.core.PDF

Exponentially Modified Gaussian PDF with fixed dispersion

dispersion_index = sigma**2 / rt_mean pdf(rt_mean) = calc_emg(t, rt_mean, sigma, skew)

set_pdf_pars(dispersion_index: float, skew: float)

Sets the dispersion index and skew factor

Abstract Methods

----------------

update_pdf(**kwargs)

Calculate new pdf for a given set of parameters. pdf_update_keys contains groups of possible parameter combinations.

_possible_key_groups : Sequence[Sequence[str]]

List of keys for key-value argument combinations that could be specified to update_pdf(**kwarg) function

assert_and_get_provided_kv_pairs(**kwargs)

Parameters

kwargs – Inputs to calc_pdf(**kwargs) function

Returns

Filtered kwargs so the keys contain first possible key group in _possible_key_groups and any number of optional keys from _optional_keys.

Return type

dict

Raises

ValueError – If **kwargs do not contain keys from any of the groups in _possible_key_groups.

get_p()

Get probability distribution.

Returns

p – Evaluated probability distribution function. sum(p * self._dt) == 1 Corresponding time axis starts with 0 and has a fixed step of self._dt.

Return type

np.ndarray

property log

Logger.

If logger is not set, then a DefaultLogger is instantiated. Setter also plants a data tree into passed logger.

Return type

RtdLogger

set_logger_from_parent(parent_id, logger)

Inherit logger from parent.

Parameters

• parent_id (str) –

• logger (RtdLogger) –

update_pdf(**kwargs)

Re-calculate PDF based on specified parameters.

Parameters

kwargs – Should contain keys from one of the group in self._possible_key_groups. It may contain additional keys from self._optional_keys.

ExpModGaussianFixedRelativeWidth

class bio_rtd.pdf.ExpModGaussianFixedRelativeWidth(t, sigma_relative, skew, pdf_id='')

Bases: bio_rtd.core.PDF

Exponentially Modified Gaussian PDF with fixed relative peak width

assert_and_get_provided_kv_pairs(**kwargs)

Parameters

kwargs – Inputs to calc_pdf(**kwargs) function

Returns

Filtered kwargs so the keys contain first possible key group in _possible_key_groups and any number of optional keys from _optional_keys.

Return type

dict

Raises

ValueError – If **kwargs do not contain keys from any of the groups in _possible_key_groups.

get_p()

Get probability distribution.

Returns

p – Evaluated probability distribution function. sum(p * self._dt) == 1 Corresponding time axis starts with 0 and has a fixed step of self._dt.

Return type

np.ndarray

property log

Logger.

If logger is not set, then a DefaultLogger is instantiated. Setter also plants a data tree into passed logger.

Return type

RtdLogger

set_logger_from_parent(parent_id, logger)

Inherit logger from parent.

Parameters

• parent_id (str) –

• logger (RtdLogger) –

update_pdf(**kwargs)

Re-calculate PDF based on specified parameters.

Parameters

kwargs – Should contain keys from one of the group in self._possible_key_groups. It may contain additional keys from self._optional_keys.

TanksInSeries

class bio_rtd.pdf.TanksInSeries(t, n_tanks, pdf_id='')

Bases: bio_rtd.core.PDF

Tanks in series PDF

rt_mean means flow-through time through entire unit operation (all tanks)

set_pdf_pars(n_tanks: float)

Sets number of tanks

Abstract Methods

----------------

update_pdf(**kwargs)

Calculate new pdf for a given set of parameters. pdf_update_keys contains groups of possible parameter combinations.

_possible_key_groups : Sequence[Sequence[str]]

List of keys for key-value argument combinations that could be specified to update_pdf(**kwarg) function

assert_and_get_provided_kv_pairs(**kwargs)

Parameters

kwargs – Inputs to calc_pdf(**kwargs) function

Returns

Filtered kwargs so the keys contain first possible key group in _possible_key_groups and any number of optional keys from _optional_keys.

Return type

dict

Raises

ValueError – If **kwargs do not contain keys from any of the groups in _possible_key_groups.

get_p()

Get probability distribution.

Returns

p – Evaluated probability distribution function. sum(p * self._dt) == 1 Corresponding time axis starts with 0 and has a fixed step of self._dt.

Return type

np.ndarray

property log

Logger.

If logger is not set, then a DefaultLogger is instantiated. Setter also plants a data tree into passed logger.

Return type

RtdLogger

set_logger_from_parent(parent_id, logger)

Inherit logger from parent.

Parameters

• parent_id (str) –

• logger (RtdLogger) –

update_pdf(**kwargs)

Re-calculate PDF based on specified parameters.

Parameters

kwargs – Should contain keys from one of the group in self._possible_key_groups. It may contain additional keys from self._optional_keys.