The GEMSEO concepts¶

Design space.

A design space is used to represent the optimization’s unknowns, a.k.a. design variables.

A DesignSpace describes this design space at a given state, in terms of names, sizes, types, bounds and current values of the design variables.

Variables can easily be added to the DesignSpace using the DesignSpace.add_variable() method or removed using the DesignSpace.remove_variable() method.

We can also filter the design variables using the DesignSpace.filter() method.

Getters and setters are also available to get or set the value of a given variable property.

Lastly, an instance of DesignSpace can be stored in a txt or HDF file.

class gemseo.algos.design_space.DesignSpace(hdf_file=None, name=None)[source]

Description of a design space.

It defines a set of variables from their names, sizes, types and bounds.

In addition, it provides the current values of these variables that can be used as the initial solution of an OptimizationProblem.

A DesignSpace has the same API as a dictionary, e.g. variable = design_space["x"], other_design_space["x"] = design_space["x"], del design_space["x"], for name, value in design_space["x"].items(), …

Parameters

hdf_file (str | Path | None) –
The path to the file containing the description of an initial design space. If None, start with an empty design space.

By default it is set to None.
name (str | None) –
The name to be given to the design space, None if the design space is unnamed.

By default it is set to None.

Return type

None

add_variable(name, size=1, var_type=DesignVariableType.FLOAT, l_b=None, u_b=None, value=None)[source]

Add a variable to the design space.

Parameters

name (str) – The name of the variable.
size (int) –
The size of the variable.

By default it is set to 1.
var_type (VarType) –
Either the type of the variable or the types of its components.

By default it is set to FLOAT.
l_b (float | ndarray | None) –
The lower bound of the variable. If None, use \(-\infty\).

By default it is set to None.
u_b (float | ndarray | None) –
The upper bound of the variable. If None, use \(+\infty\).

By default it is set to None.
value (float | ndarray | None) –
The default value of the variable. If None, do not use a default value.

By default it is set to None.

Raises

ValueError – Either if the variable already exists or if the size is not a positive integer.

Return type

None

array_to_dict(x_array)[source]

Convert a design array into a dictionary indexed by the variables names.

Parameters: x_array (numpy.ndarray) – A design value expressed as a NumPy array.
Returns: The design value expressed as a dictionary of NumPy arrays.
Return type: dict[str, numpy.ndarray]

check()[source]

Check the state of the design space.

Raises: ValueError – If the design space is empty.
Return type: None

check_membership(x_vect, variable_names=None)[source]

Check whether the variables satisfy the design space requirements.

Parameters

x_vect (Mapping[str, ndarray] | ndarray) – The values of the variables.
variable_names (Sequence[str] | None) –
The names of the variables. If None, use the names of the variables of the design space.

By default it is set to None.

Raises

ValueError – Either if the dimension of the values vector is wrong, if the values are not specified as an array or a dictionary, if the values are outside the bounds of the variables or if the component of an integer variable is not an integer.

Return type

None

clear() → None. Remove all items from D.

dict_to_array(design_values, variable_names=None)[source]

Convert a point as dictionary into an array.

Parameters

design_values (dict[str, numpy.ndarray]) – The design point to be converted.
variable_names (Optional[Iterable[str]]) –
The variables to be considered. If None, use the variables of the design space.

By default it is set to None.

Returns

The point as an array.

Return type

numpy.ndarray

export_hdf(file_path, append=False)[source]

Export the design space to an HDF file.

Parameters

file_path (str | Path) – The path to the file to export the design space.
append (bool) –
If True, appends the data in the file.

By default it is set to False.

Return type

None

export_to_txt(output_file, fields=None, header_char='', **table_options)[source]

Export the design space to a text file.

Parameters

output_file (str | Path) – The path to the file.
fields (Sequence[str] | None) –
The fields to be exported. If None, export all fields.

By default it is set to None.
header_char (str) –
The header character.

By default it is set to .
**table_options (Any) – The names and values of additional attributes for the PrettyTable view generated by DesignSpace.get_pretty_table().

Return type

None

extend(other)[source]

Extend the design space with another design space.

Parameters: other (gemseo.algos.design_space.DesignSpace) – The design space to be appended to the current one.
Return type: None

filter(keep_variables, copy=False)[source]

Filter the design space to keep a subset of variables.

Parameters

keep_variables (str | Iterable[str]) – The names of the variables to be kept.
copy (bool) –
If True, then a copy of the design space is filtered, otherwise the design space itself is filtered.

By default it is set to False.

Returns

Either the filtered original design space or a copy.

Raises

ValueError – If the variable is not in the design space.

Return type

DesignSpace

filter_dim(variable, keep_dimensions)[source]

Filter the design space to keep a subset of dimensions for a variable.

Parameters

variable (str) – The name of the variable.
keep_dimensions (Iterable[int]) – The dimensions of the variable to be kept, between \(0\) and \(d-1\) where \(d\) is the number of dimensions of the variable.

Returns

The filtered design space.

Raises

ValueError – If a dimension is unknown.

Return type

gemseo.algos.design_space.DesignSpace

get(k[, d]) → D[k] if k in D, else d. d defaults to None.

get_active_bounds(x_vec=None, tol=1e-08)[source]

Determine which bound constraints of a design value are active.

Parameters

x_vec (ndarray | None) –
The design value at which to check the bounds. If None, use the current design value.

By default it is set to None.
tol (float) –
The tolerance of comparison of a scalar with a bound.

By default it is set to 1e-08.

Returns

Whether the components of the lower and upper bound constraints are active, the first returned value representing the lower bounds and the second one the upper bounds, e.g.

({'x': array(are_x_lower_bounds_active),
  'y': array(are_y_lower_bounds_active)},
 {'x': array(are_x_upper_bounds_active),
  'y': array(are_y_upper_bounds_active)}
)

where:

are_x_lower_bounds_active = [True, False]
are_x_upper_bounds_active = [False, False]
are_y_lower_bounds_active = [False]
are_y_upper_bounds_active = [True]

Return type

tuple[dict[str, ndarray], dict[str, ndarray]]

get_current_value(variable_names=None, complex_to_real=False, as_dict=False, normalize=False)[source]

Return the current design value.

Parameters

variable_names (Sequence[str] | None) –
The names of the design variables. If None, use all the design variables.

By default it is set to None.
complex_to_real (bool) –
Whether to cast complex numbers to real ones.

By default it is set to False.
as_dict (bool) –
Whether to return the current design value as a dictionary of the form {variable_name: variable_value}.

By default it is set to False.
normalize (bool) –
Whether to normalize the design values in \([0,1]\) with the bounds of the variables.

By default it is set to False.

Returns

The current design value.

Raises

KeyError – If a variable has no current value.

Return type

ndarray | dict[str, ndarray]

get_indexed_var_name(variable_name)[source]

Create the names of the components of a variable.

If the size of the variable is equal to 1, this method returns the name of the variable. Otherwise, it concatenates the name of the variable, the separator DesignSpace.SEP and the index of the component.

Parameters: variable_name (str) – The name of the variable.
Returns: The names of the components of the variable.
Return type: str | list[str]

get_indexed_variables_names()[source]

Create the names of the components of all the variables.

If the size of the variable is equal to 1, this method uses its name. Otherwise, it concatenates the name of the variable, the separator DesignSpace.SEP and the index of the component.

Returns: The name of the components of all the variables.
Return type: list[str]

get_lower_bound(name)[source]

Return the lower bound of a variable.

Parameters: name (str) – The name of the variable.
Returns: The lower bound of the variable (possibly infinite).
Return type: numpy.ndarray

get_lower_bounds(variable_names=None)[source]

Generate an array of the variables’ lower bounds.

Parameters

variable_names (Sequence[str] | None) –

The names of the variables of which the lower bounds are required. If None, use the variables of the design space.

By default it is set to None.

Returns

The lower bounds of the variables.

Return type

ndarray

get_pretty_table(fields=None)[source]

Build a tabular view of the design space.

Parameters

fields (Sequence[str] | None) –

The name of the fields to be exported. If None, export all the fields.

By default it is set to None.

Returns

A tabular view of the design space.

Return type

PrettyTable

get_size(name)[source]

Get the size of a variable.

Parameters: name (str) – The name of the variable.
Returns: The size of the variable, None if it is not known.
Return type: int | None

get_type(name)[source]

Return the type of a variable.

Parameters: name (str) – The name of the variable.
Returns: The type of the variable, None if it is not known.
Return type: str | None

get_upper_bound(name)[source]

Return the upper bound of a variable.

Parameters: name (str) – The name of the variable.
Returns: The upper bound of the variable (possibly infinite).
Return type: numpy.ndarray

get_upper_bounds(variable_names=None)[source]

Generate an array of the variables’ upper bounds.

Parameters

variable_names (Sequence[str] | None) –

The names of the variables of which the upper bounds are required. If None, use the variables of the design space.

By default it is set to None.

Returns

The upper bounds of the variables.

Return type

ndarray

get_variables_indexes(variable_names)[source]

Return the indexes of a design array corresponding to the variables names.

Parameters: variable_names (Iterable[str]) – The names of the variables.
Returns: The indexes of a design array corresponding to the variables names.
Return type: numpy.ndarray

has_current_value()[source]

Check if each variable has a current value.

Returns: Whether the current design value is defined for all variables.
Return type: bool

has_integer_variables()[source]

Check if the design space has at least one integer variable.

Returns: Whether the design space has at least one integer variable.
Return type: bool

import_hdf(file_path)[source]

Import a design space from an HDF file.

Parameters: file_path (str | Path) – The path to the file containing the description of a design space.
Return type: None

items() → a set-like object providing a view on D's items

keys() → a set-like object providing a view on D's keys

normalize_grad(g_vect)[source]

Normalize an unnormalized gradient.

This method is based on the chain rule:

\[\frac{df(x)}{dx} = \frac{df(x)}{dx_u}\frac{dx_u}{dx} = \frac{df(x)}{dx_u}\frac{1}{u_b-l_b}\]

where \(x_u = \frac{x-l_b}{u_b-l_b}\) is the normalized input vector, \(x\) is the unnormalized input vector and \(l_b\) and \(u_b\) are the lower and upper bounds of \(x\).

Then, the normalized gradient reads:

\[\frac{df(x)}{dx_u} = (u_b-l_b)\frac{df(x)}{dx}\]

where \(\frac{df(x)}{dx}\) is the unnormalized one.

Parameters: g_vect (numpy.ndarray) – The gradient to be normalized.
Returns: The normalized gradient.
Return type: numpy.ndarray

normalize_vect(x_vect, minus_lb=True, out=None)[source]

Normalize a vector of the design space.

If minus_lb is True:

\[x_u = \frac{x-l_b}{u_b-l_b}\]

where \(l_b\) and \(u_b\) are the lower and upper bounds of \(x\).

Otherwise:

\[x_u = \frac{x}{u_b-l_b}\]

Unbounded variables are not normalized.

Parameters

x_vect (ndarray) – The values of the design variables.
minus_lb (bool) –
If True, remove the lower bounds at normalization.

By default it is set to True.
out (ndarray | None) –
The array to store the normalized vector. If None, create a new array.

By default it is set to None.

Returns

The normalized vector.

Return type

ndarray

pop(k[, d]) → v, remove specified key and return the corresponding value.

If key is not found, d is returned if given, otherwise KeyError is raised.

popitem() → (k, v), remove and return some (key, value) pair

as a 2-tuple; but raise KeyError if D is empty.

project_into_bounds(x_c, normalized=False)[source]

Project a vector onto the bounds, using a simple coordinate wise approach.

Parameters

normalized (bool) –
If True, then the vector is assumed to be normalized.

By default it is set to False.
x_c (numpy.ndarray) – The vector to be projected onto the bounds.

Returns

The projected vector.

Return type

numpy.ndarray

static read_from_txt(input_file, header=None)[source]

Create a design space from a text file.

Parameters

input_file (str | Path) – The path to the file.
header (Iterable[str] | None) –
The names of the fields saved in the file. If None, read them in the file.

By default it is set to None.

Returns

The design space read from the file.

Raises

ValueError – If the file does not contain the minimal variables in its header.

Return type

DesignSpace

remove_variable(name)[source]

Remove a variable from the design space.

Parameters: name (str) – The name of the variable to be removed.
Return type: None

rename_variable(current_name, new_name)[source]

Rename a variable.

Parameters

current_name (str) – The name of the variable to rename.
new_name (str) – The new name of the variable.

Return type

None

round_vect(x_vect, copy=True)[source]

Round the vector where variables are of integer type.

Parameters

x_vect (numpy.ndarray) – The values to be rounded.
copy (bool) –
Whether to round a copy of x_vect.

By default it is set to True.

Returns

The rounded values.

Return type

numpy.ndarray

set_current_value(value)[source]

Set the current design value.

Parameters

value (ndarray | Mapping[str, ndarray] | OptimizationResult) – The value of the current design.

Raises

ValueError – If the value has a wrong dimension.
TypeError – If the value is neither a mapping of NumPy arrays, a NumPy array nor an OptimizationResult.

Return type

None

set_current_variable(name, current_value)[source]

Set the current value of a single variable.

Parameters

name (str) – The name of the variable.
current_value (numpy.ndarray) – The current value of the variable.

Return type

None

set_lower_bound(name, lower_bound)[source]

Set the lower bound of a variable.

Parameters

name (str) – The name of the variable.
lower_bound (numpy.ndarray) – The value of the lower bound.

Raises

ValueError – If the variable does not exist.

Return type

None

set_upper_bound(name, upper_bound)[source]

Set the upper bound of a variable.

Parameters

name (str) – The name of the variable.
upper_bound (numpy.ndarray) – The value of the upper bound.

Raises

ValueError – If the variable does not exist.

Return type

None

setdefault(k[, d]) → D.get(k,d), also set D[k]=d if k not in D

to_complex()[source]

Cast the current value to complex.

Return type: None

transform_vect(vector, out=None)[source]

Map a point of the design space to a vector with components in \([0,1]\).

Parameters

vector (ndarray) – A point of the design space.
out (ndarray | None) –
The array to store the transformed vector. If None, create a new array.

By default it is set to None.

Returns

A vector with components in \([0,1]\).

Return type

ndarray

unnormalize_grad(g_vect)[source]

Unnormalize a normalized gradient.

This method is based on the chain rule:

\[\frac{df(x)}{dx} = \frac{df(x)}{dx_u}\frac{dx_u}{dx} = \frac{df(x)}{dx_u}\frac{1}{u_b-l_b}\]

where \(x_u = \frac{x-l_b}{u_b-l_b}\) is the normalized input vector, \(x\) is the unnormalized input vector, \(\frac{df(x)}{dx_u}\) is the unnormalized gradient \(\frac{df(x)}{dx}\) is the normalized one, and \(l_b\) and \(u_b\) are the lower and upper bounds of \(x\).

Parameters: g_vect (numpy.ndarray) – The gradient to be unnormalized.
Returns: The unnormalized gradient.
Return type: numpy.ndarray

unnormalize_vect(x_vect, minus_lb=True, no_check=False, out=None)[source]

Unnormalize a normalized vector of the design space.

If minus_lb is True:

\[x = x_u(u_b-l_b) + l_b\]

where \(x_u\) is the normalized input vector, \(x\) is the unnormalized input vector and \(l_b\) and \(u_b\) are the lower and upper bounds of \(x\).

Otherwise:

\[x = x_u(u_b-l_b)\]

Parameters

x_vect (ndarray) – The values of the design variables.
minus_lb (bool) –
Whether to remove the lower bounds at normalization.

By default it is set to True.
no_check (bool) –
Whether to check if the components are in \([0,1]\).

By default it is set to False.
out (ndarray | None) –
The array to store the unnormalized vector. If None, create a new array.

By default it is set to None.

Returns

The unnormalized vector.

Return type

ndarray

untransform_vect(vector, no_check=False, out=None)[source]

Map a vector with components in \([0,1]\) to the design space.

Parameters

vector (ndarray) – A vector with components in \([0,1]\).
no_check (bool) –
Whether to check if the components are in \([0,1]\).

By default it is set to False.
out (ndarray | None) –
The array to store the untransformed vector. If None, create a new array.

By default it is set to None.

Returns

A point of the variables space.

Return type

ndarray

update([E, ]**F) → None. Update D from mapping/iterable E and F.

If E present and has a .keys() method, does: for k in E: D[k] = E[k] If E present and lacks .keys() method, does: for (k, v) in E: D[k] = v In either case, this is followed by: for k, v in F.items(): D[k] = v

values() → an object providing a view on D's values

dimension: int: The total dimension of the space, corresponding to the sum of the sizes of the variables.

name: str | None: The name of the space.

normalize: dict[str, ndarray]: The normalization policies of the variables components indexed by the variables names; if True, the component can be normalized.

variables_names: list[str]: The names of the variables.

variables_sizes: dict[str, int]: The sizes of the variables.

variables_types: dict[str, ndarray]: The types of the variables components, which can be any DesignSpace.DesignVariableType.

class gemseo.algos.design_space.DesignVariable(size, var_type, l_b, u_b, value)

Create new instance of DesignVariable(size, var_type, l_b, u_b, value)

count(value, /)

Return number of occurrences of value.

index(value, start=0, stop=9223372036854775807, /)

Return first index of value.

Raises ValueError if the value is not present.

l_b: Alias for field number 2

size: Alias for field number 0

u_b: Alias for field number 3

value: Alias for field number 4

var_type: Alias for field number 1

class gemseo.algos.design_space.DesignVariableType(value)[source]

A type of design variable.

Optimization problem.

The OptimizationProblem class operates on a DesignSpace defining:

an initial guess \(x_0\) for the design variables,
the bounds \(l_b \leq x \leq u_b\) of the design variables.

A (possible vector) objective function with a MDOFunction type is set using the objective attribute. If the optimization problem looks for the maximum of this objective function, the OptimizationProblem.change_objective_sign() changes the objective function sign because the optimization drivers seek to minimize this objective function.

Equality and inequality constraints are also MDOFunction instances provided to the OptimizationProblem by means of its OptimizationProblem.add_constraint() method.

The OptimizationProblem allows to evaluate the different functions for a given design parameters vector (see OptimizationProblem.evaluate_functions()). Note that this evaluation step relies on an automated scaling of function wrt the bounds so that optimizers and DOE algorithms work with inputs scaled between 0 and 1 for all the variables.

The OptimizationProblem has also a Database that stores the calls to all the functions so that no function is called twice with the same inputs. Concerning the derivatives’ computation, the OptimizationProblem automates the generation of the finite differences or complex step wrappers on functions, when the analytical gradient is not available.

Lastly, various getters and setters are available, as well as methods to export the Database to an HDF file or to a Dataset for future post-processing.

class gemseo.algos.opt_problem.OptimizationProblem(design_space, pb_type='non-linear', input_database=None, differentiation_method='user', fd_step=1e-07, parallel_differentiation=False, use_standardized_objective=True, **parallel_differentiation_options)[source]

An optimization problem.

Create an optimization problem from:

a DesignSpace specifying the design variables in terms of names, lower bounds, upper bounds and initial guesses,
the objective function as a MDOFunction, which can be a vector,

execute it from an algorithm provided by a DriverLib, and store some execution data in a Database.

In particular, this Database stores the calls to all the functions so that no function is called twice with the same inputs.

An OptimizationProblem also has an automated scaling of function with respect to the bounds of the design variables so that the driving algorithms work with inputs scaled between 0 and 1.

Lastly, OptimizationProblem automates the generation of finite differences or complex step wrappers on functions, when analytical gradient is not available.

Parameters

design_space (DesignSpace) – The design space on which the functions are evaluated.
pb_type (str) –
The type of the optimization problem among OptimizationProblem.AVAILABLE_PB_TYPES.

By default it is set to non-linear.
input_database (str | Database | None) –
A database to initialize that of the optimization problem. If None, the optimization problem starts from an empty database.

By default it is set to None.
differentiation_method (str) –
The default differentiation method to be applied to the functions of the optimization problem.

By default it is set to user.
fd_step (float) –
The step to be used by the step-based differentiation methods.

By default it is set to 1e-07.
parallel_differentiation (bool) –
Whether to approximate the derivatives in parallel.

By default it is set to False.
use_standardized_objective (bool) –
Whether to use standardized objective for logging and post-processing.

By default it is set to True.
**parallel_differentiation_options (int | bool) – The options to approximate the derivatives in parallel.

Return type

None

add_callback(callback_func, each_new_iter=True, each_store=False)[source]

Add a callback function after each store operation or new iteration.

Parameters

callback_func (Callable) – A function to be called after some event.
each_new_iter (bool) –
If True, then callback at every iteration.

By default it is set to True.
each_store (bool) –
If True, then callback at every call to Database.store().

By default it is set to False.

Return type

None

add_constraint(cstr_func, value=None, cstr_type=None, positive=False)[source]

Add a constraint (equality and inequality) to the optimization problem.

Parameters

cstr_func (MDOFunction) – The constraint.
value (float | None) –
The value for which the constraint is active. If None, this value is 0.

By default it is set to None.
cstr_type (str | None) –
The type of the constraint. Either equality or inequality.

By default it is set to None.
positive (bool) –
If True, then the inequality constraint is positive.

By default it is set to False.

Raises

TypeError – When the constraint of a linear optimization problem is not an MDOLinearFunction.
ValueError – When the type of the constraint is missing.

Return type

None

add_eq_constraint(cstr_func, value=None)[source]

Add an equality constraint to the optimization problem.

Parameters

cstr_func (MDOFunction) – The constraint.
value (float | None) –
The value for which the constraint is active. If None, this value is 0.

By default it is set to None.

Return type

None

add_ineq_constraint(cstr_func, value=None, positive=False)[source]

Add an inequality constraint to the optimization problem.

Parameters

cstr_func (MDOFunction) – The constraint.
value (float | None) –
The value for which the constraint is active. If None, this value is 0.

By default it is set to None.
positive (bool) –
If True, then the inequality constraint is positive.

By default it is set to False.

Return type

None

add_observable(obs_func, new_iter=True)[source]

Add a function to be observed.

When the OptimizationProblem is executed, the observables are called following this sequence:

The optimization algorithm calls the objective function with a normalized x_vect.

The OptimizationProblem.preprocess_functions() wraps the function as a NormDBFunction, which unnormalizes the x_vect before evaluation.

The unnormalized x_vect and the result of the evaluation are stored in the OptimizationProblem.database.

The previous step triggers the OptimizationProblem.new_iter_listeners, which calls the observables with the unnormalized x_vect.

The observables themselves are wrapped as a NormDBFunction by OptimizationProblem.preprocess_functions(), but in this case the input is always expected as unnormalized to avoid an additional normalizing-unnormalizing step.

Finally, the output is stored in the OptimizationProblem.database.

Parameters

obs_func (gemseo.core.mdofunctions.mdo_function.MDOFunction) – An observable to be observed.
new_iter (bool) –
If True, then the observable will be called at each new iterate.

By default it is set to True.

Return type

None

aggregate_constraint(constr_id, method='max', groups=None, **options)[source]

Aggregates a constraint to generate a reduced dimension constraint.

Parameters

constr_id (int) – The index of the constraint in constraints.
method (str | Callable[[Callable], Callable]) –
The aggregation method, e.g. "max", "KS" or "IKS".

By default it is set to max.
groups (tuple[ndarray] | None) –
The groups for which to produce an output. If None, a single output constraint is produced.

By default it is set to None.
**options (Any) – The options of the aggregation method.

Raises

ValueError – When the given is index is greater or equal than the number of constraints or when the method is aggregation unknown.

change_objective_sign()[source]

Change the objective function sign in order to minimize its opposite.

The OptimizationProblem expresses any optimization problem as a minimization problem. Then, an objective function originally expressed as a performance function to maximize must be converted into a cost function to minimize, by means of this method.

Return type: None

check()[source]

Check if the optimization problem is ready for run.

Raises: ValueError – If the objective function is missing.
Return type: None

static check_format(input_function)[source]

Check that a function is an instance of MDOFunction.

Parameters: input_function (Any) – The function to be tested.
Raises: TypeError – If the function is not a MDOFunction.
Return type: None

clear_listeners()[source]

Clear all the listeners.

Return type: None

evaluate_functions(x_vect=None, eval_jac=False, eval_obj=True, eval_observables=False, normalize=True, no_db_no_norm=False)[source]

Compute the functions of interest, and possibly their derivatives.

These functions of interest are the constraints, and possibly the objective.

Some optimization libraries require the number of constraints as an input parameter which is unknown by the formulation or the scenario. Evaluation of initial point allows to get this mandatory information. This is also used for design of experiments to evaluate samples.

Parameters

x_vect (ndarray) –
The input vector at which the functions must be evaluated; if None, the initial point x_0 is used.

By default it is set to None.
eval_jac (bool) –
Whether to compute the Jacobian matrices of the functions of interest.

By default it is set to False.
eval_obj (bool) –
Whether to consider the objective function as a function of interest.

By default it is set to True.
normalize (bool) –
Whether to consider the input vector x_vect normalized.

By default it is set to True.
no_db_no_norm (bool) –
If True, then do not use the pre-processed functions, so we have no database, nor normalization.

By default it is set to False.
eval_observables (bool) –

By default it is set to False.

Returns

The output values of the functions of interest, as well as their Jacobian matrices if eval_jac is True.

Return type

tuple[dict[str, float | ndarray], dict[str, ndarray]]

execute_observables_callback(last_x)[source]

The callback function to be passed to the database.

Call all the observables with the last design variables values as argument.

Parameters: last_x (numpy.ndarray) – The design variables values from the last evaluation.
Return type: None

export_hdf(file_path, append=False)[source]

Export the optimization problem to an HDF file.

Parameters

file_path (str) – The file to store the data.
append (bool) –
If True, then the data are appended to the file if not empty.

By default it is set to False.

Return type

None

export_to_dataset(name=None, by_group=True, categorize=True, opt_naming=True, export_gradients=False, input_values=None)[source]

Export the database of the optimization problem to a Dataset.

The variables can be classified into groups: Dataset.DESIGN_GROUP or Dataset.INPUT_GROUP for the design variables and Dataset.FUNCTION_GROUP or Dataset.OUTPUT_GROUP for the functions (objective, constraints and observables).

Parameters

name (str | None) –
The name to be given to the dataset. If None, use the name of the OptimizationProblem.database.

By default it is set to None.
by_group (bool) –
Whether to store the data by group in Dataset.data, in the sense of one unique NumPy array per group. If categorize is False, there is a unique group: Dataset.PARAMETER_GROUP`. If categorize is True, the groups can be either Dataset.DESIGN_GROUP and Dataset.FUNCTION_GROUP if opt_naming is True, or Dataset.INPUT_GROUP and Dataset.OUTPUT_GROUP. If by_group is False, store the data by variable names.

By default it is set to True.
categorize (bool) –
Whether to distinguish between the different groups of variables. Otherwise, group all the variables in Dataset.PARAMETER_GROUP`.

By default it is set to True.
opt_naming (bool) –
Whether to use Dataset.DESIGN_GROUP and Dataset.FUNCTION_GROUP as groups. Otherwise, use Dataset.INPUT_GROUP and Dataset.OUTPUT_GROUP.

By default it is set to True.
export_gradients (bool) –
Whether to export the gradients of the functions (objective function, constraints and observables) if the latter are available in the database of the optimization problem.

By default it is set to False.
input_values (Iterable[ndarray] | None) –
The input values to be considered. If None, consider all the input values of the database.

By default it is set to None.

Returns

A dataset built from the database of the optimization problem.

Return type

Dataset

get_active_ineq_constraints(x_vect, tol=1e-06)[source]

For each constraint, indicate if its different components are active.

Parameters

x_vect (numpy.ndarray) – The vector of design variables.
tol (float) –
The tolerance for deciding whether a constraint is active.

By default it is set to 1e-06.

Returns

For each constraint, a boolean indicator of activation of its different components.

Return type

dict[gemseo.core.mdofunctions.mdo_function.MDOFunction, numpy.ndarray]

get_all_functions()[source]

Retrieve all the functions of the optimization problem.

These functions are the constraints, the objective function and the observables.

Returns: All the functions of the optimization problem.
Return type: list[gemseo.core.mdofunctions.mdo_function.MDOFunction]

get_all_functions_names()[source]

Retrieve the names of all the function of the optimization problem.

These functions are the constraints, the objective function and the observables.

Returns: The names of all the functions of the optimization problem.
Return type: list[str]

get_best_infeasible_point()[source]

Retrieve the best infeasible point within a given tolerance.

Returns: The best infeasible point expressed as the design variables values, the objective function value, the feasibility of the point and the functions values.
Return type: Tuple[Optional[numpy.ndarray], Optional[numpy.ndarray], bool, Dict[str, numpy.ndarray]]

get_constraints_names()[source]

Retrieve the names of the constraints.

Returns: The names of the constraints.
Return type: list[str]

get_constraints_number()[source]

Retrieve the number of constraints.

Returns: The number of constraints.
Return type: int

get_data_by_names(names, as_dict=True, filter_non_feasible=False)[source]

Return the data for specific names of variables.

Parameters

names (str | Iterable[str]) – The names of the variables.
as_dict (bool) –
If True, return values as dictionary.

By default it is set to True.
filter_non_feasible (bool) –
If True, remove the non-feasible points from the data.

By default it is set to False.

Returns

The data related to the variables.

Return type

ndarray | dict[str, ndarray]

get_design_variable_names()[source]

Retrieve the names of the design variables.

Returns: The names of the design variables.
Return type: list[str]

get_dimension()[source]

Retrieve the total number of design variables.

Returns: The dimension of the design space.
Return type: int

get_eq_constraints()[source]

Retrieve all the equality constraints.

Returns: The equality constraints.
Return type: list[gemseo.core.mdofunctions.mdo_function.MDOFunction]

get_eq_constraints_number()[source]

Retrieve the number of equality constraints.

Returns: The number of equality constraints.
Return type: int

get_eq_cstr_total_dim()[source]

Retrieve the total dimension of the equality constraints.

This dimension is the sum of all the outputs dimensions of all the equality constraints.

Returns: The total dimension of the equality constraints.
Return type: int

get_feasible_points()[source]

Retrieve the feasible points within a given tolerance.

This tolerance is defined by OptimizationProblem.eq_tolerance for equality constraints and OptimizationProblem.ineq_tolerance for inequality ones.

Returns: The values of the design variables and objective function for the feasible points.
Return type: tuple[list[ndarray], list[dict[str, float | list[int]]]]

get_function_dimension(name)[source]

Return the dimension of a function of the problem (e.g. a constraint).

Parameters

name (str) – The name of the function.

Returns

The dimension of the function.

Raises

ValueError – If the function name is unknown to the problem.
RuntimeError – If the function dimension is not unavailable.

Return type

int

get_function_names(names)[source]

Return the names of the functions stored in the database.

Parameters: names (Iterable[str]) – The names of the outputs or constraints specified by the user.
Returns: The names of the constraints stored in the database.
Return type: list[str]

get_functions_dimensions(names=None)[source]

Return the dimensions of the outputs of the problem functions.

Parameters

names (Iterable[str] | None) –

The names of the functions. If None, then the objective and all the constraints are considered.

By default it is set to None.

Returns

The dimensions of the outputs of the problem functions. The dictionary keys are the functions names and the values are the functions dimensions.

Return type

dict[str, int]

get_ineq_constraints()[source]

Retrieve all the inequality constraints.

Returns: The inequality constraints.
Return type: list[gemseo.core.mdofunctions.mdo_function.MDOFunction]

get_ineq_constraints_number()[source]

Retrieve the number of inequality constraints.

Returns: The number of inequality constraints.
Return type: int

get_ineq_cstr_total_dim()[source]

Retrieve the total dimension of the inequality constraints.

This dimension is the sum of all the outputs dimensions of all the inequality constraints.

Returns: The total dimension of the inequality constraints.
Return type: int

get_nonproc_constraints()[source]

Retrieve the non-processed constraints.

Returns: The non-processed constraints.
Return type: list[gemseo.core.mdofunctions.mdo_function.MDOFunction]

get_nonproc_objective()[source]

Retrieve the non-processed objective function.

Return type: gemseo.core.mdofunctions.mdo_function.MDOFunction

get_number_of_unsatisfied_constraints(design_variables)[source]

Return the number of scalar constraints not satisfied by design variables.

Parameters: design_variables (numpy.ndarray) – The design variables.
Returns: The number of unsatisfied scalar constraints.
Return type: int

get_objective_name(standardize=True)[source]

Retrieve the name of the objective function.

Parameters

standardize (bool) –

Whether to use the name of the objective expressed as a cost, e.g. "-f" when the user seeks to maximize "f".

By default it is set to True.

Returns

The name of the objective function.

Return type

str

get_observable(name)[source]

Retrieve an observable from its name.

Parameters: name (str) – The name of the observable.
Returns: The observable.
Raises: ValueError – If the observable cannot be found.
Return type: gemseo.core.mdofunctions.mdo_function.MDOFunction

get_optimum()[source]

Return the optimum solution within a given feasibility tolerances.

Returns

The optimum result, defined by:

the value of the objective function,
the value of the design variables,
the indicator of feasibility of the optimal solution,
the value of the constraints,
the value of the gradients of the constraints.

Return type

Tuple[numpy.ndarray, numpy.ndarray, bool, Dict[str, numpy.ndarray], Dict[str, numpy.ndarray]]

get_scalar_constraints_names()[source]

Return the names of the scalar constraints.

Returns: The names of the scalar constraints.
Return type: list[str]

get_violation_criteria(x_vect)[source]

Compute a violation measure associated to an iteration.

For each constraint, when it is violated, add the absolute distance to zero, in L2 norm.

If 0, all constraints are satisfied

Parameters: x_vect (numpy.ndarray) – The vector of the design variables values.
Returns: The feasibility of the point and the violation measure.
Return type: tuple[bool, float]

get_x0_normalized(cast_to_real=False)[source]

Return the current values of the design variables after normalization.

Parameters

cast_to_real (bool) –

Whether to cast the return value to real.

By default it is set to False.

Returns

The current values of the design variables normalized between 0 and 1 from their lower and upper bounds.

Return type

numpy.ndarray

has_constraints()[source]

Check if the problem has equality or inequality constraints.

Returns: True if the problem has equality or inequality constraints.

has_eq_constraints()[source]

Check if the problem has equality constraints.

Returns: True if the problem has equality constraints.
Return type: bool

has_ineq_constraints()[source]

Check if the problem has inequality constraints.

Returns: True if the problem has inequality constraints.
Return type: bool

has_nonlinear_constraints()[source]

Check if the problem has non-linear constraints.

Returns: True if the problem has equality or inequality constraints.
Return type: bool

classmethod import_hdf(file_path, x_tolerance=0.0)[source]

Import an optimization history from an HDF file.

Parameters

file_path (str) – The file containing the optimization history.
x_tolerance (float) –
The tolerance on the design variables when reading the file.

By default it is set to 0.0.

Returns

The read optimization problem.

Return type

gemseo.algos.opt_problem.OptimizationProblem

is_max_iter_reached()[source]

Check if the maximum amount of iterations has been reached.

Returns: Whether the maximum amount of iterations has been reached.
Return type: bool

is_point_feasible(out_val, constraints=None)[source]

Check if a point is feasible.

Note

If the value of a constraint is absent from this point, then this constraint will be considered satisfied.

Parameters

out_val (dict[str, ndarray]) – The values of the objective function, and eventually constraints.
constraints (Iterable[MDOFunction] | None) –
The constraints whose values are to be tested. If None, then take all constraints of the problem.

By default it is set to None.

Returns

The feasibility of the point.

Return type

bool

preprocess_functions(is_function_input_normalized=True, use_database=True, round_ints=True, eval_obs_jac=False)[source]

Pre-process all the functions and eventually the gradient.

Required to wrap the objective function and constraints with the database and eventually the gradients by complex step or finite differences.

Parameters

is_function_input_normalized (bool) –
Whether to consider the function input as normalized and unnormalize it before the evaluation takes place.

By default it is set to True.
use_database (bool) –
Whether to wrap the functions in the database.

By default it is set to True.
round_ints (bool) –
Whether to round the integer variables.

By default it is set to True.
eval_obs_jac (bool) –
Whether to evaluate the Jacobian of the observables.

By default it is set to False.

Return type

None

static repr_constraint(func, ctype, value=None, positive=False)[source]

Express a constraint as a string expression.

Parameters

func (MDOFunction) – The constraint function.
ctype (str) – The type of the constraint. Either equality or inequality.
value (float | None) –
The value for which the constraint is active. If None, this value is 0.

By default it is set to None.
positive (bool) –
If True, then the inequality constraint is positive.

By default it is set to False.

Returns

A string representation of the constraint.

Return type

str

reset(database=True, current_iter=True, design_space=True, function_calls=True, preprocessing=True)[source]

Partially or fully reset the optimization problem.

Parameters

database (bool) –
Whether to clear the database.

By default it is set to True.
current_iter (bool) –
Whether to reset the current iteration OptimizationProblem.current_iter.

By default it is set to True.
design_space (bool) –
Whether to reset the current point of the OptimizationProblem.design_space to its initial value (possibly none).

By default it is set to True.
function_calls (bool) –
Whether to reset the number of calls of the functions.

By default it is set to True.
preprocessing (bool) –
Whether to turn the pre-processing of functions to False.

By default it is set to True.

Return type

None

activate_bound_check: ClassVar[bool] = True: Whether to check if a point is in the design space before calling functions.

constraint_names: dict[str, list[str]]: The standardized constraint names bound to the original ones.

constraints: list[gemseo.core.mdofunctions.mdo_function.MDOFunction]: The constraints.

database: gemseo.algos.database.Database: The database to store the optimization problem data.

design_space: gemseo.algos.design_space.DesignSpace: The design space on which the optimization problem is solved.

property differentiation_method: str: The differentiation method.

property dimension: int: The dimension of the design space.

eq_tolerance: float: The tolerance for the equality constraints.

fd_step: float: The finite differences step.

ineq_tolerance: float: The tolerance for the inequality constraints.

property is_mono_objective: bool: Whether the optimization problem is mono-objective.

minimize_objective: bool: Whether to maximize the objective.

new_iter_observables: list[gemseo.core.mdofunctions.mdo_function.MDOFunction]: The observables to be called at each new iterate.

nonproc_constraints: list[gemseo.core.mdofunctions.mdo_function.MDOFunction]: The non-processed constraints.

nonproc_new_iter_observables: list[gemseo.core.mdofunctions.mdo_function.MDOFunction]: The non-processed observables to be called at each new iterate.

nonproc_objective: gemseo.core.mdofunctions.mdo_function.MDOFunction: The non-processed objective function.

nonproc_observables: list[gemseo.core.mdofunctions.mdo_function.MDOFunction]: The non-processed observables.

property objective: gemseo.core.mdofunctions.mdo_function.MDOFunction: The objective function.

observables: list[gemseo.core.mdofunctions.mdo_function.MDOFunction]: The observables.

property parallel_differentiation: bool: Whether to approximate the derivatives in parallel.

property parallel_differentiation_options: bool: The options to approximate the derivatives in parallel.

pb_type: str: The type of optimization problem.

preprocess_options: dict: The options to pre-process the functions.

solution: gemseo.algos.opt_result.OptimizationResult: The solution of the optimization problem.

stop_if_nan: bool: Whether the optimization stops when a function returns NaN.

use_standardized_objective: bool

Whether to use standardized objective for logging and post-processing.

The standardized objective corresponds to the original one expressed as a cost function to minimize. A DriverLib works with this standardized objective and the Database stores its values. However, for convenience, it may be more relevant to log the expression and the values of the original objective.

Examples

Driver library.

A driver library aims to solve an OptimizationProblem using a particular algorithm from a particular family of numerical methods. This algorithm will be in charge of evaluating the objective and constraints functions at different points of the design space, using the DriverLib.execute() method. The most famous kinds of numerical methods to solve an optimization problem are optimization algorithms and design of experiments (DOE). A DOE driver browses the design space agnostically, i.e. without taking into account the function evaluations. On the contrary, an optimization algorithm uses this information to make the journey through design space as relevant as possible in order to reach as soon as possible the optimum. These families are implemented in DOELibrary and OptimizationLibrary.

class gemseo.algos.driver_lib.DriverDescription(algorithm_name, internal_algorithm_name, library_name='', description='', website='', handle_integer_variables=False, require_gradient=False)[source]

The description of a driver.

Parameters

algorithm_name (str) –
internal_algorithm_name (str) –
library_name (str) –

By default it is set to .
description (str) –

By default it is set to .
website (str) –

By default it is set to .
handle_integer_variables (bool) –

By default it is set to False.
require_gradient (bool) –

By default it is set to False.

Return type

None

handle_integer_variables: bool = False: Whether the optimization algorithm handles integer variables.

require_gradient: bool = False: Whether the optimization algorithm requires the gradient.

class gemseo.algos.driver_lib.DriverLib[source]

Abstract class for DOE & optimization libraries interfaces.

Lists available methods in the library for the proposed problem to be solved.

To integrate an optimization package, inherit from this class and put your file in gemseo.algos.doe or gemseo.algo.opt packages.

deactivate_progress_bar()[source]

Deactivate the progress bar.

Return type: None

driver_has_option(option_name)

Check the existence of an option.

Parameters: option_name (str) – The name of the option.
Returns: Whether the option exists.
Return type: bool

ensure_bounds(orig_func, normalize=True)[source]

Project the design vector onto the design space before execution.

Parameters

orig_func – The original function.
normalize –
Whether to use the normalized design space.

By default it is set to True.

Returns

A function calling the original function with the input data projected onto the design space.

execute(problem, algo_name=None, eval_obs_jac=False, skip_int_check=False, **options)[source]

Execute the driver.

Parameters

problem (OptimizationProblem) – The problem to be solved.
algo_name (str | None) –
The name of the algorithm. If None, use the algo_name attribute which may have been set by the factory.

By default it is set to None.
eval_obs_jac (bool) –
Whether to evaluate the Jacobian of the observables.

By default it is set to False.
skip_int_check (bool) –
Whether to skip the integer variable handling check of the selected algorithm.

By default it is set to False.
**options (DriverLibOptionType) – The options for the algorithm.

Returns

The optimization result.

Raises

ValueError – If algo_name was not either set by the factory or given as an argument.

Return type

OptimizationResult

filter_adapted_algorithms(problem)

Filter the algorithms capable of solving the problem.

Parameters: problem (Any) – The problem to be solved.
Returns: The names of the algorithms adapted to this problem.
Return type: list[str]

finalize_iter_observer()[source]

Finalize the iteration observer.

Return type: None

get_optimum_from_database(message=None, status=None)[source]

Retrieves the optimum from the database and builds an optimization result object from it.

get_x0_and_bounds_vects(normalize_ds)[source]

Gets x0, bounds, normalized or not depending on algo options, all as numpy arrays.

Parameters: normalize_ds – Whether to normalize the input variables that are not integers, according to the normalization policy of the design space.
Returns: The current value, the lower bounds and the upper bounds.

init_iter_observer(max_iter, message)[source]

Initialize the iteration observer.

It will handle the stopping criterion and the logging of the progress bar.

Parameters

max_iter (int) – The maximum number of iterations.
message (str) – The message to display at the beginning.

Raises

ValueError – If the max_iter is not greater than or equal to one.

Return type

None

init_options_grammar(algo_name)

Initialize the options grammar.

Parameters: algo_name (str) – The name of the algorithm.
Return type: gemseo.core.grammars.json_grammar.JSONGrammar

is_algo_requires_grad(algo_name)[source]

Returns True if the algorithm requires a gradient evaluation.

Parameters: algo_name – The name of the algorithm.

static is_algorithm_suited(algorithm_description, problem)

Check if the algorithm is suited to the problem according to its description.

Parameters

algorithm_description (gemseo.algos.algo_lib.AlgorithmDescription) – The description of the algorithm.
problem (Any) – The problem to be solved.

Returns

Whether the algorithm is suited to the problem.

Return type

bool

new_iteration_callback(x_vect=None)[source]

Callback called at each new iteration, i.e. every time a design vector that is not already in the database is proposed by the optimizer.

Iterate the progress bar, implement the stop criteria.

Parameters

x_vect (ndarray | None) –

The design variables values. If None, use the values of the last iteration.

By default it is set to None.

Raises

MaxTimeReached – If the elapsed time is greater than the maximum execution time.

Return type

None

activate_progress_bar: ClassVar[bool] = True: Whether to activate the progress bar in the optimization log.

property algorithms: list[str]: The available algorithms.

class gemseo.algos.driver_lib.ProgressBar(*_, **__)[source]

Extend tqdm progress bar with better time units.

Use hour, day or week for slower processes.

Parameters

iterable (iterable, optional) – Iterable to decorate with a progressbar. Leave blank to manually manage the updates.
desc (str, optional) – Prefix for the progressbar.
total (int or float, optional) – The number of expected iterations. If unspecified, len(iterable) is used if possible. If float(“inf”) or as a last resort, only basic progress statistics are displayed (no ETA, no progressbar). If gui is True and this parameter needs subsequent updating, specify an initial arbitrary large positive number, e.g. 9e9.
leave (bool, optional) – If [default: True], keeps all traces of the progressbar upon termination of iteration. If None, will leave only if position is 0.
file (io.TextIOWrapper or io.StringIO, optional) – Specifies where to output the progress messages (default: sys.stderr). Uses file.write(str) and file.flush() methods. For encoding, see write_bytes.
ncols (int, optional) – The width of the entire output message. If specified, dynamically resizes the progressbar to stay within this bound. If unspecified, attempts to use environment width. The fallback is a meter width of 10 and no limit for the counter and statistics. If 0, will not print any meter (only stats).
mininterval (float, optional) – Minimum progress display update interval [default: 0.1] seconds.
maxinterval (float, optional) – Maximum progress display update interval [default: 10] seconds. Automatically adjusts miniters to correspond to mininterval after long display update lag. Only works if dynamic_miniters or monitor thread is enabled.
miniters (int or float, optional) – Minimum progress display update interval, in iterations. If 0 and dynamic_miniters, will automatically adjust to equal mininterval (more CPU efficient, good for tight loops). If > 0, will skip display of specified number of iterations. Tweak this and mininterval to get very efficient loops. If your progress is erratic with both fast and slow iterations (network, skipping items, etc) you should set miniters=1.
ascii (bool or str, optional) – If unspecified or False, use unicode (smooth blocks) to fill the meter. The fallback is to use ASCII characters ” 123456789#”.
disable (bool, optional) – Whether to disable the entire progressbar wrapper [default: False]. If set to None, disable on non-TTY.
unit (str, optional) – String that will be used to define the unit of each iteration [default: it].
unit_scale (bool or int or float, optional) – If 1 or True, the number of iterations will be reduced/scaled automatically and a metric prefix following the International System of Units standard will be added (kilo, mega, etc.) [default: False]. If any other non-zero number, will scale total and n.
dynamic_ncols (bool, optional) – If set, constantly alters ncols and nrows to the environment (allowing for window resizes) [default: False].
smoothing (float, optional) – Exponential moving average smoothing factor for speed estimates (ignored in GUI mode). Ranges from 0 (average speed) to 1 (current/instantaneous speed) [default: 0.3].
bar_format (str, optional) –
Specify a custom bar string formatting. May impact performance. [default: ‘{l_bar}{bar}{r_bar}’], where l_bar=’{desc}: {percentage:3.0f}%|’ and r_bar=’| {n_fmt}/{total_fmt} [{elapsed}<{remaining}, ‘

’{rate_fmt}{postfix}]’

Possible vars: l_bar, bar, r_bar, n, n_fmt, total, total_fmt,
percentage, elapsed, elapsed_s, ncols, nrows, desc, unit, rate, rate_fmt, rate_noinv, rate_noinv_fmt, rate_inv, rate_inv_fmt, postfix, unit_divisor, remaining, remaining_s, eta.

Note that a trailing “: ” is automatically removed after {desc} if the latter is empty.
initial (int or float, optional) – The initial counter value. Useful when restarting a progress bar [default: 0]. If using float, consider specifying {n:.3f} or similar in bar_format, or specifying unit_scale.
position (int, optional) – Specify the line offset to print this bar (starting from 0) Automatic if unspecified. Useful to manage multiple bars at once (eg, from threads).
postfix (dict or *, optional) – Specify additional stats to display at the end of the bar. Calls set_postfix(**postfix) if possible (dict).
unit_divisor (float, optional) – [default: 1000], ignored unless unit_scale is True.
write_bytes (bool, optional) – If (default: None) and file is unspecified, bytes will be written in Python 2. If True will also write bytes. In all other cases will default to unicode.
lock_args (tuple, optional) – Passed to refresh for intermediate output (initialisation, iterating, and updating).
nrows (int, optional) – The screen height. If specified, hides nested bars outside this bound. If unspecified, attempts to use environment height. The fallback is 20.
colour (str, optional) – Bar colour (e.g. ‘green’, ‘#00ff00’).
delay (float, optional) – Don’t display until [default: 0] seconds have elapsed.
gui (bool, optional) – WARNING: internal parameter - do not use. Use tqdm.gui.tqdm(…) instead. If set, will attempt to use matplotlib animations for a graphical output [default: False].

Returns

out

Return type

decorated iterator.

clear(nolock=False)

Clear current bar display.

close()

Cleanup and (if leave=False) close the progressbar.

display(msg=None, pos=None)

Use self.sp to display msg in the specified pos.

Consider overloading this function when inheriting to use e.g.: self.some_frontend(**self.format_dict) instead of self.sp.

Parameters

msg (str, optional. What to display (default: repr(self)).) – By default it is set to None.
pos (int, optional. Position to moveto) –
(default: abs(self.pos)).

By default it is set to None.

classmethod external_write_mode(file=None, nolock=False)

Disable tqdm within context and refresh tqdm when exits. Useful when writing to standard output stream

static format_interval(t)

Formats a number of seconds as a clock time, [H:]MM:SS

Parameters: t (int) – Number of seconds.
Returns: out – [H:]MM:SS
Return type: str

classmethod format_meter(n, total, elapsed, **kwargs)[source]

Return a string-based progress bar given some parameters

Parameters

n (int or float) – Number of finished iterations.
total (int or float) – The expected total number of iterations. If meaningless (None), only basic progress statistics are displayed (no ETA).
elapsed (float) – Number of seconds passed since start.
ncols (int, optional) – The width of the entire output message. If specified, dynamically resizes {bar} to stay within this bound [default: None]. If 0, will not print any bar (only stats). The fallback is {bar:10}.
prefix (str, optional) – Prefix message (included in total width) [default: ‘’]. Use as {desc} in bar_format string.
ascii (bool, optional or str, optional) – If not set, use unicode (smooth blocks) to fill the meter [default: False]. The fallback is to use ASCII characters ” 123456789#”.
unit (str, optional) – The iteration unit [default: ‘it’].
unit_scale (bool or int or float, optional) – If 1 or True, the number of iterations will be printed with an appropriate SI metric prefix (k = 10^3, M = 10^6, etc.) [default: False]. If any other non-zero number, will scale total and n.
rate (float, optional) – Manual override for iteration rate. If [default: None], uses n/elapsed.
bar_format (str, optional) –
Specify a custom bar string formatting. May impact performance. [default: ‘{l_bar}{bar}{r_bar}’], where l_bar=’{desc}: {percentage:3.0f}%|’ and r_bar=’| {n_fmt}/{total_fmt} [{elapsed}<{remaining}, ‘

’{rate_fmt}{postfix}]’

Possible vars: l_bar, bar, r_bar, n, n_fmt, total, total_fmt,
percentage, elapsed, elapsed_s, ncols, nrows, desc, unit, rate, rate_fmt, rate_noinv, rate_noinv_fmt, rate_inv, rate_inv_fmt, postfix, unit_divisor, remaining, remaining_s, eta.

Note that a trailing “: ” is automatically removed after {desc} if the latter is empty.
postfix (*, optional) – Similar to prefix, but placed at the end (e.g. for additional stats). Note: postfix is usually a string (not a dict) for this method, and will if possible be set to postfix = ‘, ‘ + postfix. However other types are supported (#382).
unit_divisor (float, optional) – [default: 1000], ignored unless unit_scale is True.
initial (int or float, optional) – The initial counter value [default: 0].
colour (str, optional) – Bar colour (e.g. ‘green’, ‘#00ff00’).

Returns

out

Return type

Formatted meter and stats, ready to display.

static format_num(n)

Intelligent scientific notation (.3g).

Parameters: n (int or float or Numeric) – A Number.
Returns: out – Formatted number.
Return type: str

static format_sizeof(num, suffix='', divisor=1000)

Formats a number (greater than unity) with SI Order of Magnitude prefixes.

Parameters

num (float) – Number ( >= 1) to format.
suffix (str, optional) –
Post-postfix [default: ‘’].

By default it is set to .
divisor (float, optional) –
Divisor between prefixes [default: 1000].

By default it is set to 1000.

Returns

out – Number with Order of Magnitude SI unit postfix.

Return type

str

classmethod get_lock()

Get the global lock. Construct it if it does not exist.

classmethod pandas(**tqdm_kwargs)

Registers the current tqdm class with: pandas.core. ( frame.DataFrame | series.Series | groupby.(generic.)DataFrameGroupBy | groupby.(generic.)SeriesGroupBy ).progress_apply

A new instance will be created every time progress_apply is called, and each instance will automatically close() upon completion.

Parameters: tqdm_kwargs (arguments for the tqdm instance) –

Examples

>>> import pandas as pd
>>> import numpy as np
>>> from tqdm import tqdm
>>> from tqdm.gui import tqdm as tqdm_gui
>>>
>>> df = pd.DataFrame(np.random.randint(0, 100, (100000, 6)))
>>> tqdm.pandas(ncols=50)  # can use tqdm_gui, optional kwargs, etc
>>> # Now you can use `progress_apply` instead of `apply`
>>> df.groupby(0).progress_apply(lambda x: x**2)

References

<https://stackoverflow.com/questions/18603270/ progress-indicator-during-pandas-operations-python>

refresh(nolock=False, lock_args=None)

Force refresh the display of this bar.

Parameters

nolock (bool, optional) –
If True, does not lock. If [default: False]: calls acquire() on internal lock.

By default it is set to False.
lock_args (tuple, optional) –
Passed to internal lock’s acquire(). If specified, will only display() if acquire() returns True.

By default it is set to None.

reset(total=None)

Resets to 0 iterations for repeated use.

Consider combining with leave=True.

Parameters: total (int or float, optional. Total to use for the new bar.) – By default it is set to None.

set_description(desc=None, refresh=True)

Set/modify description of the progress bar.

Parameters

desc (str, optional) – By default it is set to None.
refresh (bool, optional) –
Forces refresh [default: True].

By default it is set to True.

set_description_str(desc=None, refresh=True)

Set/modify description without ‘: ‘ appended.

classmethod set_lock(lock)

Set the global lock.

set_postfix(ordered_dict=None, refresh=True, **kwargs)

Set/modify postfix (additional stats) with automatic formatting based on datatype.

Parameters

ordered_dict (dict or OrderedDict, optional) – By default it is set to None.
refresh (bool, optional) –
Forces refresh [default: True].

By default it is set to True.
kwargs (dict, optional) –

set_postfix_str(s='', refresh=True)

Postfix without dictionary expansion, similar to prefix handling.

status_printer(file)[source]

Overload the status_printer method to avoid the use of closures.

Parameters: file (io.TextIOWrapper | io.StringIO) – Specifies where to output the progress messages.
Returns: The function to print the status in the progress bar.
Return type: Callable[[str], None]

unpause()

Restart tqdm timer from last print time.

update(n=1)

Manually update the progress bar, useful for streams such as reading files. E.g.: >>> t = tqdm(total=filesize) # Initialise >>> for current_buffer in stream: … … … t.update(len(current_buffer)) >>> t.close() The last line is highly recommended, but possibly not necessary if t.update() will be called in such a way that filesize will be exactly reached and printed.

Parameters

n (int or float, optional) –

Increment to add to the internal counter of iterations [default: 1]. If using float, consider specifying {n:.3f} or similar in bar_format, or specifying unit_scale.

By default it is set to 1.

Returns

out – True if a display() was triggered.

Return type

bool or None

classmethod wrapattr(stream, method, total=None, bytes=True, **tqdm_kwargs)

stream : file-like object. method : str, “read” or “write”. The result of read() and

the first argument of write() should have a len().

>>> with tqdm.wrapattr(file_obj, "read", total=file_obj.size) as fobj:
...     while True:
...         chunk = fobj.read(chunk_size)
...         if not chunk:
...             break

classmethod write(s, file=None, end='\n', nolock=False)

Print a message via tqdm (without overlap with bars).

property format_dict: Public API for read-only member access.

class gemseo.algos.driver_lib.TqdmToLogger(initial_value='', newline='\n')[source]

Redirect tqdm output to the gemseo logger.

close()

Close the IO object.

Attempting any further operation after the object is closed will raise a ValueError.

This method has no effect if the file is already closed.

detach()

Separate the underlying buffer from the TextIOBase and return it.

After the underlying buffer has been detached, the TextIO is in an unusable state.

fileno()

Returns underlying file descriptor if one exists.

OSError is raised if the IO object does not use a file descriptor.

flush()

Flush write buffers, if applicable.

This is not implemented for read-only and non-blocking streams.

getvalue()

Retrieve the entire contents of the object.

isatty()

Return whether this is an ‘interactive’ stream.

Return False if it can’t be determined.

read(size=- 1, /)

Read at most size characters, returned as a string.

If the argument is negative or omitted, read until EOF is reached. Return an empty string at EOF.

readable()

Returns True if the IO object can be read.

readline(size=- 1, /)

Read until newline or EOF.

Returns an empty string if EOF is hit immediately.

readlines(hint=- 1, /)

Return a list of lines from the stream.

hint can be specified to control the number of lines read: no more lines will be read if the total size (in bytes/characters) of all lines so far exceeds hint.

seek(pos, whence=0, /)

Change stream position.

Seek to character offset pos relative to position indicated by whence:: 0 Start of stream (the default). pos should be >= 0; 1 Current position - pos must be 0; 2 End of stream - pos must be 0.

Returns the new absolute position.

seekable()

Returns True if the IO object can be seeked.

tell()

Tell the current file position.

truncate(pos=None, /)

Truncate size to pos.

The pos argument defaults to the current file position, as returned by tell(). The current file position is unchanged. Returns the new absolute position.

writable()

Returns True if the IO object can be written.

write(buf)[source]

Write buffer.

writelines(lines, /)

Write a list of lines to stream.

Line separators are not added, so it is usual for each of the lines provided to have a line separator at the end.

encoding

Encoding of the text stream.

Subclasses should override.

errors

The error setting of the decoder or encoder.

Subclasses should override.

newlines