# 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.

Classes:

 DesignSpace([hdf_file, name]) Description of a design space. DesignVariable(size, var_type, l_b, u_b, value) Create new instance of DesignVariable(size, var_type, l_b, u_b, value) DesignVariableType(value) A type of design variable.
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(), …

name

The name of the space.

Type

Optional[str]

variables_names

The names of the variables.

Type

List[str]

dimension

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

Type

int

variables_sizes

The sizes of the variables.

Type

Dict[str,int]

variables_types

The types of the variables components, which can be any DesignVariableType.

Type

Dict[str,ndarray]

normalize

The normalization policies of the variables components indexed by the variables names; if True, the component can be normalized.

Type

Dict[str,ndarray]

Parameters
• hdf_file (Optional[Union[str,Path]]) –

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 (Optional[str]) –

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

Methods:

 add_variable(name[, size, var_type, l_b, ...]) Add a variable to the design space. array_to_dict(x_array) Convert the current point into a dictionary indexed by the variables names. Check the state of the design space. check_membership(x_vect[, variables_names]) Check whether the variables satisfy the design space requirements. dict_to_array(x_dict[, all_vars, all_var_list]) Convert an point as dictionary into an array. export_hdf(file_path[, append]) Export the design space to an HDF file. export_to_txt(output_file[, fields, header_char]) Export the design space to a text file. extend(other) Extend the design space with another design space. filter(keep_variables[, copy]) Filter the design space to keep a subset of variables. filter_dim(variable, keep_dimensions) Filter the design space to keep a subset of dimensions for a variable. get(k[,d]) get_active_bounds([x_vec, tol]) Determine which bound constraints of the current point are active. get_current_x([variables_names]) Return the current point in the design space. Return the current point in the design space as a dictionary. Return the current point normalized. get_indexed_var_name(variable_name) Create the names of the components of a variable. Create the names of the components of all the variables. Return the lower bound of a variable. get_lower_bounds([variables_names]) Generate an array of the variables' lower bounds. get_pretty_table([fields]) Build a tabular view of the design space. get_size(name) Get the size of a variable. get_type(name) Return the type of a variable. Return the upper bound of a variable. get_upper_bounds([variables_names]) Generate an array of the variables' upper bounds. get_variables_indexes(variables_names) Return the indexes of a design array corresponding to the variables names. Check if the current design value is defined for all variables. import_hdf(file_path) Import a design space from an HDF file. normalize_grad(g_vect) Normalize an unnormalized gradient. normalize_vect(x_vect[, minus_lb]) Normalize a vector of the design space. pop(k[,d]) If key is not found, d is returned if given, otherwise KeyError is raised. as a 2-tuple; but raise KeyError if D is empty. project_into_bounds(x_c[, normalized]) Project a vector onto the bounds, using a simple coordinate wise approach. read_from_txt(input_file[, header]) Create a design space from a text file. Remove a variable from the design space. round_vect(x_vect) Round the vector where variables are of integer type. set_current_variable(name, current_value) Set the current value of a single variable. set_current_x(current_x) Set the current point. set_lower_bound(name, lower_bound) Set the lower bound of a variable. set_upper_bound(name, upper_bound) Set the upper bound of a variable. setdefault(k[,d]) Cast the current value to complex. transform_vect(vector) Map a point of the design space to a vector with components in $$[0,1]$$. unnormalize_grad(g_vect) Unnormalize a normalized gradient. unnormalize_vect(x_vect[, minus_lb, no_check]) Unnormalize a normalized vector of the design space. untransform_vect(vector) Map a vector with components in $$[0,1]$$ to the design space. update([E, ]**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
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 (Union[str, Sequence[str], gemseo.algos.design_space.DesignVariableType, Sequence[gemseo.algos.design_space.DesignVariableType]]) –

Either the type of the variable or the types of its components.

By default it is set to FLOAT.

• l_b (Optional[Union[float, numpy.ndarray]]) –

The lower bound of the variable. If None, use $$-\infty$$.

By default it is set to None.

• u_b (Optional[Union[float, numpy.ndarray]]) –

The upper bound of the variable. If None, use $$+\infty$$.

By default it is set to None.

• value (Optional[Union[float, numpy.ndarray]]) –

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 the current point into a dictionary indexed by the variables names.

Parameters

x_array (numpy.ndarray) – The current point.

Returns

The dictionary version of the current point.

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, variables_names=None)[source]

Check whether the variables satisfy the design space requirements.

Parameters
• x_vect (Union[Mapping[str, numpy.ndarray], numpy.ndarray]) – The values of the variables.

• variables_names (Optional[Sequence[str]]) –

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 an integer.

Return type

None

clear() None.  Remove all items from D.
dict_to_array(x_dict, all_vars=True, all_var_list=None)[source]

Convert an point as dictionary into an array.

Parameters
• x_dict (Dict[str, numpy.ndarray]) – The point to be converted.

• all_vars (bool) –

If True, all the variables to be considered shall be in the provided point.

By default it is set to True.

• all_var_list (Optional[Sequence[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 (Union[str, pathlib.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 the design space to a text file.

Parameters
• output_file (Union[str,Path],) – The path to the file.

• fields (Optional[Sequence[str]]) –

The fields to be exported. If None, export all fields.

By default it is set to None.

By default it is set to .

• **table_options (Any) – The names and values of additional attributes for the PrettyTable view generated by 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 (Union[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

gemseo.algos.design_space.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 the current point are active.

Parameters
• x_vec (Optional[numpy.ndarray]) –

The point at which to check the bounds. If None, use the current point.

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

get_current_x(variables_names=None)[source]

Return the current point in the design space.

Parameters

variables_names (Optional[Iterable[str]]) –

The names of the required variables. If None, use the names of the variables of the design space.

By default it is set to None.

Raises

KeyError – If a variable has no current value.

Return type

numpy.ndarray

get_current_x_dict()[source]

Return the current point in the design space as a dictionary.

Returns

The current point in the design space as a dictionary, whose keys are the names of the variables and values are the values of the variables.

Return type

Dict[str, numpy.ndarray]

get_current_x_normalized()[source]

Return the current point normalized.

Returns

The current point as an array normalized by the bounds of the variables.

Returns

If the current point cannot be normalized.

Return type

KeyError

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 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

Union[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 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(variables_names=None)[source]

Generate an array of the variables’ lower bounds.

Parameters

variables_names (Optional[Sequence[str]]) –

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

numpy.ndarray

get_pretty_table(fields=None)[source]

Build a tabular view of the design space.

Parameters

fields (Optional[Sequence[str]]) –

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

gemseo.third_party.prettytable.prettytable.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

Optional[int]

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

Optional[str]

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(variables_names=None)[source]

Generate an array of the variables’ upper bounds.

Parameters

variables_names (Optional[Sequence[str]]) –

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

numpy.ndarray

get_variables_indexes(variables_names)[source]

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

Parameters

variables_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_x()[source]

Check if the current design value is defined for all variables.

Returns

Whether the current design value is defined for all variables.

Return type

bool

import_hdf(file_path)[source]

Import a design space from an HDF file.

Parameters

file_path (Union[str, pathlib.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

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 $$x$$.

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

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

Parameters

g_vect (numpy.ndarray) – The gradient to be normalized.

Returns

Return type

numpy.ndarray

normalize_vect(x_vect, minus_lb=True)[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 (numpy.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.

Returns

The normalized vector.

Raises

ValueError – If the array to be normalized is not one- or two-dimensional.

Return type

numpy.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

Create a design space from a text file.

Parameters
• input_file (Union[str, pathlib.Path]) – The path to the file.

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

gemseo.algos.design_space.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

round_vect(x_vect)[source]

Round the vector where variables are of integer type.

Parameters

x_vect (numpy.ndarray) – The values to be rounded.

Returns

The rounded values values.

Raises

ValueError – If the values is not a one- or two-dimensional

Return type

numpy.ndarray

set_current_variable(name, current_value)[source]

Set the current value of a single variable.

Parameters
• name – The name of the variable.

• current_value – The current value of the variable.

set_current_x(current_x)[source]

Set the current point.

Parameters

current_x (Union[numpy.ndarray, Mapping[str, numpy.ndarray], gemseo.algos.opt_result.OptimizationResult]) – The value of the current point.

Raises
• ValueError – If the value has a wrong dimension.

• TypeError – If the current point is neither a mapping of NumPy arrays, a NumPy array nor an OptimizationResult.

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)[source]

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

Parameters

vector (numpy.ndarray) – A point of the design space.

Returns

A vector with components in $$[0,1]$$.

Return type

numpy.ndarray

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

Return type

numpy.ndarray

unnormalize_vect(x_vect, minus_lb=True, no_check=False)[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 (numpy.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.

• no_check (bool) –

If True, do not check that the values are in [0,1].

By default it is set to False.

Returns

The unnormalized vector.

Raises

ValueError – If the array to be unnormalized is not one- or two-dimensional.

Return type

numpy.ndarray

untransform_vect(vector)[source]

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

Parameters

vector (numpy.ndarray) – A vector with components in $$[0,1]$$.

Returns

A point of the variables space.

Return type

numpy.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
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)

Methods:

 count(value, /) Return number of occurrences of value. index(value[, start, stop]) Return first index of value.

Attributes:

 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
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 a HDF file or to a Dataset for future post-processing.

Classes:

 OptimizationProblem(design_space[, pb_type, ...]) An optimization problem.
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, **parallel_differentiation_options)[source]

An optimization problem.

Create an optimization problem from:

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.

nonproc_objective

The non-processed objective function.

Type

MDOFunction

constraints

The constraints.

Type

List(MDOFunction)

nonproc_constraints

The non-processed constraints.

Type

List(MDOFunction)

observables

The observables.

Type

List(MDOFunction)

new_iter_observables

The observables to be called at each new iterate.

Type

List(MDOFunction)

nonproc_observables

The non-processed observables.

Type

List(MDOFunction)

nonproc_new_iter_observables

The non-processed observables to be called at each new iterate.

Type

List(MDOFunction)

minimize_objective

If True, maximize the objective.

Type

bool

fd_step

The finite differences step.

Type

float

pb_type

The type of optimization problem.

Type

str

ineq_tolerance

The tolerance for the inequality constraints.

Type

float

eq_tolerance

The tolerance for the equality constraints.

Type

float

database

The database to store the optimization problem data.

Type

Database

solution

The solution of the optimization problem.

design_space

The design space on which the optimization problem is solved.

Type

DesignSpace

stop_if_nan

If True, the optimization stops when a function returns NaN.

Type

bool

preprocess_options

The options to pre-process the functions.

Type

Dict

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 (Optional[Union[str,Database]]) –

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.

• **parallel_differentiation_options (Union[int,bool]) – The options to approximate the derivatives in parallel.

Return type

None

Methods:

 add_callback(callback_func[, each_new_iter, ...]) Add a callback function after each store operation or new iteration. add_constraint(cstr_func[, value, ...]) Add a constraint (equality and inequality) to the optimization problem. add_eq_constraint(cstr_func[, value]) Add an equality constraint to the optimization problem. add_ineq_constraint(cstr_func[, value, positive]) Add an inequality constraint to the optimization problem. add_observable(obs_func[, new_iter]) Add a function to be observed. aggregate_constraint(constr_id[, method, groups]) Aggregates a constraint to generate a reduced dimension constraint. Change the objective function sign in order to minimize its opposite. Check if the optimization problem is ready for run. check_format(input_function) Check that a function is an instance of MDOFunction. Clear all the listeners. evaluate_functions([x_vect, eval_jac, ...]) Compute the objective and the constraints. The callback function to be passed to the database. export_hdf(file_path[, append]) Export the optimization problem to an HDF file. export_to_dataset([name, by_group, ...]) Export the database of the optimization problem to a Dataset. get_active_ineq_constraints(x_vect[, tol]) For each constraint, indicate if its different components are active. Retrieve all the functions of the optimization problem. Retrieve the names of all the function of the optimization problem. Retrieve the best infeasible point within a given tolerance. Retrieve the names of the constraints. Retrieve the number of constraints. get_data_by_names(names[, as_dict, ...]) Return the data for specific names of variables. Retrieve the names of the design variables. Retrieve the total number of design variables. Retrieve all the equality constraints. Retrieve the number of equality constraints. Retrieve the total dimension of the equality constraints. Retrieve the feasible points within a given tolerance. Return the dimensions of the outputs of the problem functions. Retrieve all the inequality constraints. Retrieve the number of inequality constraints. Retrieve the total dimension of the inequality constraints. Retrieve the non-processed constraints. Retrieve the non-processed objective function. Return the number of scalar constraints not satisfied by design variables. Retrieve the name of the objective function. get_observable(name) Retrieve an observable from its name. Return the optimum solution within a given feasibility tolerances. Return the names of the scalar constraints. get_violation_criteria(x_vect) Compute a violation measure associated to an iteration. Return the current values of the design variables after normalization. Check if the problem has equality or inequality constraints. Check if the problem has equality constraints. Check if the problem has inequality constraints. Check if the problem has non-linear constraints. import_hdf(file_path[, x_tolerance]) Import an optimization history from an HDF file. Check if the maximum amount of iterations has been reached. is_point_feasible(out_val[, constraints]) Check if a point is feasible. preprocess_functions([normalize, ...]) Pre-process all the functions and eventually the gradient. repr_constraint(func, ctype[, value, positive]) Express a constraint as a string expression.

Attributes:

 differentiation_method The differentiation method. dimension The dimension of the design space. is_mono_objective Whether the optimization problem is mono-objective. objective The objective function. parallel_differentiation Whether to approximate the derivatives in parallel. parallel_differentiation_options The options to approximate the derivatives in parallel.

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

Parameters
• cstr_func (MDOFunction) – The constraint.

• value (Optional[value]) –

The value for which the constraint is active. If None, this value is 0.

By default it is set to None.

• cstr_type (Optional[str]) –

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 an equality constraint to the optimization problem.

Parameters
Return type

None

Add an inequality constraint to the optimization problem.

Parameters
• cstr_func (MDOFunction) – The constraint.

• value (Optional[value]) –

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 a function to be observed.

Parameters
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) – index of the constraint in self.constraints

• method (str or callable, that takes a function and returns a function) –

aggregation method, among (‘max’,’KS’, ‘IKS’)

By default it is set to max.

• groups (tuple of ndarray) –

if None, a single output constraint is produced otherwise, one output per group is produced.

By default it is set to None.

change_objective_sign()[source]

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 – 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

property differentiation_method

The differentiation method.

property dimension

The dimension of the design space.

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

Compute the objective and the constraints.

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 (Optional[numpy.ndarray]) –

The input vector at which the functions must be evaluated; if None, x_0 is used.

By default it is set to None.

• eval_jac (bool) –

If True, then the Jacobian is evaluated

By default it is set to False.

• eval_obj (bool) –

If True, then the objective function is evaluated

By default it is set to True.

• normalize (bool) –

If True, then input vector is considered 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.

Returns

The functions values and/or the Jacobian values according to the passed arguments.

Raises

ValueError – If both no_db_no_norm and normalize are True.

Return type

Tuple[Dict[str, Union[float, numpy.ndarray]], Dict[str, numpy.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 the database of the optimization problem to a Dataset.

The variables can be classified into groups, separating the design variables and functions (objective function and constraints). This classification can use either an optimization naming, with Database.DESIGN_GROUP and Database.FUNCTION_GROUP or an input-output naming, with Database.INPUT_GROUP and Database.OUTPUT_GROUP

Parameters
• name (Optional[str]) –

A name to be given to the dataset. If None, use the name of the database.

By default it is set to None.

• by_group (bool) –

If True, then store the data by group. Otherwise, store them by variables.

By default it is set to True.

• categorize (bool) –

If True, then distinguish between the different groups of variables.

By default it is set to True.

• opt_naming (bool) –

If True, then use an optimization naming.

By default it is set to True.

If True, then export also 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.

Returns

A dataset built from the database of the optimization problem.

Return type

gemseo.core.dataset.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[str, 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
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 (Union[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

Union[numpy.ndarray, Dict[str, numpy.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
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[numpy.ndarray], List[Dict[str, Union[float, List[int]]]]]

get_functions_dimensions()[source]

Return the dimensions of the outputs of the problem functions.

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
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
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()[source]

Retrieve the name of the objective function.

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()[source]

Return the current values of the design variables after normalization.

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

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

property is_mono_objective

Whether the optimization problem is mono-objective.

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, numpy.ndarray]) – The values of the objective function, and eventually constraints.

• constraints (Optional[Iterable[gemseo.core.mdofunctions.mdo_function.MDOFunction]]) –

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

property objective

The objective function.

property parallel_differentiation

Whether to approximate the derivatives in parallel.

property parallel_differentiation_options

The options to approximate the derivatives in parallel.

preprocess_functions(normalize=True, use_database=True, round_ints=True)[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
• normalize (bool) –

Whether to unnormalize the input vector of the function before evaluate it.

By default it is set to True.

• use_database (bool) –

If True, then the functions are wrapped in the database.

By default it is set to True.

• round_ints (bool) –

If True, then round the integer variables.

By default it is set to True.

Return type

None

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

Express a constraint as a string expression.

Parameters
• func (gemseo.core.mdofunctions.mdo_function.MDOFunction) – The constraint function.

• ctype (str) – The type of the constraint. Either equality or inequality.

• value (Optional[float]) –

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

## 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.

Classes:

 Abstract class for DOE & optimization libraries interfaces. ProgressBar(*_, **__) Extend tqdm progress bar with better time units. TqdmToLogger([initial_value, newline]) Redirect tqdm output to the gemseo logger.
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.

Constructor.

Attributes:

 algorithms The available algorithms.

Methods:

 Deactivate the progress bar. driver_has_option(option_key) Check if the option key exists. ensure_bounds(orig_func[, normalize]) Project the design vector onto the design space before execution. execute(problem[, algo_name]) Executes the driver. filter_adapted_algorithms(problem) Filter the algorithms capable of solving the problem. Finalize the iteration observer. get_optimum_from_database([message, status]) Retrieves the optimum from the database and builds an optimization result object from it. get_x0_and_bounds_vects(normalize_ds) Gets x0, bounds, normalized or not depending on algo options, all as numpy arrays. init_iter_observer(max_iter, message) Initialize the iteration observer. init_options_grammar(algo_name) Initialize the options grammar. is_algo_requires_grad(algo_name) Returns True if the algorithm requires a gradient evaluation. is_algorithm_suited(algo_dict, problem) Check if the algorithm is suited to the problem according to algo_dict. new_iteration_callback([x_vect]) 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.
property algorithms

The available algorithms.

deactivate_progress_bar()[source]

Deactivate the progress bar.

Return type

None

driver_has_option(option_key)

Check if the option key exists.

Parameters

option_key (str) – The name of the option.

Returns

Whether the option is in the grammar.

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

if True, use the normalized design space

By default it is set to True.

Returns

the wrapped function

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

Executes the driver.

Parameters
• problem – the problem to be solved

• algo_name

name of the algorithm if None, use self.algo_name which may have been set by the factory (Default value = None)

By default it is set to None.

• options – the options dict for the algorithm

Filter the algorithms capable of solving the problem.

Parameters

problem (Any) – The opt_problem to be solved.

Returns

The list of adapted algorithms names.

Return type

bool

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.

Parameters
• message

Default value = None)

By default it is set to None.

• status

Default value = None)

By default it is set to None.

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 – if True, normalizes all input vars that are not integers, according to design space normalization policy

Returns

x, lower bounds, 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

Returns True if the algorithm requires a gradient evaluation.

Parameters

algo_name – name of the algorithm

static is_algorithm_suited(algo_dict, problem)

Check if the algorithm is suited to the problem according to algo_dict.

Parameters
• algo_dict (Mapping[str, bool]) – the algorithm characteristics

• problem (Any) – the opt_problem to be solved

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 (Optional[numpy.ndarray]) –

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

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.

Methods:

 clear([nolock]) Clear current bar display. Cleanup and (if leave=False) close the progressbar. display([msg, pos]) Use self.sp to display msg in the specified pos. external_write_mode([file, nolock]) Disable tqdm within context and refresh tqdm when exits. Formats a number of seconds as a clock time, [H:]MM:SS Intelligent scientific notation (.3g). format_sizeof(num[, suffix, divisor]) Formats a number (greater than unity) with SI Order of Magnitude prefixes. Get the global lock. pandas(**tqdm_kwargs) Registers the current tqdm class with refresh([nolock, lock_args]) Force refresh the display of this bar. reset([total]) Resets to 0 iterations for repeated use. set_description([desc, refresh]) Set/modify description of the progress bar. set_description_str([desc, refresh]) Set/modify description without ': ' appended. set_lock(lock) Set the global lock. set_postfix([ordered_dict, refresh]) Set/modify postfix (additional stats) with automatic formatting based on datatype. set_postfix_str([s, refresh]) Postfix without dictionary expansion, similar to prefix handling. status_printer(file) Overload the status_printer method to avoid the use of closures. Restart tqdm timer from last print time. update([n]) Manually update the progress bar, useful for streams such as reading files. wrapattr(stream, method[, total, bytes]) stream : file-like object. method : str, "read" or "write". The result of read() and the first argument of write() should have a len(). write(s[, file, end, nolock]) Print a message via tqdm (without overlap with bars).

Attributes:

 format_dict Public API for read-only member access.
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.

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

property format_dict

Public API for read-only member access.

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

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 create 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 (Union[_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:
...         if not chunk:
...             break

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

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

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

Redirect tqdm output to the gemseo logger.

Methods:

 Close the IO object. detach Separate the underlying buffer from the TextIOBase and return it. Returns underlying file descriptor if one exists. Flush write buffers, if applicable. Retrieve the entire contents of the object. Return whether this is an 'interactive' stream. read([size]) Read at most size characters, returned as a string. Returns True if the IO object can be read. readline([size]) Read until newline or EOF. readlines([hint]) Return a list of lines from the stream. seek(pos[, whence]) Change stream position. Returns True if the IO object can be seeked. Tell the current file position. truncate([pos]) Truncate size to pos. Returns True if the IO object can be written. write(buf) Write buffer. writelines(lines, /) Write a list of lines to stream.

Attributes:

 encoding Encoding of the text stream. errors The error setting of the decoder or encoder. newlines
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.

encoding

Encoding of the text stream.

Subclasses should override.

errors

The error setting of the decoder or encoder.

Subclasses should override.

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.

newlines

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.

Returns True if the IO object can be read.

Returns an empty string if EOF is hit immediately.

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.