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:

Description of a design space. 

Create new instance of DesignVariable(size, var_type, l_b, u_b, 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
()Check the state of the design space.
check_membership
(x_vect[, variables_names])Check whether the variables satisfy the design space requirements.
clear
()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.
get_lower_bound
(name)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.
get_upper_bound
(name)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.
items
()keys
()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.
popitem
()as a 2tuple; 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_variable
(name)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
values
() 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_to_txt(output_file, fields=None, header_char='', **table_options)[source]
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.
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 byget_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
 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 \(d1\) where \(d\) is the number of dimensions of the variable.
 Returns
The filtered design space.
 Raises
ValueError – If a dimension is unknown.
 Return type
 get(k[, d]) D[k] if k in D, else d. d defaults to None.
 get_active_bounds(x_vec=None, tol=1e08)[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 1e08.
 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
 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 setlike object providing a view on D's items
 keys() a setlike 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:
\[\frac{df(x)}{dx} = \frac{df(x)}{dx_u}\frac{dx_u}{dx} = \frac{df(x)}{dx_u}\frac{1}{u_bl_b}\]where \(x_u = \frac{xl_b}{u_bl_b}\) 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:
\[\frac{df(x)}{dx_u} = (u_bl_b)\frac{df(x)}{dx}\]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)[source]
Normalize a vector of the design space.
If minus_lb is True:
\[x_u = \frac{xl_b}{u_bl_b}\]where \(l_b\) and \(u_b\) are the lower and upper bounds of \(x\).
Otherwise:
\[x_u = \frac{x}{u_bl_b}\]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 twodimensional.
 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 2tuple; 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 (Union[str, pathlib.Path]) – The path to the file.
header (Optional[Iterable[str]]) –
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
 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 twodimensional
 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
 unnormalize_grad(g_vect)[source]
Unnormalize a normalized gradient.
This method is based on the chain rule:
\[\frac{df(x)}{dx} = \frac{df(x)}{dx_u}\frac{dx_u}{dx} = \frac{df(x)}{dx_u}\frac{1}{u_bl_b}\]where \(x_u = \frac{xl_b}{u_bl_b}\) 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)[source]
Unnormalize a normalized vector of the design space.
If minus_lb is True:
\[x = x_u(u_bl_b) + l_b\]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:
\[x = x_u(u_bl_b)\] 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 twodimensional.
 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:
Alias for field number 2
Alias for field number 0
Alias for field number 3
Alias for field number 4
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 postprocessing.
Classes:

An optimization problem. 
 class gemseo.algos.opt_problem.OptimizationProblem(design_space, pb_type='nonlinear', input_database=None, differentiation_method='user', fd_step=1e07, parallel_differentiation=False, **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 aDatabase
.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 nonprocessed objective function.
 Type
 constraints
The constraints.
 Type
List(MDOFunction)
 nonproc_constraints
The nonprocessed 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 nonprocessed observables.
 Type
List(MDOFunction)
 nonproc_new_iter_observables
The nonprocessed 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
 solution
The solution of the optimization problem.
 design_space
The design space on which the optimization problem is solved.
 Type
 stop_if_nan
If True, the optimization stops when a function returns NaN.
 Type
bool
 preprocess_options
The options to preprocess 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 nonlinear.
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 stepbased differentiation methods.
By default it is set to 1e07.
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
()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.
execute_observables_callback
(last_x)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 nonprocessed constraints.
Retrieve the nonprocessed 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 nonlinear 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, ...])Preprocess all the functions and eventually the gradient.
repr_constraint
(func, ctype[, value, positive])Express a constraint as a string expression.
Attributes:
The differentiation method.
The dimension of the design space.
Whether the optimization problem is monoobjective.
The objective function.
Whether to approximate the derivatives in parallel.
The options to approximate the derivatives in parallel.
 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 (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_eq_constraint(cstr_func, value=None)[source]
Add an equality constraint to the optimization problem.
 Parameters
cstr_func (gemseo.core.mdofunctions.mdo_function.MDOFunction) – The constraint.
value (Optional[float]) –
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 (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_observable(obs_func, new_iter=True)[source]
Add a function to be observed.
 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) – 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]
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 – 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 preprocessed 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_to_dataset(name=None, by_group=True, categorize=True, opt_naming=True, export_gradients=False)[source]
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
andDatabase.FUNCTION_GROUP
or an inputoutput naming, withDatabase.INPUT_GROUP
andDatabase.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.
export_gradients (bool) –
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
 get_active_ineq_constraints(x_vect, tol=1e06)[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 1e06.
 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 nonfeasible 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 andOptimizationProblem.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 nonprocessed constraints.
 Returns
The nonprocessed constraints.
 Return type
 get_nonproc_objective()[source]
Retrieve the nonprocessed objective function.
 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
 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 nonlinear 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
 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 monoobjective.
 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]
Preprocess 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. 


Extend tqdm progress bar with better time units. 

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:
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_adapted_algorithms(problem)
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
 is_algo_requires_grad(algo_name)[source]
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 nonTTY.
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 nonzero 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.
close
()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
format_num
(n)Intelligent scientific notation (.3g).
format_sizeof
(num[, suffix, divisor])Formats a number (greater than unity) with SI Order of Magnitude prefixes.
get_lock
()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.
unpause
()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 : filelike 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:
Public API for readonly 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.
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
 property format_dict
Public API for readonly 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) –
Postpostfix [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/ progressindicatorduringpandasoperationspython>
 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 : filelike 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).
 class gemseo.algos.driver_lib.TqdmToLogger(initial_value='', newline='\n')[source]
Redirect tqdm output to the gemseo logger.
Methods:
close
()Close the IO object.
Separate the underlying buffer from the TextIOBase and return it.
fileno
()Returns underlying file descriptor if one exists.
flush
()Flush write buffers, if applicable.
getvalue
()Retrieve the entire contents of the object.
isatty
()Return whether this is an 'interactive' stream.
read
([size])Read at most size characters, returned as a string.
readable
()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.
seekable
()Returns True if the IO object can be seeked.
tell
()Tell the current file position.
truncate
([pos])Truncate size to pos.
writable
()Returns True if the IO object can be written.
write
(buf)Write buffer.
writelines
(lines, /)Write a list of lines to stream.
Attributes:
Encoding of the text stream.
The error setting of the decoder or encoder.
 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 readonly and nonblocking 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(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.