# Release Notes#

## Upcoming Version#

## Version 0.3.15#

The group dimension when grouping by a pandas dataframe is now always group. This fixes the case that the dataframe contains a column named name.

## Version 0.3.14#

Ensure compatibility with xarray >= v2024.07.0, which has drop the

`squeeze`

argument from the`groupby`

function.

## Version 0.3.13#

Follow-up release to properly fix all deprecations from multiindexed data assignments in xarray datasets.

Fix typing relevant import for non-default highs dependency in vanilla installation

## Version 0.3.12#

Support for warmstart in HiGHS using basis or solution files, including support for writing basis and solution files of a solved model.

Linopy now uses mypy for type checking allowing for a more secure and stable code base.

The creation of solution files with gurobi, scip and mindopt is now supported.

## Version 0.3.11#

The writing and reading from netcdf files was fixed to correctly handle the model parameters field.

## Version 0.3.10#

The classes Variable, LinearExpression and Constraint now have a new getitem method that allows selecting a subset of the object in the same way as xarray objects, i.e. by integer labels or boolean index. Example usage: x[[1, 2]] or x[x.indexes[“some_index”] > 5].

The class Constraint now has a new method .loc to select a subset of the constraint by labels.

Selecting a single variable with the getitem ([]) method now raises a FutureWarning that the return type will change to Variable instead of a ScalarVariable in the future. To get a ScalarVariable in the future, use the at[] method.

A new module examples was added which contains example models. For example, you can call m = linopy.examples.benchmark_model().

A new memory-efficient and super fast LP file writing method was added which uses the Polars package. It is still in experimental mode but seems to be very promising. Activate it with the io_api=”lp-polars” argument in the solve function.

The Constraint class now supports the methods assign, assign_attrs, assign_coords, broadcast_like, chunk, drop_sel, drop_isel, expand_dims, sel, isel, shift, swap_dims, set_index, reindex, reindex_like, rename, rename_dims, roll, stack. These methods allow to manipulation of a (anonymous) constraint more flexibly.

The Variable, expressions and Constraint classes now have new methods swap_dims and set_index. The swap_dims method allows to swap the dimensions of the object. The set_index method allows to set a new index for the object. Both methods are useful for reshaping the object more flexibly.

## Version 0.3.9#

The matrices accessor of the Model class now has a new function dual which returns the dual values of the constraints if the underlying model was optimized and dual values are existent.

The Variables class now has a new function get_solver_attribute which parses solver-specific attributes of the variables. For now, this function only works for Gurobi solver_model`s. For example, the function allows retrieving the variable fields `SAObjUp or RC.

The constraint assignment with a LinearExpression and a constant value when using the pattern model.add_constraints(lhs_with_constant, sign, rhs) was fixed. Before, the constant value was not added to the right-hand-side properly which led to the wrong constraint behavior. This is fixed now.

`nan`s in constants is now handled more consistently. These are ignored when in the addition of expressions (effectively filled by zero). In a future version, this might change to align the propagation of `nan`s with tools like numpy/pandas/xarray.

Up to now the rhs argument in the add_constraints function was not supporting an expression as an input type. This is now added.

Linopy now supports python 3.12.

**Deprecations**

The argument dims in the .sum function of variables and expressions was deprecated in favor of the dim argument. This aligns the argument name with the xarray convention.

## Version 0.3.8#

**New Features**

The LinearExpression and QuadraticExpression class have a new attribute solution which returns the optimal values of the expression if the underlying model was optimized.

It is now possible to access variables and constraints, that don’t have python variable name format, as attributes from the corresponding containers. Therefore, a new formatting scheme was introduced which converts dashes and white spaces into underscores. For example, a variable was added to the model with the label “my-variable”. This variable can now be accessed with model.variables.my_variable. In particular, the autocompletion function of the IPython console is aware of this new formatting scheme. This allows easy access to variables and constraints with long labels.

Variables and LinearExpressions now have a new method dot, which allows computing the dot product of two objects. This multiplies objects and sums over common dimensions.

The matmul operator @, which runs the dot operation, is now supported for Variables and LinearExpression.

**Bugfixes**

The multiplication of two linear expression with non-zero constants led to wrong results of the cross terms. Given the multiplication (v1 + c1) * (v2 + c2) with v being a variable and c a constant, the operation did not calculate the cross terms v1 * c2 + v2 * c1. This is fixed now.

## Version 0.3.7#

**New Features**

A direct interface to the Mosek solver was added. With this change, a new conversion function model.to_mosek was added to convert a linopy model to a mosek model. The solve function now supports the mosek solver with io_api=”direct”.

It is now possible to create LinearExpression from a pandas.DataFrame, pandas.Series, a numpy.array or constant scalar values, e.g. linopy.LinearExpression(df). This will create a LinearExpression with constants only and the coordinates of the DataFrame, Series or array as dimensions.

**Bugfixes**

When grouping an expression or a variable by a pandas.DataFrame or a xarray.DataArray, the coordinates of the groupby object were not properly aligned. So in cases, when the groupby object was not indexed in the same way as the variable/expression, the groupby operation led to wrong results. This is fixed now.

## Version 0.3.6#

The handling of pandas objects was improved. As pandas objects are fully aware of coordinates, their index and columns are now strictly taken into account. For example, when multiplying a pandas.DataFrame with a variable, linopy now checks the alignment of indexes and reindexes accordingly. Previously, if the axis shapes were the same, the indexes of the variable were inserted and the pandas indexes were effectively ignored. A warning has been added for cases where users should expect changes to the results with this version.

**Important**: This does not apply to overwriting the coordinates when one expression is added to another, e.g. “x + df” still overwrites the index of “df” when the dimensional shapes are aligned.The .mask attribute of the Constraint class was fixed to return a proper boolean xarray.DataArray object.

The printout of masked constraints was fixed.

## Version 0.3.5#

The return type of

`coord_dims`

for expressions and constraints was changed from set to tuple to align with the xarray convention.The printout of transposed expressions and constraints was fixed.

Variables and LinearExpressions now support the chaining operations .add, .sub, .mul, .div.

Variables and LinearExpressions now have support for the power operator. For example, x**2 is now supported.

## Version 0.3.4#

Solver output of CBC and GLPK is sent to logging with level INFO instead of stdout

Added support for QP problems with MOSEK and COPT.

A warning was added when linopy is not able to add pass quadratic objective terms to the highs solver. This is the case when the “ipm” solver of highs is explicitly selected.

## Version 0.3.3#

New solver interface for SCIP <https://www.scipopt.org/>. This solver is now supported by linopy and can be used with the solve function if the pyscipopt package is installed. The solver is available for free for general use. See the SCIP website <https://www.scipopt.org/> for more information.

Linopy was refactored to use the new xarray API (>=2024.01) without the deprecation warnings.

The set “quadratic_solvers” now only contains quadratic solvers which are installed and available to the user.

The solve function now throws an error instead of a warning if the set value for

`io_api`

is not available for a solver.

## Version 0.3.2#

The IO with NetCDF files was made more secure and fixed for some cases. In particular, variables and constraints with a dash in the name are now supported (as used by PyPSA). The object sense and value are now properly stored and retrieved from the netcdf file.

The IO with NetCDF file now supports multiindexed coordinates.

The representation of single indexed expressions and constraints with non-empty dimensions/coordinates was fixed, e.g. x.loc[[“a”]] > 0 where x has only one dimension. Therefore the representation now shows the coordinates.

The creation of

`LinearExpression`

and`Constraints`

was made robust against the case where the`data`

argument is a`xarray.DataArray`

with helper dimensions (like “_term” etc.) unintentionally assigned as coordinates.

## Version 0.3.1#

**New Features**

Added solver interface for MOSEK.

Support for MindOpt solver was added.

Added solver interface for COPT by Cardinal Optimizer.

Type consistency with fill values for constant values was improved, this prevent dtype warnings put out by xarray/numpy.

## Version 0.3.0#

**New Features**

It is now possible to set the sense of the objective function to minimize or maximize. Therefore, a new class Objective was introduced which is used in Model.objective. It supports the same arithmetic operations as LinearExpression and QuadraticExpression and contains a sense attribute which can be set to minimize or maximize.

The fillna function for variables was made more secure by raising a warning if the fill value is not of variable-like type.

The where and fillna functions for expressions were made more flexible: When passing a scalar value or a DataArray, the values are added as constants to the expression, where there were missing values before. If another expression is passed, the values are added to the expression, where there were missing values before.

**Breaking Changes**

The _fill_value for LinearExpression and QuadraticExpression classes was changed to

`NaN`

for the constant array (“const”). This allows to use the where function for expressions with constant values in the argument other.The functions

`ravel`

and`iter_ravel`

for Variables and Constraints were removed in favor of the`flat`

function.The property

`non_helper_dims`

for Variables and Constraints was removed in favor of the`coord_dims`

property.The function

`to_anonymous_constraint`

was removed in favor of the`to_constraint`

function.The support for python 3.8 has been dropped.

## Version 0.2.6#

The memory-efficiency of the IO to LP/MPS file was further improved. In particular, the function to_dataframe is now avoiding unnecessary data copies.

The printout of time stamps was modified to be more readable, leaving out the display of seconds and below if not necessary.

The gurobi environment is now enclosed in a context manager to avoid any unwanted use of a token.

## Version 0.2.5#

The solution getter model.solution was falsely returning integer dtype in case of non-aligned indexes. This is fixed now.

Highs is now in the set of default solvers when install linopy via pip.

## Version 0.2.4#

The IO to LP/MPS file was made more memory-efficient. In particular, the memory excessive operation to_dataframe (see pydata/xarray#6561) was replaced by an in-house implementation.

## Version 0.2.3#

**Bugfixes**

When multiplying a LinearExpression with a constant value, the constant in the LinearExpression was not updated. This is fixed now.

**New Features**

The Variable and the LinearExpression have a new method cumsum, which allows to compute the cumulative sum.

## Version 0.2.2#

The documentation was revised and extended.

A new function print_labels was added to the Variables and Constraints class. This function allows to print the variables/constraints from a list of labels.

A new function compute_infeasibilities and print_infeasibilities was added to the Model class. This function allows to compute the infeasibilities of an infeasible model and print them out. The function only supports the gurobi solver so far.

## Version 0.2.1#

Backwards compatibility for python 3.8.

Variable, LinearExpression and Constraint now have a print function to easily print the objects with larger layouts, i.e. showing more terms and lines.

## Version 0.2.0#

**New Features**

Linopy now supports quadratic programming. Therefore a new class QuadraticExpression was created, which can be assigned to the objective function. The QuadraticExpression class supports the same arithmetic operations as the LinearExpression and can be created by multiplying two Variable or LinearExpression objects. Note for the latter, the number of stacked terms must be equal to one (expr.nterm == 1).

LinearExpression’s now support constant values. This allows defining linear expressions with numeric constant values, like x + 5.

When defining constraints, it is not needed to separate variables from constants anymore. Thus, expressions like x <= y or 5 * x + 10 >= y are supported.

The new default solver will now be the first element in available_solvers.

The classes Variable, LinearExpression and Constraint now have a loc method.

The classes Variable, LinearExpression, Constraint, Variables and Constraints now have a flat method, which returns a flattened pandas.DataFrame of the object in long-table format.

It is now possible to access variables and constraints by a dot notation. For example, model.variables.x returns the variable x of the model.

Variable assignment without explicit coordinates is now supported. In an internal step, integer coordinates are assigned to the dimensions without explicit coordinates.

The groupby function now supports passing a pandas.Dataframe as groupby keys. These allows to group by multiple variables at once.

The performance of the groupby function was strongly increased. In large operations a speedup of 10x was observed.

New test functions assert_varequal, assert_conequal were added to the testing module.

**Deprecations**

The class AnonymousConstraint is now deprecated in the favor of Constraint. The latter can now be assigned to a model or not.

The ravel and iter_ravel method of the Variables and Constraints class is now deprecated in favor of the flat method.

**Breaking Changes**

The data attribute of Variables and Constraints now returns a xarray.Dataset object instead of a xarray.DataArray object with the labels only.

The deprecated groupby_sum function was removed in favor of the groupby method.

The deprecated rolling_sum function was removed in favor of the rolling method.

The deprecated eval module was removed in favor of the arithmetic operations on the classes Variable, LinearExpression and Constraint.

The deprecated attribute values of the classes Variable, LinearExpression and Constraint was removed in favor of the data attribute.

The deprecated to_array method of the classes Variable and Constraint was removed in favor of the data attribute.

The deprecated to_dataset of the LinearExpression class was removed in favor of the data attribute.

The function get_lower_bound, get_upper_bound, get_variable_labels, get_variable_types, get_objective_coefficient, get_constraint_labels, get_constraint_sense, get_constraint_rhs, get_constraint_matrix were removed in favor of the matrices accessor, i.e. ub, lb, vlabels, etc.

The LinearExpressionGroupby class now takes a different set of arguments when initializing. These are data: xr.Dataset, group: xr.DataArray, model: Any, kwargs: Mapping[str, Any].

When grouping with a xr.DataArray / pd.Series / pd.DataFrame and summing afterwards, the keyword arguments like squeeze, restore_coords are ignored.

**Internal Changes**

The internal data fields in Variable and Constraint are now always broadcasted to have aligned indexes. This allows for a more consistent handling of the objects.

The inner structure of the Variable, Variables, Constraint and Constraints class has changed to a more stable design. All information of the Variable and the Constraint class is now stored in the data field. The data field is a xarray.Dataset object. The Variables and Constraints class “simple” containers for the Variable and Constraint objects, stored in dictionary under the data field. This design allows for a more flexible handling of individual variables and constraints.

**Other**

License changed to MIT license.

## Version 0.1.5#

Add sel functions to Constraint and AnonymousConstraint to allow for selection and inspection of constraints by coordinate.

The printout of Variables and Constraints was refactored to a more concise layout.

The solving termination condition “other” is now tagged as solving status “warning”.

## Version 0.1.4#

Fix representation of empty variables and linear expressions.

The benchmark reported in [here](PyPSA/linopy) was updated to the latest version of linopy and adjusted to be fully reproducible.

## Version 0.1.3#

**Hotfix**dual value retrieval for`highs`

.The MPS file writing was fixed for

`glpk`

solver. The MPS file writing is now tested against all solvers.

## Version 0.1.2#

Fix display for constraint with single entry and no coordinates.

## Version 0.1.1#

Printing out long LinearExpression is now accelerated in the __repr__ function.

Multiplication of LinearExpression’s with pandas object was stabilized.

A options handler was introduced that allows the user to change the maximum of printed lines and terms in the display of Variable’s, LinearExpression’s and Constraint’s.

If LinearExpression of exactly the same shape are joined together (in arithmetic operations), the coordinates of the first object is used to override the coordinates of the consecutive objects.

## Version 0.1#

This is the first major-minor release of linopy! With this release, the package should more stable and consistent. The main changes are:

The classes Variable, LinearExpression and Constraint now have a __repr__ method. This allows for a better representation of the classes in the console.

Linopy now defines and uses a fixed set of solver status and termination codes. This allows for a more consistent and reliable handling of solver results. The new codes are defined in the linopy.constants module. The implementation is inspired by 0b11001111 and the implementation in this PyPSA fork

The automated summation of repeated variables in one constraint is now supported. Before the implementation for constraints like x + x + x <= 5 was only working for solvers with a corresponding fallback computation. This is now fixed.

Integer variables are now fully supported.

Support exporting problems to MPS file via fast highspy MPS-writer (highspy installation required).

The internal data structure of linopy classes were updated to a safer design. Instead of being defined as inherited xarray classes, the class Variable, LinearExpression and Constraint are now no inherited classes but contain the xarray objects in the data field. This allows the package to have more flexible function design and a reduced set of wrapped functions that are sensible to use in the optimization context.

The class Variable and LinearExpression have new functions groupby and rolling imitating the corresponding xarray functions but with safe type inheritance and application of appended operations.

Coefficients very close to zero (< 1e-10) are now automatically set to zero to avoid numerical issues with solvers.

Coefficients of variables are no also allowed to be np.nan. These coefficients are ignored in the LP file writing.

The classes Variable, LinearExpression, Constraint, ScalarVariable, ScalarLinearExpression and ScalarConstraint now require the model in the initialization (mostly internal code is affected).

The eval module was removed in favor of arithmetic operations on the classes Variable, LinearExpression and Constraint.

Solver options are now printed out in the console when solving a model.

If a variable with indexes differing from the model internal indexes are assigned, linopy will raise a warning and align the variable to the model indexes.

## Version 0.0.15#

Using the python sum() function over a ScalarVariable or a ScalarLinearExpression is now supported.

Returning None type in from_rule assignment is now supported.

Python 3.11 is now supported

Xarray versions higher and lower v2022.06. are now supported.

## Version 0.0.14#

**New Features**

Linopy now uses highspy <https://pypi.org/project/highspy/> as an interface to the HiGHS solver. This enables a direct and fast communication without needing to write an intermediate LP file.

## Version 0.0.13#

**New Features**

The function LinearExpression.from_tuples now allows ScalarVariable as input.

For compatibility reasons, the function groupby_sum now allows pandas.Series as input.

**Bug Fixes**

Filtering out zeros is now an optional feature in the solve function. The previous behavior of filtering just before the LP file writing, lead to unexpected errors for constraints with only zero terms.

## Version 0.0.12#

**New Features**

A new module was created to export basic mathematical quantities such as lb, ub, c vectors and the A matrix. Use it with the matrices accessor in linopy.Model.

For Constraints` and Variables` a ipython autocompletion function for getting items was added.

Inplace updates for constraints are now more flexible.

AnonymousConstraint can now built from comparison operations of variables with constants, e.g. x >= 5.

The Model.add_constraints function now support input of type ScalarVariable, ScalarLinearExpression and ScalarConstraint.

Terms with zero coefficient are now filtered out before writing to file to avoid unnecessary overhead.

The function sanitize_zeros was added to Constraints. Use this to filter out zero coefficient terms.

**Bug Fixes**

Solving with gurobi and io_api=”direct” lead to wrong objective assignment if the objective contained non-unique variables. This is fixed in this version.

## Version 0.0.11#

Constraints and expressions can now be created using function that iterates over all combinations of given coordinates. This functionality mirrors the behavior of the Pyomo package. For complicated constraints which are hard to create with arrays of variables, it is easier (thus less efficient) to use an iterating function. For more information see the example notebook in the documentation.

When getting the value of a variable, the value of the variable is returned as a ScalarVariable. This is useful for the above mentioned creation of expressions and constraints with iterating functions. This affect only the direct getter function, all other functions like .sel or .isel behave as known from Xarray.

The docstring examples are now part of the Continuous Integration.

Due to problems with indexing in the latest package version, the xarray dependency was set to <=v2022.3.0.

## Version 0.0.10#

Improved type security when applying xarray functions on variables linear expressions and constraints.

Correct block assignment for upcoming PIPS-IPM++ implementation.

The function

`group_terms`

was renamed to`groupby_sum`

.A new function

`rolling_sum`

was introduced to compute rolling sums for variables and linear expressions.

## Version 0.0.9#

**New Features**

Numpy

`__array_ufunc__`

was disabled in the Variable, Constraint and LinearExpression class in order to ensure persistence as the class when multiplying with numpy objects. As for pandas objects the issue pandas-dev/pandas#45803 must be solved.The Variable class got a new accessor sol which points to the optimal values if the underlying model was optimized.

The Constraint class got a new accessor dual which points to the dual values if tune underlying model was optimized and dual values are existent.

When writing out the LP file, the handling of nan values is now checked in a more rigorous way. Before linopy was skipping and therefore ignoring constraints where the rhs was a nan value. As this behavior is not very save, such cases will raise an error now.

Models can now be solved on a remote machine using a ssh tunnel. The implementation automatically stores the locally initialized model to a netcdf file on the server, runs the optimization and retrieves the results. See the example Solve a model on a remote machine in the documentation for further information.

**Bug Fixes**

linopy is now continuously tested and working for Windows machines.

## Version 0.0.8#

**New Features**

Writing out the LP was further sped up.

The LP file writing for problems with “-0.0” coefficients was fixed.

**Breaking changes**

the function

`as_str`

was replaced by`int_to_str`

and`float_to_str`

.

## Version 0.0.7#

**New Features**

Add

`get_name_by_label`

function to`Variables`

and`Constraints`

class. It retrieves the name of the variable/constraint containing the passed integer label. This is helpful for debugging.

**Bug Fixes**

The lhs accessor for the

`Constraint`

class was fixed. This raised an error before as the _term dimension was not adjusted adequately.Variables and constraints which are fully masked are now skipped in the lp-file writing. This lead to a error before.

## Version 0.0.6#

Hot fix: Assign

`linopy.__version__`

attributeHot fix: Fix sign assignment in conversion from

`LinearExpression`

to`AnonymousConstraint`

.

## Version 0.0.5#

LinearExpression has a new function densify_terms which reduces the _term axis to a minimal length while containing all non-zero coefficient values.

When summing over one or multiple axes in a LinearExpression, terms with coefficient of zeros can now be dropped automatically.

The export of LP files was restructured and is flat arrays under the hook to ensure performant export of long constraints.

Dimensions of masks passed to add_variables and add_constraints now have to be a subset of the resulting labels dimensions.

A new high-level function merge was added to concatenate multiple linear expressions.

The

`Variable.where`

function now has -1 as default fill value.The return value of most Variable functions built on xarray functions now preserve the Variable type.

The variable labels in linear expression which are added to a model are ensured to be stored as integers.

A preliminary function to print out the subset of infeasible constraints was added (only available for Gurobi, based on https://www.gurobi.com/documentation/9.5/refman/py_model_computeiis.html)

Constraints with only missing variables labels are now sanitized are receive a label -1.

Binary variables now also have a non-nan lower and upper value due compatibility.

Models can now be created using the gurobipy API, this can lead to faster total solving times.

.solve has a new argument io_api. If set to ‘direct’ the io solving will be performed using the python API’s. Currently only available for gurobi.

The Variable class now has a lower and upper accessor, which allows to inspect and modify the lower and upper bounds of a assigned variable.

The Constraint class now has a lhs, vars, coeffs, rhs and sign accessor, which allows to inspect and modify the left-hand-side, the signs and right-hand-side of a assigned constraint.

Constraints can now be build combining linear expressions with right-hand-side via a >=, <= or a == operator. This creates an AnonymousConstraint which can be passed to Model.add_constraints.

Add support of the HiGHS open source solver https://www.maths.ed.ac.uk/hall/HiGHS/ (PyPSA/linopy#8, PyPSA/linopy#17).

**Breaking changes**

The low level IO function

`linopy.io.str_array_to_file`

was renamed to`linopy.io.array_to_file`

, the function`linopy.io.join_str_arrays`

was removed.The keep_coords flag in

`LinearExpression.sum`

and`Variable.sum`

was dropped.The run_ functions in linopy.solvers now have a new set of arguments and keyword argument, in order to make solving io more flexible.

ncons and nvars now count only non-missing constraints and variables.

## Version 0.0.4#

**Package Design**

The definitions of variables, constraints and linearexpression were moved to dedicated modules: `linopy.variables`

, `linopy.constraints`

and `linopy.expressions`

.

**Internal/Data handling**

Most of the following changes are dedicated to data handling within the Model class. Users which rely on the internal structure have to expect some breaking changes.

The model class now stores variables and constraints in dedicated (newly added) classes,

`Variables`

and`Constraints`

. The`Variables`

class contains the`xarray`

datasets labels, lower and upper. The`Constraints`

class contains the datasets labels, coeffs, vars, sign and rhs. The two new class facilitate data access and helper functions.The “_term” dimension in the

`LinearExpression`

class is now stored without coordinates.As soon as a linear expression is added to a model the “_term” dimension is rename to “{constraintname}_term” in order align the model better with the contained arrays and to avoid unnecessary nans.

Missing values in the

`Model.variables.labels`

and`Model.constraints.labels`

arrays are now indicated by -1. This circumvents changing the type from int to float.`LinearExpression`

now allows empty data as input.The test_model_creation script was refactored.

**New Features**

The

`Variable`

class now has a accessor to get lower and upper bounds,`get_lower_bound()`

and`get_upper_bound()`

.A new

`Constraint`

class was added which enables a better visual representation of the constraints. The class also has getter function to get coefficients, variables, signs and rhs constants. The new return type of the`Model.add_constraints`

function is`Constraint`

.`add_variables`

and`add_constraints`

now accept a new argument`mask`

. The mask, which should be an boolean array, defines whether a variable/constraint is active (True) or should be ignored (False).A set of experimental eval functions was added. Now one can assign variable and constraints using string expressions. For further information see linopy.Model.vareval, linopy.Model.lineval and linopy.Model.coneval.

`Model`

has a new argument force_dim_names. When set to true assigned variables, constraints and data must always have custom dimension names, otherwise a ValueError is raised. These helps to avoid unintended broadcasting over dimension. Especially the use of pandas DataFrames and Series may become safer.A new binaries accessor

`Model.binaries`

was added.

## Version 0.0.3#

Support assignment of variables and constraints without explicit names.

Add support for xarray version > 0.16

Add a documentation

## Version 0.0.2#

Set up first runnable prototype.