Module “server”¶

class tc_python.server.LoggingPolicy(value)¶

Bases: enum.Enum

Logging policy that determines how the TC-Python logs are presented to the user.

FILE = 1¶: Logging to a file.

NONE = 2¶: No logging at all.

SCREEN = 0¶: Logging to the screen.

class tc_python.server.MetallurgyCalculations(metallurgy_calculations)¶

Bases: object

Provides access to the calculation objects for all Process Metallurgy calculations.

These are specialised calculations for working with metallurgical processes. Both equilibrium calculations and kinetic process simulations (Effective Equilibrium Reaction Zone model) are available.

with_adiabatic_equilibrium_calculation(database: tc_python.process_metallurgy.base.ProcessDatabase) → tc_python.process_metallurgy.equilibrium.AdiabaticEquilibriumCalculation ¶

Creates an adiabatic equilibrium calculation for Process Metallurgy.

Parameters: database – The thermodynamic database used in the calculation
Returns: A new AdiabaticEquilibriumCalculation object

with_adiabatic_process_calculation(database: tc_python.process_metallurgy.base.ProcessDatabase) → tc_python.process_metallurgy.process.ProcessSimulationCalculation ¶

Creates an adiabatic kinetic process simulation (EERZ, i.e. Effective Equilibrium Reaction Zone model).

Parameters: database – The thermodynamic database used in the calculation
Returns: A new ProcessSimulationCalculation object

with_isothermal_equilibrium_calculation(database: tc_python.process_metallurgy.base.ProcessDatabase) → tc_python.process_metallurgy.equilibrium.IsoThermalEquilibriumCalculation ¶

Creates an isothermal equilibrium calculation for Process Metallurgy.

Parameters: database – The thermodynamic database used in the calculation
Returns: A new IsoThermalEquilibriumCalculation object

class tc_python.server.ResultLoader(result_loader)¶

Bases: object

Contains methods for loading results from previously done calculations.

diffusion(path: str) → tc_python.diffusion.DiffusionCalculationResult ¶

Loads a DiffusionCalculationResult from disc.

Parameters: path – path to the folder where result was previously saved.
Returns: A new DiffusionCalculationResult object which later can be used to get specific values from the calculated result

phase_diagram(path: str) → tc_python.step_or_map_diagrams.PhaseDiagramResult ¶

Loads a PhaseDiagramResult from disc.

Parameters: path – path to the folder where result was previously saved.
Returns: A new PhaseDiagramResult object which later can be used to get specific values from the calculated result

precipitation_TTT_or_CCT(path: str) → tc_python.precipitation.PrecipitationCalculationTTTorCCTResult ¶

Loads a PrecipitationCalculationTTTorCCTResult from disc.

Parameters: path – path to the folder where result was previously saved.
Returns: A new PrecipitationCalculationTTTorCCTResult object which later can be used to get specific values from the calculated result

precipitation_single(path: str) → tc_python.precipitation.PrecipitationCalculationSingleResult ¶

Loads a PrecipitationCalculationSingleResult from disc.

Parameters: path – path to the folder where result was previously saved.
Returns: A new PrecipitationCalculationSingleResult object which later can be used to get specific values from the calculated result

property_diagram(path: str) → tc_python.step_or_map_diagrams.PropertyDiagramResult ¶

Loads a PropertyDiagramResult from disc.

Parameters: path – path to the folder where result was previously saved.
Returns: A new PropertyDiagramResult object which later can be used to get specific values from the calculated result

property_model(path: str) → tc_python.propertymodel.PropertyModelResult ¶

Loads a PropertyModelResult from disc.

Parameters: path – path to the folder where result was previously saved.
Returns: A new PropertyModelResult object which later can be used to get specific values from the calculated result

scheil(path: str) → tc_python.scheil.ScheilCalculationResult ¶

Loads a ScheilCalculationResult from disc.

Parameters: path – path to the folder where result was previously saved.
Returns: A new ScheilCalculationResult object which later can be used to get specific values from the calculated result

single_equilibrium(path: str) → tc_python.single_equilibrium.SingleEquilibriumResult ¶

Loads a SingleEquilibriumResult from disc.

Parameters: path – path to the folder where result was previously saved.
Returns: A new SingleEquilibriumResult object which later can be used to get specific values from the calculated result

class tc_python.server.SetUp(debug_logging=False)¶

Bases: object

Starting point for all calculations.

Note

This class exposes methods that have no precondition, it is used for choosing databases and elements.

disable_caching()¶

A previously set cache folder is no longer used.

Note

Within the session, caching is activated and used through the default temporary directory.

Returns: This SetUp object

get_database_info(database_short_name: str) → str¶

Obtains the short information available for the specified database.

Parameters: database_short_name – The name of the database (i.e. “FEDEMO”, …)
Returns: The short information about the database

get_database_path_on_disk(database_short_name: str) → str¶

Obtains the path to the database file on disk. TCPATH is a placeholder for the root path of the used Thermo-Calc installation.

Note

Encrypted databases (*.TDC) cannot be edited.

Parameters: database_short_name – The name of the database (i.e. “FEDEMO”, …)
Returns: The path to the database on disk

get_databases() → List[str]¶

Obtains the short names of all databases available in the used Thermo-Calc installation.

Note

Only databases with a valid license are listed.

Returns: List of the available databases

get_property_models(path_to_models: str = '') → Set[str]¶

Lists the names of all Property Models in the specified directory.

If the directory is not specified, the Property Model folder used by the normal Thermo-Calc application is used.

Parameters: path_to_models – The path where the Property Models are installed. If no value is entered, the Property Model folder used by the normal Thermo-Calc application is used.
Returns: Set containing all Property Model names

load_result_from_disk() → tc_python.server.ResultLoader ¶

Loads a previously calculated result from disk.

Note

This only works for results created by calling one of the save_result() methods on a Result class created from a calculation.

Returns: A new ResultLoader object

select_database_and_elements(database_name: str, list_of_elements: List[str]) → tc_python.system.SystemBuilder ¶

Selects a first thermodynamic or kinetic database and selects the elements in it.

Parameters

database_name – The name of the database, for example “FEDEMO”
list_of_elements – The list of the selected elements in that database, for example [“Fe”, “C”]

Returns

A new SystemBuilder object

select_thermodynamic_and_kinetic_databases_with_elements(thermodynamic_db_name: str, kinetic_db_name: str, list_of_elements: List[str]) → tc_python.system.MultiDatabaseSystemBuilder ¶

Selects the thermodynamic and kinetic database at once, guarantees that the databases are added in the correct order. Further rejection or selection of phases applies to both databases.

Parameters

thermodynamic_db_name – The thermodynamic database name, for example “FEDEMO”
kinetic_db_name – The kinetic database name, for example “MFEDEMO”
list_of_elements – The list of the selected elements in that database, for example [“Fe”, “C”]

Returns

A new MultiDatabaseSystemBuilder object

select_user_database_and_elements(path_to_user_database: str, list_of_elements: List[str]) → tc_python.system.SystemBuilder ¶

Selects a user-defined database and selects the elements in it.

Note

By using a r-literal, it is possible to use slashes on all platforms, also on Windows: select_user_database_and_elements(r”my path/user_db.tdb”, [“Fe”, “Cr”]])

Otherwise it is required to use double back-slashes on Windows as separator.

Note

On Linux and Mac the path is case-sensitive, also the file ending.

Parameters

path_to_user_database – The path to the database file (“database”.TDB), defaults to the current working directory. Only filename is required if the database is located in the same folder as the script.
list_of_elements – The list of the selected elements in that database, for example [“Fe”, “C”]

Returns

A new SystemBuilder object

set_cache_folder(path: str = '', precision_for_floats: int = 12)¶

Sets a folder where results from calculations and state of systems are saved. If at any time a calculation is run which has the exact same setting as a previous, the calculation is not re-run. The result is instead loaded from this folder.

Note

The same folder can be used in several scripts, and it can even be shared between different users. It can be a network folder.

Parameters

path – path to the folder where results should be stored. It can be relative or absolute.
precision_for_floats – The number of significant figures used when comparing if the calculation has the same setting as a previous.

Returns

This SetUp object

set_ges_version(version: int = 6)¶

Setting the version of the Gibbs Energy System (GES).

Parameters: version – The GES-version (currently version 5 or 6)
Returns: This SetUp object

set_log_level_to_debug()¶

Sets log level to DEBUG

Returns: This SetUp object

set_log_level_to_info()¶

Sets log level to INFO

Returns: This SetUp object

with_metallurgy() → tc_python.server.MetallurgyCalculations ¶

Provides access to the calculation objects for all Process Metallurgy calculations.

These are specialised calculations for working with metallurgical processes. Both equilibrium calculations and kinetic process simulations (Effective Equilibrium Reaction Zone model) are available.

class tc_python.server.TCPython(logging_policy=<LoggingPolicy.SCREEN: 0>, log_file=None, debug_mode=False, debug_logging=False, do_throw_on_backend_hard_crash=True, port_number=0)¶

Bases: object

Starting point of the API. Typical syntax:

with TCPython() as session:
    session.select_database_and_elements(...)

Note

Each usage of with TCPython() causes significant overhead (starting a new process, stopping the old one, cleaning up the temporary disk space). Usually it is recommendable to call with TCPython() only once for each process, even if working in a loop. Instead you should pass the session or calculator object into the loop and use them there.

If necessary, beginning from version 2019a it is however possible to call with TCPython safely multiple times.

tc_python.server.start_api_server(logging_policy=<LoggingPolicy.SCREEN: 0>, log_file=None, debug_mode=False, is_unittest=False, do_throw_on_backend_hard_crash=True, port_number=0)¶

Starts a process of the API server and sets up the socket communication with it.

Parameters

logging_policy – Determines if the TC-Python log output is sent to the screen (LoggingPolicy.SCREEN), to file (LoggingPolicy.FILE) or nothing is logged at all (LoggingPolicy.NONE) Default: LoggingPolicy.SCREEN. Note that the log-handlers can also be adapted through the tc_python.LOGGER object at any time.
log_file – The log-file relative to the current path or absolute, only relevant if logging_policy=LoggingPolicy.FILE. Log-output will be appended.
debug_mode – If True it is tried to open a connection to an already running API-server. This is only used for debugging the API itself.
is_unittest – Should be True if called by a unit test, only to be used internally for development.
do_throw_on_backend_hard_crash – If True an UnrecoverableCalculationException will be thrown if the Java-backend crashes hard, if False the application will simply crash with a FORTRAN-stacktrace. If `True` the exception can be caught outside of the `with`-clause and the application can continue, if `False` more information about the error is shown by the stacktrace..
port_number – The port number for the communication with the Java-backend server. This is not required to be changed by normal users.

Warning

Most users should use TCPython using a with-statement for automatic management of the resources (network sockets and temporary files). If you anyway need to use that method, make sure to call stop_api_server() in any case using the try-finally-pattern.

tc_python.server.start_matlab_server(logging_policy=<LoggingPolicy.SCREEN: 0>, log_file=None, debug_mode=False, is_unittest=False, do_throw_on_backend_hard_crash=True, port_number=0)¶

tc_python.server.stop_api_server(gateway_id: str = None)¶: Clears all resources used by the session (i.e. shuts down the API server and deletes all temporary files). The disk usage of temporary files might be significant.

Warning

Call this method only if you used start_api_server() initially. It should never be called when the API has been initialized in a with-statement using TCPython.